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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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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

Data maturity models are a crucial step for any organisation looking to improve their data, informing if your current data practices are helping, or holding back, your business. ? Click the links below to navigate to different sections What are data maturity models? Why do we need data maturity models?

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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 115
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Your Data Won’t Speak Unless You Ask It The Right Data Analysis Questions

datapine

In our cutthroat digital age, the importance of setting the right data analysis questions can define the overall success of a business. That being said, it seems like we’re in the midst of a data analysis crisis. Or even better: “Which marketing campaign that I did this quarter got the best ROI, and how can I replicate its success?”.

IT 317
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Common Business Intelligence Challenges Facing Entrepreneurs

datapine

Data analytics isn’t just for the Big Guys anymore; it’s accessible to ventures, organizations, and businesses of all shapes, sizes, and sectors. The power of data analytics and business intelligence is universal. They need to make quick and informed decisions. 1) Too expensive and hard to justify the ROI of BI.

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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.