Today’s A:360 discusses some of the common pushbacks that I often hear surrounding data, analytics, and becoming a data-driven organization. In this podcast, I’ll dispell some of these common pushbacks and explain how you can overcome these misconceptions and challenges.

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Hey everyone. Welcome to today’s A:360. My name is Brewster Knowlton, and today we’re going to be talking about the common reasons and common misconceptions that are holding you and your organization back from becoming data-driven.

One of the most common misconceptions I hear about becoming data-driven is that it costs too much money. I hear all the time that building a data warehouse costs millions and millions of dollars. But that’s simply not true for the community banks and credit unions that are in that mid-market in terms of business size.

If you’re a large company (Fortune 500, Fortune 1000 or somewhere in that scope or scale), then absolutely it’ll probably cost you that much. However, for the mid-market organizations (most credit unions and community banks) you’re not even coming anywhere near that number in terms of the initial buildout. It’s simply not true that becoming data-driven costs too much money. Especially for a data warehouse, there are companies out there (one that we partner with for credit unions is called OnApproach that have pre-built data warehouse solutions. With them, you’re not building from scratch, and it can save you a tremendous amount of money in the long run.

The next common misconception that I hear is that data warehouses and analytics projects are way too big of a project. And that’s true – if you try to do everything at once.

Would you implement a new loan origination system, a new core, a new online banking platform and merge with another organization all at the same time? Probably not. And, if you did, those would be extremely large projects to tackle at once and would probably be unbearable. Data warehouses are unbearable if you do too much at once. However, if you break it down into phases and break it down to an iterative process, building a data warehouse or building an analytics platform is not too big of a project. It simply has to be approached the right way.

Another common misconception is when people tell me there isn’t enough time to focus on analytics. They’re too busy. They can’t do it.

The average billion-dollar credit union that we work with has between 45 and 65 third-party applications. And, because of the disparate nature of this data, what that means is that there are thousands and thousands of man-hours that could be automated with a better data and analytics platform.

So, if we could get you back 5,000 hours (which has a significant time-cost associated with it) would there be enough time then? Or, think about what the value of having those people get that time back would be. Whenever I hear that there isn’t enough time to focus on analytics, that is just either a misconception or [making analytics] a lower priority.

There is enough time. In fact, you will get more time if you invest in these projects.

This next one is probably my least favorite excuse. It’s an issue that I get when discussing data and analytics with people they often say, “The way that we’ve always done it has worked, so why would we need to change? Why bother? Why should I invest in this data and analytics stuff? We’ve been doing just fine.”

In the past, that logic might have prevailed. But look how much the financial industry’s landscape has changed in just the past five years. You have peer to peer lenders. You have peer to peer payment mobile apps like Venmo. You have 100% digital banks. You have a very different landscape, and the way that those vendors – those non-traditional competitors – are successful is through an investment in analytics. And they have a lot less data about your customers than you do!

Saying “The way we have always done it works” is not going to be true in the constantly changing future environment. It requires data and analytics to be innovative and adaptive.

The last common misconception or excuse that ends up holding organizations back from becoming data driven is when they say that, “We don’t have the right culture. We don’t have a culture for using data, so why would we invest?”

That’s sort of a tautology, isn’t it? If you don’t have a data-driven culture, then you don’t have a data-driven culture. Right? It’s obvious. But like anything else, you have to develop that competency. You don’t just step into a car and automatically know how to drive. You have to learn. Therefore, as part of your analytics project, you have to take the right time to promote and grow a culture that supports, trusts, believes in and uses data and analytics. (We have a couple posts and podcasts that talk about that.)

Regardless, that cultural shift is key. Using the statement that “We don’t have the right culture for data and analytics” as an excuse to not invest in analytics presents a natural paradox. You have to grow that culture in order to make use of analytics, and every organization goes through this challenge. Nobody naturally has, unless they built it from the ground up when starting the business (see Uber, Netflix, etc.), an intrinsically data-driven organization. That is part of your analytics project, and you too can grow the proper culture to successfully deploy and leverage analytics.

Again, these are the common misconceptions or excuses that I hear for why organizations are not data-driven and what’s holding them back:

  • They say that becoming data-driven costs too much money. False.
  • They say that data warehouses are way too big of a project. False.
  • I hear that there isn’t enough time to spend on analytics. Surprise…also false!
  • My least favorite: “The way we’ve always done it works.” Well, in the past, yes. In the future, probably not.
  • They will say, “We don’t have the right culture”. You can have the right culture. You just have to build it. It is not going to inherently exist.

That’s it for today. Thanks again for tuning in to today’s A:360.

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Today’s “A:360” podcast answers the question “How do you know if you have the right KPIs?”. I discuss three quick ways you can tell if you have the right KPIs, and what key points you should consider surround your key performance indicators.

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Hey everyone. Welcome to today’s A:360. My name is Brewster Knowlton, and today we’re going to be talking about how you can tell whether or not you have the right KPIs at your organization.

There’s a famous quote by W. Edwards Deming that says, “You can’t manage what you don’t measure.” The managers of your organization will naturally manage towards driving success for what they are measured by. If our key performance indicators align with what we want our managers to actually accomplish, then, naturally, as a byproduct of having the correct KPIs, our managers will improve performance and manage towards achieving those objectives and thresholds set forth by our KPIs and key metrics.

The question therein lies, how do you know if you have the right key performance indicators? We’re going to quickly talk about three key ways that can help you figure out whether or not you’re tracking the right KPIs and the right key measures.

One of the first ways you can tell if you’re tracking the right KPIs is that you have less than 10 KPIs (and, really, five to seven is your best target). But, if you have more than 10 KPIs, you’re probably tracking too many tactical things and not enough strategic things. Or, you’re trying to tackle too many strategic objectives at once (another bad thing). A quick way to tell if you’re tracking the right KPIs is [answering the question], “Do I have between five and seven? Or at least less than 10?” That tells me that you’re tracking only the most strategic things and that you’re focusing on the most important measures that you need to be tracking.

The next way you can tell you have the right KPIs is that your key performance indicators align with your strategic goals. I alluded to this in the last point, but your KPIs must directly support the strategic objectives of the organization whether they be for the next year or the next three to five years. What are those key objectives? And then, ask yourself if your KPIs support those key objectives.

If you think back to Deming’s quote, “You can’t manage what you don’t measure”, it emphasizes the importance of KPIs measuring progress towards strategic goals, because then, naturally, our teams will manage towards success and achieving those strategic goals. It is, therefore, critically important that your key performance indicators align with your strategic goals.

One of the last ways you can tell that you’re measuring the right KPIs is that your KPIs are different than everyone else’s. That’s not to say that there’s not going to be some overlap, but your KPIs need to be unique to your organization and the specific strategic objectives that your organization has.

Benchmarking is a critical aspect of a lot of peer comparisons in many industries, but that doesn’t mean that those benchmarking measures should be your KPIs. Credit Union A, for example, might have high deposits and, therefore, are more interested in loan growth. Credit Union B, however, might have high loans and would be more interested in deposit growth. You have to assess what objectives your organization has and only your organization. Focus not on what your competitors track, but what is best for your organization. Once you determine those strategic objectives, identify those five to seven key performance indicators that will monitor and track your progress towards success in those objectives.

Wrapping up, you can tell if you have the right KPIs by these three points:

  • You have less than 10 KPIs. Ideally we want between five and seven.
  • Second, your KPIs align with your strategic goals.
  • Third, your KPIs aren’t the same as everyone else’s. They accomplish the unique objectives and help you track your progress towards your unique strategic objectives only.

That’s it for today! Thanks again for listening to today’s A:360.

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For starters, yes the title is a terrible play on “when life gives you lemons, make lemonade”.

Bad jokes aside, I hear too frequently how organizations need more and more data. I’m a data guy – I’m all about data. But there is a subtle difference between having more data and more information.

Below is one of my favorite quotes about data:

Without data, you’re just another person with an opinion. – W. Edwards Deming

Opinions are the foundation of subjectivity, and subjectivity fundamentally is devoid of data as support. Decisions driven by opinions without data is counter to everything The Knowlton Group (and I personally) stand for. Our primary mission is to enable every organization to become data-driven.

But as much as I like Deming’s quote, I also love this quote from a 2015 Forbes article:

“Without an opinion, you’re just another person with data” – Milo Jones and Philippe Silberzahn

The converse of Deming’s quote is equally accurate. Having data without an opinion or interpretation of that data is as bad as forming opinions without any data to back you up. With all of this data out there, it is foolish to believe that data will tell us what we need to do. All data requires interpretation and opinions to be formed before it can be practically applied.

The Proper Way to Use Data

Though this section is a gross oversimplification, it boils down the proper way to use data into a few simple steps. Following this process will enable you to turn data into information.

1. Ask a Question

Every actionable use of data must start with a question. These questions can be relatively simple like “how many members do we have” – unless there is no consistent definition for a member! .

The questions you ask can also be more complicated like “do we need a new branch?”. Regardless of what the question is, properly leveraging data and analytics requires asking a question to which you hope to discover an answer.

2. Form a Hypothesis

Like the null hypothesis in statistics, I strongly believe that you must start with a hypothesis. This is where your opinion and subjectivity can come into play. Use this hypothesis as a method to which you test your analysis against. Be sure that confirmation bias doesn’t play into your analysis. If your hypothesis turns out to be incorrect, who cares!?.

“I have not failed. I’ve just found 10,000 ways that won’t work” – Thomas Edison

3. Test your Hypothesis

With a question asked and a hypothesis formed, now you can begin to discover an answer to your question and test your hypothesis. This is where we can start to dig into the data (and the fun really begins). Simpler questions may require you to gather data from a single source. More complicated questions can require analyzing data from multiple data sources. For the more complicated questions, a data warehouse really starts to prove its worth!

Avoid gathering more data than you need and overcomplicating the process. Paralysis by analysis is a real thing. Gather and analyze only the data you need to test your hypothesis. Here are some of my favorite quotes/sayings on avoiding unnecessary complexity to motivate you:

“KISS – Keep it Simple Stupid” – Kelly Johnson, Late Lead Engineer for Lockheed Skunk Works

“Simplicity is the ultimate sophistication” – Leonardo Da Vinci

“Make simple tasks simple!” – Bjarne Stroustrup

4. Analyze Findings and Provide an Explanation

This is one of the most overlooked steps of the process. Asking the right question of your data is crucial, but you must provide an explanation or recommendation based on your analysis. Too often I see fancy documents put together with many pages and loads of charts and spreadsheets. In these instances, you’ve written a lot but said little.

Be concise, be clear, and provide an answer (for simple business questions) or a recommendation (for more complicated business questions).

Wrapping Up

Properly using data and analytics is becoming invaluable and, ultimately, necessary for most organizations. To begin developing a data-driven culture, have your staff read this article to learn how to properly turn data into information.

There is A LOT of data out there. The most successful companies (and people) know how to turn that data into information.

Need help turning data into information? Contact us by filling out the form below or sending us an email!

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I’ve thought more and more about this question in the past few months: can traditional financial institutions (retail banks and credit unions, specifically) become (or, at minimum, act like) technology companies?

Critique to this article might very well be, “who cares whether traditional FIs can be classified as technology companies.” Admittedly, the debate is predicated on an entirely semantic argument, but the question goes much deeper than a superficial definition. Regardless, I’m going to give it a shot.

We first have to come up with a definition of a technology company – no small feat – and determine whether or not banks and credit unions could ever fulfill that definition. Once you’ve finished reading my take on this question, comment and let us know where you stand.

The Definition of a Technology Company

Unsurprisingly, the definition of a technology company is a bit of an enigma. In a recent article, four respected technology executives, analysts, and venture capitalists gave their takes:

“You are a technology company if you are in the business of selling technology–if you make money by selling applied scientific knowledge that solves a concrete problem.”

Alex Payne, Co-Founder, Simple

“It’s generally a company whose primary business is selling tech or tech services. A more nuanced definition is a company with tech or tech services as a key part of its business. It’s a hard question.”

Todd Berkowitz, VP of Research, Gartner

“A tech company uses technology to create an unfair advantage in terms of product uniqueness or scale or improved margins. Ask the question: Could this company exist without technology? If the answer is no, it has to be a tech company.”

Greg Bettinelli, Partner, Upfront Ventures

“I think there’s a false dichotomy in the idea that a company either is or is not a tech company. I think it’s possible for a company to be a hybrid if tech is giving it an edge over incumbents.”

Hayley Barna, Venture Partner, First Round Capital

Each response to the question of what defines a technology company never fully answers the question. Payne’s response aligns more with a way of approaching problems using a scientific approach – a common theme amongst companies who proclaim they are technology companies. Berkowitz’ more nuanced definition is an interesting thought but is difficult to rationalize if “technology” is not equated to “software” in the context of his definition.

The point is that nearly every definition of a “technology company” is deficient in some way.

Let’s try a different approach where we dig deeper into leading technology companies instead of superficially trying to define them. I’d equate this approach to answering the question of “Who is John Smith” not by superficially replying “he is a 5’11” male who works at ABC corp…” but rather by saying “John Smith is a father of two who enjoys spending time with his family while balancing work and life without sacrificing what is most important to him”. It’s a very different way of approaching the same problem, whereby the essence of John Smith is more descriptive than how he appears.

Instead of trying to apply a simple definition to a clearly more complicated classification, let’s look at the common characteristics of companies we tend to mostly agree are “technology companies”. Perhaps we can create a definition driven around the essence of what makes a technology company successful.


Apple is one of the most likely candidates for the company that is front-of-mind when you hear “technology company”. What defines Apple is their ability to innovate and challenge the status quo. This innovation is their competitive advantage. Whether they are designing the iPhone or challenging the music industry with iTunes, Apple has shown time and time again that they can effectively change the course of industries.

Creating a bigger iPhone is not innovation – innovation is taking phone, email, messaging, calculators, alarm clocks and dozens of other applications and technologies and putting it in the palm of your hand with a “smart” phone. Whether it be the personal computer, smart phone, wearables or a variety of other ventures Apple invests in, their innovation – not their products – is what makes them so successful.

Alphabet (Google)

Google has become a generic trademark for search engines. How often do you say “I’m going to search for something online using a search engine?” Never. You say “I’ll Google it”.

It’s no secret that Google is much more than just a search engine. Back in October of 2015, Larry Page and Sergey Brin reorganized Google into Alphabet. This holding company structure better enables each business line to grow independently. From the “traditional” Google products and services to YouTube to Google X, Nest, and the other subsidiaries of Alphabet, the freedom to grow and innovate each business line independently is critical to Alphabet’s master plan.

At the heart of all each product and service is innovation. Susan Wjocicki, current CEO of YouTube and 16th Google employee, penned a great article several years back (pre-Alphabet) titled ”The Eight Pillars of Innovation”. She discusses the various ways Google (now Alphabet) continues to be an innovative company despite its sprawling operations that span tens of thousands of employees, multiple continents, and an ever increasing number of products and services. In hindsight, Susan’s article could certainly have foreshadowed the impending reorganization.


Bill Gates is arguably one of the top business people of the 20th century. It takes quite a leader to put “a computer on every desk and in every home. He has certainly contributed to making that goal a reality when he first started Microsoft back in April of 1975.

Considering that the large “computers” of 1975 were primarily used by larger companies, the thought that he could put one on a desk in every home is arguably crazy. It takes an innovative genius to be able to drive a vision that could have been considered science fiction at the time. While the mid-2000s might have taken away some of Microsoft’s “innovation points”, the rise of Satya Nadella to the CEO position after Balmer’s departure is bringing innovation back to the forefront of Microsoft’s business model. Azure, HoloLens, opening up Microsoft’s development tools, and a variety of other new products, services, and ventures has placed Microsoft back into the conversation of leading innovative companies.

Innovation is what got them to where they were; Nadella is betting on it to bring them back to the top of the technology world.

What Does This Have to Do with Financial Institutions?

Are you noticing the not-so-subtle theme? Every leading technology company is defined not by their software but by their innovation. We can’t deny that these original technology companies didn’t develop great software and technology, but the innovation that drove the technology is what defined them. It’s no surprise that Walter Isaacson fantastic book, “The Innovators: How a Group of Hackers, Geniuses, and Geeks Created the Digital Revolution” emphasizes the roles of Steve Jobs, Larry Page, and Bill Gates (Apple, Google (Alphabet), and Microsoft) in making the modern, digital world.

Let’s establish the unstated though, hopefully, obvious point I am trying to make: a “technology company” is defined through its innovation and not by its products and services. The products and services (read technology) are there to simply support their innovative endeavors.

So how does this apply to banks and credit unions?

The most innovative advances in banking and finance are being developed by companies that are not traditional financial institutions. Companies like Square, Sofi, LendingClub, and Stripe are challenging services that have traditionally been provided by banks and credit unions.

Why are they successful? Because they are challenging the way that things have always been done. In my work helping financial institutions design and implement data strategies, I get to interview dozens of staff from the CEO to the customer and member-facing staff. I cringe when I ask why a current tool is used or process still exists and the response I get is “because we’ve always done it this way”.

The intersection of business and technology has blurred the line of the archaic definition of a technology company. To answer the question asked in the first paragraph, “can banks and credit unions become technology companies?” My answer is a resounding yes.

The answer is yes but not by the definition of what investors will use to classify tech companies on the stock market. I answer yes based on the definition that leading technology companies are as successful as they are because of their innovation and not their technology. By my definition, companies like Tesla, Bayer, and Marriott are also technology companies (see this list here for others that would certainly satisfy this definition).

Be Steve Jobs and challenge the status quo. Be Bill Gates and realize a vision so grand that people think you are crazy. Be Sergey Brin and Larry Page and revolutionize the world.

Traditional financial institutions can compete with financial technology startups by being as innovative as they are. The software and technology is but a byproduct of their innovation; embrace an innovative culture within your bank or credit union and you too can change the industry.

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In our previous post, we introduced you to the data and analytics management methodology, “centrally driven, broadly distributed”. Having discussed the reasons why a “centrally driven” approach to data and analytics is optimal, this post will dive into the second half of the quote, “broadly distributed”.

“Broadly Distributed” Analytics Takes Down Silos

In many organizations without a robust data and analytics program, analytics is typically managed within silos as discussed in part 1. The reporting and analysis is then rarely distributed to the rest of the organization. Rather, it is kept “close to the chest” of the department that originated the analysis. This creates a “broadly driven, centrally distributed” approach whereby analytics originates in departmental silos and never leaves those silos.

Organizational transparency is critical to a strong data and analytics program. Marketing can no longer operate in a vacuum without support and communication with IT, Lending or Operations (to name a few in a banking context). These interdepartmental efforts create the need to view reporting and analytics across department reporting lines as well.

A “broadly distributed” approach to data and analytics ensures that data can be consumed by all departments in the organization. Of course, security measures must be ensured for particularly sensitive data, but operationally data transparency is of critical importance. If a new lending promotion is being planned, Lending shouldn’t have to jump through hoops to get Marketing data. Similarly, Marketing shouldn’t have to jump through hoops to get data from the Lending team.

Silos are bad.

From “I” to “We”

In organizations where analytics is not front-of-mind, there lacks an enterprise-wide view of processes, goals and operations. When data and analytics is “broadly distributed”, staff start to see how their department’s efforts contribute to the organization’s strategic goals.

Enterprise wide distribution of reporting and analytics enables inefficient processes to be identified early and reduce their negative impacts. For example, if a new lending product is not interfacing with the core properly, Lending, IT, Marketing, Operations or a staff member of any department could assist in identifying this process flaw.

More often than not, we tend to find that staff are willing and able to adopt an enterprise level view of their efforts. The challenge often lies in giving them the tools and access to fully embrace a view outside of their own department’s efforts.

A “broadly distributed” data and analytics program enables your employees to no longer think only about their department’s operations but about the enterprise’s operations with the big picture in mind.

Accurate Across Reporting Lines

In part 1 of this post, we described a situation where various members of different departments are asked the same business question, “how many members do we have?”. In a “broadly driven” (instead of “centrally driven”) data and analytics model, each staff member would go to their own data sources to retrieve the “correct” answer. Rarely does each staff member produce the same answer.

A “broadly distributed” approach in conjunction with a “centrally driven” model ensures that each employee retrieves data and analytics from the same, single source of truth (add link to data warehouse article). By distributing the “centrally driven” repository of data and analytics throughout the organization, accurate and consistent reporting proliferates throughout the company.

Wrapping Up…

Combined with a “centrally driven” solution, a “broadly distributed” model is unquestionably the optimal approach to data and analytics. Eliminating silos, reducing inconsistencies and inaccuracies and ensuring an organizational mindset are but a few of the positive outcomes of this design. Coupled with the benefits described in our post on a “centrally driven” solution, our hope is that you and your internal team’s see the value in approaching enterprise analytics the proper way.

If your team needs any form of assistance in formulating your data and analytics strategy, give us a call at 860-593-7842 or send an email to!

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In Thomas Davenport’s “Competing on Analytics: The New Science of Winning”, one of the first chapters of the book defines the common attributes of analytically-driven organizations. Davenport discusses that one of the critical aspects to success in analytics revolves around taking an enterprise-level approach to managing data and analytics. He then quotes Harrah’s Entertainment’s management approach to enterprise data and analytics, calling the approach “centrally driven, broadly distributed”.

Focusing on “Centrally Driven”

In this article – part 1 of a two part post – we will discuss the first half of that quote. Having data and analytics be “centrally driven” is of the utmost importance to success and sustainability of your analytics programs.

Inconsistencies Abound

If you are an executive of any industry, I know for certain you have experienced the following scenario: you ask the same data and analytics question to different departments and you get several different responses.

If you are in the banking industry, ask Marketing, Lending, IT, Finance, Commercial, and Retail Operations how many customers/members you have.

How many different answers do you think you will get?

In most organizations, each department would independently retrieve the information that you asked of them. Marketing might go to their MCIF, IT might go to the core, Finance might look at the GL application – rarely will each department go to a centralized, single source of truth to retrieve the “correct” answer. Since each application is managed separately with different data contained within, there is no way to ensure consistency and accuracy without a central data warehouse or repository.

Without a centralized, enterprise-level platform of data and analytics, it is nearly impossible to ensure consistent, accurate reporting.

Spreadsheet Errors

I love Excel as much as the next guy. Quite a few organizations across nearly every industry live and die by their Excel spreadsheets (investment banking first year associates know this all too well). But how many Excel spreadsheets are 100% correct and error-free? Some studies estimate that nearly 9 in 10 Excel files have errors!

Especially in the banking industry, I see organizations littered with complicated spreadsheets. Analysts will extract Excel files from different data sources and then hope they’ve defined their VLOOKUPs accurately to be able to consolidate the data sets. I’ve personally witnessed high-level, executive reports with wildly inaccurate formulas that effectively rendered the spreadsheets useless. This is, sadly, not an anomaly.

How do you resolve this issue? By taking a centrally driven approach to managing data, users will not be required to consolidate data from various sources or manually define complicated, error-prone formulas. Data will be consolidated and validated reducing or eliminating the vast majority of these overly-complex Excel spreadsheets. Data warehouses combined with BI and data visualization tools (like Tableau, InformationBuilders, Qlik, and many others) provide an enterprise-level, industry-leading platform for data and analytics.

Definitions and Data Governance

Whenever I get the opportunity to speak with a credit union, I eventually always ask the same question, “what is your definition of a member? Does everyone have the same definition as you?” This seems similar to the inconsistencies idea brought up at the beginning of the article, but even with the same data source, there is no assurances that everyone will define a key business term the same.

Marketing might only focus on members that have not been placed on a “do not contact” list. What about the same individual (i.e. unique SSN) that has multiple accounts and member/customer numbers? How are they counted for aggregation purposes? These subtleties are critical to a strong data and analytics program. A cross-departmental team must agree on key definitions that are used throughout the organization. This centrally driven approach to defining key business terms helps ensure accountability and consistency.

Wrapping up

A centrally driven approach to data and analytics ensures consistently accurate reporting with key business definitions universally understood by everyone in the organization.

In our second part of this post, we talk about why having a “broadly distributed” data and analytics solution is vital.

Check back soon or subscribe to our blog for updates!


There are plenty of data and analytics consultants in the market. The choice of a data and analytics consultant can be critical to the success of your project, so how you can be sure you have chosen the best team to partner with?

At The Knowlton Group, we specialize in working with financial institutions. We know your challenges, the technology you utilize, and can provide solutions backed by expertise and experience.

But so can a handful of other qualified consultancies.

What the other consultancies don’t have is what we call “The Knowlton Group Advantage”.

The Knowlton Group Advantage and Why It Matters

“The Knowlton Group Advantage” is the closest thing to a full service data and analytics solution that can be provided to financial institutions. With our partner ecosystem and in-house expertise, we can help you manage and implement each component of a data and analytics program.

The Knowlton Group Advantage

There are several key steps involved with any data and analytics program.

Data and Analytics Strategy

You must have a plan of action and a strategic direction defined before commencing any data and analytics project. A data and analytics strategy is required to drive the strategic direction of the program for the next several years. Combining execution and strategy, this offering is critical to long-term success.

Data and Analytics Talent Acquisition and Staffing

Third party vendors and consultants can only do so much. Eventually, an organization must have the right internal talent to drive the strategic direction of the data and analytics program. The Knowlton Group can source, assess, and recommend the best data and analytics talent based on the cultural fit and skills required for the role.

Data Warehouse Implementation and Customization

Organization’s that have a data warehouse in place often require additional integrations or customizations after the initial implementation. Need to integrate your new loan origination system or other third-party application into your data warehouse? No problem.

If you don’t have a data warehouse currently implemented, we have existing relationships with vendors who have been handpicked based on their ability to provide the highest quality product.

Data-Driven Business Strategies

You have a data warehouse in place and a data and analytics team staffed. Now what?

Let our team and partners work with you to define how best to leverage the data and analytics platform in place. We have helped each and every business unit within the bank or credit union drive business decisions and strategies through the use of data and analytics.

We can help you put your data to use.

Statistical Modeling and Analytics

There are times when advanced statistical modeling and predictive analytics requires a highly specialized skill set and expertise. From segmentation to product propensity analysis to advanced machine learning applications, we can recommend the top experts in these areas (and more!) to ensure seamless delivery and execution.

Why it matters

There are many moving parts in a data and analytics program. This can present significant challenges to organizations without the in-house experience and expertise. Through our own internal capabilities and the strengths of our relationships with the best vendors in the market, we can fully support and manage the entire data and analytics lifecycle.

Take the stress out of your data and analytics program with The Knowlton Group Advantage.

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?


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

The terms business intelligence, data and analytics, and big data strike a combination of excitement and fear into business leaders’ minds. My goal for this post is to alleviate some of the fears associated with business intelligence and analytics (big data is a whole different world). Community banks and credit unions, regardless of size, can start using business intelligence (BI) today to yield tangible benefits. All you need to do is start with reporting.

Reporting: All You Need is Excel

While a data warehouse is often the foundation of a robust business intelligence platform, as long as you have a single SQL database (like Microsoft SQL Server, Oracle, MySQL, DB2, etc.) you can begin to use business intelligence.

Let’s assume that your core’s database is a SQL database (your IT team should be able to answer this question for you very quickly). Even if you don’t have a program like iDashboards, SSRS, Tableau, PowerBI, or some other data visualization and reporting application, you can still begin to create reports. Microsoft Excel 2010 and above have a fantastic feature, PowerPivot, that allows you to create and monitor KPIs right in Excel. You can create SQL queries, return data, create charts and graphs – all without leaving Excel.

Let me reiterate this: to get started with reporting and business intelligence, all you need is Excel.

Is this the best long term solution? Probably not. But it is important to start small when thinking about how to expand business intelligence in your community bank or credit union.

SSRS – You Probably Already Own It

SSRS, SQL Server Reporting Services, is a reporting tool built right into Microsoft SQL Server (one of the most common types of databases deployed at community banks and credit unions). This browser-based reporting application allows you to easily create, manage, and deploy reports to users throughout your organization.

Did we mention you probably already own it?

The theme here is to start small and use the tools already available to you. If you have SSRS, start to experiment with the program. Find someone in your company who wants a report built and design a small proof-of-concept. Once interest is piqued, one report becomes ten and then ten becomes one hundred. Soon, the use of business intelligence will spread like wildfire throughout your community bank or credit union.

Start Learning SQL

Learning SQL seems much harder than it really is. We’ve provided some great free SQL lessons throughout our site to help you get started. Looking for something a little more comprehensive and hands-on? Take a look at our top-rated online SQL course; it’s a great value for SQL beginners.

Once you have learned SQL, you can start writing queries to retrieve data from a database. You can use SQL to pull data into Excel to then build reports, charts, and other visuals like we mentioned before. If you want to start using SSRS, start by creating some very simple SQL queries. Once you write the query, you will find that SSRS is very intuitive for new users.

Don’t feel like you need to become a SQL expert. Learn enough to start making an impact, and you can learn more complicated features as you go.

To Begin, Begin!

Using business intelligence is just as much a capability to the $100 million community bank as it is to the $5 billion credit union. Keep things simple and start working with Excel. Once you have made some basic reports with Excel, start to experiment with SSRS. Learn enough SQL to get started, and make incremental progress. Business intelligence can be as simple as reporting – something every bank and credit union can get better at.

Need help getting started? Give us a call at 860-593-7842 or send an email to!

Over the past few weeks, we have released posts describing how different members of the C-suite can make use of data and analytics. We started out discussing five ways CMOs can use data and then talked about the CLO and lending analytics. In this post, we want to highlight the ways that a CFO (Chief Financial Officer) can make use of data and analytics.

How Can a CFO Use Data?

CFOs can benefit in several ways from data and analytics. From near real-time access of key data to addressing data quality issues, CFOs – especially in community banks and credit unions – have great opportunities to leverage data and analytics.

1. Lose the Overly Complex Excel Workbooks

As a CFO, does your finance team have incredibly complex Excel workbooks? When data and/or requirements change, how time-consuming is it for your team to quickly deliver you updated results?

With the growth of data and analytics and the increasing use of data warehouses in combination with visualization tools, CFOs and their teams can lose the overly complex Excel workbooks. Using industry-standard (and affordable!) visualization tools, ad-hoc reporting can eliminate complexity and manual effort to get even some of the more complicated data points. Working with your internal business intelligence team, processes can be automated to allow your financial analysts to spend less time fussing with Excel files and more time actually analyzing the data.

2. Repeatable, Automated Processes

Similar to some of the concerns mentioned in the first point, CFOs and their teams tend to have manual, multi-step processes built to create reports and retrieve information daily, weekly, monthly, and quarterly. Now imagine giving that time back to you and your team to increase productivity.

With a data and analytics platform, a CFO can enable his team to automate what tend to be very time-consuming and cumbersome processes. If a team member is out for some reason, the automated process still gets completed. This eliminates concerns that may arise if there is turnover in the CFO’s team as processes are removed from any one individual. Processes to gather information and produce reports are created more quickly, more efficiently, more accurately, and in an automated fashion.

3. Integration of Data

With a data warehouse, CFOs can begin to understand their organization’s operations more holistically. With an integrated data and analytics platform, a CFO can examine financials while “drilling across” to view loan, deposit, or channel usage details. As more community banks and credit unions look to stay competitive in such an evolving industry, the insights provided by an integrated data platform will become critical for CFOs.

4. Data Quality

How many CFOs trust the data they get from their MCIF? Probably not a ton of you, right?

But many community banks and credit unions use an MCIF as their primary and sole tool for business intelligence. This naturally presents a challenge for CFOs because an MCIF (Marketing Customer Information File) is primarily designed for marketing. Sure they may have some financial capabilities built into them, but MCIFs are not designed to be a financial reporting tool.

With a enterprise data and analytics program built, a data warehouse or central data repository of some kind will be created. This platform will enable CFOs to look at data without the MCIF’s scope and filters affecting data quality and accuracy.

5. Compliance and Regulatory Reporting

It’s no secret that compliance and regulatory pressures will continue to impact community banks and credit unions. As a CFO, these pressures will become increasingly important to monitor, audit, and track.

With a well-defined data and analytics platform, CFOs can help their teams do a better job of tracking and ensuring compliance for key policies. For example, dashboards can be built to automatically track Risk-Based Capital ratios and other financial details auditors will undoubtedly be focused on. As CECL implementation and standards timelines inch closer, data and analytics will enable CFOs to monitor, track, and conform to the appropriate standards.

6. Data-Driven Pricing Models

Companies like Deep Future Analytics are starting to bring this idea to many banks and credit unions. By leveraging your historical data, statistical models can be applied to optimize pricing while maintaining (or even reducing) your organization’s credit risk. Based on past loan performance, your bank or credit union can identify whether or not you can offer better rates on C or D quality loans while ensuring adequate ALLL.

We anticipate a sharp increase in the number of community banks and credit unions performing this type of price modeling as data and analytics become more commonly leveraged throughout the industry. Large financial institutions have been doing this type of work for years; as data and analytics technology becomes more practical for smaller organizations, these advanced models should become more frequently adopted. Increased collaboration between the CFO and CLO can maximize the power and effectiveness of these pricing models.


CFOs, like all members of the executive team, can benefit from data and analytics in several ways. We believe that all community banks and credit unions can and should become data-driven. By understanding some of the ways the CFO can use data and analytics, our hope is that your organization takes a more focused approach to building out your business intelligence program.