Credit Unions are at a unique crossroads. We are behind on a handful of key trends. We must balance our technological needs with our focus on service, growth, and profit. At the same time, we have outside factors impacting us including fintechs, compliance, IT security, and ever-changing regulations.
One advantage shared by credit unions is their access to data. Individually, they generate quite a bit of data. Collectively, they generate tremendous sums. Even with all of that, we remain behind woefully in leveraging our data.
What to Do with All That Data
Here’s the challenge: artificial intelligence and machine learning thrive on structured data. We have the most structured data of any industry, yet we barely leverage it.
Even worse, every single fintech we talk to is using machine learning in their product, pushing ahead of us technologically, even though most are lacking our access to data. All of them develop on cloud platforms and leverage the opensource NLP (Natural Language Processing) and ML (Machine Learning) models available on Google, Amazon, and Microsoft’s clouds.
So, if all of your new competition is leveraging cloud and AI, what is your credit union doing? What is your path to catch up?
Here’s what you should be doing: A) get on the cloud, and B) get your analytics in order. If you don’t, you’re going to get crushed by the innovation curve of AI-driven fintechs and big banks in the next 5–10 years.
So, with all that in mind, you might be wondering, “how do I pick the right analytics provider or strategy for my credit union?”
That’s what we’re here to help with.
Evaluating Data Analytics Providers
There are many ways to evaluate data analytics providers.
Especially if you’re just starting your data analytics journey, your first step should be to figure out what you want to accomplish with analytics. Some credit unions look at analytics, data-driven culture, and digital transformation as a goal.
But they’re not goals—they’re ways of reaching your goals. That said, you’ll need to figure out what kind of projects you plan to augment with analytics. Knowing your goals will help you choose the tools best for achieving them.
We’ve ranked data analytics vendors by seven different variables:
- Normalization of data
- Existing credit union models
- App store
- Reporting and data visualization support
- Support and roadmap
- Credit union knowledge
- Time to deploy and gain actionable data
This guide is designed to help you review data analytics providers in key categories. Good luck!
Data Analytics Vendor Comparison Guide
| Provider | Rankings | ||||||
| Normalization of Data | Existing Credit Union Models | App Store | Reporting and Data Visualization Support | Support and Roadmap | Credit Union Knowledge | Time to deploy and gain actionable data | |
| AdvantEdge Analytics | 3 | 4 | 2 | 2 | 1 | 3 | 4 |
| CU Rise | 5 | 4 | 4 | 5 | 5 | 5 | 4 |
| CU*Answers | 5 | 5 | 3 | 5 | 4 | 5 | 4 |
| Fiserv – iVue | 3 | 4 | 4 | 3 | 3 | 3 | 3 |
| IBM Planning Analytics | 3 | 2 | 2 | 1 | 5 | 3 | 1 |
| Lodestar | 5 | 5 | 1 | 5 | 4 | 4 | 4 |
| Trellance | 4 | 5 | 5 | 4 | 4 | 5 | 5 |
| Prism | 3 | 3 | 3 | 3 | 3 | 4 | 3 |
| Raddon | 3 | 5 | 3 | 5 | 4 | 4 | 3 |
| Saggezza – TruVantageTM | 2 | 3 | 2 | 2 | 3 | 4 | 3 |
| Visible Equity | 3 | 4 | 4 | 5 | 4 | 5 | 4 |
| DIY (do it yourself) | varies | varies | 1 | varies | varies | 5 | 1 |
Data Analytics Criteria Definitions
This section goes into detail about the seven different metrics by which we ranked the data analytics criteria above.
Normalization of Data
Now, we’re not saying you should necessarily prioritize any of these metrics over the others, but let’s be real: this is a big one.
Normalization of data essentially refers to the quality and integrity of data. Is it well organized? Is it accurate? Is it accessible? And, is it… extensive?
If you’re going to make data-driven decisions, then you want good data. In many ways, you can think of this category as the “performance” evaluation of data. After all, better data = better analytics.
Existing Credit Union Model Depth
Does the data analytics provider have existing solutions? Do they have experience in addressing your credit union’s goals?
For example, do they have loan origination models? Do they have predictive analytics for CECL? Have they or their clients built anything to solve your marketing needs? Can they provide knowledgeable KPIs for loan officers?
The existing credit union model depth measures the vendor’s experience with specific campaigns and goals. If you’re looking to implement a particular analytics solution, you should look into vendors who have relevant experience.
App Store
I mentioned earlier that data analytics is a vehicle, not a destination. Access to an app store or an equivalent can help kickstart your data analytics journey. Ready-made apps can leverage your data to make predictions, recommendations, and more, all without extensive internal development.
Does the data analytics vendor have or support an app store? Are the apps relevant to your credit union and its goals?
Reporting and Data Visualization
Most people are not data scientists. Instead, they rely on intuitive user interfaces, dashboards, and visuals to understand data. Is the data readable? Is it accessible? Does the readout make sense to the people using it?
Data analytics vendors that report data in intuitive, intelligible, comprehensive ways will make it easier on your team. You’ll spend less time wondering what the numbers mean, and more time deciding what the numbers are saying.
Support and Roadmap
In some industries, vendors can get away with simply selling a product and leaving the buyer to their own devices. Data analytics is not one of those industries.
First, does the data analytics vendor provide ongoing support? Are they invested in your success? Your analytics vendor should be committed to helping your credit union reach its analytics goals.
Second, does the analytics vendor have a stable roadmap? Are they looking ahead, or are they stuck in the present? The financial industry is changing rapidly, as is the industry’s relationship to data. Your vendor shouldn’t just help you catch up—they should also be ready to help you adapt to the future and get ahead.
Credit Union Knowledge
Here’s a facile dichotomy that should prove telling nonetheless:
There are two kinds of analytics vendors: those that specialize in credit unions, and those that don’t.
Which vendor do you choose? Of course, just because a vendor doesn’t specialize in credit union data, issues, and pain points doesn’t mean they can’t help. However, some knowledge of the credit union industry may help.
Vendors who understand the credit union arena may be better positioned to assist with industry-specific goals, support industry-specific features, and lend their industry-specific experience to your credit union’s data analytics program.
Time to Deploy and Make Actionable
The world of financial technology is evolving rapidly. With the explosion of data and analytics, plus the advent of machine learning and other AI tools, the whole industry is experiencing an accelerating pace of change.
The longer it takes to implement a data analytics solution, the further behind your credit union gets. This metric measures how quickly you can expect to get set up and using your data. Faster is definitely better.
Conclusion
There are several vendors to compare, here. Plus, there are as many metrics by which to measure their products.
The first step is to narrow down the list to a few who look promising to your credit union. Then, we recommend finding vendors who hit your key criteria, then doing further research. It will be important to choose a data analytics vendor who fits your credit union’s needs and project goals.
After you’ve narrowed your list, it’s time to do some demos and move forward. At the current pace of technological change, time is of the essence!