Matt Aslett's Analyst Perspectives

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

Oracle Positions to Address Any and All Data Platform Needs

Posted by Matt Aslett on May 5, 2022 3:00:00 AM

I recently described how the operational data platforms sector is in a state of flux. There are multiple trends at play, including the increasing need for hybrid and multicloud data platforms, the evolution of NoSQL database functionality and applicable use-cases, and the drivers for hybrid data processing. The past decade has seen significant change in the emergence of new vendors, data models and architectures as well as new deployment and consumption approaches. As organizations adopted strategies to address these new options, a few things remained constant – one being the influence and importance of Oracle. The company’s database business continues to be a core focus of innovation, evolution and differentiation, even as it expanded its portfolio to address cloud applications and infrastructure.

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Topics: Analytics, Business Intelligence, Data Integration, Data, AI and Machine Learning, data platforms

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: business intelligence, Analytics, 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

Starburst Accelerates Analysis of Distributed Data

Posted by Matt Aslett on Apr 12, 2022 3:00:00 AM

I recently described the growing level of interest in data mesh which provides an organizational and cultural approach to data ownership, access and governance that facilitates distributed data processing. As I stated in my Analyst Perspective, data mesh is not a product that can be acquired or even a technical architecture that can be built. Adopting the data mesh approach is dependent on people and process change to overcome traditional reliance on centralized ownership of data and infrastructure and adapt to its principles of domain-oriented ownership, data as a product, self-serve data infrastructure and federated governance. Many organizations will need to make technological changes to facilitate adoption of data mesh, however. Starburst Data is associated with accelerating analysis of data in data lakes but is also one of several vendors aligning their products with data mesh.

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

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: 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

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 Integration, Data, data lakes, data operations, AI & Machine Learning, 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: business intelligence, Analytics, Data Integration, Data, data lakes, data operations, AI & Machine Learning, data platforms, Streaming Data & Events

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

AWS Cloud Data Platform Services Expand Workload Placement Options

Posted by Matt Aslett on Jan 19, 2022 3:00:00 AM

Few trends have had a bigger impact on the data platforms landscape than the emergence of cloud computing. The adoption of cloud computing infrastructure as an alternative to on-premises datacenters has resulted in significant workloads being migrated to the cloud, displacing traditional server and storage vendors. Almost one-half (49%) of respondents to Ventana Research’s Analytics and Data Benchmark Research currently use cloud computing products for analytics and data, and a further one-quarter plan to do so. In addition to deploying data workloads on cloud infrastructure, many organizations have also adopted cloud data and analytics services offered by the same cloud providers, displacing traditional data platform vendors. Organizations now have greater choice in relation to potential products and providers for data and analytics workloads, but also need to think about integrating services offered by cloud providers with established technology and processes. Having pioneered the concept, Amazon Web Services has arguably benefitted more than most from adoption of cloud computing, and is also in the process of expanding and adjusting its portfolio to alleviate challenges and encourage even greater adoption.

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Topics: Analytics, Business Intelligence, Data Governance, Data Integration, Data

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