How to measure data quality
Learn the nuts and bolts of how to measure data quality from expert Jim Harris.
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, analytics and humans: The insight equation
Jim Harris shows how data, analytics and humans work together to form the "insight equation."
Why Ops-ing is in (DevOps, ModelOps, DataOps)
Jim Harris explains the relevance of DevOps, DataOps and ModelOps for data analytics practitioners.
4 data tagging best practices
Read about the value of data tagging and learn best practices for doing it effectively.
Top 5 ways business glossaries make analytics better
A business glossary improves data quality – one of the top five ways it makes analytics better.
Data-driven to business insights
Data-driven businesses use technology as an insight platform to empower nontechnical users.
The convergence of data and model governance
Could data governance policies for analytics be the foundation for a model governance program?
How data can change your business – if you let it
Jim Harris explains why true transformation requires more than just being data-driven.
How to minimize data usage
Jim Harris shares some simple tips about how to minimize data usage.
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.
How data quality improves artificial intelligence
Jim Harris shares examples of how and why AI applications are dependent on high-quality data.
Data stewardship for artificial intelligence
Jim Harris says curating AI’s curriculum is the responsibility of data stewards.
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.