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

Jet Global

Are you making decisions based on bad data? It’s hard to answer that question because, truth be told, you don’t know you’re using bad data until it’s too late. . states that about 40 percent of enterprise data is either inaccurate, incomplete, or unavailable. Because bad data is the reason behind poor analytics. .

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Your 5-Step Journey from Analytics to AI

CIO Business Intelligence

Most organizations have come to understand the importance of being data-driven. To compete in a digital economy, it’s essential to base decisions and actions on accurate data, both real-time and historical. But the sheer volume of the world’s data is expected to nearly triple between 2020 and 2025 to a whopping 180 zettabytes.

Analytics 115
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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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Data Mesh vs. Data Fabric: A Love Story

Alation

And now, arguably the greatest rivalry the world (well, at least the data community) has ever witnessed: Data Fabric vs Data Mesh! Data fabric and data mesh are both having a moment. Gartner calls data fabric the Future of Data Management 1. Gartner on Data Fabric. Thoughtworks on Data Mesh.

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What Is a Data Fabric and How Does a Data Catalog Support It?

Alation

Data fabric is now on the minds of most data management leaders. In our previous blog, Data Mesh vs. Data Fabric: A Love Story , we defined data fabric and outlined its uses and motivations. The data catalog is a foundational layer of the data fabric.

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

CIO Business Intelligence

Topping the list of executive priorities for 2023—a year heralded by escalating economic woes and climate risks—is the need for data driven insights to propel efficiency, resiliency, and other key initiatives. 2] Foundational considerations include compute power, memory architecture as well as data processing, storage, and security.

Analytics 137