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Why Not Hearing About Data Errors Should Worry Your Data Team

DataKitchen

Why Not Hearing About Data Errors Should Worry Your Data Team In the chaotic lives of data & analytics teams, a day without hearing of any data-related errors is a blessing. Your team is on top of things, deliveries are on schedule (you think), and no major complaints are making their way to your desk.

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KPI Management And Best Practices: How To Find The Perfect KPI Solutions?

datapine

4) How to Select Your KPIs 5) Avoid These KPI Mistakes 6) How To Choose A KPI Management Solution 7) KPI Management Examples Fact: 100% of statistics strategically placed at the top of blog posts are a direct result of people studying the dynamics of Key Performance Indicators, or KPIs. 3) What Are KPI Best Practices?

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10 Examples of How Big Data in Logistics Can Transform The Supply Chain

datapine

Table of Contents 1) Benefits Of Big Data In Logistics 2) 10 Big Data In Logistics Use Cases Big data is revolutionizing many fields of business, and logistics analytics is no exception. The complex and ever-evolving nature of logistics makes it an essential use case for big data applications. Did you know?

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Augmented Analytics Must Provide Data Quality and Insight!

Smarten

How Can I Ensure Data Quality and Gain Data Insight Using Augmented Analytics? There are many business issues surrounding the use of data to make decisions. One such issue is the inability of an organization to gather and analyze data.

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Integrating AI into Asset Performance Management: It’s all about the data

IBM Big Data Hub

Imagine a future where artificial intelligence (AI) seamlessly collaborates with existing supply chain solutions, redefining how organizations manage their assets. If you’re currently using traditional AI, advanced analytics, and intelligent automation, aren’t you already getting deep insights into asset performance?

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Building cyber resiliency for your data with IBM FlashSystem

IBM Big Data Hub

In this blog, we will explore the concept of cyber resiliency from an IBM® perspective and how our approach can help organizations protect themselves in an ever-changing cybersecurity landscape. Their expertise in developing secure systems ensures that proactive defense measures are in place.

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The Ultimate Guide to Modern Data Quality Management (DQM) For An Effective Data Quality Control Driven by The Right Metrics

datapine

1) What Is Data Quality Management? 2) Why Do You Need DQM? 4) Data Quality Best Practices. 5) How Do You Measure Data Quality? 6) Data Quality Metrics Examples. 7) Data Quality Control: Use Case. 8) The Consequences Of Bad Data Quality. 9) 3 Sources Of Low-Quality Data.