The market for data and predictive analytics is growing fast— and for a good reason. According to the Aberdeen Group, companies using predictive analytics enjoyed a 73% higher lift in revenue than companies that do not use this technology (1).

Many credit union executives we work with use data analytics to increase their market share against the competition. They understandably use this member data to focus on gaining new members. Instead of focusing on acquiring new members, we ask, “how can credit unions use data analytics to further engage existing members to improve retention rates?” After all, retaining existing members is often easier and less expensive than onboarding new members.

Enter predictive analytics. Predictive analytics is the use of data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data. The credit union’s goal is to go beyond knowing what has happened to creating predictions of what will happen in the future.

By taking a predictive approach, credit unions can dramatically improve their ability to anticipate member behavior and key life events at an individualized basis. When properly deployed, credit unions can offer the right product at the right time via the right channel, all based on what the data predicts will best motivate the member to act. This enables credit unions to capture the member’s attention and trigger the desired behavior while also increasing loyalty due to marketing messages being relevant and personalized.

How can your credit union use predictive capabilities to provide an appropriate range of services for your members when they experience significant life events? Here are a few considerations as you get started:

Start with segmentation: 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. From there, you can develop targeted marketing campaigns that create relevant, personalized experiences that improve engagement.

Analyze spending and behavior patterns: Studying member product usage and targeting the right product is a task. By employing predictive analytics, credit unions can quickly isolate different member segments to create highly individualized and relevant messages tailored to each member’s profile resulting in a higher response rate. This helps credit unions in targeting the right product for the right member. For example, promoting low interest auto loans properly targeted at a person who has shown a trend of purchasing a new car every three to four years will be happy to receive timely and relevant information on auto loans. These messages can be deployed using different marketing channels such as e-mail, call center, direct mail, mobile banking, etc. based on the members preferred marketing channel.

• Leverage Cross Sell/Up Sell Opportunities: An analysis of existing member behavior can lead to efficient cross-sell of products. By mining existing member product and service mix in conjunction with segmentation, credit unions can identify targeted cross-sell and up-sell opportunities. These efforts can drive an increase in member engagement while ensuring your credit union is the member’s primary financial institution.

• Maintain exceptional service: Members expect their credit union to provide top-notch service. Predictive analytics benefit any decision by providing credit union executives with the tools to forecast changes in a member’s life. From member purchasing likelihood to targeted marketing and up-selling to sales and revenue forecasting, there really is no limit to the potential benefits data analytics tools provide. In many competitive markets, if your credit union is able to provide members with the right products and financial services when they need it most, member loyalty, engagement, and retention will soar.

Shifting Your Focus in 2018

Credit unions that can better manage their member interactions while armed with in-depth and customized knowledge about each individual member will have a tremendous advantage to compete in a fast-paced, competitive market. Shifting to a more analytics-oriented culture to better understand and anticipate member needs is not an overnight task.

If your credit union is seeking outsourced talent, advice and best practices as you enter the evolving world of data analytics, call or email the Knowlton Group today. We will provide you with the tools and action plans to accurately analyze and act upon member and product insights so you can respond to your members’ needs faster than the competition.

 

Sources: 1. Aberdeen Group research group.

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If data isn’t governed properly, your credit union could be at risk. Consider these best practices for a well-managed data governance strategy.

The foundation for a successful analytics program is data. Without accurate, 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 monitor, manage and control data’s integrity becomes even more mission-critical. Establishing a data governance strategy and data quality procedures within the credit union ensures dependable data flows throughout the credit union.

The lack of data governance within the credit union can have serious impacts within every area of the organization. The Data Warehouse Institute estimates that data quality problems cost U.S. businesses more than $600 billion a year.(1) In most credit unions, the volumes of data have multiplied over time and the data governance structure has not kept pace with business change or the credit union’s growth. Many credit unions also rely on legacy systems that cannot be updated or integrated with new sources of data.

In my last piece, I covered why data governance is critical to the credit union’s analytic strategy. Now, we’ll share some best practices on how to create a data governance program through a combination of data committees, corporate policies, and accountability.

Establish a Data Governance Committee: Data governance is not just an IT issue – everyone in the credit union is responsible for maintaining accurate, accessible, and secure data to support the credit union’s business objectives. With the help of dedicated employees, create a trusted data governance committee that is tasked with establishing data review processes and data quality standards. The data governance committee will ensure responsibility, accountability and sustainability of data best practices. They will oversee the preservation, availability, security, confidentiality and usability of the credit union’s data. The data governance committee can be a powerful force for setting the tone for data quality within the credit union and for establishing the internal top-down support to ensure that employees are properly educated and trained on data literacy and how the data is collected and then used.

Develop and Enforce Policies and Procedures: 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. Policies help prevent employees from violating data quality guidelines while helping the credit union meet regulatory requirements. Such policies must include a comprehensive set of rules governing the proper collection, use and disposal of the credit union’s data. Ensuring that everyone in the organization is adhering to policies is important to data’s success.

Set Data Governance Mission and Objectives:  What do you want data governance to accomplish? For most credit unions, the objectives include: enabling better decision-making, reducing operational waste, meeting the needs of current and future members, educating management and staff to adopt common approaches to data issues, reducing costs, and ensuring data quality throughout the organization. Analytics strategies are meaningless if the data powering them is unreliable. Placing data governance at the heart of your data and analytics strategy will ensure quality data translates directly into better business value.

Agree on Key Terms and Definitions: As a best practice, a component to an effective data governance program hinges on the development of strong and consistent data definitions and terms. It is important to data’s success to establish standardized data definitions across the organization that everyone understands and adheres to. The implementation of a data dictionary can be established within the database system, making it a priority to ensure that all data terms and business glossaries are the same. For example, if three different people in your organization are asked “what is a member”, those three people should provide the same, consistent response.

Perform a Data Quality Audit: Data quality audits can be time-consuming, but it is a valuable activity that certifies the data’s accuracy. Technical quality issues such as inconsistent structure, missing data, typos or other errors in the data fields are easy to spot and correct. However, more complex issues should be approached with more defined processes. Starting small in profiling data quality is recommended. I generally advise clients to start with a focus on a very narrow data set — perhaps a certain line of business, or a subset of member data. Make sure you establish a standardized method to share your findings and key steps to correct inaccurate data. Develop a process whereby business users can report data quality issues and then work with the data governance committee to research the error’s source and develop a resolution.

Developing a successful data governance strategy requires careful planning, the right people, ownership and appropriate tools and technologies. The key to an ongoing data governance program is to take an incremental approach, ensure buy-in across all business and IT departments, and hold key business units accountable.

If your credit union needs support in establishing a data governance program, The Knowlton Group can help. We believe that the best data and analytics program starts with a great strategy, a clearly defined roadmap and implementation plan, and a methodology for ensuring data’s accuracy.

Source:
1. Data Quality and the Bottom Line- The Data Warehouse Institute

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For any credit union that aspires to derive insight and business value from their data, the discipline of data governance is essential.

What Is Data Governance?

Think of data governance as a quality control discipline that governs the overall management, usage, storage, monitoring, and protection of the credit union’s data. Without a dedicated governance process, overtime, poor data quality leads to inferior service, reduced employee productivity, missed opportunities, and increased costs.

If you’re in the midst of building a case for greater data governance within the credit union, consider the following payoffs of stewarding the data.

Improved Revenue: Using high quality, clean data, credit unions can accurately analyze the spending patterns and changes in life stage of their members. This allows the credit union to pinpoint the right message at the right time to offer new products, up sell and cross-sell services, and even provide financial advice. Promptly acting on these opportunities will lead to profitable business outcomes.

Better Decision Making: Drawing accurate conclusions from the data is hindered when data is scattered across a variety of departments, processes and applications. This results in incomplete, incorrect and inconsistent information. When data is maintained and managed with proper data governance controls, quality data can be translated into actionable insights that decision makers can use to evaluate opportunities and execute strategies.

Greater Confidence: Data and analytics can be worthless when the data is not properly governed. When your data is accurate, effective, complete and trustworthy, employees and decision-makers will rely on it to formulate strategies for member engagement opportunities, new member acquisition programs, product and service development, and much more.

Improved Member Service: Today’s credit unions must understand what products and services their members might need. What types of investments may interest members? What types of products and services are they looking for? Having accurate and timely information on member preferences and concerns is vital to providing good service and nurturing member relationships.

Competitive Advantage: To address changing member preferences, credit unions need to develop new capabilities. If you can make better decisions because your decisions are backed by data that have been robustly collected, cleaned and analyzed, you can beat the competition by predicting next best product opportunities, new markets and demographics as well as effective marketing and new member acquisition campaigns.

Reduced Expenses: One of the major benefits to governing data is cost reduction. Improved member data and the consolidation of data into a single source can lead to significant operating cost savings. Using the data to find and eliminate wasteful practices, wrong mailing addresses, redundancy and inefficiency, the credit union can save time and money and reinvest that savings into enhancing member service.

Do you have a team responsible for data’s accuracy, accessibility, consistency, and completeness? The Knowlton Group can help your data governance initiatives remain on track as you continue to deliver a return on your data and analytics investments.  Send us an email or give us a call at 860-593-7842 today to learn how we can help.

Stay tuned! Our next article will provide a tip sheet on data governance best practices.

 

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Achieving success with data analytics isn’t just a matter of acquiring robust BI software or hiring a data scientist and teams of analysts. It requires a shift in how the credit union embraces data which requires the participation of all employees at every level. It also requires transparency, solid processes, and buy-in from executive decision-makers.  Transitioning to a genuine data-driven culture still poses a challenge for many organizations.  The process is not an overnight switch, but through perseverance and recruiting the right people to lead the way, your credit union can soon reap the rewards of a data-driven culture.

The following steps are suggestions for how to implement a sustainable data-driven culture– one credit union employees will understand and self-reinforce–along the way.

Hire Right:  The first step in the process is to hire the right people who are visionaries and data-driven.  Recruit data savvy experts and develop a team that will not only report and analyze the data but refine member data into actionable models and provide insights to make more proactive and informed decisions.

Embrace Data:  Becoming data-driven requires leadership to place data at the heart of the credit union. Today, there are still plenty of skeptical executives who prefer trusting their gut instinct rather than data analytics. Executive decision-makers need to believe that insights and opportunities born from the data can improve everything from operations and marketing to risk exposure and member loyalty.

Instill Transparency and Knowledge:  In a data-driven culture, everyone can be a data analyst. The data needs to be transparent and employees need the tools to access it. Spend time and effort winning employees over on the power of the data. Make sure key resources have access to dashboards and analytic tools to extract insights from the data.  Educate employees on the power and possibilities the data can bring to the credit union and how it can actually make their jobs easier. Once they learn how to extract insights from data, encourage them to share their ideas with all areas of the credit union.

Provide Training and More Training: Data reports can be difficult to interpret without proper training. Employees from all departments should be trained in data literacy and how to interpret raw data into knowledge and actionable insights. With proper and ongoing training, employees will use the data analytics tools to their fullest capabilities.  Eventually they will understand why these tools and the insights they bring are valuable and will use it routinely and effectively.

Track, Measure, and Manage: Adopting a data driven culture requires managing what you measure. Many organizations use data to support their decisions instead of driving their actions. But why? After all, data’s value and trackable return on investment comes from the ability to translate data into actionable insights and business opportunities. Select the metrics that support your objectives. Determine what you want from your data, establish a plan, set goals and targets while continually fine-tuning how you measure results.

Communicate the Vision:  This may seem like a simple step, but at times it’s the most overlooked in the process of revamping a culture. The credit union’s mission and vision around data and analytics needs to be communicated continuously. From executive management to the front line, everyone needs to understand why the culture change is happening and what impact it will have on their daily jobs. If employees understand and believe in the vision – rather than fear that the data analytic tools will replace them – the entire credit union will be motivated to use the data and will strive to hit goals and targets.

Let the Culture Shift Begin
Credit Unions must be able to gather, process and analyze data in real-time for better decision-making to remain competitive. Change management is a tough task for many organizations, but, if leadership embraces analytics and communicates the vision consistently across all departments, a data-driven transformation will occur.

If your credit union has hit some obstacles along the journey, it’s best to partner with experts who can develop a strategy and a roadmap for accountability.  This is what the Knowlton Group does exceptionally well.  Our clearly defined strategy, roadmap and implementation plan will 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.

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Warren Buffet famously once said: “Price is what you pay, value is what you get.”  Consider the last technology and software investments your credit union made to keep the organization running.  Are you getting true value from the dollars spent?  Are you maximizing all capabilities these IT investments offer? Have you calculated a measurable ROI?

Many financial technology firms are offering best-of-breed software solutions that have functionality and features that are typically under-utilized or forgotten in the urgency of deployment and day-to-day “firefighting”. In most cases 60-90% of the product features are often unused despite paying for 100% of the product. Ironically, most of these unused features are precisely the reason for selecting the product in the first place.

I call this problem the “ten percent syndrome”. Many financial institutions will purchase software and IT systems yet only use ten percent of its true functionality.  Then in a couple years, management devises a budget for the next new, shiny software investment and will only use ten percent of its functionality. This syndrome repeats itself time after time, draining profits and potentially causing compliance hazards.

Deeply embedded within credit unions is the ongoing search for ways to better serve their members.  Since improving efficiencies and member experiences lead to a greater competitive advantage, many credit unions are investing more on IT and software applications in the pursuit of better business outcomes. However, if these software solutions are not fully leveraged, they often end up being shelved. Waste always inhibits our progress in serving members better, faster or cheaper. Consequently, eliminating waste drives improvement in all three objectives.

Fix the Strategy – Not the Technology

It’s time to focus more on process and strategy rather than purchasing continuously under-utilized software.

Using new software and technology requires change in processes and in the workplace culture – two areas often not considered before implementation.  In many organizations, the teams reviewing technology and its capabilities may not be the same team using and deploying the technology.  This leads to implementation, training, and process challenges later on in the deployment process.

Technology is not the solution to business problems.  Process, ownership, accountability and a defined strategy are the solution.  During these next few weeks as executive management teams review budgets, technology spending and other software investments, consider these steps to ensure you’re spending your IT assets wisely.

 Step 1:  Align the Strategic Technology Direction with the Credit Union’s Overall Strategic Business Direction

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 technology solutions that fit your requirements both today and, more importantly, where you want to be in the future. Otherwise, you run the risk of investing in software that doesn’t fit the credit union’s vision, and the cost of converting later can be significant.

Too often, executive management teams have an unclear strategic direction with regards to their technology 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 by accident.

Step 2:  Appoint an Evaluation Team

Once you have documented and agreed upon the annual strategic direction, identify the capable individuals within the credit union who, given the time and resources, can select an appropriate technology vendor, software upgrade or technology investment. This effort should not be driven from a technology perspective — instead it must be a business-led effort based upon the strategic priorities of the credit union.  Senior managers and employees that represent each of the major business functions can bring broad knowledge of the business, operations and existing technologies to the process.

Step 3:  Commit to a Technology Assessment

A current technology audit is integral to understanding the present software environment and identifying the gaps and overlaps that need to be resolved by new software or the existing, under-utilized software.  The technology assessment should include an inventory of your credit union’s current IT systems, software and hardware, and a review of overall costs. Additionally, you should assess employees’ and management’s overall satisfaction with current technology and systems. Most importantly, an assessment of key business processes and the current environment’s alignment with those processes is a prerequisite for maximizing each software investment’s ROI.

Step 4: Prepare a Needs Assessment

This step defines those issues and needs that are specific to your strategic plan. Every software investment will have its own unique features and functionality, so focus on what’s important to the credit union. This will guide the rest of the decision-making.

Step 5: See the System and Software In Action

Request that each of the software sales representatives make detailed demonstrations of their capabilities. Experienced technology purchasers should go into these meetings armed with information.  It’s helpful for the employee end-users of the technology to attend vendor presentations and be given the opportunity to ask questions.  Request the sales representatives provide a detailed analysis of the various features and functions of their IT solutions and software.

Avoid Buyers’ Remorse in 2018

Once you have selected the right solutions, be sure you fully understand the capabilities and extent of each product. Don’t get stuck trying to make a square-peg process fit into a round-peg software solution.

As the cost to win, retain, and serve your members continues to skyrocket, a true assessment of your technology needs, functionality and expenses could be a major place for cost savings. Reverse the ten percent syndrome and take these above steps to ensure you do not have buyers’ remorse with your next IT purchase.

At The Knowlton Group, we specialize in working with credit unions. We know your challenges, the technology you utilize, and can provide solutions backed by expertise and experience. Contact us today to discuss any upcoming IT or business intelligence purchasing decisions.

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Understanding The Benefits & Problems with Data Lakes

As more credit unions want to up their competitive game through data and analytics, the debate between data warehouses and data lakes continues. While solution providers and analysts line up on both sides of the discussion, understanding the advantages and drawbacks of a data lake can help your credit union determine if it’s truly the best fit for your needs.

To clear any confusion, let’s recap the main distinctions between a data warehouse and a data lake from our previous article. Both serve as data repositories, however, the data warehouse integrates primarily structured data from multiple data sources into one centralized, single-source of truth. It is then made available to run complex queries fast and efficiently. The data lake, on the other hand, offers credit unions the ability to store vast amounts of raw and unstructured data in its native form until it is ready for use when it is then transformed for analytics, reporting and visualization.

The Data Lake Upsides:

Boosts Competitive Advantage: As a tool, the data lake is helping to redefine the way credit unions analyze heaps of unstructured data for business decision-making. With the tremendous increase in competition, the need to analyze and utilize member data from all sources will be crucial. The data lake facilitates quick decision-making, advanced predictive analytics, and agile data-backed determinations.

Converges Data Sources: Data lakes can help resolve the nagging problem of accessibility and data integration. Credit unions can start to pull together massive volumes of data from various sources for analytics or to store for undetermined future uses. Rather than having dozens of independently managed collections of data, you can combine these sources into the unmanaged data lake.

Delivers Fast Results: Data lakes provide a platform to transform mountains of information for business benefits in near real-time. The data extracted from the data lake can be queried for information and analysis and further decision-making.

Reduces Expense: A data lake built in a public or hybrid cloud environment can help reduce some of the cost required to store raw data. Additionally, the data lake can potentially help cut costs through server and license reduction.

The Data Lake Draw Backs:

Lacks Compatibility: The capability of a data lake to be able to store data in a way that it’s constantly retrievable and queried must be built in to the data lake through unique metadata tags. Without these tags, the data lake quickly dissolves into what many have dubbed the data swamp.

Requires Expertise: Data lakes are only as good as the person fishing in them. Someone with extensive skills must be tasked with ingesting the data, cleansing it, analyzing it and acting upon it. A data lake, at this point in its maturity, is best suited for the trained data scientists. To effectively transform the raw data into useful information, it requires the expertise that many credit unions do not have in-house today.

Hinders Security: By its definition, a data lake accepts any data, from any source, without oversight or governance. Data lakes focus on storing disparate data but do not focus on how or why data is governed, defined and secured. Experts agree that data lakes are a target for hackers. Since the technology and security capabilities are still emerging, it could put the credit union at risk and pose compliance problems.

Skews Results: Since the data stored in a data lake is unstructured and has potential data quality issues, the credit union runs the risk of the analytics being misinterpreted, inaccurate or imprecise.

Creates Data Graveyards: The reality for many credit unions is that data lakes are becoming data retention ponds. It is quite possible that the credit union can discover that they are simply just storing heaps of raw data, unable to make use of the data for problem-solving and business growth. Data lakes require solid cleaning and archiving practices. Without implementing a data analytics roadmap for how to use the data and a solid business intelligence strategy, the data lake can quickly become an expensive repository.

Consumes Time: Since data lakes hold mountains of unstructured data they can potentially squander the valuable time of the data scientist if most of their initial efforts are spent preparing and cleaning the data before any analysis can even begin.

Know Your End Goal
Credit Unions should enter any new technology investment armed with questions and answers. In today’s fiercely competitive financial environment, where every single scrap of data matters, it’s important to stay abreast of all the data analytics tools available. However, we caution our readers to do their homework before diving in. Credit Unions should be careful of jumping right into data lakes and using them as the main integration source for analytics. While the vision for data lakes has been focused on making large amounts of data available quickly, the credit union needs to first strategically assess their current and future business goals, consider the pros and cons of the data lake, and then determine the best tool for the job.

Are you looking for more tips and helpful advice on data management? At the Knowlton Group, we believe that the best data and analytics program starts with a great strategy and clearly defined roadmap and implementation plan. Our personalized approach to each engagement ensures that the specific needs and goals of your financial institution are captured for maximum results. Contact us today to get started.

It has been a long journey from the early days of credit union business intelligence solutions. As your credit union leverages the benefits of a data analytics program, you will need all the capabilities required to make it easy for management teams, data scientists, and analysts to store data and extract information of any size, shape, and speed across multiple platforms. When it comes to planning and budgeting for the right tools, it’s important to know what products and tools are available and the differences between them.

Two key terms you may have been hearing:  Data Warehouse and Data Lake. Both have many definitions from various business savvy techies, but let’s dig deeper to help you understand what they are and how they are different.

What is a Data Warehouse?

Credit Unions use reports, dashboards, and analytics tools to extract insights from their data, monitor member transaction activity, and to support decision making. These reports, dashboards and analytics tools are most effective and efficient when powered by data warehouses which store modeled and structured data efficiently to deliver results quickly.

The data warehouse integrates data from multiple data sources including the core, loan origination systems, online banking platforms, CRM systems, and more  into one centralized, single source of truth. The data that is uploaded each day to the data warehouse is then made available to run complex queries fast and efficiently.  Information stored in a data warehouse is historical, spanning member and transaction information that has occurred over time. The data warehouse aggregates and structures information to provide a 360-degree view of your membership including their products, services, online banking utilization, credit and debit card usage and so much more.  With a data warehouse, the credit union can instantly gain insightful information for better decision-making, leading to improved business outcomes.

What is a Data Lake?

Like a data warehouse, a data lake is also a data storage repository. However, a data lake stores raw (both structured and unstructured) data using a flat architecture for storing data. In a nutshell, a data lake is a data storage and processing system where a credit union can place internal and external data that does not fit into a typical data warehouse.  In a data lake, vast amounts of raw data in its native form is stored.  The data lake retains ALL data and keeps it in its unrefined state that is then transformed and defined only when ready to consume.  Since the data lake stores data of all kinds, this allows highly skilled analysts and data scientists to explore the raw data in new ways, helping with projects that have diversified data. The data lake allows for complex algorithms to identify patterns and trends that will power real-time decision-making analytics and business opportunities.

Now that you are more familiar with the key basic differences between a data warehouse and data lake, the next step is to determine your organization’s needs and objectives to identify which one is right for the credit union. Stay tuned for our next article that outlines the pros and cons of a data lake.

The Knowlton Group can ensure your business goals are met with a data strategy assessment and a business intelligence roadmap.  To learn more about how data analytics can help drive your business practices, contact us today.

 

 

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For years, the idea that “big data” could somehow create real business opportunities for banks and credit unions lurked in the background. How could analyzing statistics and volumes of data replace historical information, gut instincts, and industry experience?

Consider this scenario: In 2013, the executive management team at a community financial institution embarked on an ambitious project to answer a simple question: What percentage of all the products and services they offered were actually profitable? Now imagine after extensive research coming to the gut-wrenching conclusion that only 48% of the products were generating a profit!

Fortunately, the management team eventually figured out how to make their products more valuable and profitable on their own and then complementary to business relationships. How? Through an aggressive use of data analytics.

Balancing Instinct with Data

When making critical decisions, sound business experience should certainly weigh into the equation, but, today there is simply too much at stake to discount factual data-backed information. This is especially true with the increased accessibility of data analytics tools and business intelligence software that helps credit unions quickly and easily compile and analyze data through comprehensive dashboards and analytical reports. This gives the credit union powerful data-driven answers, encouraging decision makers to take these insights into account before embarking on a new business venture.

Data analytics tools are extremely valuable in their ability to gather the tremendous volumes of disparate data types from various sources, process them at record speeds, and analyze and use that data for vital knowledge. With new and more powerful tools to harness today’s data explosion, a credit union can use data analytics to control expenses, boost revenue, and provide better member service all while competing locally and nationally.

Consider these basic advantages of relying on data analytics for business decision making:

Better risk management:
Data analytics tools provide the credit union with new insights into their systems, transactions, customers and environments to help avoid certain risks and to quickly detect pattern changes that are potential risk indicators.

Cost-effective marketing: Using data analytics helps the credit union develop more effective marketing programs and lead generation campaigns that can target the right member with the right product at the right time through the right channel. (Learn more about The 4 R’s of Analytics-Driven Digital Marketing) Having a system that allows the credit union to segment, manage, and track activity will enhance the marketing ROI.

Improved Member Service: With the volumes of member data available, the credit union can analyze timely information about purchasing decisions, behaviors and product needs about each of their members—proactively. This translates into improved member relationships and member loyalty.

Greater Efficiency: Data analytics can be used not only for member-facing activities but also for internal efforts. For example, data can be used to analyze and assess internal processes. By measuring key processes, the credit union can reduce inefficiencies and, therefore, streamline operations. This often allows credit unions to achieve greater throughput and performance without requiring additional staff.

Many CEOs and business leaders will admit they rely on intuition to make business decisions. History shows that some tremendous wins have been gained by doing so. However, we are fortunate to be in a data-driven era where advanced data analytic tools are readily available to help us make more-informed business decisions. And, with the expertise of data scientists, we no longer need to rely solely on instinct. Perhaps the harmonious balanced approach works best: making data-informed gut decisions!

To learn more about how data analytics can help drive your business practices, contact The Knowlton Group today.

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As I travel around the country working with financial institutions that are ready to implement a data analytics program, I realize a common denominator throughout them. Many have yet to align their data analytics strategy with their overall corporate/business strategy.

When the business objectives and analytics strategy do not operate from the same playbook, it creates competing priorities, putting the various departments within a financial institution at cross-purposes. Even worse, when these strategies do not blend, the credit union will struggle to see a return on the data analytics investment! If you’re ready to put your strategy into action, here are seven steps to keep in mind as you implement your data analytics initiatives:

1. Start with a Roadmap

The first step is to attain a thorough understanding of the credit union’s strategic business initiatives and ascertain how data analytics will help achieve those objectives. Knowing your business goals today, and in the coming 2, 5 and even 10 years will help the credit union devise a strategic plan and roadmap that ultimately aligns analytics with key business imperatives. During the strategic planning and goal setting phase, make sure key objectives for the data analytics program are documented and communicated throughout the organization.

2. Understand Line-of-Business Needs

Take the time to understand each line-of-business’ needs regarding what information is required from the data and how to act on insights. Establish what systems and processes are needed to drive the flow of data throughout the credit union. Each line-of-business will require different data outputs to hit their set goals. By providing easy access to the data, and a streamlined process to put the data into action, alignment of the overall business initiatives becomes routine.

3. Establish Metrics to Track Performance, Measure Progress & ROI

Tracking and measuring the ROI of an analytic programs 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 opportunities, resulting in an immediate boost to revenue and member 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.

4. Understand the Data & Application Environment

The credit union’s application environment contains a massive collection of resources that hosts applications including, the core, consumer LOS, a CRM system, commercial and mortgage loan origination systems, phone systems, bill pay and many other disparate systems. Understanding where and how data resides in each system along with how data flows through these applications is crucial in prioritizing data integrations into an analytics platform, accomplishing strategic business intelligence goals, and allowing the credit union to become data-driven. Some data resides on-premise while other data sets reside in hosted applications by the credit union’s vendors. It is imperative that this due diligence is performed in advance of any development or software acquisition endeavors.

5. Create an Analytics Committee

When the analytics strategy is shared by the entire team, it is easier to navigate the near-term tasks of planning, goal-setting, and performance management. Consider establishing a leadership committee responsible for defining and driving the analytics program for maximum value. The committee will set teams up for success by demonstrating how to effectively use the system to extract insight and drive action. Once the staff has a better understanding of how to use the data analytics program, they can then focus their efforts on addressing key business objectives.

6. Allow Open Communication

Data analytics isn’t just for your IT folks. From C-level to member-facing staff — all should be able to access, share and see data to monitor performance, challenges or emerging opportunities. Open, transparent communication about the strategy and long-term goals leads to a more aligned team, and prevents information silos. In turn, workplace culture is improved and a collaborative environment is created.

7. Hire a Chief Data Officer/Analytics Officer

By enlisting the help of an experienced CDO/CAO, the credit union is better equipped to reach their business objectives. Whether hiring a full-time executive or outsourced consultant, view them as a trusted advisor and strategic partner. A CDO/CAO will work with your data analytics teams to analyze the data and train your staff to uncover and act on new opportunities that lead to your end goals. These experts can advise your decision-makers to act on immediate opportunities, monitor progress, track success, establish best practices and improve your institution’s overall competitive advantage.

Has your credit union hit success with an aligned business and analytics strategy? If you need support with these key steps, The Knowlton Group can help. Let us create a data analytics strategy and implementation roadmap to ensure your business goals are met and that your analytics investment pays-off.

Today’s A:360 discusses the importance of the “crawl, walk, run” progression when getting started with analytics. Feel free to read the summary of the podcast below or scroll towards the bottom of the page to watch or listen!

Like most major projects and strategies, realistic goals and timelines need to be adhered to for the greatest chance of success. Many organizations may want to immediately start using “big data” and “data science” yet they haven’t even tackled the basics of traditional business intelligence. This is where the crawl, walk, run mentality comes into the picture.

The “crawl” phase is what I would consider traditional business intelligence. This is the phase where the organization stops living and dying by their Excel VLOOKUPs and starts to use relational databases and common reporting tools. Visualization tools (like WebFOCUS, PowerBI or Tableau) are implemented allowing the organization to consume information in a fashion other than a spreadsheet.

The “walk” phase of analytics is where “real” analytics can begin. Data governance has been set in place so key definitions and terms are understood by all within the organization. Data is no longer stored and reported on in silos. Data transparency and data integration allow the organization to see a 360-degree view of the member. At this phase, your staff does not need to go to ten different places to get data for a report. Near real-time or real-time analytics can be developed. Questions that are asked are not “what did we do last month?” but “what will we do next month?”.

The “run” phase of analytics is where data science and statistical models become fully realized and leveraged. This is the point where your organization may employ or work with outsourced data scientists. Statistical models are developed based on your underlying data structures to do any number of things including:

  • Advanced Member Attrition Analysis – who is likely to leave the credit union (or no longer use us as their PFI) and when?
  • Refined Risk Modeling – is using credit score really the best way to manage risk and maximize NIM? Can we layer in other attributes in the origination process to underwrite traditionally “riskier” loans without impacting our risk profile?
  • Advanced analysis of card transaction data to identify opportunities for improved interchange income and greater utilization (i.e. top of wallet) by the member.

There are about a hundred other examples of “run” phase analytics that could be leveraged. The idea, however, is that in the “run” phase the data is working for us in every way imaginable. At this phase, the organization has the structure, the culture, and the skills to make full use of the data’s potential.

Watch and Listen

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Listen to the Podcast

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