I Wish I Knew Where to Start

“I wish I knew where to start”

This is a common statement I hear from credit union colleagues who are interested in doing their own data analytics, building a data analytics role, or maybe even a team.

How many times have we also heard:

“Just start somewhere”

“There’s low-hanging fruit in your data”

“Ask questions of your data, then act on it”

There are many in the credit union industry who have already made it past these early thoughts and made great strides toward building analytics resources and talents at their credit union. But what about those who are still beginners – is that you? Do you know you want to do this, but HOW?! You’re not alone, and you’re not without support. And YES, it is overwhelming to begin developing these roles and skills.

You need a person (or persons?)

You need a curious, methodical, purposeful person who is a good communicator. Is that you? If you’re an executive, you do probably have all those character qualities but depending on your credit union’s situation it may make more sense to delegate the work.

If it’s not you, does anybody else at your credit union fit this picture? If nobody comes to mind right away think about who at your organization has that spark of curiosity. Someone who is often looking for new, interesting, or better ways to do things. These people like to ask “Ok, why? How does this fit into the bigger picture of my job/the credit union/etc?” when they are asked to do things.

This part—finding your person(s)—is more important than you might think because it really is about the soft skills rather than the technical skills. You can train technical skills into anybody who has a curious mind. It is harder to train an analytical mind into someone who doesn’t have that inclination already.

Easy wins, low-hanging fruit, work smarter not harder

All those clichés. They are easy to say but can be frustrating to people like me because they make promises and give no clues or instructions on how to deliver. I’m certainly capable of coming up with the game plan myself but do the people who say these things realize that the “game planning” even still needs to be done? Time, introspective thought, inventorying resources, interviewing interested parties, brainstorming and prioritizing ideas, etc.

That said, I’ll be guilty of saying these clichés myself occasionally. There’s merit to the ideas for sure. But someone (maybe you) has to actually translate the ideas into the realness of individual objectives and then the tasks to accomplish them.

To that end, I would like to suggest considering the following ideas for tangible ways to begin.

1 – the business plan.

What are your credit union’s business goals for the current fiscal year? Ask for a copy of your credit union’s business plan and interview executives to hear their perspectives and their top priorities. There is no guarantee their top priorities will be “easy” to analyze. But sometimes an easy win can be found by opening their eyes to a report or dashboard that they’d never seen before.

Key Point – Try not to promise there will be research or an analysis on every idea.

In this early phase you are getting your bearings around this whole idea of data analytics and how it fits into your credit union. Write down anybody’s ideas including any you’re inspired to have off your conversations. After you’ve finished this gathering and brainstorming process, evaluate the entire list to look for the ones which truly will be “easy wins” or “low hanging fruit” and start with those.

The goal early on should be to receive ideas and perspective, not to promise or deliver anything yet. Otherwise you’ll end up on rabbit trails or starting a complicated project that eats all your time without any opportunity to actually deliver quick value with low-hanging fruit or easy win projects. If you’re an executive reading this blog – this means it may not be the best idea to start your new analyst on defining and building your CECL model right away. Necessity is necessity, but understand committing to a huge long-term project right away for the analyst has a high risk of entirely killing the “easy win” and “low-hanging fruit” forward motion that helps with those early goals of winning over other staff, process development, and driving internal excitement about data analytics.

2 – don’t spend money yet; use your free resources

It’s tempting to look at what analytics firms offer and reason that spending $ for their software or analysis packages will provide an easy win. But don’t forget the hidden costs of spending time learning new software, and outsourcing the work of analyzing your own data generally means you are still exactly where you started from a skills perspective.

Don’t get me wrong, I work on a team that you can pay to do analysis for you and I absolutely see the value in outsourcing certain projects for time-savings when you don’t have staff to do your data analytics. But if you’re trying to be purposeful about developing data analytics skills and talents internally, make sure the firm you hire will take the time to educate you on the process and variables of results as they hand off the work.

I work mostly with credit unions on the CU*BASE data processing core and there are hundreds of free data sources and analysis tools to begin your journey. Contact me or anyone else on our team with a topic you have in mind if you’d like some help narrowing it down to 5-10 most relevant options for your credit union’s business objectives.

3 – start with finding and graphing trends 

Those spikes and valleys across time can speak volumes! One, it’s a visual representation of the data which often is more helpful for quickly communicating results to others. Two, there is astronomically huge value in the historical context that a trend line gives. You instantly see patterns and build an opinion of what “normalcy” is for this data point with today’s data compared to historical snapshots in time.

This concept of “normal” is huge. Being a smart data consumer and analyzer means you have at least some idea of what’s a normal stat for your credit union on certain topics. Having that baseline understanding can help you to begin quickly noticing when values start jumping up or down toward abnormal. Scan for patterns you wouldn’t have expected to see—you may have just found a problem or maybe you found a reason to celebrate!

Is that loan product portfolio balance tanking too fast? That needs further research and the immediate attention of your loan staff. Do you see a 20% jump in eStatement enrollments after the end of that last promotional campaign? Sing the praises to the entire credit union.

If you are a CU*BASE core user, these five tools are some of my favorites for quick and powerful trends:

473 Loan Risk Score Analysis

844 Teller Activity by Day of Month

846 Teller Activity by Time of Day

856 Tiered Services Monthly Comparison (in addition to monthly, you can also do quarterly or yearly comparisons)

1011 10-Year Trends by GL Account

If you are a subscriber of our Analytics Booth software you also have 75 data points trended daily which can be set up to receive as email alerts, plus hundreds more available in the software’s Dashboards section with data on products, services, and your credit union’s financials.

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