Working with data scientists to meet future data protection needs

A fintech company wants to be ahead in the future, that is, they want to be able to predict the market, understand their customers’ behaviours and want to be able to merge market expectations and deliver exacting products to their customers. However, they want to do this in the right fashion, meaning they: (a) want to build products that are meaningful and respect the rights of individuals (b) they want to maintain brand reputation and be transparent about how they use data and (c) they want to compete with other companies by releasing products that would match their risk appetite. To meet this, the fintech company is wondering who has the best set of resources for these goals. To accomplish this, the company needs a data scientist with a bit of data protection help or a data scientist with a knowledge of the principles of data protection as a minimum.

The company wants to understand the structure of data that would enable it to meet its business trajectory. For example, let’s say they want to create a feature for their clients to reduce backlogs from payment and help their clients service their business end-users in a timely manner. To achieve this goal, they would collect live data from their portal to first understand the hourly transactions and how many seconds or minutes it takes to complete payments. And here lies the big question, what would the data scientist capture?

First, let’s understand the role of a data scientist and why this fintech company needs their expertise. A data scientist will collect, analyse, and interpret complex digital data using statistics and other programmatic features to help the business-decision making. The data scientist can, with the data, help the product team come up with a new feature for the fintech company. But typically, what transpires is that data scientists sometimes have the tendency to take as much data as possible for them to be close enough to achieve the business outcomes.

That shouldn’t be the case. Hence, why the data scientist needs to work closely with the privacy professional in that fintech company to first establish the scope of what the company is trying to achieve, understand the exact data they need to achieve this and establish how the data will be used and for how long. Succinctly put, there needs to be a clear governance approach embedded in the whole data capturing and analysis stage. In previous articles, I have highlighted some of these governance expectations in capturing and analysing data.

For companies that are willing to collect data to make robust business decision they need to employ the expertise of their privacy professional so that (a) the right data sets are collected for the right business expectations; (b) that they meet the transparency principle of data protection; and (c) establish the workflow for the management of data lifecycle.

There is no one fit approach, but companies will start doing well to embed data privacy in their data management and more importantly, create a synergy between data protection and the entire data team, especially their data scientist.

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