Data and Analytics strategy framework

Data and Analytics Strategy Framework for Credit Unions and Banks

Properly building a data and analytics solution is no easy task.

Properly deploying a data and analytics solution (so that it actually gets used!) is no easy task.

When execution is devoid of strategy, the solution will fail.

When strategy lacks execution, the solution will fail.

At this point, you are probably saying to yourself, “Ok, we get it; data and analytics is pretty tricky. So what?”. (Or, you might be saying how negative this guy writing the post must be!).

Data and analytics implementations require the right combination of strategy and execution. Rich Jones of Leading2Leadership and I have worked with several financial institutions to merge the tactical and the strategic. In fact, our involvement and experience in defining and executing on data and analytics strategies has enabled us to clearly define a data and analytics strategy framework that works.

The Knowlton Group Data and Analytics Strategy Framework

Data and Analytics strategy framework

The Knowlton Group Data and Analytics Strategy Framework

Simple and concise, “The Knowlton Group Data and Analytics Strategy Framework” brings clarity to what can be an intimidating process. What follows is a description of each step in the framework that can be used by every organization that hopes to become data-driven.

1. Discover

Every major project or implementation must start with discovery. We recommend organizations begin by creating a data inventory and a report inventory to understand the existing data environment.

Business and reporting processes are becoming increasingly data-driven. Be mindful of how data can be used to track, monitor, and improve key processes. With a greater understanding of the processes driving the bank or credit union, the requirements for the final data and analytics solution will become clearer.

2. Strategize

The strategize step is one of the most critical steps in the framework that is almost always overlooked. Remember what we said at the beginning of this article: “When execution is devoid of strategy, the solution will fail.”

Before beginning the development of the data and analytics solution, you must define an overarching strategy that will drive the development, deployment, and utilization of the solution for the next several years. You must ask question such as:

  1. Do we have the right data and analytics talent?
  2. How quickly can we implement each piece of the solution?
  3. Do we have a culture that embraces data?
  4. Does our budget support our timeline?
  5. Does our executive team believe in the power and value of data and analytics?

Without a clearly defined strategy, your team is like a contractor without a blueprint.

3. Develop

With discovery completed and a strategy in place, the solution can begin to be developed. What gets developed and the order in which development occurs is going to be entirely unique to each organization.

For most banks and credit unions, we tend to suggest that a data warehouse (or, at a minimum, some type of central data repository) be designed and implemented. We find that most banks and credit unions have somewhere between 30 and 50 third-party applications or data sources housing data. Because of this expanse of third-party applications, the data becomes very disparate. Getting a 360-degree view of the member or customer becomes nearly impossible and/or incredibly cumbersome. A data warehouse will resolve these issues and provide a centralized, single source of truth for your data.

Be sure that both technical and business resources are members of your development team. A data and analytics solution is neither completely business-driven nor completely technology-driven. Rather, it requires a blend of the knowledge and skills of both areas of the organization to design the optimal solution.

4. Deploy

Data and analytics solutions are not a “if you build it, they will come” solution.

Let me repeat that. Data and analytics solutions are not a “if you build it, they will come” solution.

When we hear of data and analytics programs that fail, one of the common reasons is that the team responsible never deployed the solution to the organization effectively.

Would you implement a new core without training users throughout the organization? Of course, not! But we find that BI teams do not provide the appropriate amount of education and training to the rest of the company.

Your team could build the greatest data and analytics platform in the world, but if no one uses it – who cares? The “Deploy” phase requires your team to interface with the consumers of data throughout the bank or credit union to help them understand how they can use the solution that has been developed. Show them how they can use data to improve processes, make better business decisions, or visualize data and reports quickly and more efficiently.

5. Analyze

You’ve completed discovery, defined a strategy, developed the solution and deployed the solution. You’re done, right?

WRONG!

Your data and analytics solution is a living, breathing organism. The needs of the business users will constantly change, technology constantly changes, and new ways to use data to drive business growth are always being explored. Your data and analytics team must constantly be analyzing the effectiveness of the solution and determining new and improved ways to use data throughout the organization.

Constantly questioning how you can make the solution better is critical to any sustainable success.

6. Iterate

After the analyze phase, you may find that some key data is missing or a new application is being deployed that must be brought into the solution. At this point, you must cycle back to the “Discover” phase of the framework.

This doesn’t mean that you are completely redesigning the solution, but you need to figure out how to integrate this new application into the solution. This requires discovery, strategy, development, deployment, and analysis just as the framework describes. In fact, we have a whole post dedicated to why data and analytics is an iterative process because it is that critical to long-term success.

Like a small child, your solution requires constant nurturing and guidance. Iterating through the entire framework ensures that your data and analytics solution remains a success for years to come.


The Knowlton Group’s Data and Analytics Strategy Framework is designed to be simple, concise, and help every organization to become data-driven. By following this framework, we strongly believe (and have witnessed first-hand) that your bank or credit union can become data-driven in as little as eighteen months.

If you feel like you need a little bit of help getting started or guidance during any step in the process, ask how we can help by calling 860-593-7842 or emailing Brewster at brewster@knowlton-group.com.

Posted in Banks, Credit Unions, DW/BI, Strategy.

3 Comments

  1. Great framework and thoughts. I’d add one specific thought – quality vs. quantity of the data. While we’d like to see all the data combined, I see a number of banks/CUs more concerned with getting everything into the database, rather than ensuring the data is clean/accurate.

    Don’t get so wrapped up in quantity. I would suggest you think of the data as “need to know” vs. “nice to know”. “Nice to know”s are those things that you say “yep, that would be cool to know”….but is it really something that is actionable? Start with “need to know” information, it makes your information much more actionable and manageable. It also enables you to execute, rather than getting bogged down in the database build.

    • Mike,

      That is an excellent point. In the discover phase, the goal is to truly understand those “need to haves” versus the “want to haves”. That’s part of the power that the Data Inventory and Report Inventory bring to the entire process.

      Take a look at one of our older posts (http://knowlton-group.com/common-reasons-bi-projects-fail/) and see the third point that we discuss. It highlights the exact problem that you brought up!

      Thanks for the comment!
      Brewster

  2. Pingback: Centrally Driven, Broadly Distributed - Part 1 - The Knowlton Group

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