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. 1
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.
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.).
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.
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.
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?
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 email@example.com to learn more!
Here is a surprising statistic I recently read: more than 90% of strategic plans are not successfully accomplished with 67% of failed plans attributed to a breakdown in execution. (1)
I’m a firm believer that strategy without execution will fail and that execution without strategy will also fail. But few organizations have figured out the antidote to close the gap—especially when formulating plans to become data-driven. For those financial institutions ready to implement analytics initiatives, success requires a top-down approach. You should focus first strategically, from a higher level, before you start focusing only on the operational, the technology and some of the tactical components that analytics requires.
Sounds simple, right? If only.
The antidote requires masterful integration and alignment and deployment of high-level goals down to more tactical objectives. Key strategic leaders need to make a top-down commitment to the implementation of data and analytics through communication with the gatekeepers of key operational processes, constant training, reinforcing a culture of accountability, and doing all these things in a manner consistent with a shared long-term plan.
So, how does this correlate to credit unions and community banks working to implement a successful data analytics program? Start with the end in mind by crafting a roadmap to guide you along the journey. These key steps will help navigate your course:
Step 1: Determine your objectives:
The first step in crafting your data analytics roadmap is to clearly understand your financial institution’s strategic business objectives and outline how data analytics will help achieve and/or measure progress towards those objectives. Knowing your strategic business goals now (i.e. deposit growth, opening new branches, new products, digital channel alignment, etc.) and in the coming years will help your organization devise a living, breathing roadmap that aligns analytics with key business objectives.
Establishing your goals from the starting point will provide direction, motivation and a clear path to success.
Step 2: Create a long-term budget:
In budgeting for your BI initiatives, remember that this is not a one-time purchase. A successful data analytics program requires continuous investment as the data needs of your FI grow. In most BI/data strategy projects, plan for a minimum of 18-36 month roadmap. Planning and developing the implementation this way ensures greater success from a development perspective but also allows time for cultural shifts in the organization to take place.
Step 3: Build awareness:
Once you have clearly outlined the long-term strategic goals, during the planning and goal-setting phase, make certain objectives for the data analytics program are documented and communicated with all personnel who will be involved in the initiative. All too often, I’ve seen business and IT leaders develop their own priorities and silos which is a large reason why so many data analytics projects fail.
One of the first orders of business is to ensure the entire organization understands and is aware of why you are building an analytic-driven organization and how it will support the overall business and growth goals.
Step 4: Appoint a committee
Once you have documented and agreed upon the strategic direction, identify the capable individuals within the FI who, given the time and resources, can select an appropriate technology vendor, software upgrade or technology investment for your data analytics program. 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 FI. 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 table.
Step 5: Bridge the talent gap
Too often analytics projects fail due to lack of resources or the right analytics talent. Answer critical questions such as: “Does our organization have the right technical team and data savvy talent to achieve the goals we’ve established?”
Analytics projects often require different skill sets especially with some of the new tools and technologies that are available. Drill down and make sure you have the right people in your organization to transform data analytics into profitable insights and actionable information.
As your organization begins to fully leverage data and analytics for decision-making, key staff such as a chief data officer, data stewards, and a data governance team will become increasingly important.
Additionally, when you start combining business, IT, data, and corporate strategy issues all on the same project, you need clear and experienced leadership. Recommended the organization hire experienced outside consultants and third-party partners that can help assess your staff, technology capabilities, and readiness before you launch any data analytics program.
Stay tuned as we provide the final critical steps in deploying your data analytics roadmap.
Does it sound complex so far? Yes, data analytics can be complex especially if you don’t have a roadmap in place to guide your strategy. But, if executed well, analytics systems can have an enormously positive impact for your organization.
Still skeptical? The Knowlton Group can help. Our expertise, years of working with FIs on assessing and implementing a proven data analytics strategy, can work for you.
Contact us today to learn how!
(1) The Balanced Scorecard, authors David Norton and Robert Kaplan
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.
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.
1. Data Quality and the Bottom Line- The Data Warehouse Institute
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.
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.
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.