Ventana Research Analyst Perspectives

Tamr Directs Data Integrity

Posted by Matt Aslett on Feb 8, 2023 3:00:00 AM

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 There can be numerous siloed, incomplete and outdated data sources that result in inaccurate results.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

SnapLogic Promotes Intelligent Automation for All

Posted by Matt Aslett on Jan 31, 2023 3:00:00 AM

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

Acceldata Enables Data Observability

Posted by Matt Aslett on Jan 10, 2023 3:00:00 AM

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

SQream if You Want to Analyze Data Faster

Posted by Matt Aslett on Dec 8, 2022 3:00:00 AM

One of the most significant considerations when choosing an analytic data platform is performance. As organizations compete to benefit most from being data-driven, the lower the time to insight the better. As data practitioners have learnt over time, however, lowering time to insight is about more than just high-performance queries. There are opportunities to improve time to insight throughout the analytics life cycle, which starts with data ingestion and integration, includes data preparation and data management, as well as data storage and processing, and ends with data visualization and analysis. Vendors focused on delivering the highest levels of analytic performance, such as SQream, understand that lowering time to insight relies on accelerating every aspect of that life cycle.

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Topics: Business Intelligence, Data, AI and Machine Learning, data operations, Analytic Data Platforms

Teradata Goes Cloud Native with VantageCloud Lake

Posted by Matt Aslett on Dec 1, 2022 3:00:00 AM

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

Mind the Gap Between Data and Analytics

Posted by David Menninger on Nov 16, 2022 3:00:00 AM

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

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

Actian Manages Avalanches of Data

Posted by Matt Aslett on Nov 1, 2022 3:00:00 AM

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

Cloudera Embraces SaaS with Data Lakehouse Launch

Posted by Matt Aslett on Oct 18, 2022 3:00:00 AM

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

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

Enhancing Data Catalog with AI

Posted by David Menninger on Sep 22, 2022 3:00:00 AM

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

Ascend.io Automates Data Engineering

Posted by Matt Aslett on Aug 9, 2022 3:00:00 AM

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

Data-Driven Agenda for Organizations

Posted by Matt Aslett on Jul 21, 2022 3:00:00 AM

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

Zoho Unifies Data and Analytics

Posted by David Menninger on Jul 7, 2022 3:00:00 AM

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

Ahana Offers Managed-Services Approach to Simplify Presto Adoption

Posted by Matt Aslett on Jun 29, 2022 3:00:00 AM

I previously described the concept of hydroanalytic data platforms, which combine the structured data processing and analytics acceleration capabilities associated with data warehousing with the low-cost and multi-structured data storage advantages of the data lake. One of the key enablers of this approach is interactive SQL query engine functionality, which facilitates the use of existing business intelligence (BI) and data science tools to analyze data in data lakes. Interactive SQL query engines have been in use for several years — many of the capabilities were initially used to accelerate analytics on Hadoop — but have evolved along with data lake initiatives to enable analysis of data in cloud object storage. The open source Presto project is one of the most prominent interactive SQL query engines and has been adopted by some of the largest digital-native organizations. Presto managed-services provider Ahana is on a mission to bring the advantages of Presto to the masses.

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Topics: Analytics, Business Intelligence, Cloud Computing, Data, Digital Technology, data lakes, AI and Machine Learning, data operations, data platforms, Analytics & Data

Semantic Models Benefit Analytical Processes

Posted by David Menninger on Jun 28, 2022 3:00:00 AM

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

Disentangling and Demystifying Data Mesh and Data Fabric

Posted by Matt Aslett on Jun 2, 2022 3:00:00 AM

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

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

Real-Time Data Processing Requires More Agile Data Pipelines

Posted by Matt Aslett on Apr 26, 2022 3:00:00 AM

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

The Benefits of Data Mesh Extend to Organizational and Cultural Change

Posted by Matt Aslett on Mar 29, 2022 3:00:00 AM

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

Yellowbrick Paves the Way to Distributed Cloud

Posted by Matt Aslett on Mar 22, 2022 3:00:00 AM

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

Bigeye Provides Visibility into Data Reliability

Posted by Matt Aslett on Mar 1, 2022 3:00:00 AM

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

Data Observability is Key to Ensuring Healthy Data Pipelines

Posted by Matt Aslett on Feb 22, 2022 3:00:00 AM

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

Incorta Unifies Data Processing to Accelerate Analytics & BI

Posted by Matt Aslett on Feb 16, 2022 3:00:00 AM

As I stated when joining Ventana Research, the socioeconomic impacts of the pandemic and its aftereffects have highlighted more than ever the differences between organizations that can turn data into insights and are agile enough to act upon it and those that are incapable of seeing or responding to the need for change. Data-driven organizations stand to gain competitive advantage, responding faster to worker and customer demands for more innovative, data-rich applications and personalized experiences. One of the key methods that accelerates business decision-making is reducing the lag between data collection and data analysis.

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Topics: Analytics, Business Intelligence, Data Integration, Data, data lakes, AI and Machine Learning, data operations, data platforms, Streaming Data & Events

AtScale Universal Semantic Layer Democratizes and Scales Analytics

Posted by David Menninger on Feb 10, 2022 3:00:00 AM

Organizations of all sizes are dealing with exponentially increasing data volume and data sources, which creates challenges such as siloed information, increased technical complexities across various systems and slow reporting of important business metrics. Migrating to the cloud does not solve the problems associated with performing analytics and business intelligence on data stored in disparate systems. Also, the computing power needed to process large volumes of data consists of clusters of servers with hundreds or thousands of nodes that can be difficult to administer. Our Analytics and Data Benchmark Research shows that organizations have concerns about current analytics and BI technology. Findings include difficulty integrating data with other business processes, systems that are not flexible enough to scale operations and trouble accessing data from various data sources.

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Topics: Analytics, Business Intelligence, Data Integration, Data, data lakes, AI and Machine Learning, data operations, Streaming Analytics

Managing Data Effectively in 2022: Ventana Research Market Agenda

Posted by Matt Aslett on Feb 1, 2022 3:00:00 AM

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

ThoughtSpot Enables Simpler Analytics with AI and NLP

Posted by David Menninger on Jan 21, 2022 3:00:00 AM

Organizations today have huge volumes of data across various cloud and on-premises systems which keep growing by the second. To derive value from this data, organizations must query the data regularly and share insights with relevant teams and departments. Automating this process using natural language processing (NLP) and artificial intelligence and machine learning (AI/ML) enables line-of-business personnel to query the data faster, generate reports themselves without depending on IT, and make quick decisions. Some organizations have started using NLP in self-service analytics to quickly identify patterns and simplify data visualization. Our Analytics and Data Benchmark Research finds that about 81% of organizations expect to use natural language search for analytics to make timely and informed decisions.

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Topics: embedded analytics, Analytics, Business Intelligence, Data Integration, Data, natural language processing, data lakes, AI and Machine Learning, data operations, data platforms

Hydroanalytic Data Platforms Power Data Lakes’ Strategic Value

Posted by Matt Aslett on Dec 23, 2021 3:00:00 AM

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

Data Platforms Landscape Divided Between Analytic and Operational

Posted by Matt Aslett on Dec 14, 2021 3:00:00 AM

As I noted when joining Ventana Research, the range of options faced by organizations in relation to data processing and analytics can be bewildering. When it comes to data platforms, however, there is one fundamental consideration that comes before all others: Is the workload primarily operational or analytic? Although most database products can be used for operational or analytic workloads, the market has been segmented between products targeting operational workloads, and those targeting analytic workloads for almost as long as there has been a database market.

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Topics: business intelligence, Analytics, Data, data lakes, AI and Machine Learning, data operations, data platforms

Automating Workflows for a Better Customer Experience

Posted by Keith Dawson on Dec 1, 2021 3:00:00 AM

Any organization that relies heavily on a large labor force looks to automation to reduce costs, and contact centers are no exception. They handle interactions at such large scale that almost any effort to automate some part of the process can deliver measurable efficiencies. Two factors have ratcheted up attention on automating customer experience workflows: the dramatic expansion of digital interaction channels, and the development of artificial intelligence and machine learning tools to facilitate workflow deployment.

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Topics: Customer Experience, Voice of the Customer, Analytics, Data Integration, Contact Center, Data, AI and Machine Learning, agent management, data operations, Digital Business, Experience Management, Customer Experience Management, Field Service

The Potential for Hybrid and Multicloud Platforms to Safeguard Data

Posted by Matt Aslett on Nov 24, 2021 3:00:00 AM

It has been clear for some time that future enterprise IT architecture will span multiple cloud providers as well as on-premises data centers. As Ventana Research noted in the market perspective on data architectures, the rapid adoption of cloud computing has fragmented where data is accessed or consolidated. We are already seeing that almost one-half (49%) of respondents to Ventana Research’s Analytics and Data Benchmark Research are using cloud computing for analytics and data, of which 42% are currently using more than one cloud provider.

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Topics: Data, data lakes, data operations, data platforms

Analytic Ops: The Last Mile of Data Ops

Posted by David Menninger on Nov 24, 2021 3:00:00 AM

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

Google Cloud Advances to Multi-Cloud

Posted by Matt Aslett on Nov 11, 2021 3:00:00 AM

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

Viamedici Named a Vendor with Merit in the 2021 PIM Value Index

Posted by Mark Smith on Aug 13, 2021 3:00:00 AM

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

Contentserv is a Vendor with Merit in the 2021 PIM Value Index

Posted by Mark Smith on Aug 11, 2021 3:00:00 AM

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

Perfion Named a Vendor with Merit in the 2021 PIM Value Index

Posted by Mark Smith on Aug 9, 2021 3:00:00 AM

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

Winshuttle is a Vendor with Merit in the 2021 PIM Value Index

Posted by Mark Smith on Aug 6, 2021 3:00:00 AM

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

inRiver is a Vendor of Assurance in the 2021 PIM Value Index

Posted by Mark Smith on Jul 30, 2021 3:00:00 AM

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

Riversand is Exemplary and a Leader in the 2021 PIM Value Index

Posted by Mark Smith on Jul 21, 2021 3:00:00 AM

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

Magnitude is Exemplary in the 2021 PIM Value Index

Posted by Mark Smith on Jul 19, 2021 3:00:00 AM

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

Salsify is Exemplary and a Leader in the 2021 PIM Value Index

Posted by Mark Smith on Jul 15, 2021 3:00:00 AM

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

Pimcore Named Exemplary and Overall Leader in 2021 PIM Value Index

Posted by Mark Smith on Jul 13, 2021 3:00:00 AM

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

Akeneo Rated Exemplary Leader in the 2021 PIM Value Index

Posted by Mark Smith on Jul 12, 2021 3:00:00 AM

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

Informatica is Exemplary and Leader in the 2021 PIM Value Index

Posted by Mark Smith on Jul 9, 2021 3:00:00 AM

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

Data in 2021: Ventana Research Market Agenda

Posted by David Menninger on Feb 26, 2021 3:00:00 AM

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

DataOps: Managing the Process and Technology

Posted by David Menninger on Oct 7, 2020 3:00:00 AM

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

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