data analytics - CULytics Community2024-03-29T06:37:23Zhttps://culytics.com/blogs/feed/tag/data+analyticsUncover the Power of Proxy KPIs - Learn from David Lee Roth's Clever Trickhttps://culytics.com/blogs/uncover-the-power-of-proxy-kpis2023-08-23T15:07:46.000Z2023-08-23T15:07:46.000ZNaveen Jainhttps://culytics.com/members/Naveen<div><p class="zw-paragraph heading0"><a href="{{#staticFileLink}}12207622087,RESIZE_710x{{/staticFileLink}}"><img class="align-center" src="{{#staticFileLink}}12207622087,RESIZE_710x{{/staticFileLink}}" alt="12207622087?profile=RESIZE_710x" width="260" height="283" /></a>Today, I want to share an intriguing story that ties into a critical concept in the world of business - Proxy KPIs.</p>
<p class="zw-paragraph heading0">Have you ever heard about the legendary rock star <strong>David Lee Roth'</strong>s clever trick involving "NO BROWN M&Ms" on the snack table? It may sound unusual, but it actually holds a valuable lesson about decision-making strategies and uncovering hidden insights. Read the story here - <a href="https://mac-av.com/game-theory-why-there-are-no-brown-mms/">https://mac-av.com/game-theory-why-there-are-no-brown-mms/</a></p>
<p class="zw-paragraph heading0">In the Eighties, Van Halen's concerts were among the world's most challenging rock shows, with massive setups of truss, lights, and cables. To ensure safety, David Lee Roth inserted a unique clause in the band's rider - "No Brown M&Ms". If he saw any brown M&Ms backstage, he knew the promoters hadn't read the contract rider, and this prompted them to do a serious line check. It was a brilliant application of proxy KPIs - a strategy to reveal critical information that others might want to keep hidden.</p>
<p class="zw-paragraph heading0">In the business world, we often use Proxy KPIs to measure the successful achievement of objectives when direct measurement becomes impractical. One remarkable example in the banking world is the "Net Promoter Score" (NPS), which serves as a powerful indicator of customer loyalty and satisfaction.</p>
<p class="zw-paragraph heading0">We understand the power of Proxy KPIs and their role in driving business success. That's why we've designed an exclusive workshop series tailored to credit union leaders like you. Our executive workshops focus on defining your organization's objectives and key outcomes, providing practical strategies to elevate your performance.</p>
<p class="zw-paragraph heading0">In these workshops, we will explore various concepts, including Proxy KPIs, and how they can transform your decision-making process. Gain valuable insights on aligning your objectives with your vision, identifying areas for improvement, and fostering innovation in your data analytics practice.</p>
<p>To learn more about our workshop series and how we can help your credit union thrive, I encourage you to reach out to me directly. Together, let's unlock the potential of Proxy KPIs and propel your organization towards greater success.</p></div>Unmasking Biases: A Guide to Data Analysis and KPI Definition in Credit Unionshttps://culytics.com/blogs/unmasking-biases-a-guide-to-data-analysis-and-kpi-definition2023-08-18T15:30:08.000Z2023-08-18T15:30:08.000ZNaveen Jainhttps://culytics.com/members/Naveen<div><p class="zw-paragraph heading0"><strong><a href="{{#staticFileLink}}12199358470,RESIZE_930x{{/staticFileLink}}"><img class="align-center" src="{{#staticFileLink}}12199358470,RESIZE_710x{{/staticFileLink}}" alt="12199358470?profile=RESIZE_710x" width="333" height="220" /></a></strong>In the realm of data-driven decision-making, credit unions are no strangers to the power of analytics and key performance indicators (KPIs). However, beneath the surface of these seemingly objective insights lie potential pitfalls – biases that can distort interpretations and undermine the very foundation of accurate decision-making. In this article, we dive into the biases that credit unions should be aware of when analyzing data and defining KPIs, equipping them with the tools to extract more meaningful and actionable insights.</p>
<p class="zw-paragraph heading0"><strong>1. Sampling Bias: The Silent Distorter</strong></p>
<p class="zw-paragraph heading0">Sampling bias occurs when the data collected for analysis is not representative of the entire member base. For instance, relying solely on data from active or high-value members can skew insights, neglecting the experiences of the broader membership.</p>
<p class="zw-paragraph heading0"><strong>2. Confirmation Bias: Where Perception Meets Reality</strong></p>
<p class="zw-paragraph heading0">Confirmation bias is the tendency to favor data that aligns with pre-existing notions. In a credit union setting, this could lead to cherry-picking data that supports desired outcomes while ignoring conflicting evidence.</p>
<p class="zw-paragraph heading0"><strong>3. Survivorship Bias: Missing the Bigger Picture</strong></p>
<p class="zw-paragraph heading0">Survivorship bias arises when only successful cases are considered, ignoring data from failures or dropouts. This can lead to an overly optimistic view of outcomes, overlooking valuable lessons from less successful ventures.</p>
<p class="zw-paragraph heading0"><strong>4. Response Bias: Beneath the Surface of Surveys</strong></p>
<p class="zw-paragraph heading0">Surveys and member feedback are invaluable, but response bias can distort results. Members might provide responses they perceive as desirable, rather than offering candid feedback.</p>
<p class="zw-paragraph heading0"><strong>5. Anchoring Bias: Starting with a Fixed Perspective</strong></p>
<p class="zw-paragraph heading0">Anchoring bias occurs when analysis begins with a preconceived notion. This can limit exploration and prevent uncovering insights that don't align with initial expectations.</p>
<p class="zw-paragraph heading0"><strong>6. Ethical Bias: A Balancing Act</strong></p>
<p class="zw-paragraph heading0">Ethical considerations can also introduce bias. Decisions about which data to include or exclude based on ethical concerns can inadvertently skew analysis.</p>
<p class="zw-paragraph heading0"><strong>7. Contextual Bias: Understanding the Bigger Picture</strong></p>
<p class="zw-paragraph heading0">Neglecting the context in which data is collected can lead to misinterpretation. Consider external factors that could influence trends before drawing conclusions.</p>
<p class="zw-paragraph heading0"><strong>8. Cultural Bias: Perspectives That Shape Insights</strong></p>
<p class="zw-paragraph heading0">Cultural assumptions or perspectives can influence how data is interpreted, leading to misrepresentations in diverse member groups.</p>
<p class="zw-paragraph heading0">Navigating these biases demands a conscious effort to ensure that data analysis and KPI definitions remain as objective as possible. To tackle biases head-on:</p>
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<p class="zw-list zw-paragraph heading0"><strong>Diversify Perspectives</strong>: Form multidisciplinary teams to challenge biases from various angles.</p>
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<p class="zw-list zw-paragraph heading0"><strong>Transparent Methodologies</strong>: Clearly document the data sources, methodologies, and assumptions in analysis and KPI creation.</p>
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<p class="zw-list zw-paragraph heading0"><strong>Validation</strong>: Regularly validate findings against real-world scenarios to ensure accuracy.</p>
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<p class="zw-list zw-paragraph heading0"><strong>Continuous Learning</strong>: Stay updated on emerging biases and methodologies in the field.</p>
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</ul>
<p>The path to meaningful insights and accurate KPIs requires vigilance. By being aware of these biases and implementing proactive strategies to mitigate them, credit unions can unlock the true potential of their data-driven decision-making processes.</p></div>Lending Transformation: Old Vs Newhttps://culytics.com/blogs/lending-transformation-old-vs-new2020-12-07T15:19:03.000Z2020-12-07T15:19:03.000ZMedhavi Singlahttps://culytics.com/members/MedhaviSingla<div><p><a href="{{#staticFileLink}}8258007654,RESIZE_710x{{/staticFileLink}}"><img class="align-center" src="{{#staticFileLink}}8258007654,RESIZE_710x{{/staticFileLink}}" width="626" alt="8258007654?profile=RESIZE_710x" /></a></p>
<p>COVID-19 has helped us to understand how data, analytics, and technology can simplify everything – right from placing orders online to getting door-step deliveries, having video chats with the family to running a business smoothly via digital mediums among others. Similar effects can be seen when it comes to lending in the financial industry. These transformational changes are triggered by emerging customer behavior, growing expectations, fintech disruption, newer channel proliferation, as well as the adoption of cutting-edge and innovative technologies, and rapid digitization of business and community.</p>
<p>So, for a better understanding of lending transformation; a webinar was conducted by CULytics. It is about how most progressive financial organizations are responding to challenges and making decisions to stay relevant and serve the best.</p>
<p>Jason Pieratt (Data Analysis Manager at Park Community Credit Union) hosted the sessions with the speakers - Jade Beckman (Vice President Consumer Lending at Mountain America Credit Union), Vishal Kaistha (Product Manager at First Tech Federal Credit Union), David Mitchell (Senior Manager at Portfolio Risk and Operations Alliant Credit Union), and Patrick Wiginton (Loan Origination System Officer at America’s First Federal Credit Union).</p>
<p>Followings are the highlights of the session:</p>
<ul>
<li><strong>Lending Transformation:</strong>
<p>Due to COVID, acceleration to Digital has increased as new technology and vendor partnerships increased. Let the members know what are the offers, how to access them, and accept those offers. Help them understand that things can be done digitally through mobile banking, transactions, etc. Also, get associated with the best-of-breed service providers, mobile providers, and bring in the best-in-class third-party vendors & plug in with mobile vendors to enhance the digital experience of the members. COVID-19 has helped people know digital things more and come online to perform actions that were previously completed through physical branches. This will help the management more comfortable to put things together and make plans from a strategic standpoint.</p>
<p>Some big changes like data entry can be automated and the workforce can be reserved to support and solve queries of the members and vendors to enhance the digital experience of the members. The use of mobile banking has increased due to COVID, so take things online to avoid physical interaction/ contact.</p>
</li>
<li><strong>The Lending Lifecycle </strong>
<p>The lifecycle of any element comprises of steps taken to make and maintain that particular element. For example- The loan cycle begins when a prospective borrower inquires about it, and it ends when the borrower pays off the loan. Similar is applicable for Credit Cards. Members can be incentivized to encourage the use of credit cards and make timely payments. For this, use the data and make suitable decisions. It is seen that the response rate increases when members are incentivized.</p>
</li>
<li><strong>Post-COVID Changes </strong>
<p>The COVID-19 crisis has increased the need for digital lending platforms and processes than ever before. So, to respond to the needs of the existing borrowers and future borrowers, it is required to simplify the process and offer more options to borrowers. Also, financial institutions need to take preventive measures to be in a safe place. For example- before COVID, no income verification was required or generally asked but it will be good to check in this time. Make observations based on the data. To find out the member behavior, a trial was done by observing 30, 60, and 90-day activity of members, who took the card to find out- What they are doing with the card? Results were interesting as about 80% did balance transfer that was as per expectations.</p>
<p>To bridge the gap between old ways and new ways of doing things can be considered with 2 Ps i.e. Patience and Progress. Be patient at every step of progress as transformation doesn’t happen overnight. Look at the data available and coming from the usage of products and make decisions keeping a user-friendly interface, quick initial decisioning, cloud integration, and advanced metrics at the core. Personalization can be done to meet the expectations of the members. Know the needs of the members and take measures to fulfill them to build strong and long-lasting relations. Use data, tie together data points to come to a conclusion to simplify a lot of manual work.</p>
</li>
</ul>
<p><a href="https://culytics.com/articles/webinar-lending-transformation" target="_blank">Visit Here for the Complete Webinar.</a></p></div>Report Inconsistencies are Frustratinghttps://culytics.com/blogs/report-inconsistencies-are-frustrating2018-06-28T18:34:13.000Z2018-06-28T18:34:13.000ZRichard Joneshttps://culytics.com/members/RichardJones<div><p>One of the biggest frustrations found in most credit unions is the numbers between two or more departments don't jive. The conversation almost always leans toward trying to figure out which data set is correct, which one to believe.</p><p> </p><p>The ultimate goal for these organizations is to find that elusive "one source of truth."</p><p> </p><p>So, why don't our numbers balance between reports?</p><p> </p><p><strong>Definitions</strong></p><p>When we talk with credit unions, we find they have different definitions for common data terms and fields. Anytime the definition varies the count of that field will also vary. Having one consistent definition for these common terms is essential to getting to one source of truth. Establishing one common definition is complicated by the reality that different third-party software systems often use different definitions for these common terms. To get to one common definition will require a process to "normalize" the extracts from each of these systems and this is best done during the implementation and integration process. </p><p><strong>Timing</strong></p><p>To achieve consistent numbers the data must be reporting at the same time and date stamp. Any variation in the time stamp will result in data inconsistencies. This is often complicated when different systems generate updates and append the data at different times. The way to standardize this is to collect the data with the same timestamp. This may be impossible with some systems, so reporting the time stamps of each data source will help management understand the reason for the data inconsistencies.</p><p><strong>Query Logic</strong></p><p>We all know statistics can be used to tell lies as well as truth. The easiest way to manipulate data is to change the query logic. Often, query logic is a primary cause of report imbalances. Standardizing the queries on common reports can mitigate the risk of queries being used to "abridge" the story. To help management understand the differences in reports, a footnote with any query logic variances should be noted.</p><p>Getting to "one source of truth" should be an objective of any data strategy. But, like most strategies, this does not happen overnight and it is not an objective that can be solved with a software program. It takes a dedicated effort to find uniformity in queries, definitions, and report timing.</p></div>Is Your Culture Ready for Data Analytics?https://culytics.com/blogs/is-your-culture-ready-for-data-analytics2018-06-27T16:32:07.000Z2018-06-27T16:32:07.000ZRichard Joneshttps://culytics.com/members/RichardJones<div><p> </p><p> </p><p> </p><p><a href="https://storage.ning.com/topology/rest/1.0/file/get/48510182?profile=original" target="_blank" rel="noopener"><img class="align-full" src="https://storage.ning.com/topology/rest/1.0/file/get/48510182?profile=original"/></a></p><p> </p><p><span style="font-size: 12pt;">When most of us think about data, we think about numbers, charts, graphs, and reports. However, we need to talk about CULTURE.</span></p><p><span style="font-size: 12pt;">Collecting, aggregating, normalizing, manipulating, and presenting data seems to consume the conversation. However, here is the question we need to ask ourselves, "Is our culture ready to become truly data-driven?" Too often organizations spend lots of money on software, tools, and expertise, only to learn the culture was not ready to be data-driven.</span></p><p><span style="font-size: 12pt;">How do I get the culture ready?</span></p><ol><li><span style="font-size: 12pt;">Remove the data silos. Too often I see data owned by departments. Lending owns lending and collection data, marketing owns marketing data, finance owns finance data, and call center owns call center data. The problem data silos bring to the organization is the loss of data integrity. If the Chief Lending Officer is responsible for making lending and collection goals and they own the data in a silo, there is a risk that the data can be manipulated to tell the story lending wants to show. The only way to wean the credit union from silos is to have the data centrally collected, managed, cleansed, and normalized, to have the definitions of commonly used data terms standardized, and to have the data report queries standardized. Centralizing data is a painful process because some of these silo owners may not like this neutral party controlling the story the data is telling the organization.</span></li><li><span style="font-size: 12pt;">Make all data accessible to the organization. Data is an organizational asset and needs to be available for all to see. This accessibility and availability of data will allow anyone in the organization to see what is happening and to even ask questions of the silo owner or the data for clarification or understanding.</span></li><li><span style="font-size: 12pt;">Create a Business Intelligence (BI) unit in your organization. Their job is to collect, aggregate, store, cleanse, and manage all data. The BI staff does not write reports but are the data experts. With accessibility, tools and training are available to the subject matter experts, whether they are marketers, lenders, operational staff, or finance staff, to run their reports. However, the queries, definitions, and timing of reports are understood so variances in reports can be easily explained. This method of data management is called centrally managed, widely distributed or the "hub and spoke model."</span></li></ol><p><span style="font-size: 12pt;">The organization needs to see Business Intelligence the same way they see Human Resources, IT, or Marketing. It is an independent business unit that makes data available to the entire organization in a way they can access and query the data. The organization structure needs to include a strategic owner, usually an SVP or EVP, and a tactical owner. The strategic owner keeps data projects, software, and tools visible in the planning and budgeting process the tactical owner owns the work of collecting, aggregating, normalizing, cleansing, and monitoring of the data, definitions, and queries.</span></p><p><span style="font-size: 12pt;">Data is the credit unions most valuable asset; it needs to be right and has a strategic focus.</span></p></div>Data Analytics Use Cases for Credit Unions (infographic)https://culytics.com/blogs/data-analytics-use-cases-for-credit-unions-infographic2017-06-26T08:11:35.000Z2017-06-26T08:11:35.000ZRaghavan Madabusihttps://culytics.com/members/RaghavanMadabusi<div><p><a href="http://storage.ning.com/topology/rest/1.0/file/get/2161032?profile=original" target="_self"><img src="http://storage.ning.com/topology/rest/1.0/file/get/2161032?profile=RESIZE_1024x1024" width="750" class="align-center"></a></p></div>Data/Analytics Opportunities in Credit Union businesshttps://culytics.com/blogs/data-analytics-opportunities-in-credit-union-business2017-06-25T02:26:31.000Z2017-06-25T02:26:31.000ZNaveen Jainhttps://culytics.com/members/Naveen<div><p>In our changing and uncertain world, it is more evident than ever that the long-term success of our credit unions depends on transforming how we operate, engage with our members and delight them with our products and experiences. </p>
<p dir="ltr"><span>Data is a powerful catalyst for digital transformation. Credit unions have very rich data available to them as a result of the footprint that members leave behind at the digital, retail and other channel interactions. When this data is properly managed, then it can be leveraged to better understand members, improve overall business function, and achieve better returns on any investment.</span></p>
<p dir="ltr"><span>With appropriate focus on right technology, processes and people training, data can be incorporated into everyday business and can transform the success of any credit union. Data can provide us an edge in competing with deep pockets of big banks by reducing cost and improving outcomes.</span></p>
<p dir="ltr"><span>Recently, I have been cataloging various opportunities that exists in various business functions across the organization to drive discussions and engagements. Here is the draft mind map. This map is by no means exhaustive and I believe it is just scratching the surface and the opportunities exists through out the organization, in each and every business function.</span></p>
<p dir="ltr"><span><a href="http://storage.ning.com/topology/rest/1.0/file/get/2160987?profile=original" target="_self"><img src="http://storage.ning.com/topology/rest/1.0/file/get/2160987?profile=RESIZE_1024x1024" width="750" class="align-full"></a></span></p>
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<p><a href="http://storage.ning.com/topology/rest/1.0/file/get/2160996?profile=original" target="_self">Analytics Opportunities.xmind</a></p></div>