Among today’s data-driven businesses, there’s no lack of support for Data Governance. But that enthusiasm doesn’t always make its way to the top of the management chain. “If you don’t have senior leadership sponsorship and understanding of your Data Governance program, your program is going to be at risk,” said Robert S. Seiner, president and principal of KIK Consulting and Educational Services, at a recent Real-World Data Governance webinar. “Some other shiny object, some other sexy project will come along and will steal the thunder from Data Governance.”
A thought leader in the data community, Seiner has long been on a mission to create meaningful connections between upper management and governance team members. During the webinar, he laid out a roadmap for getting executive buy-in for Data Governance.
Evaluating Data Governance Best Practices
To get leadership on board with a Data Governance program, Seiner believes that the very first step – “100% of the time, not even 98% of the time” – should be conducting a best practice assessment. Which data industry best practices does the organization already follow, and which strategies already exist within the culture in a non-invasive manner? Rather than reinventing the wheel, top management will always be happier to leverage available resources, “and then look for where there’s opportunity to improve the organization,” explained Seiner.
There are two yardsticks for evaluating Data Governance best practices. First, a best practice should seem feasible and practical for the organization in its current state. If you present senior management with a course of action that will require too much structural retrofitting, they are likely to hesitate before supporting its sponsorship. Second, the most attractive best practices are always laid out with a clear plan for allocating resources and providing adequate administration over the long term. “You’re not going to have somebody run a program for six months, and then have that program magically run itself,” quipped Seiner. When you do not give corporate leadership a clear timeline attached to the scope of goals, role definitions, expectations, and metric assessment, you risk your governance proposal being ignored.
Articulating Data Governance Requirements
As in any relationship, leadership and governance will get along best when needs are clearly communicated from the start, and then modified as needed in response to real-time contingencies. First, leadership needs to see that governance has a clear vision of needs and a strategy to manifest that vision. Depending on where the most essential data elements are generated and deployed within the company, this vision may be best advocated by anyone from a chief data officer, to a data analytics officer, to even a senior VP with a proven relationship with data. After leadership is affirmed in a sense of direction, they need to be convinced that governance can provide guarantees of the quality and accuracy of data.
“In order to know what’s right and what’s wrong, we need to start with what’s right,” said Seiner. “Leadership expects that the data on their dashboards has been validated and verified.”
If vision and reliability are common-sense directives, compelling management with the exigencies of compliance and risk management is, for Seiner, “the gimme, the no-brainer.” While arguments concerning efficiency and corporate missions may serve as a seductive carrot, government fines and punitive measures amount to a stick that bottom-line-driven CEOs will want to avoid at all costs. When leadership is convinced that its governance program acts as a reliable bulwark against security breaches or mishandling of sensitive data elements, the need for governance is spelled out in a language that key decision-makers won’t need translated. After leadership sees unequivocally how crucial governance can be in eliminating risks and liabilities, it becomes less of an abstract mission to gain full endorsement for Data Governance.
Transforming Business Challenges into a Case for Data Governance
There is no better way to showcase positive business outcomes than by tracking the ways in which good governance can help tackle obstacles over time. The most obvious of such tracking methods is a data audit. Though an audit may be slightly daunting in terms of its invasiveness in operations, it can be indispensable in uncovering lapses in data quality and risky security gaps in storage and retention. You can cover much of the same territory more informally – and less invasively – through interviews and surveys with stakeholders in the company. With a more open-ended, personalized intake of challenges in governance, these modes of recording can capture the nuances that arise in data integration and glitches in system compatibility, and they’re more likely to harvest the sorts of idiosyncratic insights that might fall through the cracks of a formal audit.
Indeed, while Seiner advocates for methods of recording that fall on the more facts-and-figures end of the spectrum – single-issue tracking, analytics, and monitoring – he finds that “one of the most successful ways of doing assessments is simply to talk to people. When you present them a list of potential challenges, they open up and share what types of challenges they’re having within the organization.” This may take place within the confines of a formal Data Governance council meeting, or even on the fly. Seiner even distills the work of governance tracking into three questions that you can pose across roles and contexts:
- What are you unable to do because of a shortfall in current data?
- What new business prospects would you tackle if you were more data-empowered?
- How might Data Governance address your answers to these two questions?
To bring all these challenges back home in a way that will gain the trust of leadership, advocates of Data Governance need to align them with business objectives. Once again, governance should be first and foremost “linked to risk management,” asserted Seiner, “because that’s what keeps leadership up at night. Make certain they understand that the risk isn’t going to manage itself.” When governance teams can walk into meetings armed with metrics that clearly show how their program cuts down on data-related incidents within the company, this too is a powerful case for governance in that it crystallizes policies and practices in the form of dollars and cost-benefits. Finally, to take the lead, governance boosters should boil down the activities of the program into more actionable opportunities at all relevant levels of the enterprise.
Conclusion
Winning over top management comes down to a few actionable steps of implementation. Make sure to map out a Data Governance policy through the most granular vision, aligning it clearly with current business objectives. Set up a framework of governance operations that lays all risks and benefits on the table and provides easily accessible (and understandable) metrics that continue to reveal the power of governance performance through solid indicators. Following these basic commandments, a competent governance team will receive the support and recognition it deserves.
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