What Is Your Bank’s Big Data IQ?

Many banks and credit unions feel pretty confident that they are effectively using analytics to drive their business. But the truth is, they are usually thinking about old school concepts and basic models, not big data. Take this self-test to see how you really stack up.

Subscribe TodayWhen was the last time your analytics program had a thorough check-up? Are you following best practices and making the most out of the resources you have when it comes to collecting, analyzing and exploiting unique insights? Particularly those related to big data?

Various methodologies have been created to help companies review their analytic strengths and weaknesses. The jargon in the industry for this type of evaluation is often called a “maturity model.” These examinations are essentially a report card — comprised of questions that assign various values for varying levels of analytics competency.

One such model is based on the work of both IBM and MIT. In this model, there are six categories addressing the various functional areas that affect the impact of data analytics inside your organization. The model not only covers both the technology aspect of data processing and the business factors that help create a successful analytics platform.

Data/Information. Using big data to support decision making is the core concept of this section. Simply put, data is raw material. Once it has been analyzed and interpreted, it becomes information; taking on definition and context. Information, along with the corresponding wisdom, lays the foundation to make data-driven decisions and improve results.

Core Analytics. This is a prerequisite to understanding the rationale of current customer behaviors, and the ability to predict future behaviors. At the base level of this category, financial institutions can carry out basic reporting for regulatory requirements and to share performance values. Banks and credit unions that score higher in this section already use analytics to optimize their marketing strategy and tactical implementation. This functionality is relevant to all customer facing departments — improving sales, marketing and operational efficiency.

Business Focus. It is important that your big data analytics platform be designed to support the bank’s overall business strategy. The analysis of the data must be designed so that meaningful insights can be used to drive improvements and efficiencies throughout the bank. Having a business focus is critical because any of the insights derived from analytics must be relevant to the business challenges the organization is facing.

Content Architecture. This measures the institution’s analytics platform to see to what degree it can grow and expand. It is important that internal stakeholders have accurately ascertained the level and scope of their future needs. To score high in this category, they must be able to manage and scale each of the four characteristics of big data: velocity, veracity, volume and variety.

Administrative/Governance. The governance of big data is critical — as much if not more so than a conventional analytics platform. All data and organizational insights gathered must be used ethically. Policies that govern the use, security, insights and applicability of data must be covered, and continually updated and matched against any potential threats (legal, technological or regulatory).

Company Culture. The ability to translate big data into insights with value hinges on the organization’s culture. A culture where senior management accepts calculated risk along with a positive working environment and a high degree of trust with respect to data skillsets will score higher in this category.

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6-Point Self-Examination

So are you ready? Great, then let’s get started and take the big data maturity test.

I – Data/Information (score 1-5)

  1. The organization uses historical structured data to observe its business.
  2. Information is used to effectively manage the business.
  3. Information is applied to improve operational processes and client engagement.
  4. Relevant information in context is used as a differentiator.
  5. Information is used as a strategic asset.

II – Core Analytics (score 1-5)

  1. Analytics is limited to describing what has happened.
  2. Analytics is used to inform decision-makers why something in the business has happened.
  3. Analytical insight is used to predict the likelihood of what will happen.
  4. Predictive analytics is used to help optimize decision making, resulting in the best actions that maximize value.
  5. Analytical insight optimizes business processes and is automated wherever possible.

III – Business Focus (score 1-5)

  1. Use of data is limited to financial and regulatory reporting. Big data is discussed but not reflected in business strategy.
  2. The business strategy recognizes that data can be used to generate business value and ROI, however it is not used consistently.
  3. The business strategy encourages the use of insight from data within business processes.
  4. The business strategy realizes competitive advantages using client-centric insight.
  5. Data drives continuous innovations and at times can even be disruptive inside our organization.

IV – Content Architecture (score 1-5)

  1. There is no single coherent/unified information architecture.
  2. A framework exists, but does not extend to new/emerging data sources or advanced analytic capabilities.
  3. Architectural systems for big data have been applied in certain areas.
  4. Information architecture is well-defined for most of the volume, variety, velocity and veracity of our data.
  5. Information architecture fully underpins business strategies to enable complete disruption.

V – Administrative & Governance (score 1-5)

  1. Information governance is largely manual and barely sufficient to stand up to audits or legal/regulatory scrutiny.
  2. Understanding of data and its ownership are defined and managed in a piece-meal fashion.
  3. Policies and procedures are implemented to manage and protect core information through its life.
  4. The degree of confidence in information and the resulting insights is reflected in the majority of decisions.
  5. Information governance is integrated into all aspects of the business.

VI – Company Culture (score 1-5)

  1. The application of analytic insight is the choice of the individual, and has little effect on how the company operates.
  2. The company understands the causes behind observations in the data, but is resistant to take advantage of insights.
  3. There are limited business decisions using analytics insight to improve operational efficiency and generate incremental value.
  4. Decision-makers are will informed with insight from analytics and the organization is capable of acting to maximize resulting business value.
  5. The organization and its business processes continuously adapt and improve based on analytical insights, and does so in ways that align with strategic business objectives.

Now total up the scores from yours answers in each of the six categories. Your total score doesn’t just tell you how well your organization can crunch numbers, but how effectively your institution is positioned to win the big data game. Based on the total score of your responses, your bank or credit union will fall into one of the following three classifications: Analytically Challenged, Analytical Practitioners, or Analytical Leaders/Innovators.

Total Score: 1 to 10 – Analytically Challenged

You have a work to do. Developing and implementing a data analytics strategy should be the next thing on your to do list. In this category, you risk losing business and market share to your competitors.

  • Lacking appropriate data management- and analytical skills.
  • Rely more on management experience than data analytics.
  • Simple approach to analytics with insights that are mostly descriptive.
  • Focused on cost reductions/savings in use of analytics.
  • Suffering from data quality and/or access issues.

Total Score: 11 to 20 – Analytical Practitioners

You are doing a good job — actually, better than most. However, there is still room for improvement. Look at which of your categories came in the lowest and brainstorm ways to bring those scores up.

  • More complex and savvy approach to analytics, with some predictive applications.
  • Working to become more data-driven.
  • Primarily operational in the application of data analytics.
  • Have data that is “just good enough.”

Total Score: 21 to 30 – Analytic Leaders/Innovators

Congratulations. You are in the top 10% of organizations when it comes to big data. But don’t get too comfortable. Changes in the marketplace, technology, and consumer attitudes happen with amazing speed, and you’ll have to keep up the hard work to stay ahead of the curve.

  • Possess advanced levels of data management and analytics skills.
  • Sophisticated approach to analytics — focused on prediction and prescription.
  • Data has a high value placed on it.
  • Analytics culture drive by mandate from senior leadership.
  • Always strategic in the application of analytics.

Frank Koechlein is the Managing Director at Velocity Marketing Analytics and coauthor of the marketing resource book, “The New Marketing Analytics.” Frank has 40 years of marketing experience in the financial services industry. He has held several senior marketing positions including SVP, Marketing with the Dreyfus Corp and Director of Marketing with Prudential Direct, Prudential’s in-house, direct response agency, as well as, community bank and credit union marketing positions. If you’d like to contact Frank, you can send him an email.

This article was originally published on March 30, 2017. All content © 2018 by The Financial Brand and may not be reproduced by any means without permission.

Comments

  1. Christopher Gruden says:

    This is a great test! ~ Christopher Gruden, Cleveland, OH

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