- Where should a Data Governance program BEGIN ?
- How can you measure a SUCCESSFUL Data Governance program ?
- Commercial (yes, dollars) Metrics are the answer, that can DEMONSTRATE a return-on-investment (ROI) of the discipline.
Without accurate and meaningful commercial metrics, Data Governance (DG) programs are irrelevant, and lose business engagement very quickly.
Apart from marshalling your stakeholders and sponsors, and getting a clear understanding of what they want from your role, and the organisation’s information, your Data Governance program will need some commercial teeth to be deemed relevant and successful.
Funding is always the big stumbling block for many wholesome data initiatives, and Data Governance is no different. Data leaders often struggle trying to establish meaningful commercial metrics that help support Data Governance initiatives (and it’s existence).
After being involved in Data Governance programs for over 30 years, I’ve been able to crystallise a few maxims, that help drive successful business engagement with (and even successfully transition data ownership across to) business stakeholders, who eventually need to own and drive any successful Data Governance program (by that name, or any other name.. more on that later)
Over the years, in live case studies I have run, and I have collected mountains of evidence, the average organisation loses about 20% of its workforce effectiveness directly due to data failure.
These numbers range from as low at 13% of the workforce (Energy Retailer), to 23% of the workforce (Capital Markets Bank), to 24% of the workforce (Health Insurance) to as high as 32% of the workforce (a Higher Education Institution). These are staff members who cannot perform their primary job function as all, due to specific, named and identifiable data problems.
Speaking to the IT leadership of one major Australian institution, this damage equates to $40m per annum. Not a number to be ignored by anyone who cares about the economic performance (or survivability) of their organisation.
Along the way, as this evidence has been collated, other factoids have surfaced during the diagnostic process including Fraud, Regulatory Fines, Breaches of Company Privacy Protocols, Staff Attrition (ability to accurately predict), and many other commercial ($) metrics revealed in the data landscape.
Data Governance programs really need to be pinned by commercial metrics, and I consider the establishment of these metrics (which should take no more than say 4 weeks for an organization with up to say 50,000 employees) in the first 100 days of the tenure of a Chief Data Officer (CDO or data leader).
In my mind, a CDO or data leader who is unable to articulate the commercial value of their Data mission in the first 100 days is destined to unceremonious failure (or irrelevance), unless they can surface their economic credentials, and raison-d’etre.
One additional provocative thought on Data Governance, after using the term for over a decade and half, I actually find the term quite antiquated, and prefer rebranding the Data Governance mission to something more outcome oriented (and meaningful to business stakeholders) like Data Assurance, Data Health, Data Administration (a throwback to 1980’s) or Data Value Engineering (to propose something sexy)…
Question 1: If you were to rename or rebrand your Data Governance program, what would you call it ? (or more importantly, what would your business stakeholders call it ?).
Question 2: In closing, what metrics do you thing are valuable in driving a traditional Data Governance program ?
Author: Martin Spratt, 30 Mar 2017. Martin Spratt is a global data value guru, author and CDO advisor, held hostage in Melbourne by 4 women and a cat, and survives on cappuccinos. This article first appeared on ClearDQ.com