I previously wrote about the challenge facing distributed SQL database providers to avoid becoming pigeonholed as only being suitable for a niche set of requirements. Factors including performance, reliability, security and scalability provide a focal point for new vendors to differentiate from established providers and get a foot in the door with customer accounts. Expanding and retaining those accounts is not necessarily easy, however, especially as general-purpose data platform providers...
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Topics:
Analytics,
Cloud Computing,
Data,
Digital Technology,
Streaming Data & Events,
analytic data platforms,
Analytics and Data,
AI and Machine Learning
Because artificial intelligence is top-of-mind, Workday spent a great deal of time on the topic at its recent Workday Rising annual user group meeting in San Francisco. It was front and center in the general sessions, in the announcements made at the event and in the product roadmaps.
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Topics:
Office of Finance,
Business Planning,
ERP and Continuous Accounting,
AI and Machine Learning
Alteryx was founded in 1997 and initially focused on analyzing demographic and geographically organized data. In 2006, the company released its eponymous product that established its direction for what the product is today. In 2017, it went public in an IPO on the NYSE. At the time of the IPO, Alteryx was focusing much of its marketing efforts on the data preparation market, particularly to support Tableau. Throughout this time though, Alteryx offered much more than data preparation. As a...
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Topics:
business intelligence,
Analytics,
data operations,
Analytic Operations,
Analytics and Data,
AI and Machine Learning
I previously described how Databricks had positioned its Lakehouse Platform as the basis for data engineering, data science and data warehousing. The lakehouse design pattern provides a flexible environment for storing and processing data from multiple enterprise applications and workloads for multiple use cases. I assert that by 2025, 8 in 10 current data lake adopters will invest in data lakehouse architecture to improve the business value generated from the accumulated data.
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Topics:
Analytics,
Business Intelligence,
Data Governance,
Data Management,
Data,
Digital Technology,
analytic data platforms,
Analytics and Data,
AI and Machine Learning
I am happy to share insights gleaned from our latest Buyers Guide, an assessment of how well vendors’ offerings meet buyers’ requirements. The Ventana Research 2023 Augmented Analytics Buyers Guide is the distillation of a year of market and product research by Ventana Research. Drawing on our Benchmark Research, we apply a structured methodology built on evaluation categories that reflect the real-world criteria incorporated in a request for proposal to Analytics and Data vendors supporting...
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Topics:
Analytics,
Augmented Analytics,
AI and Machine Learning
The 2023 Ventana Research Buyers Guide for Augmented Analytics research enables me to provide observations about how the market has advanced.
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Topics:
Analytics,
Augmented Analytics,
AI and Machine Learning
The data platforms market may appear to have little or nothing to do with haute couture, but it is one of the data sectors most strongly influenced by the fickle finger of fashion. In recent years, various architectural approaches to data storage and processing have enjoyed a phase in the limelight, including data warehouse, data mart, data hub, data lake, cloud data warehouse, object storage, data lakehouse, data fabric and data mesh. These approaches are often heralded as the next big thing,...
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Topics:
Cloud Computing,
Data Governance,
Data Management,
Data,
Digital Technology,
data operations,
Streaming Data & Events,
analytic data platforms,
Analytics and Data,
AI and Machine Learning
Despite a focus on being data-driven, many organizations find that data and analytics projects fail to deliver on expectations. These initiatives can underwhelm for many reasons, because success requires a delicate balance of people, processes, information and technology. Small deviations from perfection in any of those factors can send projects off the rails.
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Topics:
Analytics,
Business Intelligence,
Data Management,
Data,
Digital Technology,
data operations,
AI and Machine Learning
Organizations are continuously combining data from diverse and siloed sources for analytical, artificial intelligence and machine learning projects. As the volume of data grows, it becomes challenging for organizations to manage and keep current to extract valuable insights in a timely manner.
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Topics:
Analytics,
Business Intelligence,
AI and Machine Learning
I have written before about the rising popularity of the data fabric approach for managing and governing data spread across distributed environments comprised of multiple data centers, systems and applications. I assert that by 2025, more than 6 in 10 organizations will adopt data fabric technologies to facilitate the management and processing of data across multiple data platforms and cloud environments. The data fabric approach is also proving attractive to vendors, including Microsoft, as a...
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Topics:
Analytics,
Business Intelligence,
Cloud Computing,
Data Governance,
Data Management,
Data,
Digital Technology,
analytic data platforms,
Analytics and Data,
AI and Machine Learning