I was recently told that an organization had tried to implement AI for forecasting in supply chain but had failed due to poor data. This got me thinking about exactly what the impacts of poor data would be. And whether the approaches I had applied elsewhere could help. It's probably
Learn why integrating EHR data with pharmacy and claims data improves patient care.
Learn the nuts and bolts of how to measure data quality from expert Jim Harris.
Jim Harris shows how data, analytics and humans work together to form the "insight equation."
Read about the value of data tagging and learn best practices for doing it effectively.
Surprise! The data team does more than you think to implement certain legislative actions.
Jim Harris shares three more examples of how data quality improves AI in Part 2 of his series.
Phil Simon weighs in on using data to make the most of AI.
Phil Simon says that the downsides of even a few discrepancies can be enormous.
Jim Harris shares examples of how and why AI applications are dependent on high-quality data.
Data scientists spend a lot of their time using data. Data quality is essential for applying machine learning models to solve business questions and training AI models. However, analytics and data science do not just make demands on data quality. They can also contribute a lot to improving the quality
Jim Harris says curating AI’s curriculum is the responsibility of data stewards.
Learn why Jason Simon from UNT calls data governance critical.
Expect to lose time if you don't include a data steward in your project until you're reviewing the data model.
By now you’ve seen the headlines and the hype proclaiming data as the new oil. The well-meaning intent of these proclamations is to cast data in the role of primary economic driver for the 21st century, just as oil was for the 20th century. As analogies go, it’s not too
Jim Harris says data stewards are essential to analytics, providing life cycle management for data across the enterprise.
Data management gets lost in the enthusiasm around Artificial intelligence (AI) and machine learning (ML). Not surprising, when it's an algorithm that decides what search results to show you, guides the self-driving cars on the roads, and powers the anti-fraud bots that monitor every credit card transaction we make. Charles
Reconsider conventional assumptions about data governance – three suggestions for chief data officers.
How should a data trust process work? David Loshin elaborates.
Think that the company has let up in the last two years? Think again.
Focus on data governance, quality and storage if you want to do data management for analytics right.
David Loshin raises questions about what needs to be done to ensure quality analytics.
Better decisions and analytics innovation – fringe benefits of having comprehensive data governance policies.
Kim Kaluba explains why good customer data management starts with trusted data quality.
Don't be a data hoarder. Jim Harris shares guidelines for a data retention strategy.
Todd Wright says data governance is more relevant than ever – especially in light of the GDPR.
Jim Harris asks: Do you retain and maintain data, or do you have a data retention strategy?
To get full value from analytics programs, Todd Wright says be sure you can first access, integrate, cleanse and govern your data.
Jim Harris says more reusable data quality processes mean less reliance on IT and higher productivity across the board.
You have to be able to trust the data that you are working with, whether it’s data processing or analysis that you are involved with. And there is a strong correlation between that trust and data quality. Is it possible to determine data quality without monitoring mechanisms?
