Nine Data-Driven Analytics Best Practices For Financial Institutions
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Nine Data-Driven Analytics Best Practices For Financial Institutions

Tara Kenyon, CEO, Kentara Analytics
Tara Kenyon, CEO, Kentara Analytics

Tara Kenyon, CEO, Kentara Analytics

In the Banking Analytics special issue, we explored the Plan-Predict-Perform path for data-driven analytics. In this issue, we look at the nine data-driven analytics best practices which, by following, financial institutions can provide rich context for their data.

Best Practice #1: Begin with the Past

Begin your data-driven analytics journey with the past. Go back to the inception of the organization. Why was it formed? Who was it meant to serve? Decide if those reasons still stand.

Best Practice #2:Take an Inventory of the Present

What data do you currently collect? Do they support your raison d’être? Find data that are reflective of the type of organization you have as well as who it is that you serve.

Best Practice #3: Define Expected Data Outcomes for the Future

Data-driven analytics—not the data themselves—are vital to your strategy and decision-making. Also, your Chief Information Officer needs a seat at the strategic planning table. The CIO and team have a good idea what data you can expect, and they can match data-driven analytics with plan goals and objectives in your strategic plan.

Best Practice #4: Measure, Measure, Predict

Management consultant Peter Drucker, the founder of modern management, wrote: “What gets measured, gets managed.” Measurement is essential to successful business management because measurement brings attention (good and bad) to the product, branch, line of business, etc. Data-driven analytics are key to measuring financial performance and risks and thereby help financial institutions make informed decisions.

Best Practice #5:Make Decisions Analytically

In banking, decision analysis is mostly used to analyze alternative capital allocations, product selection, technology choices, and the future consequences/benefits of those selections. Decision-making encompasses many objectives, and data-driven analytics are critical to making the right decisions.

Best Practice #6: Design a Data-Driven Analytics Support Structure

The biggest qualitative differences between high- and low-performing companies, according to a McKinsey Global Survey, relate to the leadership and organization of analytics activities. If you are serious about data-driven analytics to drive performance in your company, develop an organizational structure that supports data-driven bank analytics.

Best Practice #7: Separate Risk from Ambiguity

Frank H. Knight, in his essay, Risk, Uncertainty, and Profit, made a distinction between risks (i.e., known odds with a mathematical probability of loss) versus uncertainty (i.e., ambiguous odds—non-quantifiable). Data-driven analytics can help you on the risks, but as ambiguity cannot be quantified, you will need to make the distinction between the two. This is “What gets measured, gets managed” is in its fundamental state.

Best Practice #8: Find the Business Value in High Quality Regulation

What to do about ambiguity, then? Studies indicate that cultures which demonstrate high levels of ambiguity avoidance benefit from high quality regulatory governance, as measured by the World Bank’s Global Indicators of Regulatory Governance— which presents measures of transparency, civic participation, and government accountability across the life cycle of regulations. High quality regulatory governance results in higher firm value and enhanced financial performance, particularly in cultures that don’t tolerate ambiguity well.

Best Practice #9: Play the Hand that Culture Gives You

Peter Drucker also said: “Culture eats strategy for breakfast.” Based, in part, on the level of long-term orientation of the society, a risk culture stands mostly unaffected by changes in strategy. Instead of attempting to do the impossible in the short-term, affect uncertainties and ambiguities with internal controls and policies. Don’t make controls and accounting standards the way you measure risk. Rather, all data-driven analytics measure risks and capital for better decision-making.

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