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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? 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.

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What’s Your Data Governance ROI? Here’s What to Track

Alation

From rebranding data governance in your organization to demonstrating real business impacts, there’s a lot you can do to bring everyone in your business on board. The role of monitoring, measuring, and metrics So, you’ve got the first step done; you’ve implemented data governance and everyone in your organization is on board.

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How to Build a Data Quality Strategy to Get Executive Buy-In

Octopai

And when business users don’t complain, but you know the data isn’t good enough to make these types of calls wisely, that’s an even bigger problem. How are you, as a data quality evangelist (if you’re reading this post, that must describe you at least somewhat, right?), Tie data quality directly to business objectives.

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Data Maturity Models | Measure The Health Of Your Data

Anmut

This ultimately allows for more effective goal-setting, with targets determined according to both your data maturity right now and the desired stage you want to attain in the future. Why do we need data maturity models? A data maturity model helps your company measure its data and business health.

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The steep cost of a poor data management strategy

CIO Business Intelligence

Without it, businesses incur steep costs, but the downside, or costs, are often unclear because calculating data management’s return on investment (ROI), or upside, is a murky exercise. For many organizations, the real challenge is quantifying the ROI benefits of data management in terms of dollars and cents.

Strategy 116
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How IBM process mining unleashed new efficiencies in BoB-Cardif Life

IBM Big Data Hub

IBM Process Mining can also prioritize automation improvements based on the severity of the issue and the expected ROI, driving continuous improvement of processes by triggering corrective actions or generating RPA bots. To address specific process breakpoints, the company proposed targeted digital technology and process management measures.

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Common Business Intelligence Challenges Facing Entrepreneurs

datapine

1) Too expensive and hard to justify the ROI of BI. They also need these tools to generate a true ROI. The right business intelligence tool is a much easier ROI to sell. The ROI alone from hours saved and reduced costs of producing current reports will improve your bottom line. 2) Lack of company-wide adoption.