When used properly, data can be the fuel that propels organizations towards growth and success.  Most credit unions understand that the data that has been collected over the years can be one of their most valuable assets to improve member experiences, boost efficiency and revenue, and make faster, more accurate business decisions.

The problem we all face is having too much data that may not be properly compiled, cleansed, and organized. In other words, it isn’t information — it’s just data and raw numbers.  So, how do you remove the data clutter and get better at data analytics that transforms unorganized data into insightful, actionable information?

These tips will help you and your team transform the mountains of data into steps to improve your data analytics strategy:

Clearly Define Your Key Performance Indicators (KPIs)

Having clearly defined organizational KPIs gives your analytics efforts a guiding direction.  There are thousands of different analytic opportunities your team could explore.  But, if they don’t align with the organization’s strategic objectives (which the KPIs should directly measure), then is that effort really worth it?

If a KPI is to lower the average cost of funds, then your analytics initiatives shouldn’t focus on how to target more CD deposits but gaining greater engagement in core deposits.  This strategic-to-operational alignment needs to exist in your analytics team’s efforts to maintain clarity and focus.

Define Business Objectives

The first step in the process of turning data into information is asking questions and defining your business objectives. Data for the sake of data is meaningless. Start by outlining some clearly defined business goals, use-cases and objectives around your data analytics and analysis. Figure out the questions you would like the data to answer.

For example, how many members have obtained an auto loan or mortgage in the last 24 months but do not have an active checking account? Why are so many loan applications falling off before being approved?

To get the best results out of the data, a question (or series of questions) needs to guide your starting point.

Avoid Confirmation Bias

Many organizations use data today to add support to a belief they already hold.  When viewed from a biased perspective, it is easy to manipulate an interpretation to match a previously held belief.

Successfully embedding analytics in the decision-making process requires you to eliminate confirmation bias at the outset.  Treat each analytics use case with the scientific method approach – establish a baseline hypothesis and objectively measure whether that hypothesis is true or false.  Much organizational learning will come from this rigorous and honest approach to data.

Don’t Let Perfect Get in the Way of Good

Many organizations that are more immature in the analytics journey fixate on superficial data quality issues that arise.  Things like bad social security numbers or inconsistent addresses drive most executives crazy.  But if that data is only 90% or 95% accurate, will your decision change substantially with 100% accurate data?  The answer is almost always, no.

Strike the right balance between data quality and data integrity without chasing some ideal perfection that will never be attained.  Sometimes good enough is good enough.

Successful Analytics Programs are not Grassroots Efforts

Executive support is one of the leading reasons why some analytics succeed, and others fail.  Support for using data as decision-making tool needs to spread from the top of the org chart down.  Without this clear top-down buy-in, mid-level managers will not be required to provide data-driven justification for their recommendations.  This then proliferates further into the organization and minimizes the importance of using data.

Successful analytics initiatives requires executives to hold their teams and each other accountable for bringing data-driven discussions to the table instead of instinct-driven ideas.

 

There’s no escaping the increasing reliance on advanced data analytics. Your credit union’s data is a critical asset you have that can anticipate member needs and allow you to execute personalized interactions.

How do you make a data analytics strategy work for you even more in the coming year?What do you need to include in your 2020 initiatives?

How do you plan a strategy that delivers insights that can be turned into tangible business outcomes – insights that help you increase your credit union’s performance and drive greater efficiencies?

Most failed data analytics strategies can be traced back to the fundamental error of focusing solely on the technology and not the credit union’s vision, mission and strategic goals.  As you plan for your 2020 Data Analytics Program, consider these key action plans to ensure a successful data analytics strategy and outcome:

1. Identify organizational strategic priorities and align the technology solution accordingly.
2. Establish a data warehouse to centralize data integrated from several applications.
3. Identify and plan for tangible and measurable use cases.

Business Objectives, Strategy and Technology Alignment:


Too often, executive teams have an unclear strategic direction with regards to their data analytics investments and how they align with their corporate objectives. In these cases, I recommend that the team take a step back and work on clarifying the overall strategic direction and outlook. Without this direction as a guide, the credit union’s technology decisions end up driving the overall strategic direction instead of the strategy driving the technology.

Your data strategy and strategic priorities should include:

  • A strategy defining your how analytics will help drive the credit union’s strategic priorities
  • A tactical roadmap describing how you will accomplish the analytics goals outlined
  • Plans, tactics, and processes to develop analytics skills and create a data-driven culture that embraces the daily use of data analytics

Clearly understand and outline the long-term strategic goals of the credit union (i.e. growth, branching, new products and services, etc.) to identify and select data analytics solutions that fit your requirements both today and, more importantly, where you want to be in the future. Otherwise, you run the risk of a costly blind spot: investing in software that doesn’t fit the credit union’s vision, and the cost of converting later can be significant.

Implement a Data Warehouse:

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.  This single source of truth is the repository for all the data that has been collected and integrated from multiple sources across the organization. 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.

Measure and Monitor Success:

Tracking and measuring the ROI of an analytics program allows the credit union to re-prioritize goals and take corrective action steps along the way. By measuring the action taken from the data’s prescriptive recommendations, the credit union can focus efforts on the most promising and revenue-driving opportunities, resulting in an immediate boost to earnings and member service and resources. Once key data segments are identified, efforts should be focused on the campaigns that delivered the greatest ROI and devise a set of recurring best practices for future campaigns.

Some tangible and measurable data analytics use cases The Knowlton Group clients have experienced include:

 Effective Marketing and Segmentation:

Deeper, data-driven member insights are critical to tackling challenges like improving member conversion rates, personalizing campaigns to increase revenue, predicting and avoiding member churn, and lowering member acquisition costs. Deploying member segmentation such as geographic, demographic, behavioral and other categories will help your organization target the right products and services and help reduce member churn. Using data analytics, you can successfully segment your members data, and invest more resources into those members who are most likely to respond to your product offerings.  You can then further refine your messaging and product/service offering to retain their loyalty.

 Enhance Member Experience:

Analytics also has the potential to identify the needs of new and existing members, so the credit union can effectively match the best products for them. For example, the credit union can use call center contact information, new member questionnaires and other behavioral attributes to predict what type of products would be the best fit for a new and existing member.  By matching members with the right products and services, the member is less likely to have complaints or start looking for better services elsewhere.

Elevate Efficiency:

Advanced data analytics provides credit unions with intelligence to make better, faster and smarter decisions. This knowledge leads to reducing duplicative systems, manual reconciliation tasks and redundant information technology costs.

 Improve Analysis of Transaction Data:

Your members’ transaction data is a goldmine of information and opportunity.  Deploying transaction categorization and classifications allows for the combining of ACH, Debit Card, and Credit Card transactions into a single view.  You get clean and standardized merchant information to gain the most value out of the data.  And you can identify where your members are spending their money and to which competitors’ payments are being made.  By categorizing member data into tiers, you have the greatest opportunity for deep analysis of transaction behavior.

Refine Member Engagement

Utilizing a member engagement analytics model, the credit union can assign an engagement score to each member based on all member activity.  It will enable you to separate members into defined segments based on their engagement and can create recommended action to take for each segment so you can continuously improve member engagement scores.

 The Right Guidance Leads to Success

Looking for help with developing your data strategy and plan for analytics?  The Knowlton Group is staffed by resources with both extensive technical analytics skills and decades of a line-of-business strategic leadership knowledge.  We work with many credit unions– helping them understand the full impact of data analytics to their business model, define a compelling data analytics strategy and ultimately provide results. From strategy, to conceptualization to full implementation, we are ready to make your credit union a data-driven organization. Contact us today.

Many credit unions are starting to see returns on their data and analytics initiatives while improving the decision-making process across the organization. But despite some standout success stories, I still witness far too many failing data initiatives. Begging the question…why do so many data analytics initiatives fail?

Consider these roadblocks to success, and how your credit union can avoid them!
1. Too big of an initial scope.
Very large goals have a downside. They can set us up for failure if we set too lofty of an initial scope to achieve, or if we don’t break the focus down and work towards each goal systematically. Start with a clear idea of exactly what you want to do with the data from a business perspective that will drive value and growth. Some common initial use cases include: attract new members, improve member onboarding, improve member engagement, reduce member attrition, core deposit growth, risk analytics and process improvement.

Analytics initiatives can quickly grow out of control since discovering value from data prompts wanting more data. Pick an initial goal and scale your efforts and focus around a refined list of clear objectives.

2. Heavy reliance on the software solution:
Industry data warehouse products and other off-the shelf solutions shouldn’t be viewed as the silver bullet that will magically solve all data issues.

For successful initiatives, data analytics needs to become a business-driven, not an IT-driven, journey. In many credit unions, the teams reviewing the technology and its capabilities may not be the same team using and deploying the technology. This leads to implementation, training, and process challenges later in the deployment process.
Technology is not the solution to business problems. Process, ownership, accountability and a defined strategy are the solution. Decisions around your analytics initiative should revolve around business goals and objectives rather than a technology or software solution.

3. Lack of internal talent:
Credit Union leaders know that if you want strategic execution, you need the right people and teams. To avoid this fixable problem, create a roadmap that gradually builds the skills, talent, and responsibilities that the credit union will need now and in the future. But before staffing up, you need to take a step back and look at what you want to do with your data, and then assign key roles and responsibilities for data collection, management, and analytics.

As the credit union grows and looks to remain competitive, there’s an obvious need to hire the right data talent who are skilled in analytics, who can interpret data, and make recommendations that offer tangible business value. In a previous article I broke down the responsibilities of data talent and key roles including: Chief Data Officer, Data Scientist, Data Analyst, ETL Developer more. Create a team that strikes the perfect balance between business and technology with the right blend of strategic thought and a tactical mindset.

4. Absence of executive support.
While data analytics has gone mainstream, the C-suite and senior leadership needs to drive the cultural changes that will empower utilization of analytics. Successful analytics initiatives require a shift in how the executive management embraces data. There are many steps you can take to foster a sustainable data-driven culture– one credit union employees will adopt and self-reinforce along the way. Becoming data-driven means that leadership should place data at the heart of the credit union.

Executive management should consistently foster the idea that the insights and opportunities born from the data can improve everything from operations and marketing to risk exposure and member loyalty. What gets measured gets done, and this can only be reinforced from the top down.

If your credit union has hit some obstacles along your analytics journey, it’s best to partner with experts who can develop a strategy and a roadmap. The Knowlton Group specializes in helping credit unions navigate through the analytics journey.

Let’s transform your organization into a data-driven credit union. If you are ready to become data-driven, send us an email or give us a call at 860-593-7842.

Data Analytics is becoming the main driver of innovation in the financial services industry. A recent report shows that data analytics investments in the banking sector totaled $20.8 billion dollars in 2017 and will certainly continue to rise as credit union executives leverage the wealth of potential that utilizing consumer data and developing successful, sustainable data strategies enables.

While more and more credit unions are realizing the value and future potential of data analytics, they are still grappling with some barriers, including: lack of in-house talent, how to appropriate their analytics budget, and formulating a data analytics strategy that sticks.

Plotting the Best Course

In my years of consulting credit unions (and work with organizations in the healthcare, retail, distribution, and government sectors) to help maximize the value of their data, one common thread I see among many is the lack of a strategy and a road map that plots the organization’s best course.  What is a data analytics strategy?

The strategy allows your organization to establish goals from the starting point to provide direction, alignment and a clear path to success. I’ve reviewed several of the key benefits of creating a strategy in a past post. Simply put, your strategy will help you create a solid plan, determine your objectives, allow you to bridge the talent gap, communicate the data analytics objectives and mission to your team, set and measure benchmarks, assess technology capabilities and data quality, and so much more.

Additionally, having a data strategy ensures that data is managed and used as an asset and not simply as a byproduct of your organization’s processes. By establishing common methods, practices, and processes to manage, use and share data across the credit union in a consistent way, a data strategy ensures that the goals and objectives to use data effectively and efficiently are aligned.

Making a Data Strategist

Often, I see credit unions attempt to formulate a data strategy internally using two approaches.  The first approach is to assign the task of establishing the data strategy to a line-of-business head. Perhaps, a Chief Lending Officer or Chief Marketing Officer – someone in a strategic position that sees the big picture for the credit union.  The second approach is to pass it off to IT or another technologist.  This individual tends to build the strategy from the ground-up – focusing on the tactical and technical challenges without necessarily knowing the full strategic picture.

The problem with the first approach – using a line-of-business strategic leader – is that these individuals rarely understand the tactical and technical aspects of day-to-day operations that must be factored into any data strategy.  They might be quite adept in their LOB (i.e. marketing, lending, retail, etc.), but they will struggle to adequately understand how the daily use of data and processes must be addressed by a data strategy.  The outcome from these individuals tends to be a very strategic and conceptual deliverable for what data analytics could do for the credit union – rarely does it address the how.

The flaws of the second approach – using a technologist or IT resource to formulate the data strategy – is that these individuals do not have strategic insight of the credit union nor the enterprise-wide perspective that is necessary.  They may know certain systems or processes inside and out – valuable, no doubt – but lack the big picture for how integration prioritization, deployment and change management planning, and other more strategic decisions need to be factored into the establishment of the strategy.  These individuals tend to place too much focus and emphasis on the technology decisions and not enough focus on other critical aspects that a data strategy entails.

What’s The Best Resource/Team to Create a Data Strategy ?

The best individual or team to establish your credit union’s data strategy is one that balances the strategic business needs of the organization with the technical and tactical requirements that must be addressed.  The best team/individual understands that the technology choices you make are no more critical than how you handle cultural change management throughout the analytics solution implementation.  Striking the perfect balance between business and technology with the right blend of strategic thought and a tactical mindset are key.

The catch? I rarely see this work using only the credit union’s internal resources.  Every vendor has a “analytics solution” now and every company that does anything in consulting or IT has a “data strategy service”.  It’s difficult for individuals within an organization who lack the deep understanding of the analytics solution and service marketplace to formulate the right data strategy that isn’t heavily dependent on sales material alone.

Unfortunately, many data strategy service offerings are led by either line-of-business strategic leaders (approach one from above) or by pure technologists (approach two).  Just like this doesn’t work within a credit union, it certainly doesn’t work with your consultants.

Find a firm that has the right resources from both the technology/tactical realm as well as those with deep strategic line-of-business expertise.  Only with that blend of skills will you yield a data strategy that can guide your credit union to both short-term and long-term analytics success.

Looking for help with developing your data strategy and plan for analytics?  The Knowlton Group is staffed by resources with both extensive technical analytics skills and decades of a line-of-business strategic leadership knowledge.  We’ve worked with many credit unions in the past by helping them understand the full impact of data analytics to their business model, define a compelling data analytics strategy and ultimately provide results. From strategy, to conceptualization to full implementation, we are ready to make your credit union a data-driven organization. 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.

Sources:

  1. Gartner Chief Data Officer Survey

 

It’s a harsh reality.  Many U.S. consumers are struggling financially.  According to the Federal Reserve Board, four in ten Americans can’t cover a $400 emergency expense.  As consumer debt rises and savings rates decline, credit unions are facing a growing issue and opportunity: how to quickly address financial wellness among members.

Consider these staggering stats: (2)

  • 70% of Americans live paycheck to paycheck
  • 24% of pay is spent on non-mortgage debt such as credit cards
  • 42% are living with no retirement savings

Data & Financial Health
Managing finances is one of the greatest areas of concern for many Americans. Fortunately, there is a significant opportunity for credit unions to develop analytics-driven programs that can spot those at-risk members and help provide solutions through financial education and special arrangements.  The first step begins with the credit union analyzing member data to recognize those members encountering financial hardships and then provide temporary assistance to help.

Here are some ways credit unions can maximize data analytics to guide members to better financial health:

1. Create data-driven profiles and member segmentation. In order to best determine who each individual member is, what their needs are, and how to best engage them, leveraging and analyzing member data is the first step. A combination of demographic data along with financial behavior-driven patterns that can be segmented from financial account and transaction data (like income, spending patterns, direct deposit data, and loan debt) will ultimately be the most helpful when segmenting members and compiling profiles. By profiling high-risk members, the credit union can know who specifically needs financial education tools and guidance.

2. Tracking FICO Score Migration. FICO Scores can reveal much about members and borrowers financial wellness. Many credit unions do not track the migration of credit scores for a given individual.  By saving each members’ FICO score with a date stamp, the credit union can extrapolate a potential financial wellness concern.  Factors that are considered in making FICO scores are changing. There is more emphasis on trends in someone’s credit history than before. Now consumers will be rewarded for making larger payments and getting rid of debt, but those who are accumulating more debt will be penalized (even if they are making the minimum payments).

3. Spot and track behavioral changes. Changes in deposit and ACH patterns are another tell-tale sign of a change in individual’s cash flow. Whether faced with unemployment, or a business is struggling and on the verge of bankruptcy, these deposit trends and spending changes can be analyzed through your CU’s analytics program. If the trend persists, this is an opportunity for the credit union to reach out and offer financial wellness tools and support before the situation becomes more dire.

4. Understand members’ debt-to-income ratio. According to Smart Assets, any debt to income ratio higher than 43% suggests a risky borrower or someone in need of financial wellness education. For those members who use the credit union as their primary checking account, the CU has an opportunity to monitor ACH payroll income, utility, cell phone, loan payments and other expenditures. Analyzing members’ debt-to-income ratio the credit union can determine how well certain members manage monthly debts and who could potentially struggle to repay loans.

Using Data for Good

Thanks to today’s data analytics and AI capabilities, credit unions not only have the potential to get an instant snapshot of members’ financial health, but they can offer personalized advice or guidance on how specific members can improve their spending and savings.

The credit union can then create special arrangements to help those at-risk members such as:

  • Postponed payments
  • Loan restructuring or change in interest rate
  • Temporary overdrafts or line of credit
  • Debt consolidation
  • Access to term deposit accounts
  • Refinance options

Make it a goal to explore members’ attitudes toward financial matters, determining their awareness of financial management, and their willingness to use financial wellness tools.  This creates a tremendous opportunity for the credit union to become a trusted advisor and to create services centered on helping members achieve financial peace.  As member-owned community cooperatives, credit unions are perfectly positioned to use analytics for good and improve the financial wellness of all their members.

If your credit union needs assistance on utilizing data analytics to help those members in need of financial wellness support, contact the Knowlton Group.

Sources:

1. CNN Money: 40% of Americans can’t cover a $400 emergency expense

2. Top Money Statistics 

3.  Fortune: Why Your Credit Score Could Soon Go Up

4. Smart Asset