Across business and industry sectors, data analytics is a hot topic. The advantages of becoming a data-driven organization are both exciting and scary—exciting given the potential to delight customers while garnering insights about how to manage the business more effectively and scary in thinking about how your firm’s data analytics capabilities stack up against the competition. Deploying data analytics across a business organization is not a project but a journey, best navigated in partnership between the Chief Executive Officer and Chief Data & Analytics Officer (CDO). In this Q&A, John Janclaes and Juan Gorricho share insights from their work together at Partners Federal Credit Union and offer lessons learned, positive and negative, during the credit union’s four-year (and counting) trek to cultivate a thriving data analytics practice.
Q. How do you measure the value of data and analytics?
Janclaes: Certainly we can look at return on investment and those types of calculations, but that’s a pretty tactical assessment. A more strategic, higher-level approach is to measure how becoming a data-driven organization sharpens our view through two familiar prisms: (1) informing smarter business decisions by providing a more complete risk–reward appraisal and (2) enhancing our understanding of members’ financial needs, preferences, and expectations. It’s been difficult to quantify these areas in the past, but data gives us a clearer and more comprehensive picture. We can weave more into what we know about members and move closer to a real-time view.
The wider scope and immediacy of data has allowed us to move from customization to personalization. We used to tailor offers based on broad demographics, like products likely to appeal to millennials. But now we can distinguish between a young attorney working at the Disney Studios in California and a recent Haitian immigrant, who is unbanked, speaks English as a second language, and works in hospitality at a Florida resort. And we can deliver a member experience that suits their very different needs for financial services.
We know our members—where they live, where they work, where they shop. If we can see and act on that information in real time, we can transform the member experience. That’s the strategic value of data analytics, which goes well beyond return on investment. We’re building new capabilities for our business to keep up with—and in some cases, surpass—the competition. But growing these capabilities takes time.
Gorricho: Some companies may be doing data analytics because of all the hype. It sounds like a really cool thing to do, and that is why they jump into it. However, many times companies are ignoring that a key success factor is to measure the value of their data and analytics strategies. There are two ways to measure the value of data and analytics. On one side of the spectrum, there is hard value, which is usually translated by how much incremental revenue or reduced cost the implementation of data and analytics brought to the company. That said, data and analytics leaders need to consciously link the implementation of their initiatives to the measures of value from the get-go. Data and analytics leaders should commit to value creation and follow-through. This will ensure that they align their efforts with the most impactful and less costly efforts that will drive value creation.
On the other side of the spectrum is a strategic or qualitative value: It’s important for us to do this, and here’s our plan for how to approach it. Here is how the culture of the company will change by becoming data-driven, and here are the benefits of changing in this direction. Again, some companies just jump into data analytics and buy this technology and hire a data team based on the belief that these investments will automatically create value, but they don’t lay the strategic groundwork or try to quantify its impact up-front to guide planning and implementation. CEOs and boards of directors increasingly are demanding more specifics about the value that data analytics will bring—and they’re right to do so.
The reality is that data analytics is not a silo that can create value on its own. It creates value through interactions with other operating lines of the business. Data and analytics leaders cannot create value on their own, and that is why their definition of success needs to be holistic and include the execution of actions led by operating areas and the impact of those actions.
Q. What are the key components of a successful roadmap for data and analytics?
Gorricho: Business alignment with strategy is the first step. People hear the words data analytics and they immediately jump to, “Oh, it’s a technology project. It’s about tracking down big data, buying software, and hiring data scientists.” In reality, data analytics strategy is no different than any other type of strategy in that it all starts with the question: What are the goals of our business? Financial growth? Market penetration? Cost reductions and improved efficiency? Data analytics must be driven by business strategy.
Next is the issue of timing. Momentum is more important than a perfect trajectory. I admit I borrowed that statement from another CDO, but it’s an elegant way to recommend implementing data analytics in small chunks with key milestones along the way, so everyone can see progress and value being created. You build momentum over time instead of putting all the systems in place and then collecting all the data and then, three years later, finally beginning to see some results. The CEO and board don’t have that kind of patience. They want to see results more quickly.
A third component is alignment between people, processes, and technology: the right people on data teams and on the operations side, the right processes to manage the data, and the right technology to do the job. With proper alignment, you will see more value created as a result of your investments.
Finally, your data analytics roadmap needs to be business-driven and maintain a holistic view: What business problems are we trying to solve? What data do we need? Where is it and what shape is it in? How do we bring it in, and how do we make it work for this problem?
Janclaes: At Partners, we understood the need to start with a data analytics strategy and execution plan, but we were unsure of the best way to do that. So we began working with DecisionPath Consulting (www.decisionpath.com). Their expertise was a key component to help us get things right from the beginning: To identify two primary goals—business insights and personalization—and then start planning our strategy and organizational design and weigh our options for in-house vs. some combination of a build–buy solution.
Our commitment to start by developing a strategy has helped keep us on track, and we’ve found in our research that, according to a study by MIT Sloan, a formal strategy for analytics is a crucial differentiator for successful data-driven companies.
[LINK: http://sloanreview.mit.edu/projects/the-hard-work-behind-data-analytics-strategy/]
Q. What is the role of the CEO in the implementation of data analytics strategies?
Janclaes: From the beginning, my aim has been to lead this effort on three primary fronts: (1) to be really clear on our top strategies—to stay competitive and to serve our members; (2) to set expectations and milestones with the message that “This is going to take a while—it’s a journey”; and (3) to work with staff to identify their part in executing our data analytics strategy. We all need to understand data analytics. We all are responsible for becoming more data-driven, for learning how to make the most of the data related to our business lines, and for becoming part of the governance process and curating the data. We aren’t aiming for perfection out of the gate. Instead, we’re working together to build the capabilities as we get smarter and smarter in using data to run our business.
I began with the leadership team, talking individually with executives on how they can best use data to improve decisions and operations in their divisions. And then they do the same with their teams and work with them to put the “plumbing,” or infrastructure of data analytics, in place to connect all the data sources. It’s a process of cascading all these ideas through the organization and getting practical feedback. A manager might say, “I understand our two large prisms are personalization and smart business. This is what we’ll need to operate smarter in our division.” Our staff helps define the requirements because they may know those aspects of the business better than anyone.
Gorricho: Overall, the role of the CEO is to be the champion of the data analytics strategy. In my experience, the most successful companies in implementing analytics are the ones where the CEO and board are, in effect, sponsoring the program.
Beyond that, data analytics done right changes the way you do business, so the CEO must also be a champion for change: “We will do things differently because we now rely on data. We will make changes in our processes, our culture, and our people to support the data strategy.”
Data analytics is also about innovation, about experimenting with and testing new things, so the CEO must lead here as well with messages like: “We will experiment. We will try new things.” As a recent Harvard Business Review article noted, “Analytics can enable breakthrough innovations but only if the environment supports open discovery and experimentation. If the analytics effort is anchored to traditional learning processes, it will not move fast enough to achieve meaningful change and competitive advantage.”
[link: https://hbr.org/2016/07/how-ceos-can-keep-their-analytics-programs-from-being-a-waste-of-time]
At Partners we often talked about “myth busting.” The credit union might have been working under assumptions about members’ preferences—until the data showed something different. Two things could happen when those disparities arise. The CEO could say, “I don’t believe this. My gut tells me differently.” Or, “The data is right, and we need to do things differently in response to what we’ve learned.” The sooner the latter happens, the better because change will come about much earlier in the process, and the organization will adapt sooner to being data-driven.
Q. What role should the CIO play in data and analytics? How should CDO and CIO interact?
Janclaes: This is one area that could derail the data analytics program if this relationship goes awry. There’s a lot of discussion about where the CDO should report—to the CIO or directly to the CEO. Some people say the position should report to the CIO because it’s all about technology. At Partners, we hold a different view. Data analytics relies somewhat on technology, but the foremost aim of the Chief Data Officer is to tell stories based on what’s going on with members in terms of data and personalization and bringing insights to the business to help us run better. The CIO’s job in implementing data analytics is to build the infrastructure, the servers and the software we use to visualize the data. The CDO’s job is to help the business units see the stories in the data.
Looking back, we didn’t get the data analytics leadership structure right out of the chute. We were conservative, relying on an AVP to get going on both streams, start putting in the infrastructure and start working with business owners to get the data. The problem was that we didn’t have someone continually working with the executive team, so we could all embrace our roles in setting strategy, educating staff, and monitoring implementation. That’s a full-time job, especially at the beginning. After about a year and a half, we realized that getting that advice on a part-time basis just didn’t work up-front. That’s when we went looking for some talent to take on the strategy role. Juan joined us for two years, and that’s when our practice really took off. Our understanding, our data, and our roadmap all got sharper with his leadership. Our experience is consistent with a McKinsey Global Survey in which companies with successful data analytics programs identified senior-management involvement as a key factor.
[Link: https://goo.gl/VYm4YR]
Gorricho: There’s been a lot of research on the best way to structure the CDO and CIO relationship. Success happens when these executives have a strong partnership. They understand their boundaries, and they understand that there’s a lot they can accomplish if they structure the relationship the right way—as a partnership. Success does not happen in an atmosphere of competition, when the CIO may come to believe that the CDO is eroding his or her influence in the company. In that atmosphere, it becomes the charge of the CEO to build bridges and to spell out how we are going to work together and where the CDO and CIO need to do individually what they do best.
Of necessity, the data analytics team deals with technology and software development. We do that because we have to, but the message needs to be clear to the CIO that we have only this limited role with technology and that we will not deviate from the organizational technology strategy the CIO is leading. As CDO, I have some ideas about how the technology behind data analytics will work, but I am much more focused on the business aspects, so we work together to see how everything will fit within the organization’s technology framework.
At any rate, the CIO already has a full plate because so much of the organization relies on technology. So one final message might be: Let’s work together to see how data analytics can help your department manage all of this.
Q. There seems to be a lot of noise about big data. How much of it is hype versus reality? How can companies truly realize the value and promise of big data?
Gorricho: There may be more hype than reality when it comes to big data. In some cases vendors may be behind some of the hype, promoting the message, “All you have to do is buy this technology and all your problems will be solved.” Those promises are far from reality.
To a great extent, data analytics comes down to having the right data to solve the right problems. It is true that the more our business interactions become digitized, the more data is available. The challenge for a business launching data analytics is to identify the data it needs to solve the problems the team has identified. It’s not about big data or little data—it’s about the right data.
Even the term big data is problematic. “Big” data is relative. In the early days of developing our data analytics at Partners, I would tell John that some of the data we had in-house was “big data,” in that it was beyond our maturity and capabilities at the time to act on it. The size and complexity of data is relative to the capabilities of a company to do something with it and the maturity of the company’s data analytics program.
Janclaes: Financial service providers are unique in that we are swimming in data. We have data all over the place—in deposits, in lending, in payments. We can see members’ lives in motion in we really want to. And through third-party partners like FICO and Experian, we could buy tons more data. But our first priority has been to clean up our own data.
For example, we found that we needed to have a definition of a member. What does a member in good standing look like, as defined by the available data? We cleaned up our data so that when we aggregated loan and deposit data under each member, we all agreed we had an accurate view. Some people use the term golden copy to describe this view of a member. It took us about two years with all our disparate systems—from lending, deposits, wealth management, payment streams, even our own social media interactions—to bring all of those pieces together and agree, “That is a good look at the member.”
At the same time, you need to stay on strategy. At Partners, we are committed to an Omni-Channel strategy, so that members can open and access their accounts from any device any time anywhere and have the same seamless experience. Our BI team focused on that data set and quickly realized that we had batch data when we really needed it in real time. Let’s say a member is opening an account online and, three minutes into the process, gets stuck. With real-time data, we could reach out immediately via that channel and ask, “How can we help?” That has the potential to take some friction out of the member experience. But with batch data, all you can do is look at a report the next day and say, “Oh, look at that. A member got stuck.” Staying on strategy helps to optimize your data analytics investment by steering you to areas that promise to have the greatest impact on your organization’s financial health. Those components need to be tied together to support a sustainable strategy.
Q. How should data and analytics teams be structured?
Gorricho: Data teams can be structured around three basic pillars. The first is that data teams have to have a product management function focused on adoption of the data solutions. As we’ve noted previously, data analytics on its own doesn’t create value. It’s not just about a nice-looking dashboard. Value comes from business teams using the data and acting on it. The data team may create a model to use data to predict customer behavior, but the customer service teams are the ones taking action with the outputs of the model. In this regard, the product management function is constantly looking at how the data products are performing, what new features are needed, how operating teams are using the products, and what opportunities for innovation may be arising.
The second pillar involves managing the infrastructure needed to support data analytics—all the “pipes,” all the repositories, all the tools, all the software. And the third pillar is change management—in effect, to “market” internally the positive changes that come from using data analytics. Data teams are hybrids between technology and business, but they have to have primarily a business orientation. A lot of my time as Chief Data Officer at Partners was spent just talking to my peers and understanding their needs. How can they best use the data? How do they think about data management?
Janclaes: We have both a strategic and tactical piece to our data analytics team. Based on our experience, one recommendation is to get your team out of the infrastructure business as soon as possible. If your IT department can handle most of the infrastructure, that’ll really speed up the storytelling and insights by your BI team.
Q. What about data monetization? How is that term defined, and how do you accomplish it? Does that mean selling data?
Janclaes: To me, selling data is the last thing we’d think about. There’s a lot of discussion out there about whether that’s the right thing to do. Our credit union right now is not selling data. We have a covenant with our members—a data agreement—that their data is theirs and we wouldn’t sell their data without getting their permission to do that.
But you can monetize data in other ways by getting a rate of return on using it. We look at the different initiatives we’re doing and consider if each will really improve our business decisions and member experience. Lending is our No. 1 business. We make 70 percent of our revenue there. We want to stay on our lending strategy by enhancing and personalizing the member experience. As a result, we expect that we will make more loans and more money. Data analytics complements and accelerates our efforts there. There’s your monetization.
Gorricho: There’s a lot of noise around monetization. The first thing that might come to mind is a company selling its data, but that’s just one angle of monetization, a very low maturity angle. Collecting data on consumers just to sell it is one way to lose their trust. A more sophisticated view of monetization is that a company uses the data generated through its interactions with customers to create greater value for them, which by extension increases revenue or reduces costs.
Q: Why should businesses consider data analytics?
Janclaes: There’s a confluence now of declining costs of computing and the introduction of new data visualization tools that makes it so much easier to highlight insights. An organization of our size ($1.5 billion in assets, 136,000 members) can get into the practice, make a difference, and see a good rate of return. And there are so many more services out there today that can help you on your way.
In adjacent industries, our members are already experiencing personalization in all of their buying activities and in their lives. They look to their financial service providers and say, “You should be able to do that, too.” Consumers expect the type of experience data analytics can help deliver. One other reason to get started now on the journey of data analytics is that it takes a while to get this program up and running and to gain traction. We’re in our fourth year now, and it wasn’t until year 2 that we really started seeing results in the form of improving the business, gaining insights, improving the member experiences, and decreasing the costs of data handling. We are committed to becoming a data-driven business so that we know before our members can even articulate it what they need so we can be there for them. Data analytics has changed our business, and we expect even more profound changes to come.
Gorricho: Up until about three years ago, you could say that data analytics was optional. Now it is becoming table stakes. Many start-up companies today are data-driven from the get-go. They rely on data as part of their DNA. Other businesses may have to catch up and keep pace, or their market share will be eroded. The CEOs of those businesses must champion data analytics, understand how data analytics supports strategy and returns value to the organization, and advocate for change and innovation and the concept of “failing forward fast.”
Key Learnings from this Journey
- Start with strategy clearly stating business objectives and priorities to help drive the data analytics roadmap.
- Understand that organizational design for data analytics teams is evolving across industries. It’s supposed to be that way, so relax!
- Work to ensure role clarity, which can be challenging in getting specialists from across domains (e.g., business-owners, information technology, risk management, and data analytics) to collaborate. Accelerate this process with role charters and other tactics.
- Experiment with available data before attempting to formalize the organization’s data analytics capabilities. Think progress vs. perfection. Think agile vs. waterfall. Think fail-forward to gain momentum.
- Establish data as an asset through working with board members, the CEO, the CIO, and other C-level executives to develop a shared vision of data as an enterprise-wide asset.
- Employ myth busting as a great way for an organization to make the shift to being data-driven. Intuition and data-driven processes yield the best outcomes.
- Learn and grow through an intentional and ongoing effort to evolve the organization’s understanding of data analytics and its contribution to the overarching mission.
- Commit to communication processes focused on clarity, shared vision, and tempo.
About the authors
Juan F. Gorricho
Group Executive, Data & Analytics
TSYS
Juan F. Gorricho is currently group executive, data & analytics, global product innovation at TSYS. In this role, Juan leads the development and implementation of TSYS data and analytics strategy aimed at enhancing existing and developing new data products for TSYS clients.
Prior to this role, Juan was Chief Data & Analytics Officer for Partners Federal Credit Union, where he led the development and execution of Partners’ data strategy. In this role, Juan led the data and analytics strategy development and execution for Partners, one of the top credit unions in the country. Juan has more than 15 years of experience in the data and analytics space, including multiple speaking engagements. In his prior roles with Disney, Juan led multiple multimillion-dollar projects to implement business intelligence and analytical solutions for key lines of business such as Labor Operations and Merchandise. Juan has an Industrial Engineering degree from Universidad de los Andes in Bogotá, Colombia, and an MBA from the Darden Graduate School of Business Administration at the University of Virginia. Juan is married and lives with his wife and two children in Atlanta, Georgia.
John Janclaes
President, CEO
Partners Federal Credit Union
Partners Federal Credit Union serves the employees of The Walt Disney Company. Currently, Partners serves 136,000 members throughout the United States with over $1.5 billion in assets. John joined Partners in June 2004 as President and Chief Executive Officer. John has more than 30 years of financial services experience spanning retail banking, brokerage, and insurance. Before joining Partners, John held leadership roles at Logix (formerly Lockheed Federal Credit Union), Coast Federal Bank, and Valley Federal Savings.
John earned a bachelor’s degree in Business Administration from the University of Redlands and a master’s in Business Management from the Peter F. Drucker and Masatoshi Ito Graduate School of Management at Claremont Graduate University.
John is author of the book Doing What Matters: How Leaders Help Individuals and Organizations Thrive and founder of The CEO Corner (www.theceocorner.com), an online forum for leadership development.
A native Californian, John resides in Temecula, California, with his spouse, Jackie, pursuing a passion for equestrian sports.
