Master data management may not attract the same level of excitement as fashionable topics such as DataOps or Data Platforms, but it remains one of the most significant aspects of an organization’s strategic approach to data management. Having trust in data is critical to the ability of an organization to make data-driven business decisions. Along with data quality, MDM enables organizations to ensure data is accurate, complete and consistent to fulfill operational business objectives.
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Topics: Data Governance, Data Management, Data, data operations
Data Operations (DataOps) has been part of the lexicon of the data market for almost a decade, with the term used to describe products, practices and processes designed to support agile and continuous delivery of data analytics. DataOps takes inspiration from DevOps, which describes a set of tools, practices and philosophy used to support the continuous delivery of software applications in the face of constant changes. DataOps describes a set of tools, practices and philosophy used to ensure the quality, flexibility and reliability of data and analytics initiatives, with an emphasis on continuous measurable improvement, as well as agility, collaboration and automation. Interest in products and services that support DataOps is growing. I assert that by 2025, one-half of organizations will have adopted a DataOps approach to their data engineering processes, enabling them to be more flexible and agile.
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Topics: Data Governance, Data Management, Data, data operations
As data continues to grow and evolve, organizations seek better tools and technologies to employ data faster and more efficiently. Finding and managing data remains a perennial challenge for most organizations, and is exacerbated by increasing volumes of data and an expanding array of data formats. At the same time, organizations must comply with a growing list of national and regional rules and regulations, such as General Data Protection Regulation and the California Consumer Privacy Act. While these regulations protect consumers, they increase complexity for governing and providing access to data.
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Topics: Data Governance, Data Management, Data, data operations
I have previously written about the importance of data democratization as a key element of a data-driven agenda. Removing barriers that prevent or delay users from gaining access to data enables it to be treated as a product that is generated and consumed, either internally by employees or externally by partners and customers. This is particularly important for organizations adopting the data mesh approach to data ownership, access and governance. Data mesh is an organizational and cultural approach to data, rather than a technology platform. Nevertheless, multiple vendors are increasingly focused on providing products that facilitate adoption of data mesh and promote data democratization. Amazon Web Services is one such vendor, thanks to the recent launch of Amazon DataZone, one of the figurehead analytics and data announcements made during the company’s recent re:Invent customer event.
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Topics: Cloud Computing, Data Governance, Data Management, Data, Digital Technology, data operations, Analytics & Data
Now more than ever, effective data management is crucial to enable decision-makers to better assess information and take calculated actions. It is also important to keep up with the latest trends and technologies to derive higher value from data and analytics and maintain a competitive edge in the market. However, every organization faces challenges with data management and analytics. And as organizations scale, the complexity only increases, creating a need for better data governance, data quality and streamlined and automated processes. DataOps can help solve many of the challenges organizations encounter when trying to unlock the power of data by expanding data use to various parts of an organization. Hitachi Vantara offers DataOps technology that enables organizations to improve data agility and automation. It provides cloud-ready infrastructure, advanced data management software and a broad range of support services.
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Topics: Analytics, Data Governance, Data Management, Data, data operations, Analytic Data Platforms
The market for data and analytics products is constantly evolving, with the emergence of new approaches to data persistence, data processing and analytics. This enables organizations to constantly adapt data analytics architecture in response to emerging functional capabilities and business requirements. It can, however, also be a challenge. Investments in data platforms cannot be constantly written-off as organizations adopt new products for new approaches. Too little change can lead to stagnation, but too much change can be chaotic, leading to silos of data and data integration complexity. This is one reason why there is growing interest in the concept of data fabric for managing and governing data across distributed environments. In addition to supporting hybrid and multi-cloud strategies, data fabric enables organizations to manage and generate insight from data spread across a combination of long-standing and new data platforms. Promethium focuses on automating data management and data governance across a distributed architecture with a combination of data fabric and self-service augmented analytics capabilities.
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Topics: Data Governance, Data Management, Data, data operations
Organizations across various industries collect multiple types of data from disparate systems to answer key business questions and deliver personalized experiences for customers. The expanding volume of data increases complexity, and data management becomes a challenge if the process is manual and rules-based. There can be numerous siloed, incomplete and outdated data sources that result in inaccurate results. Organizations must also deal with concurrent errors – from customers to products to suppliers – to create a complete view of the data. Many vendors, including Tamr, have turned to artificial intelligence and machine learning to overcome the challenges associated with maintaining data quality amid the growing volume and variety of data. I assert that by 2026, more than three-quarters of organizations’ data management processes will be enhanced with artificial intelligence and machine learning to increase automation, accuracy, agility and speed.
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Topics: Data Governance, Data Management, Data, data operations, Analytic Data Platforms
Ventana Research recently announced its 2023 Market Agenda for Data, continuing the guidance we have offered for two decades to help organizations derive optimal value and improve business outcomes.
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Topics: Cloud Computing, Data Governance, Data Management, Data, Digital Technology, data operations, Analytics & Data, Streaming Data & Events, operational data platforms, Analytic Data Platforms
Analytics processes are all about how organizations use data to create metrics that help manage and improve operations. Yet, the discipline applied to analytics processes seems to be lacking compared to data processes. I’ve pointed out that the weak link in data governance is often analytics. Organizations can also do a better job tying AnalyticOps to DataOps and do more to define and manage metrics. Our research has shown that creating and managing metrics in a semantic model improves analytics processes.
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Topics: Analytics, Business Intelligence, Data Governance, Data, Digital Technology, Analytics & Data
In previous perspectives in this series, I’ve discussed some of the realities of cloud computing including costs, hybrid and multi-cloud configurations and business continuity. This perspective examines the realities of security and regulatory concerns associated with cloud computing. These issues are often cited by our research participants as reasons they are not embracing the cloud. To be fair, the majority of our research participants are embracing the cloud. However, among those that have not yet made the transition to the cloud, security and regulatory concerns are among the most common issues cited across the various studies we have conducted.
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Topics: Analytics, Business Intelligence, Cloud Computing, Data Governance, Digital Technology, AI and Machine Learning, Analytics & Data, Governance & Risk
Organizations are increasingly utilizing cloud object storage as the foundation for analytic initiatives. There are multiple advantages to this approach, not least of which is enabling organizations to keep higher volumes of data relatively inexpensively, increasing the amount of data queried in analytics initiatives. I assert that by 2024, 6 in ten organizations will use cloud-based technology as the primary analytics data platform, making it easier to adopt and scale operations as necessary.
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Topics: Teradata, Data Governance, Data Management, Data, data operations, operational data platforms, Analytic Data Platforms, Object storage, Vantage platform
Almost all organizations are investing in data science, or planning to, as they seek to encourage experimentation and exploration to identify new business challenges and opportunities as part of the drive toward creating a more data-driven culture. My colleague, David Menninger, has written about how organizations using artificial intelligence and machine learning (AI/ML) report gaining competitive advantage, improving customer experiences, responding faster to opportunities and threats, and improving the bottom line with increased sales and lower costs. One-quarter of participants (25%) in Ventana Research’s Analytics and Data Benchmark Research are already using AI/ML, while more than one-third (34%) plan to do so in the next year, and more than one-quarter (28%) plan to do so eventually. As organizations adopt data science and expand their analytics initiatives, they face no shortage of options for AI/ML capabilities. Understanding which is the most appropriate approach to take could be the difference between success and failure. The cloud providers all offer services, including general-purpose ML environments, as well as dedicated services for specific use cases, such as image detection or language translation. Software vendors also provide a range of products, both on-premises and in the cloud, including general-purpose ML platforms and specialist applications. Meanwhile, analytic data platform providers are increasingly adding ML capabilities to their offerings to provide additional value to customers and differentiate themselves from their competitors. There is no simple answer as to which is the best approach, but it is worth weighing the relative benefits and challenges. Looking at the options from the perspective of our analytic data platform expertise, the key choice is between AI/ML capabilities provided on a standalone basis or integrated into a larger data platform.
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Topics: Data Governance, Data Management, Data, AI and Machine Learning, data operations, Analytics and Data, Analytic Data Platforms
If you’ve ever been to London, you are probably familiar with the announcements on the London Underground to “mind the gap” between the trains and the platform. I suggest we also need to mind the gap between data and analytics. These worlds are often disconnected in organizations and, as a result, it limits their effectiveness and agility.
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Topics: embedded analytics, Analytics, Business Intelligence, Data Governance, Data Management, data operations, Analytics & Data
I have previously written about growing interest in the data lakehouse as one of the design patterns for delivering hydroanalytics analysis of data in a data lake. Many organizations have invested in data lakes as a relatively inexpensive way of storing large volumes of data from multiple enterprise applications and workloads, especially semi- and unstructured data that is unsuitable for storing and processing in a data warehouse. However, early data lake projects lacked structured data management and processing functionality to support multiple business intelligence efforts as well as data science and even operational applications.
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Topics: Business Intelligence, Data Governance, Data Management, Data, AI and Machine Learning, Streaming Data & Events, Analytic Data Platforms
I have written recently about the similarities and differences between data mesh and data fabric. The two are potentially complementary. Data mesh is an organizational and cultural approach to data ownership, access and governance. Data fabric is a technical approach to automating data management and data governance in a distributed architecture. There are various definitions of data fabric, but key elements include a data catalog for metadata-driven data governance and self-service, agile data integration.
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Topics: Business Intelligence, Cloud Computing, Data Governance, Data Management, Data, AI and Machine Learning, data operations, operational data platforms
Ventana Research’s Data Lakes Dynamics Insights research illustrates that while data lakes are fulfilling their promise of enabling organizations to economically store and process large volumes of raw data, data lake environments continue to evolve. Data lakes were initially based primarily on Apache Hadoop deployed on-premises but are now increasingly based on cloud object storage. Adopters are also shifting from data lakes based on homegrown scripts and code to open standards and open formats, and they are beginning to embrace the structured data-processing functionality that supports data lakehouse capabilities. These trends are driving the evolution of vendor product offerings and strategies, as typified by Cloudera’s recent launch of Cloudera Data Platform (CDP) One, described as a data lakehouse software-as-a-service (SaaS) offering.
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Topics: Business Intelligence, Data Governance, Data Management, Data, AI and Machine Learning, data operations, Analytics and Data, Streaming Data & Events, operational data platforms, Analytic Data Platforms
Organizations are collecting data from multiple data sources and a variety of systems to enrich their analytics and business intelligence (BI). But collecting data is only half of the equation. As the data grows, it becomes challenging to find the right data at the right time. Many organizations can’t take full advantage of their data lakes because they don’t know what data actually exists. Also, there are more regulations and compliance requirements than ever before. It is critical for organizations to understand the kind of data they have, who is handling it, what it is being used for and how it needs to be protected. They also have to avoid putting too many layers and wrappers around the data as it can make the data difficult to access. These challenges create a need for more automated ways to discover, track, research and govern the data.
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Topics: Business Intelligence, Data Governance, Data Management, AI and Machine Learning, data operations
The data catalog has become an integral component of organizational data strategies over the past decade, serving as a conduit for good data governance and facilitating self-service analytics initiatives. The data catalog has become so important, in fact, that it is easy to forget that just 10 years ago it did not exist in terms of a standalone product category. Metadata-based data management functionality has had a role to play within products for data governance and business intelligence for much longer than that, of course, but the emergence of the data catalog as a product category provided a platform for metadata-based data inventory and discovery that could span an entire organization, serving multiple departments, use cases and initiatives.
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Topics: business intelligence, Data Governance, Data Management, Data, data operations, Analytics and Data
I recently wrote about the need for organizations to take a holistic approach to the management and governance of data in motion alongside data at rest. As adoption of streaming data and event processing increases, it is no longer sufficient for streaming data projects to exist in isolation. Data needs to be managed and governed regardless of whether it is processed in batch or as a stream of events. This requirement has resulted in established data management vendors increasing their focus on streaming data and event processing through product development as well as acquisitions. It has also resulted in streaming and event specialists, such as Confluent, adding centralized management and governance capabilities to their existing offerings as they seek to establish or reinforce the strategic importance of streaming data as part of a modern approach to data management.
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Topics: Big Data, Cloud Computing, Data Governance, Streaming Analytics, Streaming Data & Events
The analytics and business intelligence market landscape continues to grow as more organizations seek robust tools and capabilities to visualize and better understand data. BI systems are used to perform data analysis, identify market trends and opportunities and streamline business processes. They can collect and combine data from internal and external systems to present a holistic view.
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Topics: Analytics, Business Intelligence, Data Governance, Data Management, AI and Machine Learning, Analytics & Data
When joining Ventana Research, I noted that the need to be more data-driven has become a mantra among large and small organizations alike. Data-driven organizations stand to gain competitive advantage, responding faster to worker and customer demands for more innovative, data-rich applications and personalized experiences. Being data-driven is clearly something to aspire to. However, it is also a somewhat vague concept without clear definition. We know data-driven organizations when we see them — the likes of Airbnb, DoorDash, ING Bank, Netflix, Spotify, and Uber are often cited as examples — but it is not necessarily clear what separates the data-driven from the rest. Data has been used in decision-making processes for thousands of years, and no business operates without some form of data processing and analytics. As such, although many organizations may aspire to be more data-driven, identifying and defining the steps required to achieve that goal are not necessarily easy. In this Analyst Perspective, I will outline the four key traits that I believe are required for a company to be considered data-driven.
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Topics: embedded analytics, Analytics, Business Intelligence, Data Governance, Data Integration, Data, Digital Technology, natural language processing, data lakes, AI and Machine Learning, data operations, Digital Business, Streaming Analytics, data platforms, Analytics & Data, Streaming Data & Events
We’ve recently published our latest Benchmark Research on Data Governance and it’s fair to say, “you’ve come a long way, baby.” Many of you reading this weren’t around when that phrase was introduced in 1968 to promote Virginia Slims cigarettes, but you may have heard the phrase because it went on to become a part of popular culture. We’ve learned a lot about cigarettes since then, and we’ve learned a lot about data governance, too.
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Topics: Big Data, Data Governance, Data Management, Analytics & Data
Organizations are continuously increasing the use of analytics and business intelligence to turn data into meaningful and actionable insights. Our Analytics and Data Benchmark Research shows some of the benefits of using analytics: Improved efficiency in business processes, improved communication and gaining a competitive edge in the market top the list. With a unified BI system, organizations can have a comprehensive view of all organizational data to better manage processes and identify opportunities.
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Topics: business intelligence, embedded analytics, Data Governance, Data Management, natural language processing, AI and Machine Learning, data operations, Streaming Analytics, operational data platforms
Organizations are scaling business intelligence initiatives to gain a competitive advantage and increase revenue as more data is created. Lack of expertise, data governance and slow performance can impact these efforts. Our Analytics and Data Benchmark Research finds some of the most pressing complaints about analytics and BI include difficulty integrating with other business processes and flexibility issues. Kyvos is a BI acceleration platform that enables BI and analytics tools to analyze massive amounts of data. It offers support for online analytical processing-based multidimensional analytics, enabling workers to access large datasets with their analytics tools. It operates with major cloud platforms, including Google Cloud, Amazon Web Services and Microsoft Azure.
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Topics: Business Intelligence, Data Governance
I recently wrote about the potential benefits of data mesh. As I noted, data mesh is not a product that can be acquired, or even a technical architecture that can be built. It’s an organizational and cultural approach to data ownership, access and governance. While the concept of data mesh is agnostic to the technology used to implement it, technology is clearly an enabler for data mesh. For many organizations, new technological investment and evolution will be required to facilitate adoption of data mesh. Meanwhile, the concept of the data fabric, a technology-driven approach to managing and governing data across distributed environments, is rising in popularity. Although I previously touched on some of the technologies that might be applicable to data mesh, it is worth diving deeper into the data architecture implications of data mesh, and the potential overlap with data fabric.
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Topics: Analytics, Business Intelligence, Data Governance, Data Integration, Data, AI and Machine Learning, data operations, data platforms, Streaming Data & Events
The data governance landscape is growing rapidly. Organizations handling vast amounts of data face multiple challenges as more regulations are added to govern sensitive information. Adoption of multi-cloud strategies increases governance concerns with new data sources that are accessed in real time. Our Data Governance Benchmark Research shows that organizations face multiple challenges when deploying data governance. Three-quarters (73%) of organizations report disparate data sources as the biggest challenge, and half of the organizations report creating, modifying, managing and enforcing governance policies as the second biggest challenge.
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Topics: Data Governance, Data Management, data operations
I recently wrote about the importance of data pipelines and the role they play in transporting data between the stages of data processing and analytics. Healthy data pipelines are necessary to ensure data is integrated and processed in the sequence required to generate business intelligence. The concept of the data pipeline is nothing new of course, but it is becoming increasingly important as organizations adapt data management processes to be more data driven.
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Topics: Analytics, Business Intelligence, Data Governance, Data Integration, Data, Digital Technology, Digital transformation, data lakes, AI and Machine Learning, data operations, Digital Business, data platforms, Analytics & Data, Streaming Data & Events
Data governance is an issue that impacts all organizations large and small, new and old, in every industry, and every region of the world. Data governance ensures that an organization’s data can be cataloged, trusted and protected, improving business processes to accelerate analytics initiatives and support compliance with regulatory requirements. Not all data governance initiatives will be driven by regulatory compliance; however, the risk of falling foul of privacy (and human rights) laws ensures that regulatory compliance influences data-processing requirements and all data governance projects. Multinational organizations must be cognizant of the wide variety of regional data security and privacy requirements, not least the European Union’s General Data Protection Regulation (GDPR). The GDPR became enforceable in 2018, protects the privacy of personal or professional data, and carries with it the threat of fines of up to 20 million euros ($22 million) or 4% of a company’s global revenue. Europe is not alone in regulating against the use of personally identifiable information (other similar regulations include The California Consumer Privacy Act) but Ventana Research’s Data Governance Benchmark Research illustrates that there are differing attitudes and approaches to data governance on either side of the Atlantic.
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Topics: Analytics, Data Governance, Data
I recently described the growing level of interest in data mesh which provides an organizational and cultural approach to data ownership, access and governance that facilitates distributed data processing. As I stated in my Analyst Perspective, data mesh is not a product that can be acquired or even a technical architecture that can be built. Adopting the data mesh approach is dependent on people and process change to overcome traditional reliance on centralized ownership of data and infrastructure and adapt to its principles of domain-oriented ownership, data as a product, self-serve data infrastructure and federated governance. Many organizations will need to make technological changes to facilitate adoption of data mesh, however. Starburst Data is associated with accelerating analysis of data in data lakes but is also one of several vendors aligning their products with data mesh.
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Topics: Business Continuity, Analytics, Business Intelligence, Data Governance, Data Integration, Data, Digital Technology, data lakes, Digital Business, data platforms, Analytics & Data
Data mesh is the latest trend to grip the data and analytics sector. The term has been rapidly adopted by numerous vendors — as well as a growing number of organizations —as a means of embracing distributed data processing. Understanding and adopting data mesh remains a challenge, however. Data mesh is not a product that can be acquired, or even a technical architecture that can be built. It is an organizational and cultural approach to data ownership, access and governance. Adopting data mesh requires cultural and organizational change. Data mesh promises multiple benefits to organizations that embrace this change, but doing so may be far from easy.
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Topics: Analytics, Business Intelligence, Data Governance, Data Integration, Data, Digital Technology, Digital transformation, data lakes, data operations, Digital Business, data platforms, Analytics & Data, Streaming Data & Events
Despite widespread and increasing use of the cloud for data and analytics workloads, it has become clear in recent years that, for most organizations, a proportion of data-processing workloads will remain on-premises in centralized data centers or distributed-edge processing infrastructure. As we recently noted, as compute and storage are distributed across a hybrid and multi-cloud architecture, so, too, is the data it stores and relies upon. This presents challenges for organizations to identify, manage and analyze all the data that is available to them. It also presents opportunities for vendors to help alleviate that challenge. In particular, it provides a gap in the market for data-platform vendors to distinguish themselves from the various cloud providers with cloud-agnostic data platforms that can support data processing across hybrid IT, multi-cloud and edge environments (including Internet of Things devices, as well as servers and local data centers located close to the source of the data). Yellowbrick Data is one vendor that has seized upon that opportunity with its cloud Data Warehouse offering.
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Topics: Analytics, Business Intelligence, Data Governance, Data, AI and Machine Learning, data operations, data platforms
As businesses become more data-driven, they are increasingly dependent on the quality of their data and the reliability of their data pipelines. Making decisions based on data does not guarantee success, especially if the business cannot ensure that the data is accurate and trustworthy. While there is potential value in capturing all data — good or bad — making decisions based on low-quality data may do more harm than good.
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Topics: Data Governance, Data Integration, Data, Digital Technology, data lakes, data operations, Analytics & Data
I recently described the emergence of hydroanalytic data platforms, outlining how the processes involved in generating energy from a lake or reservoir were analogous to those required to generate intelligence from a data lake. I explained how structured data processing and analytics acceleration capabilities are the equivalent of turbines, generators and transformers in a hydroelectric power station. While these capabilities are more typically associated with data warehousing, they are now being applied to data lake environments as well. Structured data processing and analytics acceleration capabilities are not the only things required to generate insights from data, however, and the hydroelectric power station analogy further illustrates this. For example, generating hydroelectric power also relies on pipelines to ensure that the water is transported from the lake or reservoir at the appropriate volume to drive the turbines. Ensuring that a hydroelectric power station is operating efficiently also requires the collection, monitoring and analysis of telemetry data to confirm that the turbines, generators, transformers and pipelines are functioning correctly. Similarly, generating intelligence from data relies on data pipelines that ensure the data is integrated and processed in the correct sequence to generate the required intelligence, while the need to monitor the pipelines and processes in data-processing and analytics environments has driven the emergence of a new category of software: data observability.
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Topics: Analytics, Data Governance, Data, Digital Technology, data lakes, AI and Machine Learning, data operations, data platforms, Streaming Data & Events
Many organizations invest in data governance out of concern over misuse of data or potential data breaches. These are important considerations and valid aspects of data governance programs. However, good data governance also has positive impacts on organizations. For example, I have previously written about the valuable connection between the use of data catalogs and satisfaction with an organization’s data lake. Our most recent Analytics and Data Benchmark Research demonstrates some of the beneficial links between data governance and analytics. In this Perspective, I’ll share some of the correlations identified in our research.
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Topics: embedded analytics, Analytics, Data Governance, Data, Digital Technology
Ventana Research recently announced its 2022 Market Agenda for Data, continuing the guidance we have offered for nearly two decades to help organizations derive optimal value and improve business outcomes.
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Topics: Data Governance, Data Integration, Data, data lakes, data operations, data platforms, Streaming Data & Events
Few trends have had a bigger impact on the data platforms landscape than the emergence of cloud computing. The adoption of cloud computing infrastructure as an alternative to on-premises datacenters has resulted in significant workloads being migrated to the cloud, displacing traditional server and storage vendors. Almost one-half (49%) of respondents to Ventana Research’s Analytics and Data Benchmark Research currently use cloud computing products for analytics and data, and a further one-quarter plan to do so. In addition to deploying data workloads on cloud infrastructure, many organizations have also adopted cloud data and analytics services offered by the same cloud providers, displacing traditional data platform vendors. Organizations now have greater choice in relation to potential products and providers for data and analytics workloads, but also need to think about integrating services offered by cloud providers with established technology and processes. Having pioneered the concept, Amazon Web Services has arguably benefitted more than most from adoption of cloud computing, and is also in the process of expanding and adjusting its portfolio to alleviate challenges and encourage even greater adoption.
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Topics: Analytics, Business Intelligence, Data Governance, Data Integration, Data
Organizations today are working with multiple applications and systems, including enterprise resource planning (ERP), customer relationship management (CRM), supply chain management (SCM) and other systems, where data can easily become fragmented and siloed. And as the organization increases its data sources and adds more systems and custom applications, it becomes challenging to manage the data consistently and keep data definitions up to date. This increases the need to use master data management (MDM) software that can provide a single source of truth to drive accurate analytics and business operations.
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Topics: Analytics, Business Intelligence, Data Governance, Data Integration, Data, Digital Technology, Analytics & Data
Data lakes have enormous potential as a source of business intelligence. However, many early adopters of data lakes have found that simply storing large amounts of data in a data lake environment is not enough to generate business intelligence from that data. Similarly, lakes and reservoirs have enormous potential as sources of energy. However, simply storing large amounts of water in a lake is not enough to generate energy from that water. A hydroelectric power station is required to harness and unleash the power-generating potential of a lake or reservoir, utilizing a combination of turbines, generators and transformers to convert the energy of the flowing water into electricity. A hydroanalytic data platform, the data equivalent of a hydroelectric power station, is required to harness and unleash the intelligence-generating potential of a data lake.
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Topics: Analytics, Business Intelligence, Cloud Computing, Data Governance, Data Integration, Data, Digital Technology, data lakes, AI and Machine Learning, data operations, data platforms
TIBCO is a large, independent cloud-computing and data analytics software company that offers integration, analytics, business intelligence and events processing software. It enables organizations to analyze streaming data in real time and provides the capability to automate analytics processes. It offers more than 200 connectors, more than 200 enterprise cloud computing and application adapters, and more than 30 non-relational structured query language databases, relational database management systems and data warehouses.
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Topics: embedded analytics, Analytics, Collaboration, Data Governance, Information Management, Data, Digital Technology, data lakes, AI and Machine Learning
The need for a COVID-19 vaccination “passport” has prompted some to suggest using blockchain technology as a means of reliably verifying an individual’s status at an international level. There are precedents: for example, until smallpox was eradicated, all international travelers were obliged to carry an immunization record for that disease on a standard paper form to gain entrance to a country. With the likelihood that COVID-19 will remain endemic for many years, a reliable digital record with universal accessibility would be a boon to everyone, especially to international travelers. Vaccination records are just one part of the broader topic of using blockchain technology for medical identity management.
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Topics: Data Governance, Information Management, Data, blockchain
Organizations have become more agile and responsive, in part, as a result of being more agile with their information technology. Adopting a DevOps approach to application deployment has allowed organizations to deploy new and revised applications more quickly. DataOps is enabling organizations to be more agile in their data processes. As organizations are embracing artificial intelligence (AI) and machine learning (ML), they are recognizing the need to adopt MLOps. The same desire for agility suggests that organizations need to adopt AnalyticOps.
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Topics: business intelligence, Analytics, Data Governance, Data, Digital Technology, data operations, data platforms
Talend is a data integration and management software company that offers applications for cloud computing, big data integration, application integration, data quality and master data management. The platform enables personnel to work with relational databases, Apache Hadoop, Spark and NoSQL databases for cloud or on-premises jobs. Talend data integration software offers an open and scalable architecture and can be integrated with multiple data warehouses, systems and applications to provide a unified view of all data. Its code generation architecture uses a visual interface to create Java or SQL code.
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Topics: Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, Data, Digital Technology, data lakes, AI and Machine Learning
Enterprises looking to adopt cloud-based data processing and analytics face a disorienting array of data storage, data processing, data management and analytics offerings. Departmental autonomy, shadow IT, mergers and acquisitions, and strategic choices mean that most enterprises now have the need to manage data across multiple locations, while each of the major cloud providers and data and analytics vendors has a portfolio of offerings that may or may not be available in any given location. As such, the ability to manage and process data across multiple clouds and data centers is a growing concern for large and small enterprises alike. Almost one-half (49%) of respondents to Ventana Research’s Analytics and Data Benchmark Research study are using cloud computing for analytics and data, of which 42% are currently using more than one cloud provider.
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Topics: Analytics, Cloud Computing, Data Governance, Data Integration, Data, Digital Technology, data lakes, AI and Machine Learning, data operations, data platforms
Databricks is a data engineering and analytics cloud platform built on top of Apache Spark that processes and transforms huge volumes of data and offers data exploration capabilities through machine learning models. It can enable data engineers, data scientists, analysts and other workers to process big data and unify analytics through a single interface. The platform supports streaming data, SQL queries, graph processing and machine learning. It also offers a collaborative user interface — workspace — where workers can create data pipelines in multiple languages — including Python, R, Scala, and SQL — and train and prototype machine learning models.
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Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Information Management, Data, data lakes, AI and Machine Learning
The technology industry throws around a lot of similar terms with different meanings as well as entirely different terms with similar meanings. In this post, I don’t want to debate the meanings and origins of different terms; rather, I’d like to highlight a technology weapon that you should have in your data management arsenal. We currently refer to this technology as data virtualization. Other similar terms you may have heard include data fabric, data mesh and [data] federation. I’ll briefly discuss these terms and how I see them being used, but ultimately, I’d like to share with you some research that shows why data virtualization can be valuable, regardless of what you call it.
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Topics: Analytics, Data Governance, Data Integration, Data, Digital Technology, data lakes
Data governance is a hot topic these days. In fact, we are conducting benchmark research on the subject here. With increasing regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), organizations face more external oversight of their data governance practices. The risk of significant fines associated with these and other regulations, coupled with organizations’ internal compliance requirements, has brought more attention to data governance practices. We anticipate through 2023, three-quarters of Chief Data Officers’ primary concerns will be governing the privacy and security of their organization’s data.
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Topics: Analytics, Data Governance, Data, Digital Technology
Collibra is a data governance software company that offers tools for metadata management and data cataloging. The software enables organizations to find data quickly, identify its source and assure its integrity. Line-of-business workers can use it to create, review and update the organization's policies on different data assets. Collibra’s software uses a microservice architecture and open application programming interfaces to connect to various data ecosystems. Its data intelligence cloud platform can automatically classify data from various sources such as online transaction processing databases, master repositories and Excel files without moving the data, so the information assets stay protected.
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Topics: Analytics, Business Intelligence, Data Governance, Data Preparation, Information Management, Data, data lakes, AI and Machine Learning
The annual Ventana Research Digital Innovation Awards showcase advances in the productivity and potential of business applications, as well as technology that contributes significantly to the improved processes and performance of an organization. Our goal is to recognize technology and vendors that have introduced noteworthy digital innovations to advance business and IT.
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Topics: Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, Data, Digital Technology, blockchain, data lakes, AI and Machine Learning
Environmental, social and governance reporting by public corporations has become a top-of-mind issue for senior executives and boards of directors as countries increasingly consider or mandate its implementation in some form. The fundamental rationale for ESG reporting is rooted in the inability of purely financial measures to capture externalities (such as greenhouse gas emissions) or provide metrics that enable an objective assessment of management’s ability to properly determine trade-offs between short-term results and long-term sustainability. And, while in the United States the Sarbanes-Oxley Act mandates that auditors assess governance, the focus of this assessment is on preventing financial fraud as opposed to broader objectives that may be important to the functioning of the company as a sustainable entity.
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Topics: Human Capital Management, Office of Finance, Business Intelligence, Data Governance, Data Preparation, Data, Financial Performance Management, ERP and Continuous Accounting
We are happy to share some insights about Viamedici EPIM drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
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Topics: Marketing, Data Governance, Data, Product Information Management, Price and Revenue Management, Digital Commerce, Operations & Supply Chain, Enterprise Resource Planning, continuous supply chain, Office of Sales, data operations
The annual Ventana Research Digital Innovation Awards showcase advances in the productivity and potential of business applications, as well as technology that contributes significantly to the improved processes and performance of an organization. Our goal is to recognize technology and vendors that have introduced noteworthy digital innovations to advance business and IT.
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Topics: Voice of the Customer, Collaboration, Data Governance, Data Preparation, Information Management, Data, Product Information Management, Digital Marketing, Digital Commerce, blockchain, data lakes, intelligent marketing
We are happy to share some insights about Contentserv CS drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
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Topics: Marketing, Data Governance, Data, Product Information Management, Price and Revenue Management, Digital Commerce, Operations & Supply Chain, Enterprise Resource Planning, continuous supply chain, Office of Sales, data operations
We are happy to share some insights about Perfion drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
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Topics: Marketing, Data Governance, Data, Product Information Management, Price and Revenue Management, Digital Commerce, Operations & Supply Chain, Enterprise Resource Planning, continuous supply chain, Office of Sales, data operations
A year of business uncertainty, lockdowns and operational disruptions forced finance and accounting organizations to adapt and change in many ways that are proving to be permanent. The need to operate virtually resulted in some organizations accelerating their adoption of technology, bringing them closer to achieving a transformation of the finance and accounting function: reshaping the department into an organization that is more forward-looking and strategic. Strategic in the sense of providing greater visibility into how the company and each of its business units is performing and insight into how to achieve better results going forward. Its focus is on what is happening next and not merely on what just happened. It does not only explain past results but uses that context to provide guidance about the choices executives and managers have, and the likely impact of those choices. To truly achieve this degree of transformation requires a different departmental structure, one that incorporates a Finance IT capability.
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Topics: Office of Finance, Business Intelligence, Data Governance, Data Preparation, Business Planning, Financial Performance Management, ERP and Continuous Accounting, blockchain, robotic finance, Predictive Planning, AI and Machine Learning
We are happy to share some insights about Winshuttle EnterWorks drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
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Topics: Marketing, Data Governance, Data, Product Information Management, Price and Revenue Management, Digital Commerce, Operations & Supply Chain, Enterprise Resource Planning, continuous supply chain, Office of Sales, data operations
The annual Ventana Research Digital Innovation Awards showcase advances in the productivity and potential of business applications, as well as technology that contributes significantly to the improved processes and performance of an organization. Our goal is to recognize technology and vendors that have introduced noteworthy digital innovations to advance business and IT.
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Topics: Customer Experience, Human Capital Management, Marketing, Office of Finance, Voice of the Customer, Continuous Planning, embedded analytics, Learning Management, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, Internet of Things, Business Planning, Contact Center, Data, Product Information Management, Sales Performance Management, Workforce Management, Financial Performance Management, Price and Revenue Management, Digital Technology, Digital Marketing, Digital Commerce, Operations & Supply Chain, Enterprise Resource Planning, ERP and Continuous Accounting, Revenue, blockchain, natural language processing, data lakes, Total Compensation Management, robotic finance, Predictive Planning, employee experience, candidate engagement, Conversational Computing, Continuous Payroll, AI and Machine Learning, collaborative computing, mobile computing, continuous supply chain, Subscription Management, agent management, extended reality, intelligent marketing, sales enablement, work experience management, lease and tax accounting, robotic automation
We are happy to share some insights about inRiver PIM drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
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Topics: Marketing, Data Governance, Data, Product Information Management, Price and Revenue Management, Operations & Supply Chain, Enterprise Resource Planning, continuous supply chain, Office of Sales, data operations
We are happy to share some insights about Riversand PX 360 drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
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Topics: Marketing, Data Governance, Data, Product Information Management, Price and Revenue Management, Digital Commerce, Operations & Supply Chain, Enterprise Resource Planning, continuous supply chain, Office of Sales, data operations
We are happy to share some insights about Magnitude Agility PIM drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
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Topics: Marketing, Data Governance, Data, Product Information Management, Price and Revenue Management, Digital Commerce, Operations & Supply Chain, Enterprise Resource Planning, continuous supply chain, Office of Sales, data operations
I am happy to share insights gleaned from our latest Value Index research, an assessment of how well vendors’ offerings meet buyers’ requirements. The Ventana Research Value Index: Mobile Analytics and Data 2021 is the distillation of a year of market and product research by Ventana Research. See our prior post for a description of our methodology and which vendors are included.
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Topics: business intelligence, Analytics, Collaboration, Data Governance, Information Management, Data, Digital Technology, natural language processing, mobile computing
We are happy to share some insights about Salsify ProductXM drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
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Topics: Marketing, Data Governance, Data, Product Information Management, Price and Revenue Management, Digital Commerce, Operations & Supply Chain, Enterprise Resource Planning, continuous supply chain, Office of Sales, data operations
We are happy to share some insights about Amazon QuickSight drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
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Topics: embedded analytics, Analytics, Collaboration, Data Governance, Data Preparation, Information Management, Data, natural language processing, AI and Machine Learning
We are happy to share some insights about Pimcore Platform drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
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Topics: Marketing, Data Governance, Data, Product Information Management, Price and Revenue Management, Digital Commerce, Operations & Supply Chain, Enterprise Resource Planning, continuous supply chain, Office of Sales, data operations
We are happy to share some insights about Google Looker drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
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Topics: embedded analytics, Analytics, Collaboration, Data Governance, Data Preparation, Information Management, Data, natural language processing, AI and Machine Learning
We are happy to share some insights about Akeneo Serenity drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
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Topics: Marketing, Data Governance, Data, Product Information Management, Price and Revenue Management, Digital Commerce, Operations & Supply Chain, Enterprise Resource Planning, continuous supply chain, Office of Sales, data operations
We are happy to share some insights about ThoughtSpot drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
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Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, Data, natural language processing, AI and Machine Learning
We are happy to share some insights about Informatica's Product 360 drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
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Topics: Marketing, Data Governance, Data, Product Information Management, Price and Revenue Management, Digital Commerce, Operations & Supply Chain, Enterprise Resource Planning, continuous supply chain, Office of Sales, data operations
We are happy to share some insights about TIBCO Spotfire drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
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Topics: business intelligence, embedded analytics, Analytics, Collaboration, Data Governance, Data Preparation, Information Management, Data, AI and Machine Learning
We are happy to share some insights about Sisense drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
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Topics: business intelligence, embedded analytics, Analytics, Collaboration, Data Governance, Data Preparation, Information Management, Data, natural language processing, AI and Machine Learning
We are happy to share some insights about Microsoft Power BI drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
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Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, Data, natural language processing, AI and Machine Learning
We are happy to share some insights about Tableau drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
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Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, Data, natural language processing, AI and Machine Learning
We are happy to share some insights about SAS drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
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Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, Data, natural language processing, AI and Machine Learning
We are happy to share some insights about SAP drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
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Topics: business intelligence, embedded analytics, Analytics, Collaboration, Data Governance, Data Preparation, Data, natural language processing, AI and Machine Learning
We are happy to share some insights about Board International drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
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Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, Data, natural language processing, AI and Machine Learning
We are happy to share some insights about Yellowfin drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
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Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, Data, natural language processing, AI and Machine Learning
We are happy to share some insights about Domo drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
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Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, Data, natural language processing, AI and Machine Learning
We are happy to share some insights about Oracle Analytics Cloud drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
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Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, Data, AI and Machine Learning
We are happy to share some insights about Qlik drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
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Topics: business intelligence, embedded analytics, Analytics, Collaboration, Data Governance, Data Preparation, Data, Information Management (IM), natural language processing, AI and Machine Learning
We are happy to share some insights about Information Builders’ WebFOCUS Business Intelligence and Analytics Platform drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
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Topics: business intelligence, embedded analytics, Analytics, Collaboration, Data Governance, Data Preparation, Information Management, Data, natural language processing, AI and Machine Learning
We are happy to share some insights about MicroStrategy drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
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Topics: business intelligence, embedded analytics, Analytics, Collaboration, Data Governance, Data Preparation, Information Management, Data, natural language processing, AI and Machine Learning
We are happy to share some insights about IBM drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
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Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Information Management, natural language processing, AI and Machine Learning
Everyone talks about data quality, as they should. Our research shows that improving the quality of information is the top benefit of data preparation activities. Data quality efforts are focused on clean data. Yes, clean data is important. but so is bad data. To be more accurate, the original data as recorded by an organization’s various devices and systems is important.
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Topics: Data Governance, Data Preparation, Data, Information Management (IM), data lakes
Machine learning is valuable for organizations, but it can be hard to deploy. Our Machine Learning Dynamic Insights research identifies that not having enough skilled resources and difficulty building and maintaining ML systems are pressing challenges organizations face in applying ML. Traditional ML model development is resource-intensive, requiring significant domain knowledge and time to produce and compare dozens of models. And as the number of ML models grow, their management becomes difficult. By bringing automation to ML, organizations can reduce the time it takes to create production-ready ML models. AutoML can also enable organizations to make data science initiatives more accessible across the organization.
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Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Data, AI and Machine Learning
The amount of data flowing into organizations is growing exponentially, creating a need to process more data more quickly than ever before. Our Data Preparation Benchmark Research shows that accessing and preparing data continues to be the most time-consuming part of making data available for analysis. This can potentially slow down the organizational functions which depend on the analysis results. Trying to get ahead of the backlog with incremental improvements to existing approaches and traditional technologies alone can be frustrating.
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Topics: business intelligence, embedded analytics, Analytics, Collaboration, Data Governance, Data Preparation, Data, Information Management (IM), data lakes
Organizations are becoming more and more data-driven and are looking for ways to accelerate the usage of artificial intelligence and machine learning (AI/ML). Developing and deploying AI/ML models can be complicated in many ways, often involving different tools and services to manage these solutions from end to end. Accessing and preparing data is the most common challenge organizations face in this process, and consequently, AI/ML vendors typically incorporate tools to address this part of the process. But there are many other steps in the process as well, such as coordinating the handoff between data scientists and IT or software engineers for deployment to production. This can potentially slow down the entire data-to-insights process. End-to-end platforms for AI offer the promise of simplifying these processes, allowing teams that work with data to improve organizational results.
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Topics: business intelligence, Analytics, Collaboration, Data Governance, Data Preparation, Data, AI and Machine Learning
Organizations are accelerating their digital transformation and looking for innovative ways to engage with customers in this new digital era of data management. The goal is to understand how to manage the growing volume of data in real time, across all sources and platforms, and use it to inform, streamline and transform internal operations. Over the years, the adoption of cloud computing has gained momentum with more and more organizations trying to make use of applications, data, analytics and self-service business intelligence (BI) tools running on top of cloud-computing infrastructure in order to improve efficiency. However, cloud adoption means living with a mix of on-premises and multiple cloud-based systems in a hybrid computing environment. The challenge is to ensure that processes, applications and data can still be integrated across cloud and on-premises systems. Our research shows that organizations still have a significant requirement for on-premises data management but also have a growing requirement for cloud-based capabilities.
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Topics: business intelligence, embedded analytics, Analytics, Collaboration, Data Governance, Information Management, Internet of Things, Data, natural language processing, AI and Machine Learning
Organizations are increasingly using data as a strategic asset, which makes data services critical. Huge volumes of data need to be stored, managed, discovered and analyzed. Cloud computing and storage approaches provide enterprises with various capabilities to store and process their data in third-party data centers. The advent of data platforms previously discussed here are essential for organizations to effectively manage their data assets.
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Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Lake, Data Preparation, Data, AI and Machine Learning, Microsoft Azure
Since customer data platforms (CDP) emerged in the marketplace about five years ago, there has been debate about what roles they fill, especially within customer service organizations. They were originally developed by small software firms to provide marketing teams with a comprehensive view of customer records. Those records could be scattered throughout an organization, siloed by system and department. CDPs were an attempt to shortcut integration processes that are long, expensive and often custom-designed.
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Topics: Customer Experience, Marketing, data artisan, Data Governance, Data Lake, Data Preparation, Data, Information Management (IM), intelligent marketing
Ventana Research recently announced its 2021 Market Agenda for data, continuing the guidance we have offered for nearly two decades to help organizations derive optimal value and improve business outcomes.
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Topics: Data Governance, Data Preparation, Information Management, Data, data lakes, Streaming Data, data operations, Event Data, Data catalog, Event Streams, Event Stream Processing
Data is becoming more valuable and more important to organizations. At the same time, organizations have become more disciplined about the data on which they rely to ensure it is robust, accurate and governed properly. Without data integrity, organizations cannot trust the information produced by their data processes, and will be discouraged from using that data, resulting in inefficiencies and reduced effectiveness.
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Topics: business intelligence, Analytics, Data Governance, Data Preparation, Information Management, Data, data lakes
Organizations are dealing with exponentially increasing data that ranges broadly from customer-generated information, financial transactions, edge-generated data and even operational IT server logs. A combination of complex data lake and data warehouse capabilities are required to leverage this data. Our research shows that nearly three-quarters of organizations deploy both data lakes and data warehouses but are using a variety of approaches which can be cumbersome. A single platform that can provide both capabilities will help address organizations’ requirements.
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Topics: PROS Pricing, embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, Data, data lakes, AI and Machine Learning
Traditional on-premises data processing solutions have led to a hugely complex and expensive set of data silos where IT spends more time managing the infrastructure than extracting value from the data. Big data architectures have attempted to solve the problem with large pools of cost-effective storage, but in doing so have often created on-premises management and administration challenges. These challenges of acquiring, installing and maintaining large clusters of computing resources gave rise to cloud-based implementations as an alternative. Public cloud is becoming the new center for data as organizations migrate from static on-premises IT architectures to global, dynamic and multi-cloud architectures.
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Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Data, data lakes, AI and Machine Learning
Organizations are always looking to improve their ability to use data and AI to gain meaningful and actionable insights into their operations, services and customer needs. But unlocking value from data requires multiple analytics workloads, data science tools and machine learning algorithms to run against the same diverse data sets. Organizations still struggle with limited data visibility and insufficient insights, which are often caused by a multitude of reasons such as analytic workloads running independently, data spread across multiple data centers, data governance, etc. In our ongoing benchmark research project, we are researching the ways in which organizations work with big data and the challenges they face.
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Topics: business intelligence, embedded analytics, Analytics, Collaboration, Data Governance, Data Preparation, Data, Information Management (IM), data lakes, AI and Machine Learning
The annual Ventana Research Digital Innovation Awards showcases advances in the productivity and potential of business applications, as well as technology that contributes significantly to improved efficiency and productivity in the processes and the performance of an organization. Our goal is to recognize technology and vendors that have introduced noteworthy digital innovations that advance business and IT.
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Topics: Analytics, Collaboration, Data Governance, Data Lake, Data Preparation, IOT, Data, Information Management (IM), Digital Technology, blockchain, Conversational Computing, AI and Machine Learning, collaborative computing, mobile computing, extended reality
A data lake is a centralized repository designed to house big data in structured, semi-structured and unstructured form. I have been covering the data lake topic for several years and encourage you to check out an earlier perspective called Data Lakes: Safe Way to Swim in Big Data? for background. Our data lake research has uncovered some points to consider in your efforts, and I’d like to offer a deeper dive into our findings.
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Topics: Data Governance, Data Lake, Information Management, Data, Digital Technology
Every organization performing analytics with multiple employees needs to collaborate. They should be collaborating in the analytics process and in communicating the results of those analyses. As I continue my evaluation of analytics and data vendors, I have to admit some disappointment at the level of collaborative capabilities some analytics vendors provide. To be fair, the level of capabilities vary widely, but I expected collaborative capabilities to be more uniformly available as a standard feature in analytics technologies by now. I had anticipated that three-quarters of analytics vendors would include collaboration capabilities. More than half the vendors I have evaluated support some comments and discussion in their products, only a few have incorporated social recognition and wall posting as part of their collaborative capabilities. So, what impact does a lack of analytics collaboration have on organizations undergoing digital transformation?
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Topics: business intelligence, Analytics, Collaboration, Data Governance, Data Preparation, Information Management, Data, Digital Technology, collaborative computing
For decades, data integration was a rigid process. Data was processed in batches once a month, once a week or once a day. Organizations needed to make sure those processes were completed successfully—and reliably—so they had the data necessary to make informed business decisions. The result was battle-tested integrations that could withstand the test of time.
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Topics: Data Governance, Data Integration, Data Preparation, Information Management, dataops, data operations
Ventana Research has been evaluating analytics and business intelligence (BI) software for a long time—almost 20 years. Our methodology for these assessments is referred to as a Value Index. We use weightings derived from our benchmark research about how you, as buyers of these technologies, value and evaluate vendors. You can view our 2019 Value Index results here. I am in the process of completing the 2020 evaluation now.
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Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, natural language processing, Conversational Computing, AI and Machine Learning, collaborative computing
In this analyst perspective, Dave Menninger takes a look at data lakes. He explains the term “data lake,” describes common use cases and shares his views on some of the latest market trends. He explores the relationship between data warehouses and data lakes and share some of Ventana Research’s findings on the subject. He also provides an assessment of the risks organizations face in working with data lakes and offers recommendations for maximizing the potential of data.
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Topics: Big Data, Data Warehousing, Analytics, Business Analytics, Business Intelligence, Data Governance, Data Management, Data Preparation, data lakes
Supercharging the customer experience (CX) is more than just an opportunity. It’s essential for every organization that looks to optimize engagement at every moment of the customer journey. In times such as these, when business continuity is a top priority, organizations must address the customer experience, especially if it has not been a focal point of the executive team.
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Topics: Sales, Customer Experience, Human Capital Management, Marketing, Office of Finance, Voice of the Customer, Analytics, Collaboration, Data Governance, Data Preparation, Information Management, Internet of Things, Contact Center, Data, Digital Technology, Digital Commerce, Operations & Supply Chain, Intelligent CX, Conversational Computing
Effectively managing data privacy and security is a high-stakes matter. When an organization doesn’t get it right, it often becomes front-page news and occasionally becomes a subject of litigation. Yet organizations face an equally challenging imperative to ensure that business users have easy access to the data they need. Depending on how they are implemented, data governance policies can inhibit access to data, making it harder to find and utilize the data assets of an organization.
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Topics: Big Data, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, Internet of Things
I was recently asked to identify key modern data architecture trends. Data architectures have changed significantly to accommodate larger volumes of data as well as new types of data such as streaming and unstructured data. Here are some of the trends I see continuing to impact data architectures.
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Topics: Analytics, Business Intelligence, Data Governance, Data Preparation, Data, Information Management (IM), Digital Technology, data lakes, AI and Machine Learning
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Topics: Analytics, Business Intelligence, Collaboration, Data Governance, Data, Digital Technology, Conversational Computing
Ventana Research recently announced its 2020 research agenda for data, continuing the guidance we’ve offered for nearly two decades to help organizations derive optimal value and improve business outcomes. Data volumes continue to grow while data latency requirements continue to shrink. Meanwhile, virtually every organization is confronting a need for good data governance.
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Topics: Collaboration, Data Governance, Data Preparation, Information Management, Data, blockchain, data lakes
For interactions with customers to go well, organizations must manage an ever-increasing array of engagement channels. Our research finds that organizations expect to see interaction volumes increase on all channels, especially digital ones such as text-based messaging, chat, mobile and social apps. Unfortunately, the systems that manage these channels are typically disparate and uncoordinated and may not use the same underlying technology. This makes it difficult for organizations to coordinate customer interactions consistently and provide the best possible customer experience.
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Topics: Customer Experience, Voice of the Customer, business intelligence, embedded analytics, Analytics, Collaboration, Data Governance, Data Preparation, Information Management, Internet of Things, Contact Center, Data, Digital Technology, Digital Commerce, blockchain, natural language processing, data lakes, Intelligent CX, AI and Machine Learning, Subscription Management, agent management, extended reality
Using customer analytics effectively involves several challenges. Organizations must make it a business priority, cultivate leadership and set a course for ensuring data and analytics are being processed and governed effectively. But effectiveness also requires technology that will assist in the effective operations and management of customers and help an organization achieve its goals.
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Topics: Customer Experience, Voice of the Customer, embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, Contact Center, Data, Digital Technology, Digital Commerce, blockchain, natural language processing, data lakes, Intelligent CX, Conversational Computing, AI and Machine Learning, collaborative computing, mobile computing, Subscription Management, agent management, extended reality
Customer analytics have never been more important, but effectively creating and managing them is not easy. The data that’s required to achieve visibility into all customer activity involves many applications and systems and it’s a challenge to ensure the data used is accurate and consistent. Even once data is assembled, organizations often struggle to apply analytics to create the metrics that best represent an understanding of the past and, more importantly, the path to the future.
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Topics: Customer Experience, Voice of the Customer, embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Lake, Data Preparation, Information Management, Contact Center, Data, Digital Technology, Digital Commerce, blockchain, natural language processing, Intelligent CX, Conversational Computing, AI and Machine Learning, collaborative computing, Subscription Management, agent management, extended reality
Today’s intense competition requires that companies know as much as they can about their customers in order to anticipate their needs and deliver a superior customer experience. However, many organizations struggle to do this well. Implementing initiatives to improve customer value across any department or process involving customers requires both in-depth visibility into current operations and excellent metrics.
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Topics: Customer Experience, Voice of the Customer, business intelligence, embedded analytics, Analytics, Collaboration, Data Governance, Data Preparation, Internet of Things, Contact Center, Data, Digital Commerce, blockchain, natural language processing, data lakes, Intelligent CX, Conversational Computing, AI and Machine Learning, collaborative computing, mobile computing, Subscription Management, agent management, extended reality
Having effective analytics enables businesses to understand far better than ever before the data they’re collecting, and to do so in greater volumes and more forms. These new capabilities are especially relevant to sales organizations. When applied to sales data, analytics can help sales teams achieve quotas and forecast more consistently, as well as understand the impacts of incentives and maximize the potential of territories, all of which help improve sales performance. These benefits provide the foundation for a business case to adopt analytics tools that generate information to guide actions and decision-making for sales organizations.
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Topics: Customer Experience, Voice of the Customer, business intelligence, embedded analytics, Analytics, Collaboration, Data Governance, Data Preparation, Information Management, Internet of Things, Contact Center, Data, Digital Technology, Digital Commerce, blockchain, natural language processing, data lakes, Intelligent CX, AI and Machine Learning, Subscription Management, agent management
By itself, data isn’t useful for business; the application of analytics is necessary to transform data into actionable information. Data analysis of one sort or another has long been a core competence of finance departments, applied to balance sheets, income statements or cash flow statements. Today, however, Finance must go beyond these basics by expanding the scope of the data being examined to include all financial and operational information that can yield actionable insights. Analysis thus should include, for example, data from the systems that manage sales operations, human resources and field service and that data must be available to all departments and applications that need it.
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Topics: Customer Experience, Human Capital Management, Voice of the Customer, embedded analytics, Learning Management, Analytics, Business Intelligence, Collaboration, Data Governance, Data Lake, Data Preparation, Information Management, Internet of Things, Contact Center, Data, Product Information Management, Sales Performance Management, Workforce Management, Financial Performance Management, Price and Revenue Management, Digital Technology, Digital Marketing, Digital Commerce, ERP and Continuous Accounting, blockchain, natural language processing, robotic finance, Predictive Planning, candidate engagement, Intelligent CX, Conversational Computing, Continuous Payroll, AI and Machine Learning, revenue and lease accounting, collaborative computing, mobile computing, Subscription Management, total rewards management, intelligent marketing, intelligent sales
This year, I attended Informatica World 2019, Informatica's annual user conference. The main focus this year was on the cloud with a heavy does of AI. Under that focus, Informatica's conference emphasized capabilities across six areas (all strong areas for Informatica): data integration, data management, data quality & governance, Master Data Management (MDM), data cataloging, and data security.
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Topics: Big Data, Data Quality, Master Data Management, Data Governance, Data Management, Informatica, data lakes, Informatica World, Data Storage
Organizations now must store, process and use data of significantly greater volume and variety than in the past. These factors plus the velocity of data today — the unrelentingly rapid rate at which it is generated, both in enterprise systems and on the internet — add to the challenge of getting the data into a form that can be used for business tasks.
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Topics: Analytics, Business Intelligence, Data Governance, Data Preparation, Information Management, Internet of Things, Data, Digital Technology, blockchain, data lakes, AI and Machine Learning
“Straight-through processing” (STP) is a business process and data architecture methodology. Technology advances have made STP increasingly feasible for any business process, allowing companies to design and execute them from inception to completion in a more automated fashion, minimizing or eliminating human intervention in the process. The associated data also progresses automatically end-to-end through the process, preserving its integrity. Because there is no human intervention, data is more accurate and less prone to manipulation.
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Topics: Sales, Customer Experience, Office of Finance, Recurring Revenue, Data Governance, Financial Performance Management, Digital Commerce, ERP and Continuous Accounting, Billing and Recurring Revenue
Workiva recently introduced Wdata, a cloud facility for centralizing financial and non-financial information from multiple sources. It frees up time for finance organizations, especially financial planning and analysis (FP&A) groups, to explore conditions and trends in their business because they need to spend less of it gathering data and preparing it for analysis and reporting. Ventana Research recently awarded Workiva our Digital Innovation award for Wdata because of its transformative potential.
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Topics: Office of Finance, Recurring Revenue, Continuous Planning, Data Governance, Data Preparation, Financial Performance Management, Price and Revenue Management, Enterprise Resource Planning, ERP and Continuous Accounting, Sales Planning and Analytics, revenue recognition
Once again I attended Tableau's Users Conference, along with 17,000 other attendees, affectionately self-referred to as "data nerds". Pushing the envelope in data capabilities and access, Tableau introduced the "Ask Data" feature, allowing users to prose natural language queries and receive a response, along with new data preparation capabilities and other enhancements to help data analysts. Further, Tableau announced new developer enhancements including a new developer program to better align tools built for Tableau with Tableau's interface. For the full breakdown of Tableau User Conference 2018, and my analysis of all the largest announcements, watch my hot take video.
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Topics: Big Data, Data Governance, Data Integration, Data Preparation, Tableau Software, data lakes
This year, Teradata rebranded the Teradata users conference from "Partners" to "Analytics Universe", and there is a reason for it. For decades, Teradata has represented the high end of the analytic database, but new innovations and technologies are adding flexibility to Teradata's licensing as they compete. For the full breakdown of Teradata's Analytics Universe 2018, and my analysis of all the largest announcements, watch my hot take video.
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Topics: Big Data, Data Warehousing, Teradata, Analytics, Data Governance, Data Management, Data Preparation, Information Management, Data, Digital Technology
In 2017 Strata + Hadoop World was changed to the Strata Data Conference. As I pointed out in my coverage of last year’s event, the focus was largely on machine learning and artificial intelligence (AI). That theme continued this year, but my impression of the event was of a community looking to get value out of data regardless of the technology being used to manage that data. The change was subtle: The location was the same; the exhibitors were largely the same; attendance was similar this year and last. But there was no particular vendor or technology dominating the event.
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Topics: Big Data, Data Science, Machine Learning, Analytics, Business Intelligence, Data Governance, Data Integration, Data Preparation, Information Optimization, Digital Technology, Machine Learning and Cognitive Computing
All too often, software vendors view analytics as the end rather than the beginning of a process. I’m reminded of some of the advanced math classes I’ve taken in which the teaching process focused on a few key aspects of a mathematical proof or solution, leaving the rest of the exercise to be worked out by the students. In other contexts, you may hear people say the numbers speak for themselves.
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Topics: Data Science, Machine Learning, business intelligence, Analytics, Collaboration, Data Governance, Information Optimization, Digital Technology, collaboration for business
We now are well beyond the year depicted in 2001: A Space Odyssey, a cinematic perspective on the future of artificial intelligence in which HAL 9000, a computer, is able to simulate human behavior and control machines. Anyone reviewing the past two years of marketing around AI in the business technology industry can be forgiven for believing that we have arrived at the futuristic state Stanley Kubrick imagined. We have not.
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Topics: Big Data, Data Science, Mobile, Customer Analytics, Customer Engagement, Customer Experience, Machine Learning, Mobile Technology, Analytics, Business Intelligence, Cloud Computing, Collaboration, Customer Service, Data Governance, Data Integration, Data Preparation, Internet of Things, Contact Center, Information Optimization, Digital Technology, Machine Learning and Cognitive Computing, Cybersecurity, Billing and Recurring Revenue, Workforce Optimization, collaboration for business
Blockchains are attractive because their built-in security and trust factors make them useful for almost all business interactions involving organizations and individuals. Blockchains have two basic functions. One is as a method for handling transactions involving property such as land deeds, trademarks or other assets. The second involves exchanges of data such as identities of individuals or businesses, the location of an object at a point in time or weather conditions. All interactions involving property or assets include the transfer of data as well, of course, but some blockchain use cases are informational only.
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Topics: Big Data, Data Science, Mobile, Marketing Performance Management, Office of Finance, business intelligence, Analytics, Cloud Computing, Data Governance, Data Integration, Data Preparation, Internet of Things, Digital Technology, Digital Marketing, Digital Commerce, Operations & Supply Chain
We are have arrived at the May 25, 2018 date when the European Union’s General Data Privacy Regulations (GDPR) become enforceable, following what has been a two-year transition period. Companies were given this time to put in place reasonable measures and the systems necessary to support the legislation’s wide-ranging personal data privacy requirements, which apply to any organization with more than 250 employees that serves EU citizens. While this regulation will apply in the EU, it has implications for any organization in the world that provides services involving the personal data of any EU citizen.
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Topics: Big Data, Data Science, Mobile, Sales, Customer Analytics, Customer Engagement, Customer Experience, Marketing, Analytics, Business Intelligence, Cloud Computing, Collaboration, Data Governance, Data Integration, Data Preparation, Internet of Things, Contact Center, Digital Technology, Digital Marketing, Digital Commerce, Cybersecurity, Billing and Recurring Revenue, collaboration for business, mobile marketing
Advancing the potential of any business requires continuous improvement in the processes and technology that support it. Many companies have embraced attempts at a digital transformation, and it’s become a goal to which organizational resources and budgets have been dedicated around the globe.
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Topics: Big Data, Data Science, Mobile, Sales, Customer Analytics, Customer Engagement, Customer Experience, Human Capital Management, Machine Learning, Marketing, Marketing Performance Management, Mobile Technology, Office of Finance, Wearable Computing, Analytics, Business Intelligence, Cloud Computing, Collaboration, Customer Service, Data Governance, Data Integration, Data Preparation, Internet of Things, Contact Center, Information Optimization, Product Information Management, Digital Technology, Digital Marketing, Digital Commerce, Operations & Supply Chain, Machine Learning and Cognitive Computing, Pricing and Promotion Management, Cybersecurity, Billing and Recurring Revenue, Workforce Optimization, collaboration for business, mobile marketing
We at Ventana Research recently published our research agendas for 2018. The world of data and information management continues to evolve, as does our research on the use of these technologies to improve your organization’s operations. Relational databases are no longer the only viable enterprise data store as more organizations adopt a polyglot database infrastructure. And while their exact form may still be changing, as I have recently written, big data technologies are here to stay. Our Data and Analytics in the Cloud Benchmark Research indicates that an increasing number of organizations are opting for cloud-based deployments: A modern data infrastructure includes a hybrid of on-premises and cloud deployments for 44 percent of organizations. Our upcoming research will track how these changes are affecting data- and information-management processes.
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Topics: Data Governance, Information Management, Data, blockchain
Informatica reintroduced itself to the world at its recent customer conference, Informatica World, in San Francisco. The company took advantage of the event to showcase its new branding in an effort to change the way customers think about the company. Informatica has been providing information services in the cloud for more than a decade. Even though cloud revenue comprises a minority of Informatica’s business, in absolute terms, the revenue is significant, and company executives want the public to recognize Informatica as a leader in cloud-based data management services for enterprises. Presenters also made notable product announcements, discussed below, including the application of machine learning to the data management process.
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Topics: Big Data, Data Science, Analytics, Business Intelligence, Cloud Computing, Data Governance, Data Integration, Data Preparation, Information Optimization, Machine Learning and Cognitive Computing, Machine Learning Digital Technology
I recently attended SAS Institute’s analyst relations conference. There the company provided updates on its financial performance and its Viya platform and a glimpse into some of its future plans.
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Topics: Big Data, Data Science, Mobile Technology, business intelligence, Analytics, Cloud Computing, Collaboration, Data Governance, Data Integration, Data Preparation, Internet of Things, Information Optimization, Machine Learning and Cognitive Computing, Machine Learning Digital Technology
Cloudera provides database and enabling technology for the big data market and overall for data and information management. As my colleague David Menninger has written, the big data and information management technology markets are changing rapidly and require vendors to adapt to them. Cloudera has grown significantly over the last decade and now has approximately 1,000 customers and provides support and services in countries around the world. Its product and technology strategy is to provide a unified data management platform, Cloudera Enterprise, that can meet the data engineering and science needs for a range of analytic and operational database applications. Its primary focus is its Enterprise Data Hub, which as a data lake can handle organizations’ big data and analytical needs. As David Menninger asserts, the data lake is a safe way to invest in big data. It also helps shift the focus away from the V’s (volume, velocity and variety) of big data to the A’s, which are analytics, awareness, anticipation and action.
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Topics: Big Data, Data Science, Machine Learning, business intelligence, Analytics, Cloud Computing, Data Governance, Data Integration, Data Preparation, Internet of Things, Cognitive Computing, Information Optimization, Digital Technology
Big data has become an integral part of information management. Nearly all organizations have some need to access big data sources and produce actionable information for decision-makers. Recognizing this connection, we merged these two topics when we put together our recently published research agendas for 2017. As we plan our research, we focus on current technologies and how they can be used to improve an organization’s performance. We then share those results with our readers.
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Topics: Big Data, Data Science, Analytics, Data Governance, Data Integration, Data Preparation, Information Management, Internet of Things, Machine Learning and Cognitive Computing, Machine Learning Digital Technology
IBM recently held its inaugural World of Watson event. Formerly known as IBM Insight, and prior to that IBM Information on Demand, the annual event, attended by 17,000 people this year, showcases IBM’s data and analytics and the broader IBM efforts in cognitive computing. The theme for the event, as you might guess, was the Watson family of cognitive computing products. I, for one, was glad to spend more time getting to know the Watson product line, and I’d like to share some of my observations from the event.
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Topics: Big Data, Data Science, Machine Learning, Analytics, Cloud Computing, Data Governance, Data Integration, Internet of Things, Information Optimization, Machine Learning and Cognitive Computing
The proliferation of chief “something” officer (CxO) titles over the past decades recognizes that there’s value in having a single individual focused on a specific critical problem. A CxO position can be strategic or it can be the ultimate middle management role, with far more responsibilities than authority. Many of those handed such a title find that it’s the latter. This may be because the organization that created the title is unwilling to invest the necessary powers and portfolio of responsibilities to make it strategic – a case of institutional inertia. Or it may be that the individual given the CxO title doesn’t have the skills or temperament to be a “chief” in a strategic sense.
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Topics: GRC, Office of Finance, Chief Risk Officer, CRO, ERM, OpenPages, Operational Performance, Business Analytics, Business Collaboration, Business Performance, Cloud Computing, Data Governance, Financial Performance, IBM, compliance, Data, Risk, Financial Services, FPM
Integrated risk management (IRM) was a major theme at IBM’s recent Smarter Risk Management analyst summit in London. In the market context, IBM sees this topic as a means to differentiate its product and messaging from those of its competitors. IRM includes cloud-based offerings in operational risk analytics, IT risk analytics and financial crimes management designed for financial institutions and draws on component elements of software that IBM acquired over the past five years, notably from Algorithmics for risk-aware business decisions, Open Pages for compliance management, SPSS for sophisticated analytics, Cognos for reports, dashboards and scorecards, and Tivoli for managing all of this in a Web environment. Putting its software in the cloud enables IBM to streamline integration and maintenance, offer more flexible deployment and consumption options and potentially lower the total cost of ownership.
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Topics: Supply Chain Performance, GRC, Office of Finance, Chief Risk Officer, CRO, ERM, OpenPages, IT Performance, Operational Performance, Business Analytics, Business Collaboration, Business Performance, Cloud Computing, Customer & Contact Center, Data Governance, Governance, Risk & Compliance (GRC), IBM, Information Applications, Information Management, Operational Intelligence, compliance, Data, Risk, Financial Services, FPM
Informatica and Exterro have announced a partnership in the market for discovery of electronic data and documents (known as e-discovery). Exterro has made its reputation in e-discovery workflow and legal holds management while Informatica is a leader in data integration that our Value Index finds as the top and Hot rated provider. The partnership is designed to provide users of Exterro’s Fusion E-Discovery software with a single point of control for organizing and managing legal and preservation holds (that is, preventing electronic data from alteration or deletion) of unstructured and structured data that are held in Informatica’s Data Archive. Informatica specializes in the efficient management of information assets, which our benchmark research shows is not easy for most organizations to do because they have data spread across multiple applications and systems: Two-thirds of organizations said that this makes it difficult to manage information. By consolidating in a single repository the storage of information that is likely to be the subject of discovery, companies can simplify and cut the cost of the search process as well as reduce risk. Orchestrating legal and preservation holds can be complex since multiple people or groups within a company may be legally involved with the same data over an extended period of time. Moreover, it’s important to ensure that once the holds are no longer needed, all data that can be eliminated is eliminated.
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Topics: Sales Performance, Office of Finance, eDiscovery, Exterro, Operational Performance, Business Performance, Data Governance, Data Management, Financial Performance, Informatica, Information Applications, Information Management, Workforce Performance, compliance, Data, Information, Risk
I recently attended the annual Informatica analyst summit to get the latest on that company’s strategy and plans. The data integration provider offers a portfolio of information management software that supports today’s big data and information optimization needs. Informatica is busy making changes in its presentation to the market and its marketing and sales efforts. New executives, including new CMO Marge Breya, are working to communicate what is possible with Informatica’s product portfolio, and it’s more than just data integration.
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Topics: Big Data, Data Quality, Master Data Management, Salesforce.com, MDM, IT Performance, Business Analytics, Business Intelligence, Cloud Computing, Data Governance, Data Integration, Data Management, Governance, Risk & Compliance (GRC), Informatica, Information Applications, Information Management, Operational Intelligence, CEP, Informatica Cloud, Strata+Hadoop
Managing data efficiently across the enterprise continues to be a large challenge for both business units and IT. Organizations need data supplied in a consistent format and timely manner to help manage their activities and processes, but some do not look beyond conventional approaches to improvement. Today’s large volumes of data make it more difficult to understand the relationships among data and the role of location-related data. Our 2012 benchmark research on information management found that most organizations need to advance their data initiatives and take steps to integrate them.
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Topics: Master Data Management, Sales Performance, Social Media, MDM, IT Performance, Operational Performance, Business Analytics, Business Intelligence, Cloud Computing, Customer & Contact Center, Data Governance, Information Applications, Information Management, Location Intelligence, DG
Managing the access, storage and use of data effectively can provide businesses a competitive advantage. Last year I outlined what the big deal is in big data, as the initial focus on the volume, velocity and variety of data – what my colleague Tony Cosentino calls the three V’s – is only one small piece of how organizations should evaluate this technology. The more balanced approach is to include what he calls the three W’s – the what, so what and now what, which shifts the focus to an outcome-based view that can handle the time–to-value urgency found in business. Big data analytics can help assess the volume of data, while the velocity of data that is potentially in-motion is best handled by what we call operational intelligence. Beyond these, techniques and technology such as predictive analytics and visual discovery facilitate extracting more value from big data. Along with a wide variety of data, these tools help organizations focus on optimizing information assets. We will soon conduct benchmark research into information optimization to determine how organizations are dealing with their information today and what steps they are taking to improve. In-memory computing will surely be one of those steps, as it can significantly improve the time-to-insight equation.
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Topics: Big Data, Master Data Management, Predictive Analytics, Sales Performance, MDM, Operational Performance, Business Analytics, Business Intelligence, Business Performance, CIO, Cloud Computing, Data Governance, Data Integration, Financial Performance, Information Applications, Information Management, Location Intelligence, Operational Intelligence, Product Information Management
Informatica is a Hot vendor when it comes to our Value Index for Data Integration. When it comes to meeting the business need for consistent and high-quality product information, Informatica just raised its prospects with its announced takeover of Heiler Software. Until now, Informatica has lacked a product information management (PIM) offering. Germany-based Heiler Software, whose presence has continued to grow in Europe and North America, was recently ranked as a Hot Vendor in our Value Index for Product Information Management, as you can read in our no-cost executive report. Informatica’s purchase brings complementary products together from one supplier and will help organizations provide a higher quality and volume of product information throughout business processes, and help place business, rather than IT, in the role of creating, using and sharing information.
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Topics: Sales Performance, Supply Chain Performance, MDM, PIM, Operational Performance, Business Performance, CIO, Customer & Contact Center, Data Governance, Financial Performance, Information Management, Product Information Management
Salesforce is a global software-as-a-service (SaaS) company to be reckoned with. The swarming crowds at its Dreamforce event last week were estimated to exceed 90,000. The company is rapidly growing an ecosystem that includes Sales, Service and Marketing Clouds; Force.com for building applications; and Data.com for storing data in the cloud centrally for use across Salesforce products. It is also focusing on social computing, as I outlined at the beginning of the event. Hundreds of Salesforce partners complement and in some cases compete with the company with a large range of applications and tools available on the Salesforce AppExchange.
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Topics: Master Data Management, Sales Performance, Salesforce.com, Social Media, SnapLogic, Zyme Solutions, IT Performance, Operational Performance, Business Analytics, Business Intelligence, Business Performance, CIO, Cloud Computing, Customer & Contact Center, Data Governance, Data Integration, Governance, Risk & Compliance (GRC), Informatica, Information Builders, Information Management, Data, data integrity, database.com, Kapow
Ventana Research has just released the 2012 Value Index for Data Integration, in which we evaluate the competency and maturity of vendors and products. Our firm has been researching this software category for almost a decade. Our latest benchmark research in information management found that data integration is a critical component of information management strategies, according to 55 percent of organizations. Our benchmark research on organizations using this software not only uncovers best practices and trends, but it also highlights why IT is using data integration to advance its competencies across people and processes.
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Topics: Big Data, Master Data Management, Microsoft, Pentaho, Sales Performance, SAP, SAS, Social Media, Supply Chain Performance, Talend, SnapLogic, IT Performance, Operational Performance, Business Analytics, Business Collaboration, Business Mobility, Business Performance, Cloud Computing, Customer & Contact Center, Data Governance, Data Integration, Financial Performance, Governance, Risk & Compliance (GRC), IBM, Informatica, Information Applications, Information Builders, Information Management, Location Intelligence, Oracle, Workforce Performance, Syncsort
Ventana Research has just released our 2012 Value Index for Product Information Management (PIM), in which we evaluate the competency and maturity of vendors and products. Our firm has been researching this software category for many years, and our latest benchmark research in product information management, coming out shortly, finds PIM software providing substantive benefits in new channels of interaction with suppliers and customers.
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Topics: Master Data Management, Sales, Sales Performance, SAP, Supply Chain Performance, Enterworks, Hybris, Stibo Systems, Webon, Operational Performance, Business Analytics, Business Collaboration, Business Performance, Cloud Computing, Customer & Contact Center, Data Governance, Financial Performance, Governance, Risk & Compliance (GRC), IBM, Information Management, Location Intelligence, Oracle, Heiler, Product Information Management, Riversand
In our definition, information management encompasses the acquisition, organization, dissemination and use of information by organizations to create and enhance business value. Effective information management ensures optimal access, relevance, timeliness, quality and security of this data with the aim to improve organizational performance. This goal is not easily met, especially as organizations acquire ever more data at an ever faster pace. In our business analytics benchmark research of more than 2,600 organizations, almost half (45%) have to integrate six or more types of data in their analyses. More than two-thirds reported that they spend more time preparing data than analyzing it. To assist in dealing with these sorts of issues and others, we’ve laid out an ambitious information management research agenda for 2012.
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Topics: Data Quality, Master Data Management, Social Media, IT Performance, Business Analytics, Business Intelligence, Cloud Computing, Complex Event Processing, Data Governance, Data Integration, Information Applications, Information Life Cycle Management, Information Management, Operational Intelligence
My colleague Mark Smith and I recently chatted with executives of Tidemark, a company in the early stages of providing business analytics for decision-makers. It has a roster of experienced executive talent and solid financial backing. There’s a strategic link with Workday that reflects a common background at the operational and investor levels. As it gets rolling, Tidemark is targeting large and very companies as customers for its cloud-based system for analyzing data. It can automate alerts and enhance operating visibility, collaboratively assess the potential impacts of decisions and support the process of implementing those decisions.
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Topics: Big Data, Data Warehousing, Master Data Management, Performance Management, Planning, Predictive Analytics, Sales Performance, GRC, Budgeting, Risk Analytics, Operational Performance, Analytics, Business Analytics, Business Collaboration, Business Intelligence, Business Mobility, Business Performance, Cloud Computing, Customer & Contact Center, Data Governance, Data Integration, Financial Performance, In-Memory Computing, Information Management, Mobility, Workforce Performance, Risk, Workday, Financial Performance Management, Integrated Business Planning, Strata+Hadoop
Talend recently announced version 5 of its information management platform, which emphasizes unifying its various components. Through a combination of development activities, acquisitions and partnerships, Talend has been steadily building its portfolio of information management capabilities. In addition to its core data integration capabilities, it has added data quality, master data management, application integration and with this release business process management (BPM).
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Topics: Big Data, Data Quality, Master Data Management, Talend, Business Analytics, Cloud Computing, Data Governance, Data Integration, Information Applications, Information Management, Strata+Hadoop
Kalido recently introduced version 9 of its Information Engine product. The company has been around for 10 years but has had difficulty establishing its identity in the information management market. Kalido was perhaps ahead of its time, partly a vendor of data integration, partly master data management and partly data governance. As an example of the positioning challenge, its core product, Information Engine, while not a data integration tool, could in some cases provide sufficient capabilities to meet an organization’s data integration needs. Its real value, however, comes from authoring and management of information about the user’s data warehouse.
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Topics: Data Quality, Data Warehousing, Master Data Management, Data Governance, Data Integration, Governance, Risk & Compliance (GRC), Information Applications, Information Management, Kalido
The information management (IM) technology market is undergoing a revolution similar to the one in the business intelligence (BI) market. We define information management as the acquisition, organization, control and use of information to create and enhance business value. It is a necessary ingredient of successful BI implementations, and while some vendors such as IBM, Information Builders, Pentaho and SAP are in addition integrating their BI and IM offerings, each discipline involves different aspects of the use of information and will require it sometimes integrated and sometimes separate.
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Topics: Data Quality, Social Media, IT Performance, Analytics, Business Analytics, Business Collaboration, Business Intelligence, Business Technology, CIO, Complex Event Processing, Data Governance, Data Integration, Information Management, Information Technology, Operational Intelligence
SAP has launched its Enterprise Information Management (EIM) 4.0 release as part of its “Run Better Tour.” It includes a broad range of information management components spanning data integration, data quality, data profiling, metadata management and more. The launch was done in conjunction with SAP Business Intelligence (BI) 4.0, which got much bigger billing at the event –to the point where one might call this a stealth marketing campaign. However, the event did identify three themes intended to highlight EIM capabilities: event insight, trusted data and text processing. The goal here was to communicate the integration SAP has achieved within and between its BI and EIM products. IBM announced a similar advance with its InfoSphere products and Informatica has also invested heavily in integrating its information management products. Our Information Management benchmark research validates this approach, finding that incompatible tools create a significant obstacle to organizations’ quest for consistent sets of data.
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Topics: Data Quality, SAP, Social Media, IT Performance, Analytics, Business Analytics, Business Intelligence, Business Technology, CIO, Complex Event Processing, Data Governance, Data Integration, Information Management, Information Technology, Operational Intelligence
The proper management of data is ever more important and complex. Business people must have easy access to data from all over the enterprise, but unguarded access and distribution may enable users to bypass the IT organization’s rules for data management, copy and paste whatever they like into spreadsheets and share it in uncontrolled fashion. Firm control of enterprise data requires policies and practice for governance, yet our benchmark research found that only 12 percent of organizations are innovative in their data governance. Reaching this highest level of maturity is not easy when you have to manage a portfolio of policies and rules that span business units and IT and must take into account people, processes, information and supporting technology. Despite this it is essential to address this data governance need and as I wrote is a 2010 priority (See: “Optimized IT and Focus on Information Technology in 2010“).
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Topics: Master Data Management, MDM, IT Performance, Data Governance, Information Management, Kalido
The proper management of data is ever more important and complex. Business people must have easy access to data from all over the enterprise, but unguarded access and distribution may enable users to bypass the IT organization’s rules for data management, copy and paste whatever they like into spreadsheets and share it in uncontrolled fashion. Firm control of enterprise data requires policies and practice for governance, yet our benchmark research found that only 12 percent of organizations are innovative in their data governance. Reaching this highest level of maturity is not easy when you have to manage a portfolio of policies and rules that span business units and IT and must take into account people, processes, information and supporting technology. Despite this it is essential to address this data governance need and as I wrote is a 2010 priority (See: “Optimized IT and Focus on Information Technology in 2010“).
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Topics: Master Data Management, MDM, IT Performance, Data Governance, Information Management, Kalido
