Jim Harris shares three more examples of how data quality improves AI in Part 2 of his series.
Jim Harris discusses a key role of the data engineer – protecting sensitive personal data.
Jim Harris says learn the lineage of the data that fed the analysis before you get dazzled by visualizations or algorithms.
Jim Harris says a data-driven business can make decisions faster, using better data, with more transparency about results.
Jim Harris says more reusable data quality processes mean less reliance on IT and higher productivity across the board.
Get faster value out of your data by empowering business users to work with data on their own.
Get on with your day faster by taking a self-service approach to data preparation.
Jim Harris discusses how the lines between data management and analytics are fading.
Like getting into good shape, Jim Harris says we must carefully measure adherence to regulatory compliance – using both internal and external measures.
Platform and strategy are core to compliance, but Jim Harris says commitment from people across the organization is just as important and harder to achieve.
Corporate compliance with an increasing number of industry regulations intended to protect personally identifiable information (PII) has made data privacy a frequent and public discussion. An inherent challenge to data privacy is, as Tamara Dull explained, “data, in and of itself, has no country, respects no law, and travels freely across borders. In the
The term compliance is most often associated with control. It evokes visions of restrictions, regulations and security protecting something which is to remain private. The term open is most often associated with access, and it evokes visions of an absence of restrictions, regulations and security – making something available which is