What Does It Actually Take to Build a Data-Driven Culture?

Most people who work on data science, AI, and digital transformation are painfully aware that it is often culture, not technology, that stymies their efforts. Many even know the high-level steps they’re supposed to take to fix this problem — invest attention and money into changing people’s mindsets and how the company uses data. But once companies and leaders get into the nitty-gritty details of how to do this, it can be hard to know what implementing those steps actually looks like. To understand what it takes to change a culture and encourage a digital mindset, it’s helpful to see how another company is actually doing it. Which strategies worked and which were dead ends? What messaging landed with staff? Where should you actually start? In this article, we begin to address this gap by summarizing the first two years of a new data program at Kuwait’s Gulf Bank in which we worked to build a culture that embraced data. While two years is far too short a time to claim the job complete, hundreds of people are doing their jobs differently and using data in new, exciting ways. One of us, AlOwaish, was hired as Gulf Bank’s first chief data officer in February, 2021, as part of the strategic plan to launch a complete digital transformation of the bank’s operations, with a mandate to provide data-driven customer experiences. Within that mandate her job was to sort out Gulf Bank’s plans, build a small team, and execute. Though a successful technologist, she knew she would have to grow into the role. So, she hired the other of us, Redman, to advise. As she prepared to start her new job, she considered a common piece of advice: Score some quick wins, such as cleaning up a customer database, building a data lake to improve access, or improving regulatory reporting. But her boss, deputy CEO Raghu Menon, was an industry veteran and had seen too many data programs fail to launch when the low-hanging fruit turned out to be rotten. Instead, he counseled her to first “get the basics right.” We took two messages from Menon’s insights. First, start with data quality. Many will view this as an odd choice, but in the data space, and especially for digital transformation and data-driven customer experience, nothing is more basic than quality. Bad data is the norm. And it is a vicious killer, adding enormous costs to day-in, day-out work, and making monetization, analytics, and artificial intelligence far more difficult. The second message was to think carefully about how we would get everyone involved, the culture we wished to create, and the organizational structures needed to be effective. Specifically, we wanted to drive home two things: that everyone needs data to do their job (e.g., they are data customers), and that they also create data used downstream (e.g., they are data creators). When people step into these roles, they work together to find and eliminate the sources of bad data and quality improves quickly. Along the way, attacking data quality in this manner leads people to engage and empower themselves with data. Before charging off, we sought input from many long-term employees at all levels: Would employees find attacking data quality appealing? Would they find new roles as data customer and creator roles empowering? Their feedback told us that while some people would need convincing, plenty would find a lot to like, but we needed to provide some easy “getting started” assignments. It also encouraged us: done well, employees said, these roles could transform the bank. Building the Extended Data Team How could AlOwaish’s small team get the entire bank of 1,800 people on board? To do so, we designed a “data ambassadors” program, essentially a network of people who would lead efforts to bring data quality to their teams. To build it, AlOwaish met the bank’s management committee to explain Menon’s charge, motivate the focus on quality, and describe the profile of people she sought. She also promised to provide training and support and that the entire bank would learn along the way. AlOwaish’s “people, then technology” approach resonated with the committee, and its 13 members nominated 140 ambassadors-to-be. Even as ambassadors-to-be had been nominated by senior leaders, as predicted, many were skeptical. They saw the role as nothing but added work. So AlOwaish and her team joined up with human resources to make the work interesting, rewarding and fun. They did so in three ways: World-class training: The ambassadors were told that they would learn and do things that would serve them well throughout their careers. Covid made delivery difficult, but the training — delivered face to face in five sessions — explored their roles and responsibilities as data customers and creators, showed them how to make their first data-quality measurement, and provided a method to find and eliminate the root causes of error. The final session was a hands-on lab focused on self-serve analytics and data visualization. Each session featured an on-the-job assignment to help ambassadors get started. Media: Ambassadors received lots of publicity, as internal newsletters, social channels, and local newspapers highlighted their work. Branding: The data team reached out to marketing to create a logo for the data ambassadors program and build awareness by providing branded giveaways, such as a digital notebook. Even the most skeptical ambassadors saw opportunities for personal empowerment by the end of the first session. They came to see that data and analytics weren’t just for techies, but something they could do on their own. And they carried these messages back to their teams. Getting Everyone on Board The next target was everyone else, with special focus on those working in branches, the call center, and on sales teams, on which so much bank customer experience depended. We designed a “Data 101 program” that explained their roles as data creators and customers, and highlighted the impact of data quality on the bank’s success at all levels. Interestingly, people in these roles create much of the bank’s most important data, but never knew why. Data was the furthest thing from their minds. Finally, AlOwaish worked to ensure that Data 101 is now included in all new employee onboarding. Understanding the broader reach of their work made it more exciting than the “just make the sale” approach in most banks. For example, Fahad AlRefaei, a direct sales representative, sought AlOwaish out after a training to explain how Data 101 changed his attitude. When opening a new account after closing a sale, he now he pays extra attention to the data he doesn’t personally use, because he knows that data customers within the bank need it. Others provided similar feedback — once they learned how important quality data was, they took their responsibilities as data creators seriously. They felt empowered and better connected to the bank’s overall success. Thousands of such small steps make it easier for everyone to bring more, and more trusted, data to customer engagements. Innovation to the Fore Empowerment is a beautiful thing! As we expected, ambassadors and others across the bank began working together, making measurements, targeting data cleanups, and eliminating root causes of error. Then, somewhat organically, ambassadors and regular employees began using methods and tools provided in the training in new ways, to innovate on their own. For example, two ambassadors joined forces to improve anti-money laundering models, enhancing the customer experience in the branch, while simultaneously reducing risk and operational expense. Earlier this year, AlOwaish and her team organized the inaugural “innovation tournament” at Gulf Bank. Hundreds competed — a sure sign that engagement and empowerment are taking root. As noted above, two years is too soon to claim that a data culture has become fully embedded at Gulf Bank. Much can still go wrong. Moreover, AlOwaish and Gulf Bank have larger ambitions, including artificial intelligence, shared language, data-driven innovation, data-supply-chain management, and monetization. Many of these efforts will require big data, advanced technologies, professionals with advanced degrees, and support from ambassadors and others. Lessons Learned We are quite certain that there are many paths to building a great data culture. The U.S. Department of State has adopted a “surge philosophy,” focusing on one department at a time and others may do so by taking advantage of the excitement around artificial intelligence. Still, we think Gulf Bank’s experiences illustrate some important points. It is hard to change an existing culture and harder still if you’re fighting it every step of the way. So instead, look for things the existing culture will embrace and will move the data culture you desire forward. For example, people working in health care may be bought into “helping people lead longer, healthier lives.” Explaining how a data program will advance that mission increases your chances. It is important to start building the new culture from day one, even as doing so is not the primary mandate. This runs counter to conventional wisdom, which advises that you need quick wins to help build support. But quick win efforts often take shortcuts, running roughshod over people and culture and increasing the likelihood that these projects fail. Further, successful quick wins may lead companies to falsely conclude they need not worry about people and culture, setting themselves up for future failures. Instead, aim for “significant wins,” that fully embrace business results, structure, people, and culture. Second, to change a culture, you need to get everyone involved. At Gulf Bank, we sought out the management committee, human resources, marketing and corporate communications and received timely contributions from all. We emphasized data’s importance by delivering the training face-to-face and tailoring it to each group. Indeed, there were more than 20 versions of Data 101. Further, cultures change through deeds, not words. So we were explicit about what we wanted people to do, not just how we wanted them to think or feel. The assignments provided in our training helped people jumpstart the efforts. Third, give data quality strong consideration as the place to start, as we did. While many view data quality as the least sexy topic in all of data, it is a great way to get everyone involved and it is foundational. You cannot build a great data program atop bad data. Finally, building a culture takes persistence and courage. Expect to have some bad days, but keep the larger prize fully in mind.

What Does It Actually Take to Build a Data-Driven Culture?

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