Best Posts on Data Quality and Data Governance
- Data Quality in Six Verbs — As a list of critical topics for data quality practitioners, since I prefer to emphasize the need to take action, I propose six critical verbs: Investigate, Communicate, Collaborate, Remediate, Inebriate, and Reiterate.
- Doing Data Governance — Podcast with author John Ladley discussing how to understand the difference and relationship between data governance and enterprise information management.
- Is DG a D-O-G? — Convincing your organization to invest in a sustained data quality program implemented within a data governance (DG) framework can be so difficult that maybe the DG message is similar to a sound only dogs can hear.
- Data Quality and the Blemishing Effect — Could being honest about the existence of a small blemish enhance the true beauty of your data quality business case?
- Measuring Data Quality for Ongoing Improvement — Podcast with author Laura Sebastian-Coleman discussing bringing together a better understanding of data’s representations and expectations to improve the overall quality of data.
- Expectation and Data Quality — Could establishing an expectation of delivering high-quality data lead business users to believe that data quality thresholds have been met or exceeded?
- The Hawthorne Effect, Helter Skelter, and Data Governance — Oscillating between different levels of data governance maturity, and even occasionally coming full circle, may be an inevitable part of the evolution of a data governance program.
- The Assumption of Quality — We often leverage data on the assumption of its quality, and while this assumption should be tested whenever it can be, we have to accept that there will be many times when we will not be able to.
- Total Information Risk Management — Podcast with author Alexander Borek discussing the need to understand, measure, and protect your organization from the risks of low quality data and information assets.
- Data Quality and Anton’s Syndrome — A lot of the disconnect between business leaders, who believe they’re not blind to data quality, and data quality practitioners, who believe they’re not blind to business context, is a crisis of perception.
- Council Data Governance — Inspired by the great Eagles song Hotel California, this post “sings” about the common mistake of convening a council too early when starting a new data governance program.
How Much Quality Does Data Need?
Throughout the year I explored this question from several different perspectives across five blog posts:
- Data Quality and the OK Plateau — Data quality improvement eventually reaches a point just short of data perfection, where the diminishing returns of chasing after zero defects gives way to calling data quality as good as it needs to get.
- When Poor Data Quality Kills — A balanced perspective demands acknowledging there are times when nothing less than perfect data quality is necessary because there are times when poor data quality can have deadly consequences.
- The Costs and Profits of Poor Data Quality — Examples include the $80 million dollar missing hyphen that destroyed a spacecraft, and the $497 million dollars Google profits every year from mistyped names of popular websites.
- Sometimes Worse Data Quality is Better — Provides three examples from the world of consumer electronics (videotapes, MP3 files, and digital photography) that demonstrate how it doesn’t always pay to have better data quality.
- Keep Looking Up Insights in Data — Even when data is partially obstructed, blemished, or diminished by the turbulent and murky atmosphere of poor quality, data is still capable of providing business insights.
Best Posts on Big Data and Data Science
- Demystifying Data Science — Podcast with Melinda Thielbar, a Ph.D. Statistician, discussing what a data scientist does and straightforwardly explaining key concepts such as signal-to-noise ratio, uncertainty, experimentation, and correlation.
- On Philosophy, Science, and Data — There’s a conceptual bridge between data analysis and insight, the crossing of which is itself a philosophical exercise, which is why data needs both science and philosophy.
- The Need for Data Philosophers — As a follow-up to the previous post, despite the fact that all we hear about these days is the need for data scientists, there’s also a need for data philosophers (as well as data engineers and data artists).
- DQ-View: Microscopes, Telescopes, and Datascopes — A short video post, which was inspired by a quote about microscopes and telescopes from a Neil deGrasse Tyson book, that imagines the era of big data analytics as a datascope.
- i blog of Data glad and big — Pondering, with help from E. E. Cummings, the war of words over big data and data science expanding into realms where people and businesses were not used to enduring their influence.
- Big Data is Just Another Brick in the Wall — Big data does not obviate the need for data management best practices, but it does occasionally require adapting those best practices as well as adopting new practices.
- Big Data and the Infinite Inbox — In today’s world, where many of us strive on a daily basis to achieve Inbox Zero, unfiltered enthusiasm about big data seems rather ironic, since big data is enabling the equivalent of the Infinite Inbox.
- The Laugh-In Effect of Big Data — Channeling Arte Johnson, I deride the common phenomenon of an interesting story masquerading as a solid business case for big data that lacks any applicability beyond the specific scenario in the story.
Best of the Rest
- What an Old Dictionary teaches us about Metadata — An old dictionary reminds us that the everyday usage of language evolves, which teaches us that metadata — and the data it defines, describes, and provides a context for — also evolves.
- MDM, Assets, Locations, and the TARDIS — A master data management (MDM) analogy inspired by Doctor Who, which explains how a Location is a time and relative dimension in space where we would currently find an Asset.
- The Stone Wars of Root Cause Analysis — While root cause analysis is important to data quality improvement, too often we can get carried away riding the ripples of what we believe to be the root cause of poor data quality.
- Popeye, Spinach, and Data Quality — Could a data quality issue that was not immediately obvious at the time it was created lead to a long, and perhaps ongoing, history of data-driven decisions based on poor quality data?
- Is Poor Quality the Antihero of Data? — A post about why superhero stories seem eerily similar to data quality and pondering whether poor quality is data’s supervillain or antihero.
Thank You for Reading OCDQ Blog in 2013
In 2013, the Obsessive-Compulsive Data Quality (OCDQ) blog published 55 posts, which received 176,000 total page views, while averaging over 480 page views and 260 unique visitors a day.
Thank you for reading OCDQ Blog in 2013. Your readership was deeply appreciated.