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        Matt Aslett's Analyst Perspectives

        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...

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        Topics: business intelligence, Data Governance, Data Management, Data, data operations, Analytics & Data

        I recently wrote about the need for organizations to take a holistic approach to the management and governance of data in motion alongside data at rest. As adoption of streaming data and event processing increases, it is no longer sufficient for streaming data projects to exist in isolation. Data needs to be managed and governed regardless of whether it is processed in batch or as a stream of events. This requirement has resulted in established data management vendors increasing their focus on...

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        Topics: Big Data, Cloud Computing, Data Governance, Streaming Analytics, Streaming Data & Events

        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...

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        Topics: embedded analytics, Analytics, Business Intelligence, Data Governance, Data Integration, Data, Digital Technology, natural language processing, data lakes, data operations, Streaming Analytics, AI & Machine Learning, Digital Business, Data Platforms, Analytics & Data, Streaming Data & Events

        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...

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        Topics: Analytics, Business Intelligence, Data Governance, Data Integration, Data, data operations, AI & Machine Learning, Data Platforms, Streaming Data & Events

        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: business intelligence, Analytics, Data Governance, Data Integration, Data, Digital Technology, Digital transformation, data lakes, data operations, AI & Machine Learning, Digital Business, Data Platforms, Analytics & Data, Streaming Data & Events

        Data governance is an issue that impacts all organizations large and small, new and old, in every industry, and every region of the world. Data governance ensures that an organization’s data can be cataloged, trusted and protected, improving business processes to accelerate analytics initiatives and support compliance with regulatory requirements. Not all data governance initiatives will be driven by regulatory compliance; however, the risk of falling foul of privacy (and human rights) laws...

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        Topics: Analytics, Data Governance, Data

        I recently described the growing level of interest in data mesh which provides an organizational and cultural approach to data ownership, access and governance that facilitates distributed data processing. As I stated in my Analyst Perspective, data mesh is not a product that can be acquired or even a technical architecture that can be built. Adopting the data mesh approach is dependent on people and process change to overcome traditional reliance on centralized ownership of data and...

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        Topics: Business Continuity, business intelligence, Analytics, Data Governance, Data Integration, Data, Digital Technology, data lakes, Digital Business, Data Platforms, Analytics & Data

        Data mesh is the latest trend to grip the data and analytics sector. The term has been rapidly adopted by numerous vendors — as well as a growing number of organizations —as a means of embracing distributed data processing. Understanding and adopting data mesh remains a challenge, however. Data mesh is not a product that can be acquired, or even a technical architecture that can be built. It is an organizational and cultural approach to data ownership, access and governance. Adopting data mesh...

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        Topics: business intelligence, Analytics, Data Governance, Data Integration, Data, Digital Technology, Digital transformation, data lakes, data operations, Digital Business, Data Platforms, Analytics & Data, Streaming Data & Events

        Despite widespread and increasing use of the cloud for data and analytics workloads, it has become clear in recent years that, for most organizations, a proportion of data-processing workloads will remain on-premises in centralized data centers or distributed-edge processing infrastructure. As we recently noted, as compute and storage are distributed across a hybrid and multi-cloud architecture, so, too, is the data it stores and relies upon. This presents challenges for organizations to...

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        Topics: business intelligence, Analytics, Data Governance, Data, data operations, AI & Machine Learning, Data Platforms

        As businesses become more data-driven, they are increasingly dependent on the quality of their data and the reliability of their data pipelines. Making decisions based on data does not guarantee success, especially if the business cannot ensure that the data is accurate and trustworthy. While there is potential value in capturing all data — good or bad — making decisions based on low-quality data may do more harm than good.

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        Topics: Data Governance, Data Integration, Data, Digital Technology, data lakes, data operations, Analytics & Data
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          Each is prepared and reviewed in accordance with Ventana Research’s strict standards for accuracy and objectivity and reviewed to ensure it delivers reliable and actionable insights. It is reviewed and edited by research management and is approved by the Chief Research Officer; no individual or organization outside of Ventana Research reviews any Analyst Perspective before it is published. If you have any issue with an Analyst Perspective, please email them to ChiefResearchOfficer@ventanaresearch.com

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