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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

Which sales strategies bring in the most customers, or the most loyal customers, or the highest revenue? When business users complain that they can’t get good enough data to make these types of calls wisely, that’s a big problem. going to convince top-level management that adopting a data quality strategy pays big dividends?

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The steep cost of a poor data management strategy

CIO Business Intelligence

Such is the case with a data management strategy. That gap is becoming increasingly apparent because of artificial intelligence’s (AI) dependence on effective data management. For many organizations, the real challenge is quantifying the ROI benefits of data management in terms of dollars and cents.

Strategy 113
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Data-Driven Companies Leverage OCR for Optimal Data Quality

Smart Data Collective

OCR is the latest new technology that data-driven companies are leveraging to extract data more effectively. OCR and Other Data Extraction Tools Have Promising ROIs for Brands. Big data is changing the state of modern business. The benefits of big data cannot be overstated.

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From Excel to AI: How Liberty Dental revolutionized care management

CIO Business Intelligence

It’s a good balance between technology strategy and then applying that technology to operational areas as well. You can host data anywhere — on-prem or in the cloud — but if your data quality is not good, it serves no purpose. Data governance was the biggest piece that we took care of.

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Is the gen AI bubble due to burst? CIOs face rethink ahead

CIO Business Intelligence

Generative AI has been hyped so much over the past two years that observers see an inevitable course correction ahead — one that should prompt CIOs to rethink their gen AI strategies. Many of those gen AI projects will fail because of poor data quality, inadequate risk controls, unclear business value , or escalating costs , Gartner predicts.

ROI 143
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Unlocking the Power of AI with a Real-Time Data Strategy

CIO Business Intelligence

It’s clear how these real-time data sources generate data streams that need new data and ML models for accurate decisions. Data quality is crucial for real-time actions because decisions often can’t be taken back. report they have established a data culture 26.5%