Is DG a D-O-G?

Convincing your organization to invest in a sustained data quality program implemented within a data governance framework can be a very difficult task requiring an advocate with a championship pedigree.  But sometimes it seems like no matter how persuasive your sales pitch is, even when your presentation is judged best in show, it appears to fall on deaf ears.

Perhaps, data governance (DG) is a D-O-G.  In other words, maybe the DG message is similar to a sound only dogs can hear.

Galton’s Whistle

In the late 19th century, Francis Galton developed a whistle (now more commonly called a dog whistle), which he used to test the range of frequencies that could be heard by various animals.  Galton was conducting experiments on human faculties, including the range of human hearing.  Although not its intended purpose, today Galton’s whistle is used by dog trainers.  By varying the frequency of the whistle, it emits a sound (inaudible to humans) used either to simply get a dog’s attention, or alternatively to inflict pain for the purpose of correcting undesirable behavior.

Bad Data, Bad, Bad Data!

Many organizations do not become aware of the importance of data governance until poor data quality repeatedly “bites” critical business decisions.  Typically following a very nasty bite, executives scream “bad data, bad, bad data!” without stopping to realize the enterprise’s poor data management practices unleashed the perpetually bad data now running amuck within their systems.

For these organizations, advocacy of proactive defect prevention was an inaudible sound, and now the executives blow harshly into their data whistle and demand a one-time data cleansing project to correct the current data quality problems.

However, even after the project is over, it’s often still a doggone crazy data world.

The Data Whisperer

Executing disconnected one-off projects to deal with data issues when they become too big to ignore doesn’t work because it doesn’t identify and correct the root causes of data’s bad behavior.  By advocating root cause analysis and business process improvement, data governance can essentially be understood as The Data Whisperer.

Data governance defines policies and procedures for aligning data usage with business metrics, establishes data stewardship, prioritizes data quality issues, and facilitates collaboration among all of the business and technical stakeholders.

Data governance enables enterprise-wide data quality by combining data cleansing (which will still occasionally be necessary) and defect prevention into a hybrid discipline, which will result in you hearing everyday tales about data so well behaved that even your executives’ tails will be wagging.

Data’s Best Friend

Without question, data governance is very disruptive to an organization’s status quo.  It requires patience, understanding, and dedication because it will require a strategic enterprise-wide transformation that doesn’t happen overnight.

However, data governance is also data’s best friend. 

And in order for your organization to be successful, you have to realize that data is also your best friend.  Data governance will help you take good care of your data, which in turn will take good care of your business.

Basically, the success of your organization comes down to a very simple question — Are you a DG person?

Data Quality Project Management

OCDQ Radio is an audio podcast about data quality and its related disciplines, produced and hosted by Jim Harris.

During this episode, Adam Cox and I discuss data quality project management, avoiding data quality becoming an afterthought on data integration and data migration projects, the difference and relationship between data ownership and data stewardship, regulatory requirements for data quality, and the importance of getting buy-in from business stakeholders.

Adam Cox is a data management professional with over ten years of experience working in the public and private sector in the United Kingdom (UK).  He is an experienced project and technical manager working on large-scale projects involving significant data migration and data integration.  Adam Cox is currently working for an established UK financial institution as a Data Quality Consultant, mainly on regulatory reporting projects.

Popular OCDQ Radio Episodes

Clicking on the link will take you to the episode’s blog post:

• Demystifying Data Science — Guest Melinda Thielbar, a Ph.D. Statistician, discusses what a data scientist does and provides a straightforward explanation of key concepts such as signal-to-noise ratio, uncertainty, and correlation.

• Data Quality and Big Data — Guest Tom Redman (aka the “Data Doc”) discusses Data Quality and Big Data, including if data quality matters less in larger data sets, and if statistical outliers represent business insights or data quality issues.

• Doing Data Governance — Guest John Ladley discusses his book How to Design, Deploy and Sustain Data Governance and how to understand the difference and relationship between data governance and enterprise information management.

• Demystifying Master Data Management — Guest John Owens explains the three types of data (Transaction, Domain, Master), the four master data entities (Party, Product, Location, Asset), and the Party-Role Relationship, which is where we find many of the terms commonly used to describe the Party master data entity (e.g., Customer, Supplier, Employee).

• Measuring Data Quality for Ongoing Improvement — Guest Laura Sebastian-Coleman discusses bringing together a better understanding of what is represented in data with the expectations for use in order to improve the overall quality of data.

• The Blue Box of Information Quality — Guest Daragh O Brien on why Information Quality is bigger on the inside, using stories as an analytical tool and change management technique, and why we must never forget that “people are cool.”

• Data Governance Star Wars — Special Guests Rob Karel and Gwen Thomas joined this extended, and Star Wars themed, discussion about how to balance bureaucracy and business agility during the execution of data governance programs.

• Good-Enough Data for Fast-Enough Decisions — Guest Julie Hunt discusses Data Quality and Business Intelligence, including the speed versus quality debate of near-real-time decision making, and the future of predictive analytics.

• The Johari Window of Data Quality — Guest Martin Doyle discusses helping people better understand their data and assess its business impacts, not just the negative impacts of bad data quality, but also the positive impacts of good data quality.

• The Art of Data Matching — Guest Henrik Liliendahl Sørensen discusses data matching concepts and practices, including different match techniques, candidate selection, presentation of match results, and business applications of data matching.

• Data Profiling Early and Often — Guest James Standen discusses data profiling concepts and practices, and how bad data is often misunderstood and can be coaxed away from the dark side if you know how to approach it.

• Studying Data Quality — Guest Gordon Hamilton discusses the key concepts from recommended data quality books, including those which he has implemented in his career as a data quality practitioner.