The adoption of Kubernetes-enabled infrastructures and applications has revealed the challenge involved in enabling persistent, reliable data storage in an ephemeral compute…
Read MoreToday’s organizations are grappling with a growing volume of data—data of various types and formats that are stored in an increasing number…
Read MoreAs an enterprise area of focus, data science is one of the more sophisticated facets of data analytics. It requires the basics…
Read MoreIn recent years, the market for machine learning operations (MLOps) has seen rapid growth as businesses seek support for becoming machine learning…
Read MoreBusiness intelligence (BI) is today’s key enabler for analyzing data, deriving business insights from it, and sharing and publicizing those insights within…
Read MoreIn recent years, self-service business intelligence (SSBI) has become an increasingly important tool for organizations of all sizes. Unlike enterprise BI (EBI),…
Read MoreBusiness Intelligence (BI) as a paradigm and product category has been around for decades. Arguably, though, it is enjoying its greatest popularity,…
Read MoreThe biggest obstacle that organizations face in using data to achieve mission-critical goals is the sheer variation in datasets that they encounter…
Read MoreThe most challenging aspect of leveraging data for contemporary organizations isn’t the enormous quantity of data or the real-time speeds at which…
Read MoreThe success of any data-related project depends on the quality of data. Enterprise customers manage massive amounts of data, and it’s imperative…
Read MoreData quality reflects the completeness, accuracy, reliability, and related attributes of data. There are numerous platforms for managing data quality designed to…
Read MoreThe number of connected devices, including the machines, sensors, and cameras that make up the Internet of Things (IoT), continues to grow…
Read MoreThe diversity of the data ecosystem within modern enterprises is one of the most difficult aspects of managing data today. Companies struggle…
Read MoreData quality reflects the completeness, accuracy, consistency, usability, reliability, relevance, traceability, precision, statistical normality, verifiability, and error-free status of data. Poor data…
Read MoreIn the media and entertainment (M&E) industry, video productions invest heavily in large-scale infrastructures to store vast amounts of data, both in…
Read MoreThe last year proved to be one of explosive growth in AIOps tooling and solutions. Since our 2021 Radar report on AIOps,…
Read MoreCompetitive data-driven organizations rely on data-intensive applications to win in the digital service economy. These applications require a robust data tier that can…
Read MoreEnterprises from every industry and at every scale continue to work to leverage data to achieve their strategic objectives—whether those are to…
Read MoreMLOps is a practice for collaboration between data science and operations to manage the production machine learning (ML) lifecycles. As an amalgamation of machine learning…
Read MoreIntelligent virtual assistant (IVA) technology has transformed automated telephone response systems, such as those typically used in call centers for the first…
Read MoreThe fundamental underpinning of an organization is its transactions. They must be done well, with integrity and performance. Not only has transaction…
Read MoreThis Radar report will help enterprise buyers become familiar with data science platforms and vendor offerings. Most enterprise organizations now work with…
Read MoreThis Radar report will help enterprise buyers become familiar with data science platforms and vendor offerings. Enterprise customers manage massive amounts of…
Read MoreOver the last couple of years, ransomware has taken center stage in data protection, but very few people realize it is only…
Read MoreThis Radar report will help enterprise buyers become familiar with streaming data platforms and vendor offerings. The vendors reviewed in this Radar…
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