The Decision Maker

Data Use Cases for Credit Unions: Chapter 2

Posted by Lou Grilli on Jun 6, 2019 10:09:00 AM

Getting Out from Behind The Curve

Chapter 1 of this series considered the importance in establishing a specific goal to solve using data analytics and proving the ROI in order to justify automation and decisioning using business intelligence in a credit union. Chapter 1 also highlighted two real use cases of success credit unions have had using data analytics to solve real-world problems. According to a recent study conducted by Best Innovation Group (BIG) and OnApproach (now Trellance), 45 percent of credit unions don’t currently have a strategy in place, and those that do have a strategy still say it will take three to five years to implement. Credit unions that aren’t making the most of data analytics today could be in even bigger trouble if an economic downturn occurs, as some economists are forecasting. “As we go forward there will be a significant performance difference between those that have invested and those that have not,” says Kirk Kordeleski, senior managing partner at BIG. “We think any downturn in the economy will highlight the advantage that data-oriented FIs will have over their competitors.”

How Much Will It Cost

The survey revealed that more than half of the 85 credit unions surveyed have budgets in place for data analytics. Of those, one-third plan to spend more than $200,000, the other two-thirds plan to spend between $50k to $200k. In addition, credit unions need to consider on-going costs. A rough rule of thumb is that a CU with $500 million in assets should budget between $150,000 and $300,000 per year for three years to cover software/hardware, analytic applications, and strategy. Smaller credit unions can find some savings by relying on a CUSO to provide the analytics and associated services.

The following paragraphs are real use cases that credit unions have shown to prove out their investments.

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Topics: Business Intelligence, Credit Unions, Data

Cooperative Data Analytics Through a Semantic Layer

Posted by Nate Wentzlaff on May 30, 2019 11:04:00 AM

Credit Unions Need to Establish a Common Language to Strengthen the Credit Union Movement

On a recent trip to Malaysia, I was able to play basketball with my brother-in-law (my wife is Malaysian). As we began the game, I realized that we were all relying on a single source of truth for the rules of basketball.  Even though we are from very different parts of the globe, we were operating under the same definitions of the rules of basketball. Imagine if we all began playing according to sources of the truth that dictated different ways to play basketball. Maybe my source of truth told me that I don’t need to dribble to play the game. The other team’s source of the truth dictated that they can tackle the other team. This game would end horribly and would probably escalate into a conflict quickly. The same is true for data analytics within the credit union movement today.

Different Sources of Truth

Within most credit unions, there are many different sources of truth. Marketing departments have their sources, Accounting has theirs, and Lending has as many sources as types of loans (i.e. credit cards, mortgages, student loans, etc.). Over the course of time, every department begins establishing their own language based on their sources of truth, which are usually centered around a specific source system. For example, the marketing team has an MCIF system, which has an abundance of data regarding households and members’ profiles. The lending department relies on its loan origination system, which displays information found within a member’s loan application. All the while, the contact center relies on their CRM, which houses data collected during calls with members. When there is a need to work together to accomplish a goal, these various departments come to a meeting speaking different languages and using a separate understanding of the rules of the credit union. Like the game of basketball without a common source of truth, the project or initiative often ends horribly.

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Topics: Data Integration, Semantic Layer, Collaboration

IMMERSION19 - Top 10 Takeaways

Posted by Erika Hill on May 23, 2019 11:52:00 AM

Trellance just concluded its annual conference, IMMERSION19, where ideas and strategies took the shape of keynote presentations, breakouts and networking among credit union professionals. As in past years, there were many key takeaways. Here are the top 10 takeaways from the keynote presentations.

60% of all data analytics endeavors fail.

Tom Davis, President & CEO of Trellance, shared a warning in his opening keynote that many data analytics efforts within organizations fail more than they succeed—and cost more money than expected. Tom urged credit unions to consider partnerships to advance their moves into big data.

Take big data and make it small.

Credit unions have more data about some of their members than what Amazon knows about its customers, according to Erik Qualman, an author recognized by Forbes and Fortune as one of the Top 100 Digital Influencers. Credit unions need to be using their collective data to customize the member experience according to each member’s unique needs. That’s taking big data and making it small.

Digital leaders are made, not born.

Eric Qualman also spoke about how, with advanced technologies, everyone can exert more direct and indirect influence than ever before and become an effective digital leader — anywhere at any time.

Disrupt or die.

Former IBM Chief Innovation Officer and best-selling author Linda Bernardi gave several examples of companies that didn’t see changes coming. Nokia, Motorola, Kodak, Blackberry, Toys R Us, were just some corporations that were blindsided by changes in consumer preferences and changes in technology. Linda suggested that companies have two choices – be the innovator or get pushed out of the way.

2019 AXFI Conference, June 9-12, Minneapolis MN

If someone is going to eat your lunch, it might as well be you.

Eric Qualman also shared the same cautionary message as Linda Bernardi regarding companies that need to innovate or be left behind. His perspective, though, was to not be afraid to disrupt your own organization. He gave the example that Netflix used to be in the business of mailing out discs; at its peak in 2002 Netflix was mailing around 190,000 discs per day. But the company also saw that streaming content was about to take off, so it created a streaming video product that cannibalized its own video-by-mail business, and upended Blockbuster’s market.

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Topics: Collaboration, AXFI Conference, Disruption

Unlock the Data in your Reports

Posted by Lou Grilli on May 16, 2019 11:07:36 AM

Simple Steps To Make More Informed Decisions And Enhance Member Experience

There is a lot of buzz about big data and data analytics, but all the data in the world does no good unless it is utilized. Credit unions are behind retail and online companies in using data to make informed decisions. For example, if your forms, such as a HELOC application, do not have the fields for name, address, etc. already filled in for your members, that indicates that you are probably not using your data. You should already know this information about your members. Save them the hassle and give them the option of updating if necessary.

There’s so much more data than was available in the past that can be collected and used for purposes that can benefit the member. There are also better tools than previously available to aggregate the data to help decision makers. These two factors are bringing a wave of data analytics to credit unions. More importantly, it’s a matter of survival. Credit unions must take advantage of these opportunities to guide their sales initiatives. For example, the data can help you to decide who to target, like a new member who joined the credit union to access an auto loan, should be offered your credit union-branded credit card to maintain a sticky relationship. Or, who not to target for a specific product, like a member who already has your credit card but accessed a new loan, should not be sent another offer for the same credit card. Rich data helps you to determine who your target is for specific products and services, which helps to enhance your member experience.

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Topics: Reporting and Analytics, Business Intelligence, Data Visualization

Using Data to Create a Unique Member Experience

Posted by Erika Hill on May 7, 2019 10:59:00 AM

Enhancing member experience has been the subject of many blogs and white papers, and there is a reason why this topic is so popular. In fact, there are three good reasons why this topic is especially relevant now.

  1. Competition- Competition among financial institutions, challenger and online banks is getting fierce. Banks and online financial institutions like Marcus and Ally need your members’ deposits to fund their loan activity, and are offering higher returns for their business. Also, the huge credit card issuers want to put their cards in your members’ wallets, and are enticing them to do so with sign-up bonuses. Plus, every financial institution wants your best members’ loan activity on their income sheets.
  2. Technology– Technology has become affordable for credit unions of nearly any size. The technology to harvest data to drive decision making, segment your members, create targeted offers, and get a real-time view on each member, is much more accessible.
  3. Expectations- Your youngest members as well as the next generation that could become your newest members, demand a unique experience. They, like other customers (retail customers and e-commerce shoppers), all want a customized, digital experience. If it’s not fully digital, they want an experience that has some digital component. How do we create that experience?

The Answer is Technology

Of the three factors that make this topic especially relevant, technology is the one that drives everything else. Creating a personalized, positive experience that differs for each member relies heavily on data. This includes collecting, normalizing, and combining data from multiple sources to create a unique view of each member. This view is then used during the narrow window of opportunity you have while the member is in the branch, on the phone, in an online chat session, or engaged in online or mobile banking. This unique member-centric view is also used to determine which of your members should receive a special offer, which member should get fees waived, which members deserve bonus interest on money market accounts and other business decisions that help to enhance the member experience.

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Topics: Marketing, Membership, Data Analytics

Data Use Cases for Credit Unions: Chapter 1

Posted by Lou Grilli on Apr 29, 2019 11:53:00 AM

Data Use Cases is an ongoing series showcasing real use cases of success that credit unions have had using data analytics to solve real world problems. Data analytics was once the sole domain of giant tech companies – Amazon’s suggestions “If you bought that you might like this”; Facebook’s algorithms that determine which of your friend’s posts you most want to see on your timeline; Google’s ability to propagate data about you so that when you search for something like “hotels in San Francisco” you start seeing ads for restaurants in San Francisco on other sites. With the proliferation of data across multiple systems, the increase in computing power at a decreasing price, and tools to extract and harness data, the science of data analytics to create solutions to business problems, also known as business intelligence, is being increasingly used by credit unions to make better decisions. And it’s not just the biggest credit unions introducing business intelligence through data analytics to their staff. Credit unions with under $500 million in assets are realizing that use cases for data analytics drive ROI, better member experiences, and increased product penetration across their member base. Almost ironically, it is the smaller credit unions that absolutely need to embrace the use of data analytics – they are the ones that need to remain competitive or be merged out of existence.

Data Just For Data’s Sake – NOT!

It’s important to keep in mind that no company, regardless of what industry, invests in data analytics just for the technology. The cost of the tools, the hardware (or more commonly, the cost of cloud storage), investment in staff such as business analysts and possibly a data scientist, consulting services to help get started, can represent not just a significant up-front investment, but an on-going cost that must be justified. The justification comes in the form of use cases – individual examples of data-driven decision-making that makes a difference in how members are rewarded, or sold-to, or what products are offered, or just making a member feel more connected to their credit union through targeted, meaningful campaigns. In fact, for a credit union that’s just embarking on the data analytics journey, the best way to start is with the end in mind. Pick a single use case, a single vexing problem to solve, ideally one that has a fairly high payback if solved correctly. There are many articles that talk about the intangible benefits of business intelligence. But credit unions, especially their CFOs, want to see a return on their investment. The following paragraphs are a few real use cases that credit unions have shown to prove out their investments.

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Topics: Business Intelligence, Data Analytics, Data-Driven

The CCPA and GDPR: How These Emerging Privacy Laws will Impact the Credit Union Industry: Part II

Posted by Alex Beversdorf on Apr 9, 2019 11:02:00 AM

In the first part of this blog, we discussed technology regulation and updates regarding the legislation. With that covered, Part II will focus on what it means for your credit union and how you can prepare for the changes. 

How the CCPA Will Change the Competitive Landscape in the US

The CCPA won’t apply to all companies but will apply to a great majority, especially if one of these three thresholds are met:

  • Gross annual revenues in excess of $25 million
  • Buys, receives, sells or shares the PII of 50,000 or more consumers, households or devices for the business’s commercial purpose
  • 50% or more of the businesses annual revenue comes from selling consumer’s PII

If any of the above conditions are met, the marketers of the effected company have a great deal of work to do. Especially if they have no business tactic or strategy in place to organize all of their customer specific data. To comply with the CCPA, marketers must be able to organize and develop an efficient data scheme that compiles all of their consumer data. Consumers have the right to:

  • Know what PII is being collected regarding them
  • Know whether that is being sold and to whom
  • Say no to the sale of their PII
  • Access their own PII
  • Equal service and pay from the company, even if they exercise their own privacy rights and it requires more work to be done on the side of the business
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Topics: Data Integration, Membership, Digital, Data Ownership

The CCPA and GDPR: How These Emerging Privacy Laws will Impact the Credit Union Industry: Part I

Posted by Alex Beversdorf on Mar 28, 2019 11:54:00 AM

We are in the era of digital transformation. A time where data is being collected at exponentially growing rates all around us. A wide variety of businesses and institutions, including credit unions, have been collecting personal data on their customers/members for quite some time now. How can you receive any benefit from all of this available data? The answer lies in the use of Big Data Analytics. Big Data is used to help analyze extremely large data sets to identify patterns, trends and associations in human behavior. This method of analyzing data is very versatile and proving to become one of the most sought-after tools of today.

This has made the overall collection of consumers data much easier and more widespread. This is also commonly referred to as Personally Identifiable Information (PII) and the influx of access to it has raised some concerns. Common questions include: What data is being collected? How has it been collected? What is it being used for? Which third parties have access to it? And, how much control do we have over our own data?

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Topics: Membership, Digital, Data Ownership

To Build or Not to Build (Buy) – That is the Question for Credit Unions

Posted by Peter Keers, PMP on Jan 31, 2019 1:52:49 PM

As the Age of Analytics for credit unions rolls forward, the question of “Build or Buy” is faced almost daily by decisionmakers. It comes at all stages in the data and analytics journey, so credit unions must understand the tradeoffs in deciding to Build or Buy.

First, however, consider the question itself: Build or Buy. “Build” means the credit union uses its own resources to design, construct, launch, and maintain an application or capability. “Buy” means acquiring these same elements from an entity outside the organization.

The fast pace of technological evolution has added an innovative dimension the definition of “Buy”. Increasingly, “Buy” includes Software as a Service (SaaS) as well as on-premises implementations.

The Build Option

The perceived advantages of Build are customization and control. By keeping projects in-house, the Credit Union can design a system tailored to its unique requirements. Although all credit unions are chartered to do a specific set of services, each has its own flavor for delivering these services.

These Build option advantages favor larger credit unions with greater resources. Having the team depth of a larger organization enables greater possibilities for having both the skills and numbers to take on Build projects.

The major disadvantage of Build is cost. A custom-tailored suit is more expensive than an off-the-rack brand. Another, subtle but important disadvantage is strategic focus. A credit union is wired to be a member-oriented financial services organization. Though it may have gifted technologists on its staff, most credit unions are unlikely to have the technical breadth and depth to build a truly industrial grade application. There is also a big risk of knowledge experts leaving the organization in the current low unemployment environment.

Another cost concern is ongoing maintenance and enhancements. Experience shows custom-built applications are notoriously expensive to keep up-to-date and in efficient working order. The credit union is saddled with this ongoing burden for its data and analytics capability to keep pace with new industry trends.

See 7 Challenges to Consider When Building a Data Warehouse: http://blog.onapproach.com/7-challenges-consider-building-data-warehouse

The Buy Option

At first glance, it might be assumed the Buy option is the mirror opposite of Build. A purchased product will not be exactly customized to the credit union’s specific requirements nor will the organization have as much control over the project. However, this is a game of trade-offs driven by primarily by the size of the credit union. In order to survive, all credit unions must embark on the data and analytics journey. Those ignoring this trend will ultimately be acquired by credit unions that do take data and analytics seriously or simply become obsolete.

For the majority of credit unions, the Buy option holds significant advantages. By giving up some customization and control, the organization gains significant data and analytics capabilities at a more affordable price. In fact, not only is a tested commercial product liable to cost less up front, it also has the advantage of having the bugs worked out as the result of use at multiple sites. Therefore, the cost and headaches of the inevitable errors in complex programming code are avoided. If fact, the perception that a Build project results in a more tailored outcome may be overstated. Most commercial products are very configurable to meet specific credit union requirements.

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Topics: Credit Unions, Data Integration, Insight Platform

The Cost of Building a Data Warehouse for an Analytics Platform

Posted by CU 2.0 on Jan 25, 2019 10:02:00 AM

Credit unions can benefit greatly from collecting and storing information to leverage Big Data. The cost of building a data warehouse can be steep, though. If you’re considering building a data warehouse for your credit union, it’s important to know what you’re getting yourself into.

The benefits of building a data warehouse speak for themselves in the financial world. Getting into the data analytics game isn’t cheap, however. It’s not as simple as just buying a data warehouse and watching a video tutorial; no, getting started requires a large initial investment as well as ongoing support and upkeep costs.

Here are a couple of the common issues associated with building a data warehouse for the credit union industry.

Initial Investment Costs

There are two major expense considerations for any enterprising credit union looking to construct its own data warehouse. The most pressing of the two is the financial cost, and the second is the time invested. Because we’re talking specifically about credit unions, let’s discuss the monetary side of this investment first.

For an individual credit union, the cost of building a data warehouse or data lake for an analytics platform starts at around $500,000 at the low end. Most data warehouses and data lakes run well over the million-dollar mark. While it’s certainly a worthwhile investment, it can also be prohibitively expensive for smaller, more community-focused credit unions.

 The second major cost factor is time, though we could also say that it costs patience as well. Regardless of the size of the warehouse and the experience of the people putting it together, building a data warehouse takes an average of two or three years. If you want an analytics platform immediately, then creating one in-house from the ground up might not be your best option.

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Topics: Data Integration, Analytic Data Model, Enterprise Data Management, Data Storage, Insight Platform