Is Your Data Sitting On A Strong Foundation, Or a Flimsy Frame?

At a recent CIO Innovation Insider event roundtable, I asked about BI and Data Analytics Integration.

This is a timely discussion about a prominent topic within the community. From an innovation point of view, I want to start getting some clarity on what BI means, what data analytics means. I want to get some clarity on how you integrate data sources and bring them into a consistent platform to get meaningful information from, and how you get started.

The reason I’m saying that is because people are throwing out buzzwords of machine learning and AI, yet their backend is really kind of a mess. We’re putting fanciness on top of a weak structure, a foundation that’s not quite there.

Using a Bottom-Up-Approach to Develop Data Clarity
To launch the conversation, knowing that at least one CIO attending comes from a background entrenched in this, and who has recently jumped into a new industry. He and I met recently he told me about how he has gotten some really quick wins using a bottom-up approach – I thought this was really practical.

I would like to share with you some of this innovative CIO’s challenges and successes.

Being new to credit unions he found a lot of “silos”, with no checks and balances, so he needed to start at the beginning – starting with the banking system and ending with the connection to the member.

  • He needed to discover who used the most data.
    o He started by looking at the data flow diagram.
    o AWS has an open source data tool called Glue and he used this for the collection of data that represents the business processes.
  • He determined that Lending/Collections used the most data.
    o He started using Tableau and Alteryx and took existing reports and put them into this program.
    o Then he could then begin to make decisions based on the data; he did an analysis and dynamically produced a member map.
    o This saved the Loan Servicing department about 70 hours/week.
    o He now meets with them on a regular basis which creates more value to them and is now working on automating process APIs
  • He worked with the Marketing department and launched a new campaign in 3 days. It saved them $70,000 and they are now working to measure the data to determine their ROI.

He believes that when we begin by looking at data lakes up front, once we start understanding the data better, we can mature and graduate to AI, bots and machine learning.

Now they are working with Chat bot for customer services – to expand services availability that go across multi-channels, i.e. phone, app, and online web portal.

He landed these quick wins without spending a ton of money. It was really innovative; and now he’s freeing up dollars within the organization to go tackle some bigger initiatives.

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