Credit Unions are behind on several key trends—especially when it comes to technology. One the one hand, credit unions must balance technological needs with service, growth, and profit. Yet those needs sometimes compete with outside factors such as fintechs, compliance, IT security, and ever-changing regulations.
Fortunately, credit unions do have access to data. And what they lack individually, they can get collaboratively. But if they combine resources, they remain behind woefully in leveraging that data.
That’s where data analytics comes in. Particularly in light of the COVID-19 crisis, using data to gain insights, drive efficiency, and transform digitally. If your credit union is getting into data analytics—or switching providers—this post is for you.
Check out our new downloadable Credit Union Provider Guides here!
The World of Data Today
Artificial intelligence (AI) hasn’t just entered the financial industry—it’s already dominating it. And AI thrives on the kind of structured data that financial institutions produce. So, bigger banks and fintechs are using their data not just for analytics, but also for cloud-based AI to push ahead technologically.
That means that the competition isn’t just on the cloud, and they’re not just using data for analytics—they’re also using it for AI.
If your credit union isn’t on the cloud yet, then the competition is three steps ahead. If you are on the cloud, but you’re not using data analytics, they’re two steps ahead. 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. Simply using data-driven technology isn’t a goal in and of itself—rather, goals are things like improving interchange income, increasing auto loan portfolio size, and reducing attrition.
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!
Notes About the Guide
Surprise! There are actually two guides. The first guide is for full data analytics solutions. That is, the providers offer a data warehouse, thus offering a single source of truth. The second guide includes providers that don’t offer a data warehouse—rather, they simply work with your data to provide you the insight you need.
We generally recommend full data analytics solutions that include the warehouse. This is because when you send data, it gets manipulated and merged, and you lose the source of your data. However, for credit unions that would rather get actionable insights without working with their own data, the non-warehouse solutions may be a better option.
Additionally, we recommend against buying a data warehouse form a company that doesn’t specialize in data warehousing. Like buying a bike from Walmart, it may work and be cheap, but it will lack sorely in quality.
You may also note that we didn’t include providers such as Amazon, Azure, Google, or Snowflake. Although these solutions are technically superior, they have a few major drawbacks:
- They aren’t designed for financial institutions
- They don’t have integrations or connections with common third-party tools for credit unions
- Most credit unions don’t have the in-house expertise to implement, maintain, and maximize the investment in these solutions
Lastly, it should be noted that some larger providers with massive product suites may be trickier to integrate with products from other vendors. For example, JHA and Fiserv integrate well with other JHA and Fiserv products. However, integrations outside of their product suite can be far less straightforward.
Holistic vs. Targeted Analytics Solutions
When creating a data strategy, there are two things to look at:
- Do you need a holistic solution encompassing the normalization of all data into a central repository?
Or…
2. Do you have specific needs requiring a targeted or focused solution in one area (member attrition, loan growth, etc.)?
These two are not mutually exclusive. You can create a hybrid of the two where you put in a data warehouse and then utilize the services of third-party solutions to build on the warehouse solution you already have. If you select only option 2, then you will be giving your data (or access to your data) to those providers. From there, those third party solutions will analyze certain subsets of your data to create a actionable sets of responses with your data.
For example, there are plenty of solutions in the market today that will analyze certain sets of data to predict when a member is going to leave the credit union. Those are all solutions you can put in today, with minimal effort, that will result in a lower member attrition rate. They are great solutions, but they’re sort of a band aid to the greater analytics issue.
Option 1 serves to futureproof your credit union by standardizing and normalizing data. Once that is complete, along with proper data governance in place, you have all your data in one place. Most importantly, a holistic solution allows you to trust your data. It has been vetted, governed, and defined ahead of time. With your data in one location, you can build, buy, rent, or lease any number of predictive and prescriptive solutions to create actionable outcomes from the data analysis.
Understanding the difference between these two things is key to finding the right provider for you on the guides below.
Data Analytics Vendor Guide (with Warehouse)
| Provider | Rankings | ||||||
| Normalization of Data | Credit Union Model Depth | App Store / Collaboration | Data Visualization and Reporting | Support and Roadmap | Connectors | Time to deploy and gain actionable data | |
| AdvantEdge Analytics | 4 | 3 | 4 | 3 | 4 | 4 | 3 |
| Fiserve iVue | 4 | 3 | 2 | 3 | 2 | 3 | 2 |
| IBM Data Warehouse | 4 | 4 | 2 | 4 | 2 | 3 | 2 |
| JHA ARCU | 3 | 3 | 4 | 4 | 2 | 3 | 3 |
| LoadStar | 4 | 4 | 2 | 5 | 4 | 2 | 4 |
| Trellance | 4 | 3 | 5 | 3 | 4 | 4 | 3 |
| DIY (do it yourself) | varies | varies | 1 | varies | varies | varies | 1 |
Data Analytics Vendor Guide (No Warehouse)
| Provider | Rankings | ||||||
| Normalization of Data | Credit Union Model Depth | App Store / Collaboration | Data Visualization and Reporting | Support and Roadmap | Connectors | Time to deploy and gain actionable data | |
| CU Rise | 3 | 4 | 2 | 4 | 2 | 3 | 3 |
| CU*Answers | 3 | 3 | 3 | 3 | 2 | 2 | 3 |
| Deep Future Analytics | 4 | 2 | 1 | 4 | 3 | 1 | 3 |
| nCino (Visible Equity) | 4 | 3 | 3 | 5 | 2 | 2 | 3 |
| Prism | 4 | 3 | 3 | 2 | 3 | 3 | 3 |
| Raddon | 3 | 2 | 2 | 4 | 3 | 3 | 4 |
| Temenos | 3 | 2 | 2 | 3 | 2 | 3 | 2 |
| TruVantage | 2 | 2 | 2 | 4 | 2 | 2 | 3 |
| Twenty-Twenty Analytics | 4 | 4 | 4 | 5 | 3 | 3 | 4 |
| DIY (do it yourself) | varies | varies | 1 | varies | varies | varies | 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.
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 / Collaboration
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.
Connectors
Data connectors allow credit unions to import third-party data into their system, allowing more robust overall data. Furthermore, connectors ensure a better single source of truth in data warehousing models.
Most importantly, connectors allow visualization and reporting on imported data.
If your credit union gets data from many sources, you should find a vendor that can handle your different forms and sources!
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!
Want to hear more about credit union technology providers who have caught our attention recently? We offer free quarterly calls to discuss the fintechs and vendors that have been making the biggest impact. Contact Chris Otey to learn more.