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5 Signs You’re Using Bad Data to Make Business Decisions

Jet Global

Reduce the Risk of Bad Data with Master Data Management (MDM). Microsoft Dynamics is not built to govern data, which is why we recommend adopting Master Data Management (MDM) as a part of your business intelligence and analytics solution strategy.

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Understanding Data Entities in Microsoft Dynamics 365

Jet Global

For organizations considering a move to Microsoft Dynamics 365 Finance & Supply Chain Management (D365 F&SCM), or for those in the early stages of an implementation project, defining a clear strategy for curating data is a key to developing a comprehensive approach to reporting and analytics. What Are Data Entities?

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5 Ways Master Data Management (MDM) Can Organize Your Dynamics ERP Data for Business Intelligence

Jet Global

In today’s data-driven world, business intelligence (BI) and analytics play a huge role in better understanding your customers, improving your operations, and making actionable business decisions. Take a look at the data you need to use in order to get any value from business intelligence and analytics.

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Themes and Conferences per Pacoid, Episode 8

Domino Data Lab

Most of the data management moved to back-end servers, e.g., databases. So we had three tiers providing a separation of concerns: presentation, logic, data. Note that data warehouse (DW) and business intelligence (BI) practices both emerged circa 1990. the flywheel effect.

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Your 5-Step Journey from Analytics to AI

CIO Business Intelligence

To companies entrenched in decades-old business and IT processes, data fiefdoms, and legacy systems, the task may seem insurmountable. Develop a strategy to liberate data . Another option is a data warehouse, which stores processed and refined data. Set up unified data governance rules and processes.

Analytics 115
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10 Fundamental Web Analytics Truths: Embrace 'Em & Win Big

Occam's Razor

If nothing else convinces you, remember that clickstream data is a small part of the data you'll use to make smart Web Analytics 2.0 It is faster to fail and learn then wait for an "industry case study" or find relevancy in a "industry leader white paper" I met a small group of top companies in London recently.

Analytics 118
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The Reason Many AI and Analytics Projects Fail—and How to Make Sure Yours Doesn’t

CIO Business Intelligence

Blocking the move to a more AI-centric infrastructure, the survey noted, are concerns about cost and strategy plus overly complex existing data environments and infrastructure. 2] Foundational considerations include compute power, memory architecture as well as data processing, storage, and security.

Analytics 137