CDOs and other data leaders often struggle to communicate the tangible business value of data governance to stakeholders. Viewed as an overly academic exercise with little practical, pragmatic value, it can be difficult to show why data governance is so important to an organization.
Another challenge is that while data governance focuses on creating the processes, policies, and roles that dictate how an organization uses data, it needs to be coupled with other capabilities to actually realize business value. Capabilities include metadata management (to organize and understand data) and data quality management (to improve data quality and the business decisions that come out of it).
It’s not easy. In fact, Gartner predicts that in 2022, “only 20% of organizations investing in information governance will succeed in scaling governance for digital business”.
But organizations that are willing to do the hard work (and implement an agile data governance framework for faster results) will be rewarded. Forrester says that data-driven businesses grow by 30% each year.
What is the benefit of data governance to organizations?
A successful data governance strategy ensures that data is consistent, trustworthy, and doesn’t get misused. It also ensures that business users have access to the right information at the right time — and understand how to interpret it.
While data governance used to be focused mostly on mitigating risk in heavily legislated industries like healthcare and finance, it has in recent years been adopted by organizations across industries as a key business driver. Organizations have discovered that data governance can increase revenue and market share by identifying new business opportunities. It can also decrease or manage costs by uncovering operational inefficiencies.
Sounds great — but where’s the proof?
Three examples of data governance that provided important business value to organizations
For CDOs and other data leaders seeking to convince leadership that data governance provides value, here are three examples of tangible business value driven by data governance programs.
Example 1: How an insurance company reduced risk by prioritizing data quality
CMO Magazine published a case study by Ram Kumar, Chief Data and Analytics Officer at CIGNA, about one of the fastest growing and more successful general insurance companies in India. The case study outlines how the organization significantly reduced risk with a company-wide data quality program.
Data quality is critically important in the insurance business. Kumar’s article explains how the anonymous organization not only mitigated risk across the data lifecycle, but also ensured that agents captured accurate data to help serve the customer better.
The organization used unique and creative tactics to ensure the program succeeded, including branding it with a parrot mascot named “DeeKew”, giving out data quality awards to individual employees and the best-performing branches, and offering discounts to customers and brokers who entered quality data.
Example 2: Building a data governance program that pays for itself
At Enterprise Data Governance Online 2022, Curtis Mischler (Vice President and Chief Data Officer) and Toby Hall (Senior Vice President) of Delta Dental of Michigan delivered the keynote “Building a Data Governance Program”. They shared how they successfully launched and maintained data governance at Dental Dental of Michigan.
The presentation’s key takeaway was that their data governance program was intentionally designed to deliver business value. In fact, if any aspect of data governance didn’t have a direct effect on one of their key business drivers – including revenue growth, cost reduction, security and compliance, business decision-making, and risk reduction – they chose not to pursue it.
They also ensured that the program would “pay for itself” by adding value to the organization and addressing real business issues. Data governance isn’t just an academic exercise at Dental Dental of Michigan – it’s a living, breathing program that generates real business value.
Curtis Mischler shared how his team tracked their success metrics:
We have a massive spreadsheet… as we accomplish different things to the program, you put it up on the list. And then at some point, we’ll go in there and say, do we think it’s worth trying to quantify this into some kind of savings metric? It could be time saved, it could be real dollars, that we reduced expenditures, it could be cost avoidance, opportunity cost… ”
And it’s kind of impressive. Sometimes you pull the whole spreadsheet out and people can see the whole list by itself, even if we haven’t quantified the item on the list, just to get an appreciation of all the different things that we’ve done. And we’ve been surprised ourselves, sometimes [by] how many different things we’ve accomplished
He also shared several creative examples of benefits achieved through the data governance program, including optimized sales reports:
We have over 1000 reports that go out from our sales team every year to our customers. Sales [was] always asking questions about what’s okay to share.
We came up with what we called the “sales report catalog”, which is a tool that breaks down, based on the type of customer and based on the report what information can be sent, what needs to be held back, and what agreements such as NDAs or business associate agreements need to be in place.
It made it a lot easier for the sales team to make their decisions instead of all the back and forth. The savings for that, just in time alone, was much bigger than we thought it would be. To this day, it’s still one of the wins that sales team talks about.
Example 3: Creating relevant and meaningful consumer experiences with data governance
Cross-selling — which includes data-driven recommendation engines and hyper-targeted marketing campaigns — is another powerful value add that can only be made possible through data governance.
Any organization that wants to successfully build the trusted data sets required for effective cross-selling will need a strong data governance program.
Das Dasgupta, Chief Data & Analytics Officer of Saatchi & Saatchi, talked about how some of the most successful entertainment and retail companies in the world use recommendation engines for CDO Magazine:
There was a time when you would go to a site, and you would be served with a bunch of movies that you don’t really care about…
Netflix creates a recommendation engine based on a methodology called a “collaborative filtering process”, which takes your likes and dislikes and builds a machine learning model to know what would be the best experience for you.
Similarly, if you go to Amazon, you see products that will be more relevant to you every time you go back to the site and make a purchase… When we do this right, we create better, relevant, meaningful experiences for the consumers at large.
Robert Seiner, President and Principal, KIK Consulting and Educational Services and Publisher of TDAN.com, made the connection between data governance, cross-selling, and corporate financial statements at Enterprise Data Governance Online 2022:
I have a client now [whose] senior leadership didn’t just ask that we linked data governance to their bottom line to their corporate financial statements — they demanded it.
… So many organizations are made up of other organizations that have different products and different services, and they don’t know who buys what from whom. Amazon does a great job of this. That ability to cross-sell is what’s going to positively impact the financial statement.
How can you optimize opportunities to provide business value with data governance?
Data leaders who want to see results similar to the examples above should choose the focus of their data governance initiatives wisely.
Much like Delta Dental of Michigan, defining key business drivers – the “why” of data governance – is a valuable first step. This way, individual projects within the initiative will be achievable, scalable, and capable of delivering results that the business cares about.
Another pragmatic starting point for data governance is deceptively simple – look at the reports that the organization uses regularly to run the business. The data in these reports contain the most important metrics to the organization, so the most valuable and impactful data governance activities will concern the quality and management of those data sources.
Data leaders who start with small use cases that deliver quick wins are more likely to see their data governance initiatives succeed. Once they establish that data governance can deliver tangible business value, they can expand the scope across the rest of the organization.