In “Data governance 2.0,”, I made the argument for a rededication to the topic of data governance to advance enterprise ambitions in digital and analytical transformation. That article also explored pragmatic ways to improve the enterprise data state through practical tips from Jason Fishbain, chief data officer at University of Wisconsin at Madison. In this post, I explore the topic of analytical transformation through a dialog with Raul Padron, executive director of the Analytics Business Unit (ABU) at Grupo Financiero Banorte.
Groupo Financiero Banorte (GFNorte)
Raul Padron, executive director, Groupo Financiero Banorte (GFNorte)
Early in the year, I read a Harvard Business Review article on the analytical transformation at Banorte. I was struck by the decision by the Banorte leadership to create an Analytics Business Unit (ABU) with strong P&L expectations and robust leadership support enabled by the right incentive alignment and rigorous assessment of results. Impressed by the analytical transformation at Banorte, I reached out to Padron at Banorte to learn more about their analytics journey. We discussed a range of topics from data governance to analytics change management to talent development. Below are the excerpts from this discussion.
How do you strike the right balance between data innovation and data control when driving analytics as a key priority?
Padron: Grupo Financiero Banorte is a comprehensive financial business with diverse business units, including retail and commercial banking, insurance, public and private pension management and wealth management. It has a strong, significant and varied client base. Four years ago, the executive leadership at Banorte decided to create the ABU as a profit center with a mandate to drive top line and bottom line value from data and insights. The ABU was setup as a profit center and not as a cost center with a focus on monetization of analytics and the underlying data. The unit’s results are measured the same way as the business units’.
From the very beginning there was a strong emphasis on data governance, control and appropriate use of data. We respect and comply with all applicable regulations and laws. But it starts with a focus on careful management and protection of the client’s data and ensuring that we fully respect the client’s wishes for data protection. With this in mind we setup strong checks and controls to make data protection a reality. The chief data officer’s data governance team is responsible for managing the enterprise wide data controls and appropriate data sharing within subsidiaries. Additional checks and balances are implemented by the ABU when pursuing revenue generating/cost reduction initiatives based on analytics.
From that foundation of data control, we then focus on ensuring that there is adequate, complete and quality data available to make the best analytics possible for driving innovation.
How do you solve for the data visibility, access, and quality challenges which often plague analytical transformations in big enterprises?
Padron: Unlike other industries, the financial industry enjoys access to very rich data. However, there are still challenges in the data quality, frequency especially when the systems landscape incorporates a number of legacy systems. The ABU team works very closely with the data governance team on data centric initiatives to not only ensure that the ABU has a good supply of data, but to also make data quality improvements available to the whole enterprise. We help the data governance team prioritize what type of information should be addressed from an availability, quality perspective through a continuous loop that feedbacks challenges identified in analysis and assessments.
We continue to evolve our data cataloguing and knowledge management capabilities where we are still advancing through the curve towards maturity. We have instituted a discipline of information sharing across various analytics teams, but we are also evaluating tools that can help us advance our capabilities. As analytics efforts grow, the standardization of data definitions and business rules becomes a challenge, and the risk of inconsistency and poor control looms behind. Also, the search for increased efficiency and productivity becomes important when there is potential for reusing and sharing analytics work across teams.
There is a lot of talk about machine learning and AI in the public domain. What is your experience in adopting these in the ABU?
Padron: We are in the early stages of adopting machine learning and AI. Various incubation and pilots are underway at Banorte through which we are testing machine learning models and comparing their results to those of traditional models to understand value and implications. I like to think that one of the first takeaways of AI is that it has increased our natural intelligence in the sense that it has made us think of specific types of data that we should be tracking and collecting but haven’t so far. We still need to thoroughly understand and think about the implications of machine learning and AI to analytics, data governance, and data management.
When you established the ABU how do you ensure that the broader business community accepts and adopts the data-driven culture?
Padron: Change management is a key element in the success of the ABU at Banorte. It started with the strong top management support and mandate that the ABU received from inception. This kind of strong commitment and backing from the senior leadership is important in ensuring that the analytical transformation kicks off the right way. Next is the concept of shared success. At Banorte, the ABU’s success and results are shared with the business units, fostering cooperation rather than competition. Even with the early successes we continue to focus on changing mindsets by showing that our analytics initiatives have strong positive impacts yielding good returns. We still focus on a careful selection of projects to ensure that we focus on the highest value, highest yield initiatives.
What kind of talent strategy is needed for an organization like the ABU to be effective?
Padron: For analytics as a profit center to be effective we need to pay strong attention to team composition. We need a strong mix of talent and skills. Besides analytical and quantitative skills, we need business knowledge, strong interpersonal and communication skills, and an entrepreneurial spirit to drive change. Since it is often hard to find this combination of talent in one individual, it is a good strategy to think of building teams with the right combination of talents and continue to invest in capability building across all dimensions. For an organization like the ABU to be successful, quantitative and technical work is important but not sufficient. You need to be able to look for new opportunities to generate value from insights, influence business leaders, communicate concepts and outcomes in easily understandable ways and finally negotiate effectively with colleagues to secure implementation.
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