How to measure data quality
Learn the nuts and bolts of how to measure data quality from expert Jim Harris.
Tag: data quality
Data, analytics and humans: The insight equation
Jim Harris shows how data, analytics and humans work together to form the "insight equation."
4 data tagging best practices
Read about the value of data tagging and learn best practices for doing it effectively.
Why data management protocols are key to implementing legislative actions
Surprise! The data team does more than you think to implement certain legislative actions.
How data quality improves artificial intelligence – Part 2
Jim Harris shares three more examples of how data quality improves AI in Part 2 of his series.
Which data sources will organizations use to make AI as effective as possible?
Phil Simon weighs in on using data to make the most of AI.
How to improve data accuracy
Phil Simon says that the downsides of even a few discrepancies can be enormous.
How data quality improves artificial intelligence
Jim Harris shares examples of how and why AI applications are dependent on high-quality data.
7 ways analytical methods improve data quality
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
Data stewardship for artificial intelligence
Jim Harris says curating AI’s curriculum is the responsibility of data stewards.
The importance of data governance: An interview with UNT
Learn why Jason Simon from UNT calls data governance critical.
Data stewards – Why they’re the unsung heroes of the business
Expect to lose time if you don't include a data steward in your project until you're reviewing the data model.
No, data is not the new oil
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
Data steward: The concierge of analytics
Jim Harris says data stewards are essential to analytics, providing life cycle management for data across the enterprise.
Without data management, forget AI and machine learning in health care
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
Data governance implications for the chief data officer
Reconsider conventional assumptions about data governance – three suggestions for chief data officers.
Trust in the data: Ensuring quality analytics (continued)
How should a data trust process work? David Loshin elaborates.
Amazon's data train just keeps a rollin'
Think that the company has let up in the last two years? Think again.
Data management for analytics: Best practices and examples
Focus on data governance, quality and storage if you want to do data management for analytics right.
Trust in the data: Ensuring quality analytics
David Loshin raises questions about what needs to be done to ensure quality analytics.
Data governance policies: Key to analytics innovation?
Better decisions and analytics innovation – fringe benefits of having comprehensive data governance policies.
Data quality: The not-so-secret ingredient of customer data management
Kim Kaluba explains why good customer data management starts with trusted data quality.
Data retention strategy – Part 2
Don't be a data hoarder. Jim Harris shares guidelines for a data retention strategy.
Data governance – The topic that never dies
Todd Wright says data governance is more relevant than ever – especially in light of the GDPR.
Data retention strategy – Part 1
Jim Harris asks: Do you retain and maintain data, or do you have a data retention strategy?
The SAS platform – It all starts with data management
To get full value from analytics programs, Todd Wright says be sure you can first access, integrate, cleanse and govern your data.
Reusable data quality enables self-service data preparation
Jim Harris says more reusable data quality processes mean less reliance on IT and higher productivity across the board.
Data quality monitoring – step 1.
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?
SAS Visual Analytics: Hallo Emilia – Goodbye Daniel
Die Geburtenrate in Deutschland befindet sich derzeit auf dem höchsten Niveau seit 33 Jahren. Eine erfreuliche Entwicklung, und zugleich stellt es Eltern vor die schwere Entscheidung, welchen Namen der Nachwuchs tragen soll. Zahlreiche Webseiten und Bücher bieten Hitlisten und Namensbeschreibungen an, um die Auswahl zu erleichtern. Oder sollte man das
Self-service data preparation gives fast access to the data you need
Get on with your day faster by taking a self-service approach to data preparation.