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.
Master data management may not attract the same level of excitement as fashionable topics such as DataOps or Data Platforms, but it remains one of the most significant aspects of an organization’s strategic approach to data management. Having trust in data is critical to the ability of an organization to make data-driven business decisions. Along with data quality, MDM enables organizations to ensure data is accurate, complete and consistent to fulfill operational business objectives.
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Topics:
Data Governance,
Data Management,
Data,
data operations
The data platforms market has traditionally been divided between products specifically designed to support operational or analytic workloads, with other market segments having emerged in recent years for data platforms targeted specifically at data science and machine learning (ML), as well as real-time analytics. More recently, we have seen vendor strategies evolving to provide a more consolidated approach, with data platforms designed to address a combination of analytics and data science, as...
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Topics:
Analytics,
Business Intelligence,
Cloud Computing,
Data,
Digital Technology,
AI & Machine Learning,
Analytics & Data,
analytic data platforms,
Operational Data Platforms
The recent publication of our Value Index research highlights the impact of intelligent applications on the operational data platforms sector. While we continue to believe that, for most use cases, there is a clear, functional requirement for either analytic or operational data platforms, recent growth in the development of intelligent applications infused with the results of analytic processes, such as personalization and artificial intelligence (AI)-driven recommendations, has increasing...
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Topics:
Analytics,
Business Intelligence,
Cloud Computing,
Data,
Digital Technology,
Analytics & Data,
analytic data platforms,
Operational Data Platforms
As engagement with customers, suppliers and partners is increasingly conducted through digital channels, ensuring that infrastructure and applications are performing as expected is not just important but mission critical. My colleague, David Menninger, recently explained the increasing importance of observability to enable organizations to ensure that their systems and applications are operating efficiently. Observability has previously been the domain of the IT department but is increasingly...
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Topics:
Data Management,
Data,
Digital Technology,
Analytics & Data
I have written recently about the increasing importance of managing data in motion and at rest as the use of streaming data by enterprise organizations becomes more mainstream. While batch-based processing of application data has been a core component of enterprise IT architecture for decades, streaming data and event processing have often been niche disciplines typically reserved for organizations with the highest-level performance requirements. That has changed in recent years, driven by an...
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Topics:
Data,
Streaming Data & Events
To execute more data-driven business strategies, organizations need linked and comprehensive data that is available in real time. By consistently managing data across siloed systems and ensuring that data definitions are agreed and current, organizations can overcome the challenges presented by data being distributed across an increasingly disparate range of applications and data-processing locations. Maintaining data quality is a perennial data management challenge, often preventing...
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Topics:
Data Management,
Data,
data operations
Data Operations (DataOps) has been part of the lexicon of the data market for almost a decade, with the term used to describe products, practices and processes designed to support agile and continuous delivery of data analytics. DataOps takes inspiration from DevOps, which describes a set of tools, practices and philosophy used to support the continuous delivery of software applications in the face of constant changes. DataOps describes a set of tools, practices and philosophy used to ensure...
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Topics:
Data Governance,
Data Management,
Data,
data operations
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....
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Topics:
Data Governance,
Data Management,
Data,
data operations
Success with streaming data and events requires a more holistic approach to managing and governing data in motion and data at rest. The use of streaming data and event processing has been part of the data landscape for many decades. For much of that time, data streaming was a niche activity, however, with standalone data streaming and event-processing projects run in parallel with existing batch-processing initiatives, utilizing operational and analytic data platforms. I noted that there has...
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Topics:
Analytics,
Data,
Digital Technology,
Streaming Analytics,
Analytics & Data,
Streaming Data & Events,
analytic data platforms,
Operational Data Platforms
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...
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Topics:
Cloud Computing,
Data Governance,
Data Management,
Data,
Digital Technology,
data operations,
Analytics & Data