How containers have moved us to next-gen virtualization
With containers, you can build once, run anywhere – no worries about underlying dependencies.
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.
Alternative data may feel risky: Here's why it's not
Some businesses overlook alternative data. That's probably a mistake.
Get the scoop on data lake vs cloud
Jim Harris takes a deep dive into data lakes and how they relate to the cloud.
Building for the digital future of service
The evolution of service is simplicity and clarity. Build for the digital future of service today.
Why it's hard to say "big data" without saying "cloud"
Ready to turn big data into big business insights? Look to the cloud.
Synthetic identity fraud: What you can’t see can hurt you
Digital channels open the door to synthetic identity fraud. Luckily, artificial (AI, that is) can defeat synthetic.
What is a data catalog – and why do you need one?
Learn why a data catalog is so valuable in helping you find and use big data at your business.
Coronavirus: Know the terms, stay the course
Jim Harris examines coronavirus terms that are crucial to data-driven decisions in the pandemic.
Digging into coronavirus testing terms: What you need to know
In the third post of his series, Jim Harris looks at types, terms and timing of coronavirus tests.
Coronavirus testing: The terms you need to know
Could you pass a test on coronavirus testing terms? Jim Harris can help.
8 terms you need to understand when assessing COVID-19 data
Dashboards and metrics support data-driven decisions – if you understand the terms.
How to measure data quality
Learn the nuts and bolts of how to measure data quality from expert Jim Harris.
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.