The foundation for a successful analytics program is data. But, without accurate and quality data, it’s nearly impossible to make informed business decisions. As data becomes an even greater asset for the credit union, the ability to store large amounts of complex data in a unified, central database, known as a data warehouse, is critical.

The data warehouse is the perfect solution for credit unions that are trying to centralize data integrated from several applications. This single source of truth is the repository for all the data that has been collected and integrated from multiple sources across the organization. It puts all of your data into one place, accumulating history, and makes your information easy to access and analyze by your team. The result is that everyone in the credit union is using the same data derived from the same source, which leads to quick, easy access to accurate data and better decision-making.

Data warehousing solves the ongoing problem of analyzing separate data and converting it into actionable information you can use. Consider these benefits a single source of truth can bring to your data analytics initiatives:

Greater Data Governance and Consistency:

Creating an ongoing set of rules, policies and procedures for collecting and managing data ensures that the credit union’s data strategy and business strategy are aligned. Without a single source of truth, data governance and data quality disciplines that govern the overall management, usage, storage, monitoring, and protection of the is data is nearly impossible. Consistent, high-quality data leads to strategies that create enhanced member service, greater employee productivity, data-backed decision-making, and better business outcomes.

Improved Efficiencies:

With a data warehouse you can process, integrate and consolidate large volumes of complex data into a single stream of data. This eliminates the need to rely on multiple data sources for reports and analytics. Employee time is saved, and consistency and accuracy are greatly improved across the organization.

Enhanced Business Intelligence:

By integrating data from various sources into a single source of truth, managers and executives will no longer need to make business decisions based on limited, inaccurate data or gut instincts. In addition, data warehouses and related BI can be applied directly to business processes including marketing segmentation, member retention programs, financial management, and sales and other business reporting.

Time Saving Reporting and Process:

Data warehouses are also designed with speed of data retrieval and analysis in mind. You are able to store large amounts of data and rapidly generate results. Since data analysts and business units within the credit union can quickly access critical data from a number of sources—all in one place—they can confidently make informed decisions on key initiatives. They won’t waste time retrieving data from multiple sources and guessing which data is updated and most accurate. A single source of truth also allows your credit union to analyze the data effectively as well as trust it! It creates accuracy, dependably and unrestrains the data so that more than just the analysts and CDO/ data scientists can access it. All employees can use the data.

Positive Return on Investment:

Having a single source of truth for your data enables the credit union to generate higher amounts of revenue by making more timely, accurate and informed business decisions. As data warehouses produce greater efficiency, employee productivity and time savings, credit union can often see a clear cost savings due to the implementation of a data warehouse.

Don’t be encumbered by a number of common challenges that stem from not investing in a data warehouse for your data analytics program (e.g., impaired decision making, inaccurate data, slowed workflows, negative business outcomes, inefficient processes.)

It is clear, when a data warehouse is implemented and designed properly it leads to significant advantages for your organization. Do you need guidance on selecting the right data warehouse for your credit union and ensuring it is properly implemented? The Knowlton Group is ready to help! Contact us today.

Digital innovation is sweeping across the financial services industry and creating opportunities for banks and credit unions to leverage data as a source of competitive advantage.

Until recently, most credit unions were delegating data management and analytics to the IT department, which in turn created data silos that inhibited the enterprise use of data.

Has your credit union made the business case for creating an analytics team to spearhead important data initiatives? If so, you now need to hire or train the right talent that can turn data into value and deliver on your organization’s data strategy

Chances are you have numerous questions whirling around about how to define the key data roles and responsibilities.  When venturing outside the credit union to evaluate data leadership, this list of tips breaks down key roles and how they should align with your needs.

Chief Data Officer

The CDO is a senior executive who bears responsibility for the credit union’s enterprise data and analytics strategy, data governance, data management, and data utilization.  The CDO’s role will combine accountability and responsibility for information protection and privacy, information governance, data quality and data life cycle management, along with using member data to create business value.

This last point is arguably the most crucial.  If your analytics team is not delivering business value then you’re not achieving the team’s full potential.  The CDO should focus on measurable outcomes for specific use cases to provide the necessary cultural and change management sparks to garner enterprise-wide buy-in.

Data Scientist

A data scientist masters a whole range of skills and tasks from being able to handle the raw data and analyzing that data with the help of statistical techniques, to delivering actionable recommendations based on the underlying data.

The title “Data Scientist” has become a bit of a buzzword as of late.  If your “Data Scientist” can query a database, but the extent of the statistical knowledge is mean, median, and mode…they aren’t a data scientist.

Real” data scientists have deep knowledge of statistical and probabilistic models and know how to leverage those models for specific analytic applications.

The Data Analyst

The data analyst will retrieve and gather data, organize it and use it to reach meaningful conclusions.  The insights that data analysts bring to the credit union can be valuable in identifying and even helping to predict the needs of the credit union’s members.  They help develop effective ways to collect the data and compile key findings into reports to share with other teams within the credit union.

Think of the data analyst as the individual who translates between the technical world and the business world.  This individual needs to have basic competencies from a technical perspective, but, most importantly, they need to be able to interpret technical knowledge into practical business terms and vice-a-versa.

A good data analyst doesn’t just produce charts, graphs, and other fancy visualizations.  They produce clearly articulated meaning to describe what the visualizations mean to the business.

ETL Developer/Data Engineer

The ETL Developer/Data Engineer is a critical member of the data analytics team as they are dedicated to the fundamental process of capturing, storing and processing your data.  If your CU leverages a data warehouse as your analytics platform, then the “ETL Developer” most aptly describes the job title.  If your organization is leveraging a data lake or hybrid platform, “Data Engineer” is a more appropriate title.

In the end, this role boils down to ingesting new data sources into the platform.  This may come from non-core third-party applications (i.e. consumer LOS, real estate LOS, online banking, etc.) to external data sources (i.e. demographic data, economic indicators, social media interactions, etc.).

Report/Visualization Developer

To effectively deploy self-service reporting and analytics through your BI portal (i.e. Tableau, Power BI, Information Builders, etc.), someone must be tasked with creating these reports and dashboards. This is the critical role of the Report/Visualization Developer.

If your credit union embraces a more decentralized approach to data analytics, then these resources may reside in the business areas instead of centrally managed.  Regardless of where they reside within the organization, this is an essential function for providing a front-end to your analytics platform.

The Right Role for Your Credit Union?

As credit unions grow and look to remain competitive, there’s an obvious need to hire the right data talent who are highly skilled in analytics, who can interpret data, and insight and tangible business value. Demand for data expertise is growing every day. Be sure to understand which roles are specifically needed by your organization.  Most credit unions don’t have the necessary budget to hire each of the resources discussed.  Determine where the greatest internal need exists and identify strategic partners who can assist with the rest of the functions.

The bottom-line, all organizations have the power to become data-driven by accessing data skills – and on almost any budget.  Ready to formulate a winning data analytics strategy?  Contact The Knowlton Group to get started.


  1. Gartner Chief Data Officer Survey


For years, financial institutions have enjoyed the abundance of low-cost deposits. Today, we are in a new era of banking, with deposit acquisition becoming a significant point of focus for most credit unions.

The change represents another consequence of the Federal Reserve’s decision to raise short-term rates which influences the mortgage market, stocks and other corners of the economy. (1) Higher-rates available in money-market funds and other investments are luring consumers to move their money out of minimal interest-bearing accounts.

Adding to the challenge, the competition for acquiring new deposits is coming from not only big banks but from new digital players. In a rising rate environment coupled with a healthy economy, achieving deposit growth goals is one of the best ways to control funding costs while meeting the lending needs of members. Could data analytics be the game-changer to best understand member behavior and motivations to attract, win, and keep new deposits?

Here’s how you can maximize your member data for greater deposit growth:

Segment Your Existing Members

Member segmentation allows the credit union to divide specific target markets and member commonalities into more specific groups. By tracking and measuring important indicators in a member’s life – age, gender, marital status, income, a move, and more – you can segment members into easily targeted groups. This is important for optimizing marketing spend, effective reach and for acquiring deposits. Segmenting your members will help the credit union target the right member for the right deposit programs. Using data analytics, you can successfully segment your members’ data to create personalized, compelling marketing messages that are relevant and allow you to cater to your member’s needs—leading to potential deposit acquisition opportunities.

Create Member Acquisition Programs

Consumers have tons of options when it comes to financial services. Attracting their business requires smart, personalized marketing. Do you know which of your products consumers are more likely to use, or the segments which represent the strongest growth potential for your credit union? Through data analytics you can target the high-growth, high-opportunity member segments, create more relevant messages and product offers and promote your deposit services to the right prospective member, with the right message, at the right time.

Maximize Cross-sell Opportunities

I’ve mentioned before, the cost to acquire new members is ten times more than cross-selling/up-selling to existing members . Cross-selling deposit accounts to existing members is one of the best ways to grow core deposits. Through analytics you can gain a deep understanding of your existing members wants, needs, products and service offers and execute effective cross-selling initiatives. Data analytics helps you determine current product penetration and propensity by member as well as any gaps in products within your existing member-base. An analysis of existing member behavior can lead to efficient cross-sell of products. These efforts can drive an increase in member engagement while ensuring your credit union is maximizing all opportunities to promote high-interest yielding deposit accounts to your existing members.

Execute Retention Strategies to Keep Members

Controlling attrition is a top priority for credit unions. A change in address, marital status, or even a change in job status can lead to a member switching financial institutions. By using data analytics, credit unions can take proactive measures to understand and analyze the factors which might trigger thoughts of attrition in members. Using member data, credit unions can dramatically improve their ability to anticipate member behavior and key life events at an individualized basis. Data analytics is helping credit unions gain greater insights into their member’ needs, preferences and likely behaviors that lead to attrition. This valuable information enables the development of member loyalty strategies that strengthen the credit union’s ability to retain the members that are costly to attain.

Fine-Tune Marketing Messages

With the amount of the data now available, credit unions can create individual messaging to members and prospective members. This is a crucial factor for success in a time when deposit growth is critical. Analytics can empower the credit union to access extremely granular and detailed information on each member and allow marketing departments to send promotions on deposit services to each member segment based upon their specific needs and financial objectives. Emphasizing this personalized approach to marketing will allow members to feel a more personal connection with your credit union.

Deposits fuel revenue and the lending operations of the credit union. Without deposit growth, credit unions could face the challenge of reining in lending or pursuing more expensive funding.

Those credit unions that recognize the strategic importance to focus on deposit growth through data analytics will be the most successful in this competitive environment. Is your credit union looking for support to help maximize your member data? The Knowlton Group can help you devise a plan to put your member data to work.

1. The Biggest Banks Are Gobbling Up Deposits. Here’s Who’s Not. Wall Street Journal April 2108

Why do some data and analytics projects fail, while others go on to produce significant business outcomes? Today, many financial institutions are actively using data analytics to turn their data into actionable and profitable insights. However, the reality is most analytics projects do not always translate into easy success and big wins.  Too often, these projects will hit a plateau, unable to deliver the pot of gold or even a positive return on investment. So why the sub-optimal results?

Since deploying a data and analytics program is a complex process, there are many pitfalls and mistakes that you can make. While this list is by no means exhaustive, below are some of the most common mistakes we’ve seen as financial institutions embark on the data analytics journey.

Running before walking; walking before crawling:

Like most major projects, realistic goals and timelines need to be adhered to for the greatest chance of success.  I call this the crawl, walk, run phase of the data analytics project. The “crawl” phase is where the organization stops living and dying by their Excel VLOOKUPs and starts to use relational databases and common reporting tools.  During the “walk” phase of analytics is where the real analytics can begin. Your staff does not need to go to ten different places to get data for a report.  Near real-time analytics can be developed.  The “run” phase of analytics is where data science and statistical models become fully realized and leveraged.  During this time the data is working in every way imaginable and the organization has the structure, the culture, and the skills to make full use of the data’s potential.

Ignoring KPIs and success measures:

When you’re just getting started, it can be tempting to focus on small wins. However, it’s important to establish metrics like new business opportunities, customer satisfaction, onboarding, marketing, etc. Raw data must be turned into actionable information for it to have any real meaning.  That is why we emphasize the importance of establishing well-defined Key Performance Indicators (KPIs). KPIs are the quantifiable measures that a financial institution uses to gauge its strategic progress. The key is to keep the success measures simple, practical and relevant to the organization.  This is what will help you turn raw data into actionable, useful pieces of information so you can continually refine your KPIs for ongoing success.

Putting technology before strategy: 

When embarking on a data analytics project, one piece of advice is to not let IT drive the program. Many financial technology firms are offering best-of-breed software solutions that have features and functionality that are typically under-utilized or forgotten about in the urgency of deployment. In most cases 60-90% of the product features are often unused despite paying for 100% of the product. Clearly understand and outline the long-term strategic goals of the financial institution to identify and select technology solutions that fit your data analytics goals today and tomorrow. Otherwise, you run the risk of investing in software that doesn’t fit the financial institution’s vision, and the cost of converting later can be significant.

Overlooking data quality:

Data scientists know the importance of accurate and complete data. After all, if the data itself is unreliable, you’ll wind up making invalid conclusions based on your analysis. To avoid that pitfall, it is incumbent to spend the time and effort to diligently prepare and clean the data coming from all sources. This includes a broad range of cleansing such as: incorrect values, typos, aliases, inconsistencies, duplicate entries, outdated consumer information.  Your data need not be (nor will it ever be!) 100% clean to get started with your project.  It is, however, important to establish policies and procedures to identify and clean bad data on an ongoing basis.

Lacking data governance:

Think of data governance as a set of rules for inputting and maintaining data. It is a continuous quality control discipline that governs the overall management, usage, storage, monitoring, and protection of the financial institution’s data. Without dedicated governance processes, overtime, poor data quality will lead to inferior service delivery, reduced employee productivity, missed opportunities, and increased costs.  There are several subtopics in data governance that we have shared previously.  You can tackle data governance in a variety of different ways – just be sure not to overlook it in your analytics initiative.

Underutilizing talent:

Enlist the help of an experienced Chief Data Officer to keep everyone accountable and aligned with the overall strategic business objectives associated with your data and analytics projects.  Whether hiring a full-time executive or an outsourced expert like The Knowlton Group (who can resist a shameless self-plug!), view them as a trusted advisor and strategic partner.  Consultants and data scientists will work with your data analytics teams to pour through the data and train your staff to uncover and act on new opportunities that lead to your desired goals. These experts can advise decision-makers, monitor progress, track success and establish best practices that will position your data analytics project for success.

Underestimating the journey:

A fully-developed analytics program can take 18-to-36 months to deploy in its entirety. Deploying an advanced analytics initiative and building a data culture takes time, and many executives don’t plan for the long-term commitment.  Turning the data generated by consumers into actionable, comprehensive insights is a big task. The initial steps should include establishing short-term and long-term business goals. Once the strategy and business goals are in place, the technology infrastructure needs to have an effective and rigorous process to collect, cleanse, compute and consume the data. Staff needs to be assigned—and with the right skill-set to then use this complex data for discovery of insights and interpret results that lead to business opportunities.

What mistakes has your organization experienced while deploying your data and analytics program?

Next Steps

This list of pitfalls provides just a glimpse of the many mistakes financial institutions make when embarking on their data and analytics journey.   Before implementing any analytics solution, spend the necessary time determining how you would like data to strategically support the financial institution. Focus first on your KPIs and success measures and then explore your technological, operational, and deployment needs. With this discovery completed, you will have the foundation of what will be a working roadmap for your data and analytics journey.

Need expert help planning, designing, and implementing your data and analytics strategies and solutions? Contact The Knowlton Group today by email at to learn more!