Data stewardship for artificial intelligence
Jim Harris says curating AI’s curriculum is the responsibility of data stewards.
Author
Jim HarrisRSS
Blogger-in-Chief at Obsessive-Compulsive Data Quality (OCDQ)
Blogger-in-Chief at Obsessive-Compulsive Data Quality (OCDQ)
Jim Harris is a recognized data quality thought leader with 25 years of enterprise data management industry experience. Jim is an independent consultant, speaker, and freelance writer. Jim is the Blogger-in-Chief at Obsessive-Compulsive Data Quality, an independent blog offering a vendor-neutral perspective on data quality and its related disciplines, including data governance, master data management, and business intelligence.
Data engineers: Key role in data privacy and protection
Jim Harris discusses a key role of the data engineer – protecting sensitive personal data.
Data steward: The concierge of analytics
Jim Harris says data stewards are essential to analytics, providing life cycle management for data across the enterprise.
Data is not the only decision maker
Jim Harris says people still play a pivotal role in data-driven decision making.
Growing importance of data lineage when managing data for analytics
Jim Harris says learn the lineage of the data that fed the analysis before you get dazzled by visualizations or algorithms.
Data lake management for analytics
Jim Harris warns against allowing your data lake to become a poorly managed and ungoverned data dumping ground.
Real-time data and streaming analytics
Like hiking without GPS directions, Jim Harris says businesses that don't use streaming data analytics will be lost in a crowd of competitors.
Will GDPR shift personal perspectives on data?
Jim Harris says the GDPR may cause seismic changes in how people think of privacy and personal data.
Data retention strategy – Part 2
Don't be a data hoarder. Jim Harris shares guidelines for a data retention strategy.
Data retention strategy – Part 1
Jim Harris asks: Do you retain and maintain data, or do you have a data retention strategy?
Streaming data analytics
Jim Harris says streaming data analytics can drive better decisions and faster adaptation to changing conditions.
Data preparation for streaming data
Jim Harris considers the technical infrastructure challenges of data preparation for streaming data.
How a data-driven business supports analytics goals
Jim Harris says a data-driven business can make decisions faster, using better data, with more transparency about results.
Data governance steers a data-driven business
Jim Harris shows how data-driven businesses incorporate three aspects of data governance to guide their decisions.
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.
Discoverability enables self-service data preparation
Get faster value out of your data by empowering business users to work with data on their own.
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.
Data privacy concerns in the age of big data
Jim Harris considers whether we can save private data in the age of big data.
On the way to data lakes and Hadoop – Part 2
In part 2, Jim Harris explains more about why you should address data quality and governance issues on the way to data lakes and Hadoop.
On the way to data lakes and Hadoop – Part 1
Jim Harris advocates addressing data quality and governance issues on the way to data lakes and Hadoop.
The importance of data quality to MDM
Jim Harris explains why data quality is such a fundamental aspect of master data management.
How is MDM different from data warehousing?
Do you know how master data management and data warehouses are different? Jim Harris explains.
Streaming analytics blurs the lines between data management and analytics
Jim Harris discusses how the lines between data management and analytics are fading.
When machine learning and streaming data meet
Via streaming data, Jim Harris says machines can learn some amazing things without being programmed with domain knowledge.
Data compliance: Will you be able to measure the effectiveness of your efforts?
Like getting into good shape, Jim Harris says we must carefully measure adherence to regulatory compliance – using both internal and external measures.
Compliance: Is a solid data management strategy and platform enough?
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.
What does the requirement for data privacy mean for data scientists, business analysts and IT?
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
Can you be open and compliant at the same time?
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
Streaming to better data quality
Streaming technologies have been around for years, but as Felix Liao recently blogged, the numbers and types of use cases that can take advantage of these technologies have now increased exponentially. I've blogged about why streaming is the most effective way to handle the volume, variety and velocity of big data. That's
Managing data where it lives
Historically, before data was managed it was moved to a central location. For a long time that central location was the staging area for an enterprise data warehouse (EDW). While EDWs and their staging areas are still in use – especially for structured, transactional and internally generated data – big