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How marginal improvements in data strategy can yield tremendous results

  • The truth is brutal: data is hard, and most people don’t understand it.
  • Here's how marginal improvements to a company's data strategy can have a profound impact.
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How marginal improvements in data strategy can yield tremendous results

Christopher Aliotta, founder, president and CEO, Quantalytix

Let’s face it, you’re probably sick and tired of reading about how your bank data will make you more competitive or less risk prone. It’s not that you don’t believe it, it’s simply that the discussion is both laborious and tiresome. After all, if the benefits are so great, why is it such a difficult topic at the management and board level?

The truth is brutal: data is hard, and most people don’t understand it.

It comes as no surprise that when asked about data strategy, it is far too common to hear, “We’ll get by with what we know and what we have.” Unfortunately, according to a survey conducted by Sigma Computing, 63% of employees say they can’t get insights from their solutions in the right time frame, and a report from Forrester indicates that between 60% and 73% of all data is never used for analytical purposes.

This suggests a dismal picture where the data “rich” cannot extract value from their information, and the data “poor” cannot get the information needed. But is technology, or a lack thereof, to blame? Does one need to invest heavily in new technologies to reap the benefits of “rich” data?

I’m here to tell you that it does not take much to yield positive results from data – just marginal improvements to your data strategy can create the foundation for the achievement desired. The key is knowing where to start, and you may be surprised that it has nothing to do initially with data.

It starts with four foundational questions: 

  • Where do we want to be in the next six months?
  • How do we plan on getting there?
  • What metrics need to be measured to support these goals?
  • What information is needed to make adequate decisions?

These are anchor questions that will provide the groundwork for developing the optics and data strategy needed for execution. Let’s unpack these questions.

First, why six months, you might ask? Generally your most pressing fires are happening within that time frame. It’s hard to build an empire when the forests are burning around you. By focusing on six months, the data strategy can be seen as a series of incremental success stories, each building on the prior. Pick a data goal, prioritize it and then set it as the focus — you will be amazed.

Often, companies will try and implement large-scale monolithic data initiatives spanning many years. This is akin to trying to boil the ocean. Not only does this greatly increase the time and upfront investment needed until something useful can be produced, but you can also bet that by the time it is ready to “go live”, the solution will already be outdated, replaced by other needs or changes in business strategy.

Second, as for the “how do we plan on getting there,” this is where your business strategy comes into play. What is important to note is that banks without a clear business vision, whether it’s due to poor communication, planning, or family inheritance went wrong, will equally never have a good data strategy that is effective under any circumstance. 

In my professional career, lack of a business strategy is the number one reason banks fail to implement a sound data strategy. An organization must have a solid business strategy that is well-known and effectively communicated; otherwise, it will be impossible to know what to measure.

Lastly, the need for a solid business strategy dovetails perfectly into the final two bullet points: with the business strategy in mind, the metrics needed to make adequate business decisions are the core data requirements required to run your business. 

As a business owner or leader, what are the things you need to see to know the overall health of your business? Each of these metrics should be decomposable such that root analysis can be performed when investigating business performance. Sometimes, these measures will be hard to attain because they’ll be dependent on other data initiatives to occur; this is fine, but be patient.

We often think of using data like we would use a map when traveling. As anyone who has ever navigated with an old paper map can attest, like your data, things change, and if you are not updating your map, new, faster roads may go unnoticed.

Having a data strategy not only gives your position on the map, but with incremental improvements, can keep the map up-to-date, and even act as a GPS in case you get lost as well.

In a world where people are still using a paper map, and in some cases no map at all, marginal improvements can have a profound impact.


Christopher “Chris” Aliotta is the founder, president and CEO of Quantalytix, a Birmingham, AL-based fintech startup specializing in advanced analytics and loan management systems. With more than 16 years of banking and fintech experience, Aliotta has mastered interest rate risk management, credit risk management, leveraging and monetizing data, bank strategy, loan/deposit valuation, balance sheet profitability, relationship profitability modelling, asset liability management, relationship profitability modelling, cloud software trends, blockchain trends and data tools.

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