For many businesses, perception may be the hardest thing to change. How someone thinks about you, your product, your organization, or your data is—to that individual at least—simply the truth. Unfortunately, this is often true for enterprises that are tasked with starting a new data governance program: the data is perceived as inaccurate, that it’s not trustworthy, or, even worse, that it’s ‘data sewage’. This was the case at Genworth Financial when the data governance team began their program to improve the reliability and quality of their data.
To be successful, the team knew they would have to change the way the organization felt about data. They would need to create a culture change: one that provided clarity of purpose, defined roles, collaborative policies, consistent communication, data literacy, and tools to support their journey. The Genworth team built their framework for success and ultimately changed not only the overall perception about the quality of data, but also had an impact on the data-centric behaviors which were put in place so that their trusted, governed data could deliver value for the rest of their organization well into the future.
In our next webinar with the Data Empowerment Experts series, I’ll be joined by Christopher Corrigan, Data Governance Leader for Genworth Financial. Christopher will share Genworth’s journey, and outline how they overcame the perception of having poor quality data even as they empowered their business leaders with trusted data and operationalized an enterprise data governance program.
As I have done with previous Data Empowerment Experts, I sat down with our speaker to get his viewpoints on critical considerations for data governance professionals today.
Answer: We were required to focus on two main topics—the perception of the quality of the data and the needs for our critical business functions. So initially we focused on data quality and then migrated into the metadata aspects of the ‘In Force Action’ business. Speaking more broadly for those organizations looking to reengage or reinvigorate their programs, focusing on a particularly vital business process might be your best approach, something like your MDM (Master Data Management). Whether you own a tool or not, there is likely some data in your organization that is cross-functional, or highly shared. Building the metadata, documenting, publishing for enterprise consumption begins your common lexicon evolution. Then addressing the quality of the data sourcing the MDM and publishing those results starts the evolution on data entry value and how the data is consumed. I strongly recommend identifying a specific business initiative or two to keep a direct focus for your governance activities.It’s important to keep this in mind, as one business objective could involve half of the business operations and infrastructure—which, depending on the number of resources allocated, could present a topic that’s just too large to start. It’s better to focus on a smaller subset, which is what we did with data quality for a critical business function.
Answer: Maintaining momentum is a function of the value you are bringing to the business and transitioning ownership of success to the business through stewardship and custodial relationships. What this means is that you are always truing back to what matters most to the business, AKA the business and data strategies. Finally, to avoid losing momentum, keep the business constantly engaged through stewards, councils, objectives, and budgeting exercises. Regaining lost momentum is very difficult: the first challenge is to recognize that it is lost, and the second challenge is to discover the root cause of that loss. In my experience, the loss is often a derivative of business engagement and ownership. Typically, to regain momentum requires a degree of “reboot,” AKA Data Governance 2.0 or 3.0 or what have you. Critical to this reboot is the declaration of the prior dysfunction that leached away momentum.
Answer: There are three aspects of scale that come to mind.The first is simply raw business data, the second is in the number of data stewardship practitioners, and the third is data governance content. Regarding the raw business data, this becomes a challenge from a data classification and quality perspective. The second is the challenge of having controls in place to ensure that the content is accurate and complete, as well as that it conveys the intended meaning/knowledge. The third is the amount of business and technical metadata, as well as enabling data discovery.
Raw business data volumes require aggressive and constraining data classifications. This can be accomplished by understanding the absolutely critical business processes and the data that matters most. From this classification, you should then have a much more refined data set to apply your data quality rules. And here is a good place for technology: by capturing the metadata that classifies what data matters most, you can then dynamically build your DQ processes. Also note that through your data classifications, you can also determine the frequency of your DQ assessments.
Metadata completeness and accuracy and your stewardship community grows is a function of how many hands you allow in to “stir the pot.” The simple reality is that not everyone will be trained as technical writers, no matter what university degree they have achieved. Also, not everyone will be 100% on board with absolutely every aspect of your data governance program. And lastly but most significantly, the stewards will not be given the necessary amount of time to fulfill on all aspects of their role. So, it goes without saying that there need to be some controls built to assess the quality of the metadata. At Genworth, we will be leveraging the tools to have this kind of control, but we are also providing for operational teams whose duty it is to take change requests from stewards and construct the complete metadata according to our standards.This has the effect of allowing the business to invest as much time as they have but not to be encumbered with the many details of making the metadata complete and accurate.
The volume of metadata content is perhaps the most nebulous. On the one hand, the technical metadata and data dictionaries can be huge—but on the other hand, not everything captured in the databases is of the same value. This is another area where technology can help: for the technical metadata, we leverage Informatica’s Enterprise Data Catalog (EDC) tool. IT connects to all environments and generates a complete repository of data objects, so EDC has everything. Now at Genworth, we have branded EDC as the technical metadata tool and set the expectations that there will be an extreme number of data objects presented. Now to focus the business’ attention on what matter most, we leverage Axon. Axon is where we do all of our data classifications, definition of data quality rules, and association of physical data elements to business terms and processes. So, you can leverage your business metadata to what matters most.
Answer: Having come from GE, a company that was (and is) so focused on Six Sigma black and green belts, we learned that if we do not measure, there is no movement. The challenge then became, “what do we measure.” Our Data Governance Program Office has gone through two iterations of KPIs centered on Data Stewards, Metadata, and Data Quality. Our first version was fairly subjective and involved a great many conversations. Our current approach leverages metadata flags and physical participation in public forums as our measures.Now there are numerous maturity models and maturity model frameworks out there that offer great starting points. My only caution there is to be conscious of the magnitude of the measurement process in terms of the scope of your data governance program.
Central to our KPIs is the enterprise’s achievements in data governance. We have a data governance portal that contains the quarterly results of the KPIs, as well as access to all of our tools and documentation. It is this later avenue where we showcase our team’s achievements. Aside from the existence of new icons or documents, we highlight these in our Data Governance Council sessions. We also drive use of the tools through our data stewardship forums.
Answer: Data privacy and security is of critical and paramount importance at Genworth and as such has its own Chief Information Security Officer and Office. They currently are autonomous and leverage their own content. Our goal, however, is to enable their use of our Data Governance solutions for their oversight. Given our solution to securing the data, we have not needed to directly focus on specific data privacy regulations as they are being handled directly. Nevertheless, we do intend to enable these facets of data governance in the next 1-2 years as a way of communicating their importance and additional requirements on the data.
Answer: Driving business value has to be at the heart of any data governance program. The challenge becomes what classifications of value are relevant and when can that value materialize. When we think of business value classifications, we usually think of intangible benefits (like trusting your data) first, but these benefits have to be converted as quickly as possible into tangible benefits (such as reduced operating costs because less time is being spent assessing the quality of the data).
Using automated tools greatly enables your ability to assess the business value of your data governance program. Tools that allow you to
While there is no one tool to do all of this, Genworth has selected Axon and EDC due to its capacity to deliver, natively and out-of-the-box, much of what we need, while still providing a platform that enables the custom implementation of new connectors or resources. At the end of the day, by reducing the ROT (the redundant, obsolete, and trivial data that Peter Aiken references), we will have smaller footprints in the data center, require fewer database licenses, and need fewer resources to manage the data. Additionally, the business is not left to wonder what data they should or can use and thus is able to further streamline their processes.
At the end of the day, data governance is the very act of democratizing the data, which means in much simpler terms, the business is empowered with the knowledge of the data to evolve the business to the next great leap in innovation.
Please be sure to join us on Tuesday, April 28, 2020 for the webinar, Genworth Financial Turns Data from Byproduct to High-Value Asset with Governance, where Christopher will share his team’s journey and outline how they transformed the perception around data. As always, you can find more information about the Data Empowerment Experts Series at www.informatica.com/dataexperts