Remove glossary what-is-retail-analytics
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The Very Group adopts a data catalog to better organize and leverage its online retail capabilities

CIO Business Intelligence

Its constituent companies later moved into high-street retail, launched new mail-order brands selling clothing on credit, and even created a consumer financial data broker, later spun off like so many of the group’s other non-core activities. It was very fragmented, and I brought it together into a hub-and-spoke model.”.

IT 82
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Week in the Life of an Analyst at Gartner US IT Symposium (virtual) 2021

Andrew White

This often starts with a graphic of my mind waves (courtesy of Muse) and then you get an hour by hour run down of what I do every day. Analytics Tactics (known outcome/known data/BI/analytics v unknown outcome/unknown data/data science/ML) 11. VP Data and Analytics 1. Director Enterprise Analytics 1. Logistics 1.

IT 52
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What Is the True Value of a Data Catalog?

Alation

When considering implementing any new tool or software, the first thing many organisations will ask is what is the value add. These are companies that have tried to make something resembling a data catalog themselves — e.g., using business glossaries, spreadsheets, and other systems — but they have recognized problems with this method.

ROI 52
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Gartner Data & Analytics Summit 2022 in London: 3 Key Takeaways

Alation

What a week! Alation attended last week’s Gartner Data and Analytics Summit in London from May 9 – 11, 2022. Gartner Data & Analytics Summit 2022: Keynote Highlights. Establish what data you have. Active metadata gives you crucial context around what data you have and how to use it wisely. Introduction.

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Five benefits of a data catalog

IBM Big Data Hub

For example, imagine business analyst Alex is working on a data analytics project to help her retail company better quantify the success of shoe sales versus jewelry sales. A business glossary to explain the business terms used within a data asset. She also wants to predict future sales of both shoes and jewelry.

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PODCAST: Making AI Real – Episode 4: Unlocking the Value of Enterprise AI with Data Engineering Capabilities

bridgei2i

There is a lot of demand for data engineering teams to enable platforms that eases collaboration between analysts, data scientists, and ML-engineers to build what we call as enterprise AI solutions.’. While some companies prefer plug and play solutions, others prefer to customize their platforms by integrating with open-source tech stacks.

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In-depth with CDO Christopher Bannocks

Peter James Thomas

What I recognised very quickly was this was an area with under-investment, that was totally central the focus of that time – STP (Straight Through Processing). I have accountability for the governance and quality through those processes and for making the data available for downstream consumers, like Analytics, Risk, Finance and HR.