This webinar brings together experts from TDWI, Informatica, and Databricks to address the critical challenge of operationalizing AI at enterprise scale.

For 30+ years, TDWI has been helping data leaders and their teams transform the information and skills they need to build effective AI, BI, and data management programs.

As organizations look to utilize AI for true business impact, they are discovering that success depends on having a strong, scalable data foundation. Data quality, accessibility, governance, and ...

The Data Quality Maturity Model can help guide organizations on their data quality journey. It provides a framework for companies to understand where they are, where they’ve been, and where they still ...

The "big" in "big data" is a function of the volume, variety, and velocity of the information that constitutes it. If you read a dozen articles on big data, there's a good chance the 3 Vs -- volume, ...

Joint controller is not a new concept in the GDPR. In fact, it has been part of the law since its inception in 2018. However, the definition for the term “joint controller” has been updated as part of ...

At TDWI's Executive Summit in San Diego, Mark Madsen posed the provocative question: is there a statistically significant correlation between sales of beer and sales of diapers? Madsen, a research ...

All organizations want accurate data to run their operations. Getting data into a useful format is the focus of significant industry attention, whether that data comes from social media, structured ...

The Data Management Body of Knowledge (DMBOK), the pre-eminent reference text in the data management industry, defines data quality as “the planning, implementation, and control of activities that ...

Data Stories: Unit Charts, Multiple Axes, Chart Interpretation How to use a unit chart, a case for using multiple axes, and interpreting chart design. By Upside Staff Stacks of Symbols Considering the ...

A recent study yielded some dismal findings that won’t shock anyone who has worked in data engineering. Of the 600 data engineers surveyed, nearly all (97 percent) are experiencing burnout. In ...

A misdiagnosed patient. A denied mortgage. A fraud alert that locks out a legitimate customer. These aren’t edge cases—they’re recurring failures of systems never meant to make high-stakes decisions ...