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Jeff Stander evaluates the pros and cons of three different options you can use to prepare your organization's data for analytics.
From improving data quality to operationalizing analytics, Jim Harris explains why business glossaries make analytics better.
Data-driven businesses are driven by analytics and use technology as a platform for business insights – particularly to empower nontechnical users.
Unprepared data – it's like having driving directions in kilometers for a car measuring miles. Guest blogger Jenine Milum shares tips for preparing data.
David Loshin explains the significant role the data team plays in enforcing certain legislative actions, citing the "double-dipping" act as an example.
Jim Harris proposes that data governance policies for analytics could serve as the foundation for a formal model governance program.
Some things change – others surface repeatedly. Phil Simon reflects on a variety of data-related challenges, current and future.
David Loshin offers four suggestions for CIOs who are looking to shape successful digital transformation initiatives.
Phil Simon argues against full automation of data management – opposite the viewpoint shared in his previous post.
Jim Harris says it's not enough to be data-driven – understand which data is driving your business, and whether it's leading you to make better decisions.
New tools and automation are essential for managing today's data. Phil Simon looks at the pros of automating data management in Part 1 of this series.
In Part 2 of his series, guest blogger Khari Villela explores the common pitfalls of building a data lake and shares tips to help you skip those mistakes.
Not sure how to minimize data usage? Jim Harris shares some simple tips to help you work more efficiently and optimize data usage.
Based on three recent developments, David Loshin suggests that reference data could be the foundation of future governance structures.
Jim Harris shares three more examples of how data quality improves artificial intelligence in Part 2 of his series.
Data protection applies to different individuals in different contexts. David Loshin reminds us that we're not limited to complying with GDPR and CCPA.
Data makes artificial intelligence better and smarter. Phil Simon weighs in on using data to make the most of AI.
Phil Simon says the downsides of even a few discrepancies can be enormous. Read about four ways you can improve data accuracy.
All AI applications are data-driven and dependent on high-quality data. Jim Harris shares examples of how data quality improves artificial intelligence.