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Introducing Precisely for Data Integrity

David Menninger's Analyst Perspectives

Data is becoming more valuable and more important to organizations. At the same time, organizations have become more disciplined about the data on which they rely to ensure it is robust, accurate and governed properly.

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Your Effective Roadmap To Implement A Successful Business Intelligence Strategy

datapine

1) What Is A Business Intelligence Strategy? 4) How To Create A Business Intelligence Strategy. Odds are you know your business needs business intelligence (BI). Over the past 5 years, big data and BI became more than just data science buzzwords. Table of Contents.

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What is the Future of Business Intelligence in the Coming Year?

Smart Data Collective

As the use of intelligence technologies is staggering, knowing the latest trends in business intelligence is a must. The market for business intelligence services is expected to reach $33.5 top 5 key platforms that control the future of business intelligence impacts BI may have on your business in the future.

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5 Ways Data Modeling Is Critical to Data Governance

erwin

While it’s always been the best way to understand complex data sources and automate design standards and integrity rules, the role of data modeling continues to expand as the fulcrum of collaboration between data generators, stewards and consumers. So here’s why data modeling is so critical to data governance.

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Doing Cloud Migration and Data Governance Right the First Time

erwin

That means your cloud data assets must be available for use by the right people for the right purposes to maximize their security, quality and value. Why You Need Cloud Data Governance. Regulatory compliance is also a major driver of data governance (e.g., GDPR, CCPA, HIPAA, SOX, PIC DSS).

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What is data governance? Best practices for managing data assets

CIO Business Intelligence

Data governance definition Data governance is a system for defining who within an organization has authority and control over data assets and how those data assets may be used. It encompasses the people, processes, and technologies required to manage and protect data assets.

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How companies are building sustainable AI and ML initiatives

O'Reilly on Data

In other words, could we see a roadmap for transitioning from legacy cases (perhaps some business intelligence) toward data science practices, and from there into the tooling required for more substantial AI adoption? Data scientists and data engineers are in demand.