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From static reports to self-service BI - Texas Tech Credit Union shares Domo project breakthroughs

Alyx MacQueen Profile picture for user alex_lee January 10, 2023
The Business Intelligence landscape has changed, and it’s no longer just the domain of the analyst or IT. But the transition to self-service BI isn't a straightforward path. Brian Jackson of Texas Tech Credit Union shares how Domo helped his firm navigate the pitfalls.

Screenshot of Brian Jackson, Texas Tech Credit Union during interview
(Brian Jackson of Texas Tech Credit Union)

The quest for self-service Business Intelligence (BI) has been a Holy Grail for businesses across all industries. After all, there’s a big gap between the ability to capture and access data, and utilizing it for decision-making at all levels.

I caught up with Brian Jackson, Chief Information Officer at Texas Tech Credit Union (TTCU) who shared his experiences on the pursuit of BI to improve financial relationships with customers.

Jackson joined TTCU in 2018 as the Business Intelligence Director. When he first came on board, there was a legacy data warehouse that had been in place since 2014. It’s always easier to spot the snags in systems as a new person with a different perspective – and Jackson realized early on that the current tool and its OLAP [Online Analytical Processing] cubes weren’t going to cut it:

It was very cumbersome to maintain. The organization was growing rapidly. We were just in constant need of integrations to get the depth of our data. It's easy to say, ‘Okay, this is not going to work for us, this is not going to be scalable for the future.

Scalability and user-friendly self-service

One of the aims of TTCU’s move to Domo was to achieve a set of self-service user solutions - but how did that work in practice? I was keen to find out how the IT team got to grips with the project, given that the previous solution had been so dependent on those OLAP cubes. Jackson explained: 

Our previous solution [needed] a completely different type of skill set that we had to recruit - and in Lubbock, Texas, that was a difficult skill set to recruit. That was one of the things that made Domo easy. We have in-house full stack developers, so they can definitely leverage the Microsoft SQL infrastructure that we have.

We didn't have to have a specialized skill set to use Domo. We could turn our developers loose on it, but even just being able to simply, you know, throw something in a spreadsheet and upload that into Domo. It's really user-friendly across the board.

As to how the rest of the staff responded to Domo’s solution, for the most part, it was a smooth transition, Jackson recalled:

We had some employees that were perfectly fine with going and building their own reports, their own dashboards. We've had others that need more assistance, but just needed more training. Like, ‘Hey, your data is still here – this is just what you need to go to consume it and you can do that whenever you need it.’

Jackson saved the best example for last, however:

It's actually our CEO who's kind of taken on this effort, but we have a third party where we can pull in market data. He's built some ETL tools, and pivots, and puts it all together into one big data set so we can quickly see where we stand against our competitors in the market.

 We're doing the same thing around employee satisfaction. We've been collecting employee satisfaction data for over a year. We've taken that same approach to pull that data in, using Magic ETL to make it more actionable, so you can see what's changed - promoters, detractors - and what has changed over time. It's been really powerful to see that and something we could not have done without Magic ETL.

Although the IT team had plenty of experience in SQL, it was intriguing to hear that ETL (Extract, Transform, Load) was being used with confidence by a wider staff group who weren’t reliant on additional intervention to do the heavy lifting in data analytics.

The Magic ETL tool mentioned gives users the ability to merge any data sources using a visual interface. By simply dragging and dropping the sequenced steps, customers can create new combined datasets.

Overcoming data challenges

On the subject of data challenges, Jackson cut straight to the chase when I broached this subject.

One of the things I’ll say right off the bat is we had an existing Microsoft SQL server infrastructure, and we needed to leverage that to keep our overall implementation costs down, and Domo fits into that quite nicely. But our previous solution, SNL Banker, really handled our financial data much better than Domo did initially. So we bought Domo’s P&L app, and we paid for the development of the balance sheet side of that.

But what we found out pretty quickly is, we just couldn't look at the data like, month-to-date, quarter-to-date,  year-to-date, then back over time, quite the way we wanted to. That was an initial challenge for us early on. We were able to overcome it, since we also use Workday Adaptive Planning and Domo had a built-in integration for that. We were able to feed that data back through Workday, and then pull that data into Domo. The flexibility of Domo just made that really easy.

Jackson’s team also avoided one classic project misstep that crops up all too often - lack of attention to user training:

One of the things that we did right is we paid for onsite training. We were able to get a lot of our staff through that training, and that resulted in a lot of adoption. It was really hands on, and people walked out of there feeling really comfortable with this system. I don't think we would have had the adoption had we not invested in that.

Demonstrating real value in practice

Going back to the original objectives of the project, scalability had been an imperative when evaluating the possible solutions from vendors. Getting support when making the case for the cost of investment was a factor in the decision-making process, and the decision to move to Domo wasn’t made lightly.

Jackson explained that after some research into the systems out there, eight vendors were considered against an evaluation matrix. Although Domo clearly came out as the winner, it was a little outside the budget: 

We made do for another year and came back around to the same conclusion – this isn't scalable for the future.

In the event, Jackson had to make a strong case for the budget versus the cost of not investing. That done, the decision to go ahead with Domo was confirmed in July 2019 and the implementation went live in December the same year.

In terms of demonstrable ROI, there was one proof point that solidified exactly how the reporting tools had been working for TTCU in terms of scalability and insights, said Jackson: 

Friday after Thanksgiving when we came into work, we noticed there's not a lot of people out shopping. I wondered if there's some economic indicators here, maybe what our local economy is, and how that would compare nationally. So we had a brief conversation, and built a dataset. We spent about an hour on it, and had really actionable data that you can push into Domo. We could see that trend over the last few years – and sure enough, it was exactly what we were reading about – that sales were off by about 15% nationally. And we were able to see that happening in our local economy.

What’s next

Looking to the future, TTCU has plans to make the most of the opportunities that Domo has provided. Over half of TTCU staff are using Domo, and this will be extended to the rest of their employees over the coming months. Jackson argued: 

It's one of the most powerful tools that we have. And the mortgage markets are kind of crazy. So we've been working on trying to have a really accurate forecast and budget for 2023. Because of the ease of use with Domo, we could quickly add data points as needed to build some modeling on loan-level profitability, and have an informed budget for 2023.

The company has a member engagement model in the works, so that it can tell how many products and services are being used by members. TTCU is taking a gamified approach to this, with help from the Domo team. According to Jackson: 

We're trying to get to 3.25 products. We're at about 3.16 today.  I've been working with the Domo team on how we could apply some data science to that and make it more actionable and predictive – to see how we should be targeting our marketing efforts to see more conversions.

As to advice Jackson would give to others who may be wrangling with data challenges of their own, he concluded: 

Be a little more open-minded to some of the built-in tools. When I came into this, we already had some existing ETL processes as a part of that Microsoft SQL server infrastructure. And I thought, well, I'll never use Magic ETL that's built into Domo – I just didn't think I would ever use it - and now I find myself building stuff in there all the time.

My take

Listening to Jackson’s experiences, I was reminded of a previous job when access to a data warehouse was managed by one team. Customized reports had to be requested, although it wasn’t always clear what data was available or needed to be integrated from various systems. Decision-making based on those reports was challenging, especially when they took time to produce.

‘Data-driven’ and making decisions based on data in real time has become a commonly-used idea, but being able to take active responses to changing or emerging situations is a huge proof point of something working out the way you hope when you begin a change project.

Equally valuable is the emphasis on getting buy-in from staff across management and users. A successful implementation isn’t just about the technology; success is dependent on the people using it.

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