3 Mistakes Banks' Executives Should Avoid While Leading Big Data Projects

Big data, data analytics, data science are terms used interchangeably and often. Based on my experience, Bank Executives tend to be trapped by the same kind of dilemma: how to harness data for their organisation. In this article, I outline the 3 most common mistakes that executives make when thinking about data and developing a strategy around it.

Mistake #1: data projects should be let by IT departments. IT departments play an important role in determining how data is used and executing business strategy. However, due to the nature of the function, it can be difficult for IT departments to not only recognise what data is available, but also determine how it can be used to draw deep insights from it. It is not enough to possess data and BI software that can produce exciting dashboards and graphs – the value is viewing the data with an analytical lens to guide strategic action. And this leads to the second mistake.

Mistake #2: ignore the action for P&L. Give purpose to your data, not the other way around. Many clients, especially those in the banking sector, have asked for advice on how to price their data. It’s a reasonable question because data should be valuable. Following this logic, why can’t we consider data as assets that have a monetary value on a balance sheet? The answer is, that when data is just stored, it doesn’t generate profits. Big data is important only when it’s solving business problems. It can be used for identifying new, hidden opportunities to increase revenue, or for finding the right ways to reduce costs. Data-driven decisions leading to positive results is the holy grail of data usage, but how do you get there? The answer is: don’t let your assumptions speak louder than the data. Data is the best advisor to an executive who is trying to understand what their business, clients, and needs. Listen to Simon Sinek and “start with the why.” To determine which “why” to tackle first, listen to your data. It has hidden gems of realisation and surprise, even for the data owners. So, before starting to buy expensive big-data software, seek guidance from a data analysis specialist to identify the “why” you should focus on first to create improvement in your business.

Mistake #3: focusing on big projects instead of effective small (and cheap) initiatives. Now that you’ve listened to your data and know where to start – think small. It’s common that companies want to make a drastic leap into the digital transformation world. However, the bigger the project, the harder it is to approve, implement, and demonstrate positive results. Start small, act quickly, and solve one problem at a time. It may seem like a counterproductive advice, but if you avoid mistakes 1 and 2, you will create a huge financial impact with minimum resources. Create a list of activities and focus on P&L to estimate the financial impact versus the cost of achieving it. Focus on the cheapest opportunities first, irrespective of how big the result is. It is common that companies prioritise the high reward projects, disregarding the costs and investment needed. It can be shocking how much money it is possible to make or save, with just a few small projects before going into the heavy “dream” projects.

Pablo Morales. CEO, London Analytics

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