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.
Alation’s Data Governance Accelerates Data Intelligence
Posted by
Matt Aslett on Mar 29, 2023 3:00:00 AM
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Topics: Data Governance, Data Management, Data, data operations
AWS Enables Data Democratization with Amazon DataZone
Posted by
Matt Aslett on Mar 21, 2023 3:00:00 AM
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
Hitachi Vantara DataOps Improves Analytics and Decision-Making
Posted by
David Menninger on Mar 21, 2023 3:00:00 AM
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
Exasol Accelerates Analytics With an In-Memory Database
Posted by
Matt Aslett on Mar 7, 2023 3:00:00 AM
Organizations require faster analytics to continuously improve business operations and stay competitive in today’s market. However, many struggle with slow analytics due to a variety of factors such as slow databases, insufficient data storage capacity, poor data quality, lack of proper data cleansing and inadequate IT infrastructure. Challenges such as data silos can also decrease operational efficiency. And as the data grows, performing complex data modelling becomes challenging for users as they spend more time managing data rather than identifying insights.
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Topics: Data Management, Data, Analytic Data Platforms
More Effective Contingency Planning Improves Agility
Posted by
Robert Kugel on Feb 28, 2023 3:00:00 AM
We live in a time of uncertainty, not unpredictability. Managing an organization in uncertain times is always hard, but tools are available to improve the odds for success by making it easier and faster to plan for contingencies and scenarios. Software makes it possible to quickly consider the impact of a range of events or assumptions and devise a set of plans to deal with them. Dedicated planning and budgeting software has been around for decades but is about to become all the more useful as vendors increasingly incorporate artificial intelligence using machine learning to assist in scenario planning. Organizations can quickly investigate the impact of different contingencies and the consequences of a range of reactions to them.
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Topics: Office of Finance, Data Management, Business Planning, AI and Machine Learning, digital finance
Promethium Provides Data Fabric and Self-Service for Speed to Insights
Posted by
Matt Aslett on Feb 22, 2023 3:00:00 AM
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
Soda Provides Collaborative Approach to Data Observability
Posted by
Matt Aslett on Feb 16, 2023 3:00:00 AM
Data observability was a hot topic in 2022 and looks likely to be a continued area of focus for innovation in 2023 and beyond. As I have previously described, data observability software is designed to automate the monitoring of data platforms and data pipelines, as well as the detection and remediation of data quality and data reliability issues. There has been a Cambrian explosion of data observability software vendors in recent years, and while they have fundamental capabilities in common, there is also room for differentiation. One such vendor is Soda Data, which offers an open-source platform for self-service data observability that is focused on facilitating collaboration between business decision-makers and data teams responsible for generating and managing data to improve trust in data.
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Topics: Cloud Computing, Data Management, Data, Digital Technology, data operations, Analytics & Data
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
Despite the emphasis on organizations being more data-driven and making an increasing proportion of business decisions based on data and analytics, it remains the case that some of the most fundamental questions about an organization are difficult to answer using data and analytics. Ostensibly simple questions such as, “how many customers does the organization have?” can be fiendishly difficult to answer, especially for organizations with multiple business entities, regions, departments and applications. Increasing volumes and sources of data can hinder, rather than help. Only 1 in 5 participants (20%) in Ventana Research’s Analytics and Data Benchmark research are very confident in their organization’s ability to analyze the overall quantity of data. This is a perennial issue that data and application integration vendors, such as SnapLogic, are aiming to address – increasingly through automation and products for business users as well as data management professionals.
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Topics: Cloud Computing, Data Management, Data, AI and Machine Learning, data operations, Analytics and Data
2023 Market Agenda for Data: Accelerating Data Agility
Posted by
Matt Aslett on Jan 18, 2023 3:00:00 AM
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
Data observability is a hot topic and trend. I have written about the importance of data observability for ensuring healthy data pipelines, and have covered multiple vendors with data observability capabilities, offered both as standalone and part of a larger data engineering system. Data observability software provides an environment that takes advantage of machine learning and DataOps to automate the monitoring of data quality and reliability. The term has been adopted by multiple vendors across the industry, and while they all have key functionality in common – including collecting and measuring metrics related to data quality and data lineage – there is also room for differentiation. A prime example is Acceldata, which takes a position that data observability requires monitoring not only data and data pipelines but also the underlying data processing compute infrastructure as well as data access and usage.
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Topics: Cloud Computing, Data Management, Data, Digital Technology, data operations
InterSystems Transforming Organizations with Cloud Smart Data Fabric
Posted by
Matt Aslett on Dec 27, 2022 3:00:00 AM
The shift from on-premises server infrastructure to cloud-based and software-as-a-service (SaaS) models has had a profound impact on the data and analytics architecture of many organizations in recent years. More than one-half of participants (59%) in Ventana Research’s Analytics and Data Benchmark research are deploying data and analytics workloads in the cloud, and a further 30% plan to do so. Customer demand for cloud-based consumption models has also had a significant impact on the products and services that are available from data and analytics vendors. Data platform providers, both operational and analytic, have had to adapt to changing customer demand. The initial response — making existing products available for deployment on cloud infrastructure — only scratched the surface in terms of responding to emerging expectations. We now see the next generation of products, designed specifically to deliver innovation by taking advantage of cloud-native architecture, being brought to market both by emerging startups, and established vendors, including InterSystems.
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Topics: Business Intelligence, Cloud Computing, Data Management, Data, natural language processing, AI and Machine Learning, data operations, Analytics & Data, operational data platforms, Analytic Data Platforms
The Data Pantry Accelerates Actionable Analytics for Decision-Making
Posted by
Robert Kugel on Dec 22, 2022 3:00:00 AM
Ventana Research uses the term “data pantry” to describe a method of data storage (and the technology and process blueprint for its construction) created for a specific set of users and use cases in business-focused software. It’s a pantry because all the data one needs is readily available and easily accessible, with labels that are immediately recognized and understood by the users of the application. In tech speak, this means the semantic layer is optimized for the intended audience. It is stocked with data gathered from multiple sources and immediately available for analysis, forecasting, planning and reporting. This does away with the need for analysts to repeatedly perform data extraction, enrichment or transformation motions from the required source systems, all but eliminating the substantial amount of time analysts and business users routinely spend on data preparation.
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Topics: Continuous Planning, Business Intelligence, Data Management, Business Planning, Data, Financial Performance Management, Enterprise Resource Planning, AI and Machine Learning, continuous supply chain, data operations, digital finance, profitability management, Analytics & Data, Streaming Data & Events
Monte Carlo Bets on the Future of Data Observability
Posted by
Matt Aslett on Dec 13, 2022 3:00:00 AM
Earlier this year, I wrote about the increasing importance of data observability, an emerging product category that takes advantage of machine learning (ML) and Data Operations (DataOps) to automate the monitoring of data used for analytics projects to ensure its quality and lineage. Monitoring the quality and lineage of data is nothing new. Manual tools exist to ensure that it is complete, valid and consistent, as well as relevant and free from duplication. Data observability vendors, including Monte Carlo Data, have emerged in recent years with the goal of increasing the productivity of data teams and improving organizations’ trust in data using automation and artificial intelligence and machine learning (AI/ML).
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Topics: Business Intelligence, Cloud Computing, Data Management, Data, data operations
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
The Arguments For, and Against, In-Database Machine Learning
Posted by
Matt Aslett on Nov 23, 2022 3:00:00 AM
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
Databricks Lakehouse Platform Maximizes Analytical Value
Posted by
Matt Aslett on Nov 16, 2022 3:00:00 AM
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
IBM’s Cloud Pak for Data Builds a Foundation for Data Fabric
Posted by
Matt Aslett on Nov 8, 2022 3:03:00 AM
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
In their pursuit to be data-driven, organizations are collecting and managing more data than ever before as they attempt to gain competitive advantage and respond faster to worker and customer demands for more innovative, data-rich applications and personalized experiences. As data is increasingly spread across multiple data centers, clouds and regions, organizations need to manage data on multiple systems in different locations and bring it together for analysis. As the data volumes increase and more data sources and data types are introduced in the organization, it creates challenges to storing, managing, connecting and analyzing the huge set of information that is spread across multiple locations. Having a strong foundation and scalable data management architecture in place can help alleviate many of the challenges organizations face when they are scaling and adding more infrastructure. We have written about the potential for hybrid and multi-cloud platforms to safeguard data across heterogenous environments, which plays to the strengths of companies, such as Actian, that provide a single environment with the ability to integrate, manage and process data across multiple locations.
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Topics: Data Management, Data, data operations, Analytic Data Platforms
Orchestrating Data Pipelines Facilitates Data-Driven Analytics
Posted by
Matt Aslett on Oct 25, 2022 3:00:00 AM
I have written a few times in recent months about vendors offering functionality that addresses data orchestration. This is a concept that has been growing in popularity in the past five years amid the rise of Data Operations (DataOps), which describes more agile approaches to data integration and data management. In a nutshell, data orchestration is the process of combining data from multiple operational data sources and preparing and transforming it for analysis. To those unfamiliar with the term, this may sound very much like the tasks that data management practitioners having been undertaking for decades. As such, it is fair to ask what separates data orchestration from traditional approaches to data management. Is it really something new that can deliver innovation and business value, or just the rebranding of existing practices designed to drive demand for products and services?
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Topics: Data Management, Data, AI and Machine Learning, data operations, Analytics & Data
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
Ocient Delivers Ad Hoc Analytics on Hyperscale Workloads
Posted by
Matt Aslett on Oct 12, 2022 3:00:00 AM
I have written before about the continued use of specialist operational and analytic data platforms. Most database products can be used for operational or analytic workloads, and the number of use cases for hybrid data processing is growing. However, a general-purpose database is unlikely to meet the most demanding operational or analytic data platform requirements. Factors including performance, reliability, security and scalability necessitate the use of specialist data platforms. I assert that through 2026, and despite increased demand for hybrid operational and analytic processing, more than three-quarters of data platform use cases will have functional requirements that encourage the use of specialized analytic or operational data platforms. It is for that reason that specialist database providers, including Ocient, continue to emerge with new and innovative approaches targeted at specific data-processing requirements.
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Topics: business intelligence, Cloud Computing, Data Management, Data, Analytics and Data, Analytic Data Platforms
A predictive finance department is one that can command technology to be more forward-looking and action-oriented while still fulfilling its core role of handling the financial elements of its organization including accounting, treasury and corporate finance. Beyond just automating rote tasks, technology also facilitates a shift toward becoming a predictive finance organization. Greater amounts of information, now available in near real time, and the increasing use of artificial intelligence (AI), enable more immediate analyses and assessments of possible courses of action, providing executives and managers the ability to better anticipate change and the agility to adapt quickly to unexpected circumstances.
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Topics: Office of Finance, Business Intelligence, Data Management, Business Planning, Financial Performance Management, ERP and Continuous Accounting, AI and Machine Learning, digital finance
Astronomer’s Cloud-Based Data Orchestration Brings Efficiency
Posted by
Matt Aslett on Sep 29, 2022 3:00:00 AM
I have recently written about the organizational and cultural aspects of being data-driven, and the potential advantages data-driven organizations stand to gain by responding faster to worker and customer demands for more innovative, data-rich applications and personalized experiences. I have also explained that data-driven processes require more agile, continuous data processing, with an increased focus on extract, load and transform processes — as well as change data capture and automation and orchestration — as part of a DataOps approach to data management. Safeguarding the health of data pipelines is fundamental to ensuring data is integrated and processed in the sequence required to generate business intelligence. The significance of these data pipelines to delivering data-driven business strategies has led to the emergence of vendors, such as Astronomer, focused on enabling organizations to orchestrate data engineering pipelines and workflows.
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Topics: Cloud Computing, Data Management, Data, data operations, Analytics & Data
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 is Indispensable for Good Data Governance
Posted by
Matt Aslett on Sep 21, 2022 3:15:00 AM
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
Qlik Advances Self-Service Analytics and Business Intelligence
Posted by
David Menninger on Aug 11, 2022 3:00:00 AM
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
I have recently written about the importance of healthy data pipelines to ensure data is integrated and processed in the sequence required to generate business intelligence, and the need for data pipelines to be agile in the context of real-time data processing requirements. Data engineers, who are responsible for monitoring, managing and maintaining data pipelines, are under increasing pressure to deliver high-performance and flexible data integration and processing pipelines that are capable of handling the rising volume and frequency of data. Automation is a potential solution to this challenge, and several vendors, such as Ascend.io, have emerged in recent years to reduce the manual effort involved in data engineering.
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Topics: Big Data, Cloud Computing, Data Management, Data, data operations
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
A Data Pantry Speeds Development of Machine Learning Models
Posted by
Robert Kugel on Jul 12, 2022 3:00:00 AM
A few years ago – somewhat tongue in cheek – I began using the term “data pantry” to describe a type of data store that’s part of a business application platform, created for a specific set of users and use cases. It’s a data pantry because, unlike a general-purpose data store such as a data warehouse, everything the user needs is readily available and easily accessible, with labels that are immediately recognized and understood.
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Topics: Data Management, Business Planning, Financial Performance Management, ERP and Continuous Accounting, AI and Machine Learning, digital finance
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
I’ve never been a fan of talking about semantic models because most of the workforce probably doesn’t understand what they are, or doesn’t recognize them by name. But the findings in our recent Analytics and Data Benchmark Research have changed my mind. The research shows how important a semantic model can be to the success of data and analytics processes. Organizations that have successfully implemented a semantic model are more than twice as likely to report satisfaction with analytics (77%) compared with a 33% overall satisfaction rate. Therefore, I owe it to all of you to write about them.
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Topics: Business Intelligence, Data Management, AI and Machine Learning, data operations, Analytics & Data, semantic model
Data and Analytics Processes: Can We Get Personal?
Posted by
David Menninger on May 24, 2022 3:00:00 AM
There is a fundamental flaw in information technology, or at least in the way it is most commonly delivered. Most technology systems are developed under the assumption that all people will use the system primarily in the same way. Sure, there are some options built in — perhaps the same action can be initiated by either clicking on a button, selecting a menu item or invoking a keyboard short-cut. The problem is that when every variation needs to be coded into the system, the prospect of providing personalized software programs to every individual is impractical.
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Topics: Business Intelligence, Data Management, natural language processing, AI and Machine Learning, data operations, Analytics & Data
Accelerate Business Outcomes with Immuta Data Access Governance
Posted by
David Menninger on May 19, 2022 3:00:00 AM
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
The use of artificial intelligence (AI) using machine learning (ML) will be the single most important trend in business software this decade because it can multiply the investment value of such applications and provide vendors an important source of differentiation to achieve a competitive advantage in what are today very mature software categories. I assert that by 2025, almost all Office of Finance software vendors will have incorporated some AI capabilities to reduce workloads and improve performance. However, software vendors will be challenged to apply innovations in this area quickly while ensuring that the AI capabilities function well enough in the real world to foster rapid adoption while avoiding user frustration. The failures of the Apple Newton and Microsoft’s Clippy office assistant stand out as examples of too-ambitious-too-soon attempts at infusing intelligent automation.
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Topics: Office of Finance, embedded analytics, Data Management, Business Planning, Financial Performance Management, ERP and Continuous Accounting, AI and Machine Learning, digital finance
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
Big Data, Machine Learning and Alteryx Inspires 2019
Posted by
David Menninger on Jul 26, 2019 6:00:00 AM
Alteryx Inspire 2019, this year's user conference for Alteryx, drew around 4500 customers, partners, and prospects to Nashville’s Gaylord Opryland Resort & Convention Center in Tennessee last month. The strong attendance was a reflection of the strong growth Alteryx has experienced over the last year; roughly 50% growth year-over-year. This year's conference focused on Alteryx's evolution from data preparation to AI and machine learning, and both were front and center.
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Topics: Big Data, Data Science, alteryx, Machine Learning, Data Integration, Data Management, Alteryx Inspire
Embedded Data and Information Builders Summit 2019
Posted by
David Menninger on Jul 23, 2019 7:00:00 AM
Summit 2019, Information Builders' annual user conference, drew about 1000 attendees this year, including customers, partners and prospects all working with Information Builders' technologies. Under new leadership, Summit 2019 showcased the direction Information Builders is moving in the next couple of years.
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Topics: Big Data, embedded analytics, Analytics, Data Integration, Data Management, Information Builders, IOT, Streaming Data, Information Builders Summit 2019
Data Management on Display at Informatica World 2019
Posted by
David Menninger on Jun 12, 2019 7:00:00 AM
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
Qonnections 2019 is Qlik's annual user conference. Key news from this year's conference centered on acquisitions of Podium Data and Attunity, along with an expansion of certifications on Google Cloud Platform, AWS, and Azure, with the ability to support Red Hat OpenShift. Many of these announcements were centered on a key theme of a cloud and SaaS-first approach.
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Topics: Big Data, Analytics, Cloud Computing, Data Integration, Data Management, Information Management, Qlik, Qlik Qonnections
Domopalooza 2019 marked the first annual user conference after Domo went public, but the energy, excitement and new feature announcements have not slowed. With thousands in attendance and growing fast, this year's conference focused on five key areas: digitization, real time connectivity, driving insight based actions, applying AI & machine learning, and building applications. All of these announcements are aimed at broadening the workloads supported by Domo.
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Topics: Analytics, Business Intelligence, Collaboration, Data Integration, Data Management, Data Preparation, Domo
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
Self-Describing Data Powers B2B Blockchain Distributed Ledgers
Posted by
Robert Kugel on Feb 15, 2018 7:22:37 AM
The use of blockchain distributed ledgers in business processes is now a common theme in many business software vendors’ presentations. The technology has a multitude of potential uses. However, presentations about the opportunities for digital transformation always leave me wondering: How is this magic going to happen? I wonder this because the details about how data flows from point A to point B via a blockchain are critically important to blockchain utility and therefore the pace of its adoption.
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Topics: Planning, Predictive Analytics, Forecast, FP&A, Machine Learning, Reporting, budget, Budgeting, Continuous Planning, Analytics, Data Management, Cognitive Computing, Integrated Business Planning, AI, forecasting, consolidating
Predictive Finance Organizations Are More Valuable
Posted by
Robert Kugel on Feb 12, 2018 4:31:44 AM
Ventana Research uses the term “predictive finance” to describe a forward-looking, action-oriented finance organization that places emphasis on advising its company rather than fulfilling the traditional roles of a transactions processor and reporter. Technology is driving the shift away from the traditional bean-counting role. The cumulative evolution of software advances will substantially reduce finance and accounting workloads by automating most of the mechanical, rote functions in accounting, data preparation and reporting. (I recently summarized these in a “Robotic Finance”)
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Topics: Planning, Predictive Analytics, Forecast, FP&A, Machine Learning, Reporting, budget, Budgeting, Continuous Planning, Analytics, Data Management, Cognitive Computing, Integrated Business Planning, AI
Informatica Navigates Carefully to Broader Data Management
Posted by
Mark Smith on Dec 16, 2015 1:16:15 AM
This has been a dramatic year for Informatica, a major provider of data integration software. In August it was acquired and taken private by Permira funds and Canada Pension Plan Investment Board for about US$5.3 billion. This change was accompanied by shifts in its management. CEO Sohaib Abbasi became chairman and now has left, and many executives were replaced while Anil Chakravathy became CEO from being the Chief Product Officer. The new owners appear to have shifted the company’s strategic priorities to emphasize profitability with reduced headcount and return on the purchase investment. Despite these changes, during the past six months Informatica has made key product announcements that will impact its future and the future of data management.
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Topics: Big Data, Data Quality, Master Data Management, MDM, Operational Performance Management (OPM), Cloud Computing, Data Integration, Data Management, Data Preparation, Governance, Risk & Compliance (GRC), Informatica, Information Management, Business Performance Management (BPM), Information Optimization, Risk & Compliance (GRC)
Organizations today create and collect data at ever faster rates, and this introduces challenges in ensuring that data is not just managed but used in a consistent manner for a range of operational and analytic tasks. This is made more difficult by new data sources whose definitions vary from standard and widely used formats. Making all information available and consistent is essential to support business processes and decision-making. A key technology tool for this effort is master data management (MDM). Every business area needs MDM, whether it deals with customers, products, employees, finance or others individually or collectively in what is called multidomain MDM. It is an essential tool for data governance across an organization, which has become a focal point for improvement as many organizations spend significant time in data-related tasks. Our benchmark research on information optimization shows that preparing data for analysis (47%) and reviewing data for quality and consistency issues (45%) are the two information tasks that consume the most time. Properly used MDM enables data stewards and other IT professionals to improve the consistency and quality of departmental and enterprise data.
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Topics: Big Data, Data Quality, Master Data Management, Sales, Sales Performance, Social Media, Supply Chain Performance, Golden Records., MDM, Operational Performance, Analytics, Business Analytics, Business Performance, Cloud Computing, Customer & Contact Center, Data Management, Financial Performance, Information Applications, Information Management, Workforce Performance
Data-Driven Business Processes Essential for Optimization
Posted by
Ventana Research on Oct 16, 2014 10:29:35 AM
When applying information technology to drive better business performance, companies and the systems integrators that assist them often underestimate the importance of organizing data management around processes. For example, companies that do not execute their quote-to-cash cycle as an end-to-end process often experience a related set of issues in their sales, marketing, operations, accounting and finance functions that stem from entering the same data into multiple systems. The inability to automate passing of data from one functional group to the next forces people to spend time re-entering data and leads to fragmented and disconnected data stores. The absence of a single authoritative data source also creates conflicts about whose numbers are “right.” Even when the actual figures recorded are identical, discrepancies can crop up because of issues in synchronization and data definition. Lacking an authoritative source, organizations may need to check for and resolve errors and inconsistencies between systems to ensure, for example, that what customers purchased was what they received and were billed for. The negative impact of this lack of automation is multiplied when transactions are complex or involve contracts for recurring services.
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Topics: Big Data, Mobile, Sales Performance, Supply Chain Performance, ERP, Office of Finance, Operations, Management, close, closing, computing, end-to-end, Operational Performance, Analytics, Business Performance, Cloud Computing, Data Management, Information Applications, Information Management, CRM, Data, finance, FPM
Informatica Unveils New Platform and Tools for Information Optimization
Posted by
Mark Smith on Jun 3, 2014 9:09:23 AM
At the Informatica World 2014 conference, the company known for its data integration software unveiled the Intelligent Data Platform. In the last three years Informatica has expanded beyond data integration and now has a broad software portfolio that facilitates information management within the enterprise and through cloud computing. The Intelligent Data Platform forms a framework for its portfolio. This expression of broad potential is important for Informatica, which has been slow to position its products as capable of more than data integration. A large part of the value it provides lies in what its products can do to help organizations strengthen their enterprise architectures for managing applications and data. We see Informatica’s sweet spot in facilitating efficient use of data for business and IT purposes; we call this information optimization.
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Topics: Big Data, Master Data Management, Sales Performance, Supply Chain Performance, Operational Performance, Business Analytics, Business Intelligence, Business Performance, CIO, Cloud Computing, Customer & Contact Center, Data Integration, Data Management, Financial Performance, Informatica, Information Applications, Information Management, Workforce Performance, Information Optimization, Product Information Management, application
Many businesses are close to being overwhelmed by the unceasing growth of data they must process and analyze to find insights that can improve their operations and results. To manage this big data they find a rapidly expanding portfolio of technology products. A significant vendor in this market is SAS Institute. I recently attended the company’s annual analyst summit, Inside Intelligence 2014 (Twitter Hashtag #SASSB). SAS reported more than $3 billion in software revenue for 2013 and is known globally for its analytics software. Recently it has become a more significant presence in data management as well. SAS provides applications for various lines of business and industries in areas as diverse as fraud prevention, security, customer service and marketing. To accomplish this it applies analytics to what is now called big data, but the company has many decades of experience in dealing with large volumes of data. Recently SAS set a goal to be the vendor of choice for the analytic, data and visualization software needs for Hadoop. To achieve this aggressive goal the company will have to make significant further investments in not only its products but also marketing and sales. Our benchmark research on big data analytics shows that three out of four (76%) organizations view big data analytics as analyzing data from all sources, not just one, which sets the bar high for vendors seeking to win their business.
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Topics: Big Data, Predictive Analytics, SAS, Event Stream, Operational Performance, Analytics, Business Analytics, Business Intelligence, Business Performance, CIO, Customer & Contact Center, Data Management, Information Applications, Information Management, Location Intelligence, Operational Intelligence, Discovery
One of the potential benefits of cloud computing to access business applications and data is its potential to improve the situational awareness of executives and managers. By this I mean their understanding of what’s going on outside their company in addition to what’s happening within it. Today people have access to a trove of information about their own company, which is the result of decades of investment in an expanding range of enterprise transaction systems (ERP, CRM and supply chain management, for example) and convenient data stores that make accessing data easier than ever. But although people have access to internal information, most have big gaps in their knowledge about what’s going on in the outside world. Take, for example, market trends, information about a competitor’s, supplier’s or customer’s financials or industry-specific demand forecasts. Our benchmark research shows that while two-thirds of companies are satisfied with their ability to integrate information from standard internal sources, only 39 percent feel that way about reference or competitive data from external sources and 36 percent about text data from social media.
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Topics: Sales Performance, Supply Chain Performance, Operational Performance, Business Performance, Cloud Computing, Customer & Contact Center, Data Integration, Data Management, Financial Performance, Information Management, Workforce Performance, Data, finance, FPM
Informatica and Exterro Partner for More Effective E-discovery
Posted by
Robert Kugel on Nov 1, 2013 7:07:45 AM
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
The Personas that Matter the Most in Business Analytics
Posted by
Ventana Research on Aug 30, 2013 10:30:56 AM
As a new generation of business professionals embraces a new generation of technology, the line between people and their tools begins to blur. This shift comes as organizations become flatter and leaner and roles, context and responsibilities become intertwined. These changes have introduced faster and easier ways to bring information to users, in a context that makes it quicker to collaborate, assess and act. Today we see this in the prominent buying patterns for business intelligence and analytics software and an increased focus on the user experience. Almost two-thirds (63%) of participants in our benchmark research on next-generation business intelligence say that usability is the top purchase consideration for business intelligence software. In fact, usability is the driving factor in evaluating and selecting technology across all application and technology areas, according to our benchmark research.
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Topics: Big Data, Data Scientist, Sales Performance, Supply Chain Performance, IT Performance, Operational Performance, Analytics, Business Analytics, Business Intelligence, Business Performance, CIO, Customer & Contact Center, Data Management, Financial Performance, Information Applications, Information Management, Workforce Performance, Data
Cisco to Foster Smarter Network of Data by Acquiring Composite Software
Posted by
Mark Smith on Jun 23, 2013 1:04:54 AM
Cisco Systems has announced its intent to acquire Composite Software, which provides data virtualization to help IT departments interconnect data and systems; the purchase is scheduled to complete in early August. Cisco of course is known for its ability to interconnect just about anything with its networking technology; this acquisition will help it connect data better across networks. Over the last decade Composite had been refining the science of virtualizing data but had reached the peak of what it could do by itself, struggling to grow enough to meet the expectations of its investors, board of directors, employees, the market and visionary CEO Jim Green, who is well-known for his long commitment to improving data and technology architectures. According to press reports on the Internet, Cisco paid $180 million for Composite, which if true would be a good reward for people who have worked at Composite for some time and who were substantive shareholders.
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Topics: Big Data, Networking, IT Performance, Operational Performance, Business Analytics, Business Intelligence, Business Performance, Cloud Computing, Data Management, Information Applications, Information Management, Cisco, Composite Software, Data, Data Virtualization, Information Optimization, Internet of Everything, Strata+Hadoop
I recently attended Vision 2013, IBM’s annual conference for users of its financial governance, risk management and sales performance management software. These three groups have little in common operationally, but they share software infrastructure needs and basic supporting software components such as reporting and analytics. Moreover, while some other major vendors’ user group meetings concentrate on IT departments, Vision focuses on business users and their needs, which is a welcome difference. For me, there were three noteworthy features related to the finance portion of the program. First, IBM continues to advance its financial performance management (FPM) suite and emphasizes its Cognos TM1 platform to support a range of finance department tasks. Second, the user-led sessions illustrated improvements that finance departments can make to their core processes today, ones that improve the quality of these processes and go a long way toward enabling Finance to play a more strategic role in the company it serves. Third, the Cognos Disclosure Management product has better performance and useful new features to support the management of a full range of internal and external disclosure documents, including the extended close, which I have discussed.
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Topics: Planning, Reporting, Budgeting, closing, XBRL, Analytics, Business Performance, Data Management, Financial Performance, IBM, CFO, Financial Performance Management, FPM, SEC, TM1, Digital Technology
Informatica Establishes Order from Information Chaos
Posted by
Mark Smith on Mar 12, 2013 9:39:09 AM
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
Encountering New Bottlenecks with Oracle’s Breakthrough Technology
Posted by
Robert Kugel on Oct 3, 2012 11:43:23 AM
Two key themes that emerged from Larry Ellison’s Sunday night keynote at this year’s Oracle OpenWorld were faster processing speed and cheaper storage. An underlying purpose to these themes was to assert the importance of Oracle’s strategic vertical integration of hardware and software with the acquisitions of Sun. I try to view technology keynotes like this from the perspective of a practical business user. Advancements such of these are important because enhancing the performance and cost-effectiveness of IT infrastructure can drive substantially improved business capabilities. As I’ve noted in the past, the ability to rapidly process large amounts of data provides business users with significant new capabilities in areas such as complex event processing, social media analytics and the ability to analyze unstructured or semi-structured data. In planning, it has the potential to change how companies perform a wide range of analytics-driven processes, especially in areas such as planning, budgeting and forecasting. It makes it feasible to more fully explore the impact of different courses of action, because rather than having to wait hours or days for answers to questions that start with “What happens if we…” the answers come back in seconds. Review and planning sessions can focus more on what’s next rather than rehashing history.
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Topics: Big Data, Customer Experience, executive, IT Performance, Business Analytics, Business Performance, Data Management, Financial Performance, In-Memory Computing, Information Management, Business Process Management, Data, FPM
CIOs Need to Make Information Management a Real Priority
Posted by
Mark Smith on Aug 8, 2012 12:10:03 PM
Our recent benchmark research on information management uncovered some startling facts about the level of technology adoption necessary for efficient information-centric organizations. Chief information officers (CIO) are responsible for the availability of information to their businesses in a consistent and timely basis, but in most organizations, information management is seen as just a delegated set of tasks and is not the CIO’s top priority. This unfortunate outlook can have a lasting impact on the efficiency and profitability of a business.
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Topics: Big Data, Operational Performance, Business Analytics, Business Performance, CIO, Customer & Contact Center, Data Management, Financial Performance, Information Applications, Information Management, Data, IT
There has been a spate of acquisitions in the data warehousing and business analytics market in recent months. In May 2010 SAP announced an agreement to acquire Sybase, primarily for its mobility technology and had already been advancing its efforts with SAP HANA and BI. In July 2010 EMC agreed to acquire data warehouse appliance vendor Greenplum. In September 2010 IBM countered by acquiring Netezza, a competitor of Greenplum. In February 2011 HP announced after giving up on its original focus with HP Neoview and now has acquired analytics vendor Vertica that had been advancing its efforts efficiently. Even Microsoft shipped in 2010 its new release of SQL Server database and appliance efforts. Now, less than one month later, Teradata has announced its intent to acquire Aster Data for analytics and data management. Teradata bought an 11% stake in Aster Data in September, so its purchase of the rest of the company shouldn’t come as a complete surprise. My colleague had raised the question if Aster Data could be the new Teradata but now is part of them.
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Topics: Data Warehousing, Microsoft, RDBMS, SAS, Teradata, IT Performance, Business Intelligence, Cloud Computing, Data Management, HP, IBM, Information Management, Oracle
Secrets Revealed in Columnar Database Technology
Posted by
Ventana Research on Mar 3, 2011 9:55:23 PM
This is the second in a series of posts on the architectures of analytic databases. The first post addressed massively parallel processing (MPP) and database technology. In this post, we’ll look at columnar database technology. Given the recent announcement of HP’s plan to acquire Vertica, a columnar database vendor, there is likely to be even more interest in columnar database technology, how it operates and what benefits it offers.
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Topics: Data Warehousing, RDBMS, IT Performance, Business Intelligence, Cloud Computing, Data Management, Information Management
Living in the Era of Hadoop and Large-Scale Data
Posted by
Ventana Research on Mar 2, 2011 9:57:28 PM
It’s clear that now we are living in the era of big data. The stores of data on which modern businesses rely are already vast and increasing at an unprecedented pace. Organizations are capturing data at deeper levels of detail and keeping more history than they ever have before. Managing all of the data is thus emerging as one of the key challenges of the new decade.
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Topics: Data Warehousing, RDBMS, IT Performance, Business Intelligence, Cloud Computing, Data Management, Information Management, Strata+Hadoop
Kognitio Pablo Brings MDX to Data and Analytics
Posted by
Ventana Research on Feb 14, 2011 3:39:15 PM
Kognitio announced the addition of MultiDimensional eXpressions (MDX) capabilities for its WX2 product line. John Thompson, CEO of U.S. operations, and Sean Jackson, VP of marketing, shared some of the details with me recently. I find the marriage of MDX and large-scale data both technically challenging and potentially valuable to the market.
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Topics: Data Warehousing, MDX, RDBMS, IT Performance, Business Intelligence, Data Management, Information Management, Kognitio, MPP
MicroStrategy Combines Actions with Business Intelligence and Mobility
Posted by
Ventana Research on Feb 7, 2011 11:14:07 AM
Last week I attended MicroStrategy World 2011 in Las Vegas, the North American version of the business intelligence (BI) vendor’s annual user conference. The event was well attended, and the company claimed attendance was up 40% over last year. The purpose of the post is to recap the announcements made, highlight the areas where MicroStrategy is making investments and comment on the overall direction implied by these investments.
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Topics: Data Warehousing, MicroStrategy, IT Performance, Analytics, Business Intelligence, Cloud Computing, Data Management, Information Management
Jaspersoft Releases Version 4 and Tackles Large-Scale Data
Posted by
Ventana Research on Jan 28, 2011 12:24:46 PM
Open source business intelligence (BI) software vendor Jaspersoft recently announced general availability of its flagship product Jaspersoft 4 and earlier this week announced a new reporting project that provides data connectors to a variety of large-scale data sources.
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Topics: Data Warehousing, IT Performance, Analytics, Business Intelligence, Cloud Computing, Data Management, Information Management
Secrets Revealed in Massively Parallel Processing and Database Technology
Posted by
Ventana Research on Jan 19, 2011 11:09:13 AM
This is the first in a series of posts on the architectures of analytic databases. This is relational database technology that has been “supercharged” in some way to handle large amounts of data such as typical data warehouse workloads. The series will examine massively parallel processing (MPP), columnar databases, appliances and in-database analytics. Our purpose is to help those evaluating analytic database technologies understand some of the alternative approaches so they can differentiate between different vendors’ offerings. There is no single best solution for all analytics for all types of applications; usually the decision involves a series of trade-offs. Understanding what you might be giving up or gaining, you may be able to make a better decision about which solution is best for your organization’s needs.
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Topics: Data Warehousing, RDBMS, IT Performance, Business Intelligence, Cloud Computing, Data Management, Information Management
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