What does it really take to build a data-driven culture?

Most people working on data science, artificial intelligence, and digital transformation are painfully aware that it’s often culture, not technology, that’s holding back their efforts. Most people even know the high-level steps they need to take to solve this problem; invest attention and money in changing the way people think about and how the company uses data. But when companies and leaders get into the nitty-gritty details of how to do this, it can be hard to know what actually taking those steps actually looks like.

To understand what it takes to change the culture and encourage a digital mindset, it’s helpful to see what another company is like actually doing so. Which strategies worked and which were dead ends? What messages were received with the crew? Where do you really start?

In this article, we begin to address this gap by summarizing the first two years of a new data program at Gulf Bank in Kuwait, where we worked to create a data-embracing culture. While two years is too short a time to claim the job is done, 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 a strategic plan to initiate a complete digital transformation of the bank’s operations with a mandate to deliver data-driven customer experiences. Within that mandate, his job was to sort out Gulf Bank’s plans, build a small team and execute. Although a successful technologist, he knew he had to grow into the role. So he hired another one of us, Redman, to advise.

When he was about to start his new job, he considered a general piece of advice. Score some quick wins, like cleaning up your customer database, building a data lake to improve availability, or improving regulatory reporting. But his boss, deputy CEO Raghu Menon, was an industry veteran and had seen too many data projects fail when the low-hanging fruit turned out to be rotten. Instead, he advised her to “get the basics right” first.

We took two messages from Menon’s insight. First, start with data quality. Many will see this as an odd choice, but in the data space, and especially for digital transformation and data-driven customer experience, nothing is more fundamental than quality. Bad data is the norm. And it’s a vicious killer that adds huge costs to day-to-day operations and makes monetization, analytics, and AI much more difficult.

The second message was to think carefully about how we would engage everyone, the culture we wanted to create, and the organizational structures needed to be effective. In particular, we wanted to take home two things. that each needs data to do their job (eg, they are data consumers) and that they also create data that is used downstream (eg, they are data creators). When people take on these roles, they work together to find and eliminate sources of bad data, and quality improves rapidly. Hitting data quality along the way in this way leads people to engage and empower with the data.

Before charging, we sought the opinion of long-term employees at all levels. Will they find new roles as empowering consumers and creators of data? Their feedback told us that while some people need convincing, many will like it, we should provide some easy “getting started” tasks. It also encouraged us. Well done, employees said, these roles could transform the bank.

Building an advanced data team

How could AlOwaish’s small team take the entire bank of 1,800 on board? To do this, we’ve designed a “data ambassadors” program, essentially a network of people who will make an effort to bring data quality to their teams. To build it, AlOwaish met with the bank’s management committee to explain Menon’s charge, push for a focus on quality, and describe the profile of the people he was looking for. He 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 future ambassadors.

Even when future ambassadors were nominated by senior leaders, as predicted, many were skeptical. They saw the role as nothing more than added work. So AlOwaish and his team teamed up with human resources to make work interesting, rewarding and fun. They did this in three ways.

  • world class trainingAmbassadors were told they would learn and do things that would serve them well in their careers. Covid made delivery difficult, but the training, delivered over five face-to-face sessions, explored their roles and responsibilities as consumers and creators of data, showed them how to perform the first measurement of data quality, and provided a method to find and eliminate the root cause. reason for the error. The final session was a hands-on lab focused on self-service analytics and data visualization. Each session featured a work assignment to help ambassadors get started.
  • mediaAmbassadors received a lot of publicity as internal newsletters, social channels and local newspapers highlighted their work.
  • brandingThe data team reached out to marketing to create a logo for the data ambassador program and raise 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 saw that data and analytics were not just for technologists, but something they could do themselves. And they took these messages back to their teams.

Getting everyone seated

The next target was everyone, with a particular focus on the branch workers, call center and sales teams on whom so much of the bank’s customer experience depended. We’ve designed a “Data 101 program” that explains their role as data creators and consumers, and highlights the impact of data quality on bank success at all levels. Interestingly, people in these roles create 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 scope of their work made it more interesting than the “just make the sale” approach at most banks. For example, direct sales representative Fahad AlRefai sought out AlOwaish after the course to explain how Data 101 changed his attitude. When opening a new account after closing a sale, he now pays extra attention to data he doesn’t personally use because he knows the bank’s data customers need it. Others provided a similar opinion. when they learned how important data quality was, they took their responsibilities as data creators seriously. They felt empowered and better connected to the overall success of the bank. Thousands of small steps like these make it easier for everyone to bring more and more reliable data to customer engagement.

Innovation ahead

Empowerment is a beautiful thing. As we expected, the ambassadors and other members of the bank began to work together, taking measurements, targeting data cleaning, and eliminating root causes of error. Then, somewhat organically, ambassadors and regular employees began to use the methods and tools presented 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 at the branch while reducing risk and operational costs. Earlier this year, AlOwaish and his team organized the inaugural “innovation tournament” at Gulf Bank. Hundreds of people competed, a sure sign that engagement and empowerment is taking root.

As mentioned above, two years is too early to claim that a data culture has been fully implemented at Gulf Bank. A lot can still go wrong. Furthermore, AlOwaish and Gulf Bank have bigger ambitions including artificial intelligence, common language, data-driven innovation, data supply chain management and monetization. Many of these efforts will require big data, advanced technology, professionals with advanced degrees, and the support of ambassadors and others.

Lessons learned

We’re pretty sure there are many ways to build a big data culture. The US State Department has adopted a “philosophy of growth” by focusing on one agency while others can take advantage of the excitement surrounding artificial intelligence. However, we believe that Gulf Bank’s experience illustrates some important points.

It’s hard to change an existing culture, and even harder if you’re fighting it at every turn. So instead, look for what the existing culture will embrace and drive the data culture you want. For example, people working in the health sector can be “helping people live longer and healthier lives”. Explaining how the data program will advance that mission increases your chances.

It’s important to start building a new culture from day one, even though it’s not the primary mandate. This goes against conventional wisdom, which advises that you need quick wins to support. But quick-win efforts often take shortcuts, running roughshod over people and culture and making these projects more likely to fail. Moreover, successful quick wins can lead companies to falsely conclude that they don’t need to worry about people and culture while preparing for future setbacks. Instead, aim for “significant wins” that fully encompass business outcomes, structure, people and culture.

Second, to change the culture, you have to involve everyone. At Gulf Bank, we sought management committee, human resources, marketing and corporate communications and received timely contributions from all. We emphasized the importance of data by delivering training face-to-face and tailoring it to each group. Indeed, there were more than 20 versions of Data 101. Furthermore, cultures are changed by actions, not words. So we were saying exactly what we wanted people to do, not just how we wanted them to think or feel. The tasks given in our training helped people to intensify their efforts.

Third, pay close attention to data quality as a place to start, as we did. While many consider data quality to be the least sexualized topic of all data, it’s a great way to get everyone involved, and it’s fundamental. You can’t build a big data program on bad data.

After all, building culture takes persistence and courage. Expect a few bad days, but keep the greater prize fully in mind.

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