- Key qualities that attribute to a great data analytics solution.
- Determining the support criteria you should receive during and after implementation.
- Strategies to best analyze partner track-record, industry expertise, product customization, and pricing/fees.
Archive for category: Leadership
There’s no escaping the increasing reliance on advanced data analytics. Your credit union’s data is a critical asset you have that can anticipate member needs and allow you to execute personalized interactions.
How do you make a data analytics strategy work for you even more in the coming year?What do you need to include in your 2020 initiatives?
How do you plan a strategy that delivers insights that can be turned into tangible business outcomes – insights that help you increase your credit union’s performance and drive greater efficiencies?
Most failed data analytics strategies can be traced back to the fundamental error of focusing solely on the technology and not the credit union’s vision, mission and strategic goals. As you plan for your 2020 Data Analytics Program, consider these key action plans to ensure a successful data analytics strategy and outcome:
1. Identify organizational strategic priorities and align the technology solution accordingly.
2. Establish a data warehouse to centralize data integrated from several applications.
3. Identify and plan for tangible and measurable use cases.
Business Objectives, Strategy and Technology Alignment:
Too often, executive teams have an unclear strategic direction with regards to their data analytics investments and how they align with their corporate objectives. In these cases, I recommend that the team take a step back and work on clarifying the overall strategic direction and outlook. Without this direction as a guide, the credit union’s technology decisions end up driving the overall strategic direction instead of the strategy driving the technology.
Your data strategy and strategic priorities should include:
- A strategy defining your how analytics will help drive the credit union’s strategic priorities
- A tactical roadmap describing how you will accomplish the analytics goals outlined
- Plans, tactics, and processes to develop analytics skills and create a data-driven culture that embraces the daily use of data analytics
Clearly understand and outline the long-term strategic goals of the credit union (i.e. growth, branching, new products and services, etc.) to identify and select data analytics solutions that fit your requirements both today and, more importantly, where you want to be in the future. Otherwise, you run the risk of a costly blind spot: investing in software that doesn’t fit the credit union’s vision, and the cost of converting later can be significant.
Implement a Data Warehouse:
The foundation for a successful analytics program is data. But, without accurate and quality data, it’s nearly impossible to make informed business decisions. As data becomes an even greater asset for the credit union, the ability to store large amounts of complex data in a unified, central database, known as a data warehouse, is critical. This single source of truth is the repository for all the data that has been collected and integrated from multiple sources across the organization. Everyone in the credit union is using the same data derived from the same source, which leads to quick, easy access to accurate data and better decision-making.
Measure and Monitor Success:
Tracking and measuring the ROI of an analytics program allows the credit union to re-prioritize goals and take corrective action steps along the way. By measuring the action taken from the data’s prescriptive recommendations, the credit union can focus efforts on the most promising and revenue-driving opportunities, resulting in an immediate boost to earnings and member service and resources. Once key data segments are identified, efforts should be focused on the campaigns that delivered the greatest ROI and devise a set of recurring best practices for future campaigns.
Some tangible and measurable data analytics use cases The Knowlton Group clients have experienced include:
Effective Marketing and Segmentation:
Deeper, data-driven member insights are critical to tackling challenges like improving member conversion rates, personalizing campaigns to increase revenue, predicting and avoiding member churn, and lowering member acquisition costs. Deploying member segmentation such as geographic, demographic, behavioral and other categories will help your organization target the right products and services and help reduce member churn. Using data analytics, you can successfully segment your members data, and invest more resources into those members who are most likely to respond to your product offerings. You can then further refine your messaging and product/service offering to retain their loyalty.
Enhance Member Experience:
Analytics also has the potential to identify the needs of new and existing members, so the credit union can effectively match the best products for them. For example, the credit union can use call center contact information, new member questionnaires and other behavioral attributes to predict what type of products would be the best fit for a new and existing member. By matching members with the right products and services, the member is less likely to have complaints or start looking for better services elsewhere.
Advanced data analytics provides credit unions with intelligence to make better, faster and smarter decisions. This knowledge leads to reducing duplicative systems, manual reconciliation tasks and redundant information technology costs.
Improve Analysis of Transaction Data:
Your members’ transaction data is a goldmine of information and opportunity. Deploying transaction categorization and classifications allows for the combining of ACH, Debit Card, and Credit Card transactions into a single view. You get clean and standardized merchant information to gain the most value out of the data. And you can identify where your members are spending their money and to which competitors’ payments are being made. By categorizing member data into tiers, you have the greatest opportunity for deep analysis of transaction behavior.
Refine Member Engagement
Utilizing a member engagement analytics model, the credit union can assign an engagement score to each member based on all member activity. It will enable you to separate members into defined segments based on their engagement and can create recommended action to take for each segment so you can continuously improve member engagement scores.
The Right Guidance Leads to Success
Looking for help with developing your data strategy and plan for analytics? The Knowlton Group is staffed by resources with both extensive technical analytics skills and decades of a line-of-business strategic leadership knowledge. We work with many credit unions– helping them understand the full impact of data analytics to their business model, define a compelling data analytics strategy and ultimately provide results. From strategy, to conceptualization to full implementation, we are ready to make your credit union a data-driven organization. Contact us today.
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.
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 protected] to learn more!
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
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.
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.
Today’s A:360 discusses some of the common pushbacks that I often hear surrounding data, analytics, and becoming a data-driven organization. In this podcast, I’ll dispell some of these common pushbacks and explain how you can overcome these misconceptions and challenges.
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Hey everyone. Welcome to today’s A:360. My name is Brewster Knowlton, and today we’re going to be talking about the common reasons and common misconceptions that are holding you and your organization back from becoming data-driven.
One of the most common misconceptions I hear about becoming data-driven is that it costs too much money. I hear all the time that building a data warehouse costs millions and millions of dollars. But that’s simply not true for the community banks and credit unions that are in that mid-market in terms of business size.
If you’re a large company (Fortune 500, Fortune 1000 or somewhere in that scope or scale), then absolutely it’ll probably cost you that much. However, for the mid-market organizations (most credit unions and community banks) you’re not even coming anywhere near that number in terms of the initial buildout. It’s simply not true that becoming data-driven costs too much money. Especially for a data warehouse, there are companies out there (one that we partner with for credit unions is called OnApproach that have pre-built data warehouse solutions. With them, you’re not building from scratch, and it can save you a tremendous amount of money in the long run.
The next common misconception that I hear is that data warehouses and analytics projects are way too big of a project. And that’s true – if you try to do everything at once.
Would you implement a new loan origination system, a new core, a new online banking platform and merge with another organization all at the same time? Probably not. And, if you did, those would be extremely large projects to tackle at once and would probably be unbearable. Data warehouses are unbearable if you do too much at once. However, if you break it down into phases and break it down to an iterative process, building a data warehouse or building an analytics platform is not too big of a project. It simply has to be approached the right way.
Another common misconception is when people tell me there isn’t enough time to focus on analytics. They’re too busy. They can’t do it.
The average billion-dollar credit union that we work with has between 45 and 65 third-party applications. And, because of the disparate nature of this data, what that means is that there are thousands and thousands of man-hours that could be automated with a better data and analytics platform.
So, if we could get you back 5,000 hours (which has a significant time-cost associated with it) would there be enough time then? Or, think about what the value of having those people get that time back would be. Whenever I hear that there isn’t enough time to focus on analytics, that is just either a misconception or [making analytics] a lower priority.
There is enough time. In fact, you will get more time if you invest in these projects.
This next one is probably my least favorite excuse. It’s an issue that I get when discussing data and analytics with people they often say, “The way that we’ve always done it has worked, so why would we need to change? Why bother? Why should I invest in this data and analytics stuff? We’ve been doing just fine.”
In the past, that logic might have prevailed. But look how much the financial industry’s landscape has changed in just the past five years. You have peer to peer lenders. You have peer to peer payment mobile apps like Venmo. You have 100% digital banks. You have a very different landscape, and the way that those vendors – those non-traditional competitors – are successful is through an investment in analytics. And they have a lot less data about your customers than you do!
Saying “The way we have always done it works” is not going to be true in the constantly changing future environment. It requires data and analytics to be innovative and adaptive.
The last common misconception or excuse that ends up holding organizations back from becoming data driven is when they say that, “We don’t have the right culture. We don’t have a culture for using data, so why would we invest?”
That’s sort of a tautology, isn’t it? If you don’t have a data-driven culture, then you don’t have a data-driven culture. Right? It’s obvious. But like anything else, you have to develop that competency. You don’t just step into a car and automatically know how to drive. You have to learn. Therefore, as part of your analytics project, you have to take the right time to promote and grow a culture that supports, trusts, believes in and uses data and analytics. (We have a couple posts and podcasts that talk about that.)
Regardless, that cultural shift is key. Using the statement that “We don’t have the right culture for data and analytics” as an excuse to not invest in analytics presents a natural paradox. You have to grow that culture in order to make use of analytics, and every organization goes through this challenge. Nobody naturally has, unless they built it from the ground up when starting the business (see Uber, Netflix, etc.), an intrinsically data-driven organization. That is part of your analytics project, and you too can grow the proper culture to successfully deploy and leverage analytics.
Again, these are the common misconceptions or excuses that I hear for why organizations are not data-driven and what’s holding them back:
- They say that becoming data-driven costs too much money. False.
- They say that data warehouses are way too big of a project. False.
- I hear that there isn’t enough time to spend on analytics. Surprise…also false!
- My least favorite: “The way we’ve always done it works.” Well, in the past, yes. In the future, probably not.
- They will say, “We don’t have the right culture”. You can have the right culture. You just have to build it. It is not going to inherently exist.
That’s it for today. Thanks again for tuning in to today’s A:360.
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Most are familiar with the stages of grief that individuals go through during the grieving process. Similarly to grief, there are also stages an individual goes through when faced with change. Understanding the psychological steps involving organizational change management can help managers better assist employees through the change process.
Change vs. Transition in Change Management
One of the most important concepts to understand about the change process is that change and transition are not synonymous.
Change is situational whereas transition is psychological.
What this means is that change is a physical manipulation that occurs on the organizational level. On the other hand, transition is a psychological process that occurs on the personal level. Change occurs externally, while transition is internal. Failing to understand the difference between change and transition is a major contributor to change management failure. Understanding how employees react to and work through change and transitions will make change management more efficient for organizations.
In Managing Transitions, William Bridges outlines the three stages of transition: “Ending”, “Neutral Zone” and “New Beginning”.
The Ending Stage
The Ending is the stage where individuals are aware that there will be changes occurring and that the traditional way of doing things will no longer be the norm. It is important to properly convey this message to employees in order to avoid unneeded pushback. With the “Ending Stage”, be prepared for employees to experience denial, shock, anger, frustration, and stress. While most – if not all – of these feelings are seen as generally negative, they are part of the change process and should be embraced. The most important responsibility as a manager in this stage is to be supportive and communicate with employees. If employees feel supported and encouraged through the “Ending Stage”, they will move more quickly to the next stage.
The Neutral Zone
The “Neutral Zone” is comparable to being in limbo. Individuals have started to let go of the old way of doing things but haven’t yet adopted the new mentality or practice. With this stage comes ambivalence and skepticism. This is a good sign! These are indicators that employees have started to move on from the frustration and stress from the “Ending Stage” and are starting to have questions and mixed feelings about the new changes. At the very end of the “Neutral Zone”, individuals will develop feelings of acceptance. They will become content with the fact that the organization has let go and moved on from one process and are starting to move towards another one. During this stage, it is critical to encourage employees to ask questions!
The New Beginning Stage
The final stage in the change process is the “New Beginning” stage. In this stage, adoption of a new organizational practice or mentality is experienced. This stage starts with feelings of impatience. Though this may seem like a negative feeling, it means employees have fully moved on from old ways and are looking to adopt the new ones. Following impatience will come feelings of hope and enthusiasm. Capitalize on these feelings! Reinforce and reward this behavior as it will keep people motivated and excited to be involved in the change process.
While it can be stressful to juggle daily tasks along with the stress of carrying an entire group of employees through the transition process, it is essential in making it work. Understanding the three stages of transition will prove vital in improving the success of your change management efforts. And remember – telling people about change is not the same as implementing it.