Data governance defines the rules of the road for data, making it clear who can take what action, upon what data, in what situations, using what methods, according to the Data Governance Institute. Without effective data governance, there’s no possibility of digital transformation, data analytics, artificial intelligence, personalization, or data-driven marketing outreach. For data to fuel all of these strategic efforts, good data governance is an essential foundation, one ensuring that data is high quality, readily available, and relevant for its intended use.
Garbage in, garbage out
In the absence of good data governance, you have chaos, risks, and massive limitations around using your data. When data quality is poor, your organization has the never-ending headache of “garbage in, garbage out” when it comes to generating value (and revenues) from your data. Poor data governance leads to time wasted on boring, manual data cleanup tasks that can have your talent quitting in boredom and frustration.
Poor data governance and poor data quality have real and multiple consequences. A 2019 McKinsey survey found that poor data quality at the average company can “cost” employees about one-third of their working time (manual data cleanup is among the most monotonous tasks imaginable). At organizations with good data governance, on the other hand, cleaning up and/or fixing poor data costs employees only 5% of their time, according to that same McKinsey survey.
And while poor data governance can limit an organization’s ability to leverage its data for multiple uses, it can also trigger regulatory fines and penalties for the organization as more data privacy rules such as GDPR emerge. So it’s clear that a lack of good data governance can cost your organization in both direct and indirect ways, which begs the question: How should an organization go about creating good data governance that results in good data quality, enabling it to fully leverage data for multiple uses?
5 steps for improving data governance
A 2020 report from McKinsey, Designing Data Governance That Delivers Value, offers the following steps for driving excellence in data governance and delivering quality data that generates ROI/revenue.
1. Get buy-in from your senior leadership team for improving data governance. As with any big, important effort in business, you must begin by building a solid business case for change, which means identifying the need, explaining the costs of doing nothing, proposing a solution, and then making a plan of action to implement (and measure) the solution. Since improving data governance will impact multiple business functions and will require large investments of time, effort, people, and budget, you’ll need leadership’s approval and commitment to make it all happen.
As McKinsey explains, once you’ve identified and scoped out the problems/costs caused by poor data quality within your particular organization (we detailed some of them above), “the next step is to form a data-governance council within senior management (including, in some organizations, leaders from the C-suite itself), which will steer the governance strategy toward business needs and oversee and approve initiatives to drive improvement.” Data governance improvement needs to start with support from the top.
2. Link improved data governance efforts to ongoing digital transformation efforts. As detailed above, good data governance is foundational for ensuring that your data is high quality, relevant, and ready for use. Thus, data governance improvement will impact all ongoing initiatives involving data and digital transformation. As such, you should work to integrate your data governance improvement efforts with every ongoing initiative that involves data, because the two are inextricably linked.
As McKinsey explains it: “Rather than governance running on its own, such initiatives [to link data governance improvement with other data-related initiatives] shift data responsibility and governance toward product teams, integrating it at the point of production and consumption.” Such integration simplifies both leadership buy-in and ongoing execution of enhanced data governance.
3. Start with the “low hanging fruit” and don’t try to “boil the ocean.” Apologies upfront for the business cliches. The basic idea here is to prioritize and focus your data improvement efforts on your most important data first, instead of trying to pull in and improve all your data and data governance at once. A larger project scope means slower progress, so start smaller.
“To succeed,” according to McKinsey, “data assets should be prioritized in two ways: by domains and by data within each domain. The data council . . . should prioritize domains based on transformational efforts, regulatory requirements, and other inputs to create a road map for domain deployment. Then the organization should rapidly roll out priority domains, starting with two to three initially, and aim for each domain to be fully functional in several months.” That’s smart planning. And if you need external expertise in developing and/or executing on such plans, reach outside for help (we’re right here waiting for you).
4. Apply the “right” level of data governance to the right data, because one size of data governance does not fit all data sets. Not all data is created equally, and some data sets are more sensitive than others (such as a customer’s personal/financial information). You should apply the appropriate level of data governance for the multiple types of data you need to protect.
As McKinsey says: “the design of [data governance] programs should align with the level of regulation organizations uniquely face and the level of their data complexity. Organizations with multiple, distinct businesses spanning many geographies have more complex needs than those with a business in only one geography; similarly, a high pace of data change or low level of technology automation increases data complexity.” Build your data governance to address your specific risks and needs around your multiple data sets.
5. Iterate your way to data governance success. Take lessons learned in the early stages of your “prioritized” implementation (see step 3 above) and apply those lessons to later stages. You will try things that fail and some that succeed. Scale up successful ideas and drop the failed ones.
McKinsey offers this wisdom: “the key is to adopt iterative principles in day-to-day [data] governance. For example, if there is a backlog of known data-quality issues, review and reprioritize them daily, working to maximize the benefit to the business as priorities shift.” As we’re fond of saying here at Sojourn, “improvement is never, ever done,’” but must instead be treated as an ongoing, daily practice.
To learn more about improving your data governance and implementing the 5 steps listed above, reach out to us.