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        Matt Aslett's Analyst Perspectives

        About the Analyst

        Matt Aslett

        Matt leads the expertise in Digital Technology covering applications and technology that improve the readiness and resilience of business and IT operations. His focus areas of expertise and market coverage include: analytics and data, artificial intelligence and machine learning, blockchain, cloud computing, collaborative and conversational computing, extended reality, Internet of Things mobile computing and robotic automation. Matt’s specialization is in operational and analytical use of data and how businesses can modernize their approaches to business to accelerate the value realization of technology investments in support of hybrid and multi-cloud architecture. Matt has been an industry analyst for more than a decade and has pioneered the coverage of emerging data platforms including NoSQL and NewSQL databases, data lakes and cloud-based data processing. He is a graduate of Bournemouth University.


        Recent Posts

        As enterprises seek to increase data-driven decision-making, many are investing in strategic data democratization initiatives to provide business users and data analysts with self-service access to data across the enterprise. Such access has long been a goal of many enterprises, but few have achieved it. Only 15% of participants in Ventana Research’s Analytics and Data Benchmark Research say their organization is very comfortable allowing business users to work with data that has not been...

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        Topics: Analytics, data operations, AI & Machine Learning, Analytics & Data, Data Intelligence, Data Products, Data Democratization

        Cloud computing has had an enormous impact on the analytics and data industry in recent decades, with the on-demand provisioning of computational resources providing new opportunities for enterprises to lower costs and increase efficiency. Two-thirds of participants in Ventana Research’s Data Lakes Dynamic Insightsresearch are using a cloud-based environment as the primary data platform for analytics. 

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        Topics: Analytics, AI, AI & Machine Learning, data platforms, Analytics & Data, Generative AI, Data Intelligence

        It is well known that data integration, transformation and preparation represent a significant proportion of the time and effort required in any analytics project. Traditionally, operational data platforms are designed to store, manage, and process data to support worker-, customer- and partner-facing operational applications, and data is then extracted, transformed, and loaded (or “ETLed”) into a separate analytic data platform, which is designed to store, manage, process, and analyze data....

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        Topics: Analytics & Data, Data Intelligence

        I have previously written about the impact of intelligent operational applications on the requirements for data platforms. Intelligent applications are used to run the business but also deliver personalization, recommendations and other features generated by machine learning and artificial intelligence. As such, they require a combination of operational and analytic processing functionality. The emergence of these intelligent applications does not eradicate the need for separate analysis of...

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        Topics: Analytics, Artificial intelligence, AI & Machine Learning, data platforms, Analytics & Data, Generative AI

        The increasing importance of intelligent operational applications driven by artificial intelligence (AI) is blurring the lines that have traditionally divided the requirements between operational and analytic data platforms. Operational data platforms have traditionally been deployed to support applications targeted at business users and decision-makers to run the business, with analytic data platforms typically supporting applications used by data and business analysts to analyze the business.

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        Topics: embedded analytics, Cloud Computing, AI & Machine Learning, Analytics & Data, analytic data platforms, Operational Data Platforms

        I have previously written about thefunctional evolutionandemerging use casesfor NoSQL databases, a category of non-relational databases that first emerged 15 or so years ago and are now well established as potential alternatives to relational databases. NoSQL is a term used to describe a variety of databases that fall into four primary functional categories: key-value stores, wide-column stores, document-oriented databases and graph databases. Each is worthy of further exploration, which is...

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        Topics: Data, data operations

        In recent years, many enterprises have migrated data platform workloads from on-premises infrastructure to cloud environments, attracted by the promised benefits of greater agility and lower costs. The scale of cloud data platform adoption is illustrated by Ventana Research’s Data Lakes Dynamic Insights research: For two-thirds (66%) of participants, the primary data platform used for analytics is cloud based. As the quantity and importance of the data platform workloads deployed in the cloud...

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        Topics: business intelligence, Cloud Computing, data operations, AI & Machine Learning, robotic automation, Analytics & Data, analytic data platforms

        Ventana Research recently announced its 2024 Market Agenda for Analytics and Data, continuing the guidance we have offered for two decades to help enterprises derive optimal value and improve business outcomes.

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        Topics: embedded analytics, Business Intelligence, Data Governance, Data Management, natural language processing, data operations, Process Mining, Streaming Analytics, AI & Machine Learning, Analytics & Data, Streaming Data & Events, analytic data platforms, Operational Data Platforms

        Discussion about potential deployment locations for analytics and data workloads is often based on the assumption that, for enterprise workloads, there is a binary choice between on-premises data centers and public cloud. However, the low-latency performance or sovereignty characteristics of a significant and growing proportion of workloads make them better suited to data and analytics processing where data is generated rather than a centralized on-premises or public cloud environment. ...

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        Topics: Cloud Computing, Internet of Things, Data, Digital Technology, AI & Machine Learning, Analytics & Data, analytic data platforms, Operational Data Platforms

        As articulated in Ventana Research’s Data Platforms Buyer’s Guide and DataOps Buyer’s Guide research, the combination of cloud computing and advanced analytics has lowered the cost of storing and processing large volumes of data, accelerating the emergence of new data platform and data operations products that enable organizations to gain operational efficiency and competitive advantage. The right combination of data platform and data management products is essential to ensure that the right...

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        Topics: Data Management, Data, Digital Technology, data operations, Analytics & Data, analytic data platforms, Operational Data Platforms
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