Most people working on data science, AI, and digital transformation are acutely aware that it is often culture, not technology, that is holding them back. Many even know the rough steps to take to solve this problem: investing attention and money in changing the way people think and how businesses use data. But once you understand the core details of how companies and leaders do this, it can be difficult to know what it’s really like to implement those steps.
To understand what it takes to change culture and encourage a digital mindset, it helps to see what other companies are doing. actual are doing. Which strategies worked and which stalled? What was the message that reached your staff? Where exactly do you start?
In this article, we begin to address this gap by summarizing the first two years of a new data program that worked to build a culture of data acceptance at Gulf Bank, Kuwait. Two years is too short to claim the job is done, but hundreds of people are working differently and using data in new and exciting ways.
AlOwaish, part of us, was appointed Gulf Bank’s first Chief Data Officer in February 2021 as part of a strategic plan to launch a full digital transformation of banking, with a mission to deliver data-driven customer experiences. was adopted as Among her duties, her job was to organize Gulf Bank’s plans, build a small team, and execute. She was a successful technician, but she knew she needed to grow for her role. So she hired another of us, Redman, to advise.
As she prepared to start her new job, she considered common advice. Whether it’s cleaning up customer databases, building data lakes for better access, or improving regulatory reporting, it’s a quick win. But her boss, deputy CEO Raghu Menon, is an industry veteran who has seen too many data programs fail to launch when the easily achievable achievements prove rotten. rice field. Instead, he advised her to “get the basics right” first.
We received two messages from Menon’s insight. We start with data quality. Many would see this as an odd choice, but nothing is more fundamental to the data space, especially digital transformation and data-driven customer experiences, than quality. Bad data is the norm. And this is a vicious killer, adding huge costs to our daily work and making monetization, analytics and artificial intelligence much more difficult.
The second message was to think carefully about how you want to involve everyone, the culture you want to create, and the organizational structure that works. Specifically, we wanted her to understand two things. One is that everyone needs data to do their job (e.g. they are data customers), and they are also creating data that is used downstream (e.g. is the data creator). As people fill these roles, they work together to find and eliminate sources of bad data, resulting in rapid quality improvements. Along the way, attacking data quality in this way leaves people with: Engage and Empower own data.
Before discontinuing the activity, we solicited input from many long-term employees at all levels. Will employees be attracted to attacks on data quality? Will they find new roles that data customer and producer roles empower? A lot of people will love it, but we’ve learned that we need to provide some simple “getting started” challenges. Also, it encouraged us. If done well, employees said these roles could transform the bank.
Building an Augmented Data Team
How did AlOwaish’s small team get an entire bank of 1,800 people on board? That’s why we designed the “Data Ambassadors” program. It’s basically a network of people leading the effort to bring data quality to the team. To build it, Al-Owaish met with the bank’s management board to explain Menon’s responsibilities, motivate her to focus on quality and explain the profile of the talent she seeks. She also provided training and support, pledging the entire bank to learn along the way. AlOwaish’s “people, then technology” approach resonated with the committee, whose 13 members nominated her 140 ambassador candidates.
As expected, many were skeptical, even though the prospective ambassadors had been appointed by senior leaders. They only saw the role as additional work. So AlOwaish and her team worked with HR to make work interesting, challenging, and fun. They did it in her three ways:
- world class training: Ambassadors were told that they would learn and do things that would be useful throughout their careers. Difficult to deliver due to COVID-19, the training is delivered face-to-face in five sessions, exploring the roles and responsibilities of data customers and producers, demonstrating how to measure initial data quality, It provided a way to find and eliminate the root cause. the cause of the error. The final session was a hands-on lab focused on self-service analytics and data visualization. Each session featured a real-world challenge to help you get started on your Ambassador career.
- media: Ambassadors have received a lot of attention with their work featured in internal newsletters, social channels and local newspapers.
- branding: The Data team worked with marketing to create the Data Ambassador Program logo and increase awareness by offering branded giveaways such as digital notebooks.
Even the most skeptical ambassadors saw an opportunity for personal empowerment by the end of the first session. They began to see data and analytics as something they could do, not just a techie. And they took these messages back to the team.
get everyone involved
The next target is everyone else, especially those who work in branches, call centers and sales teams where the bank’s customer experience is highly dependent. We have designed a “Data 101 Program” that explains our roles as data producers and customers and highlights the impact of data quality on a bank’s success at every level. Interestingly, people in these roles create much of the bank’s most important data, and we never really understood why. Data was the furthest thing from their minds. Finally, AlOwaish worked to ensure Data 101 was included in all new hire training.
Realizing that their work is more extensive has made it more exciting than most banks’ “just sell” approaches. For example, Fahad Al-Rifay, a direct sales representative, visited Al-Owaish after training to explain how Data 101 changed his attitude. When opening a new account after the transaction is completed, he now pays special attention to the data that he does not use personally because he knows that the data in the bank is the data that the customer needs. I was. Others provided similar feedback. Once they realized how important quality data is, they began to take their responsibilities as data producers seriously. They felt empowered and more connected to the bank’s overall success. Thousands of small steps like these make it easier for everyone to bring more and more trusted data to customer engagement.
cutting edge innovation
Empowerment is a great thing! As we expected, Ambassadors and others across the bank began working together to take measurements, target data cleanup, and eliminate the root causes of errors. Somewhat organically, ambassadors and permanent employees then began to innovate themselves, using the methods and tools provided in the training in new ways. For example, two ambassadors have worked together to improve the anti-money laundering model and improve the customer experience at branches while reducing risk and operating expenses. Earlier this year, AlOwaish and her team hosted the inaugural “Innovation Tournament” at Gulf Bank. Hundreds of participants attended. This is a sure sign that engagement and empowerment are taking root.
As noted above, two years is too early to claim that Gulf Bank has fully embraced a data culture. Many things can still go wrong. Additionally, AlOwaish and Gulf Bank have bigger ambitions, including artificial intelligence, shared language, data-driven innovation, data supply chain management, and monetization. Many of these initiatives require support from big data, advanced technology, highly educated professionals, and ambassadors.
lesson learned
We believe there are many paths to building a great data culture. The U.S. State Department has adopted a “surge philosophy” of focusing on his one department at a time, and others may do so too, capitalizing on the excitement about artificial intelligence. Still, we believe the Gulf Bank experience demonstrates some important points.
It’s hard to change an existing culture, but it’s even harder when you’re fighting it every step of the way. So instead, look for what your existing culture will embrace and advance the data culture you want. For example, people in healthcare may be interested in “helping people live longer, healthier lives.” Possibilities are increased by explaining how the data program will advance its mission.
It’s important to start building your new culture from day one, even if it’s not your primary duty. This goes against the conventional wisdom that building support requires quick success. However, the quick cut often takes shortcuts and acts violently against people and cultures, increasing the chances of these projects failing. Furthermore, immediate success can mistakenly lead companies to believe they don’t need to worry about people and culture, setting them up for future failures. Instead, aim for “win big” that fully incorporates your business performance, structure, people and culture.
Second, culture change requires everyone to be involved. At Gulf Bank, we enlisted the Executive Committee, Human Resources, Marketing and Corporate Communications, and received timely contributions from everyone. We emphasized the importance of data by conducting training face-to-face and tailoring it to each group. In fact, Data 101 had over 20 versions. Furthermore, culture changes through deeds, not words. So we made it clear what we wanted people to do, not just how we wanted them to think and feel. The challenges provided in the training helped people galvanize their efforts.
Third, we strongly consider data quality as a starting point, as we did. Many people consider data quality to be the least sexy topic of all data, but this is a great way to get everyone on board, and it’s fundamental. You can’t build a good data program on bad data.
Finally, building culture takes persistence and courage. Expect some bad days, but keep in mind the higher prizes.
