Published by Gbaf News
Posted on January 17, 2020
5 min readLast updated: January 21, 2026
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By Ashley Bill, enterprise data consultant, Micro Focus
It wouldn’t be an exaggeration to say that the age of predictive analytics is upon us – you cannot miss it. Using insights gleaned from data, companies involved in the financial markets are able to better predict the preferences and behaviour of their target audiences to inform and maximise the potential of marketing, advertising and product development. Accurately predicting what your customers want and remaining a step ahead of competitors is the ‘holy grail’ of business success.
However, while more financial services (FS) organisations are looking to monetise data by predicting customer spending patterns, justifying the costs of conducting these analytics can be difficult. Investment in many man-hours of searching large, unstructured datasets for meaning can lead to little gain. Who said ‘panning for gold’ was easy? The answer to getting the most value from data, in fact, lies in data governance and compliance. Here’s why.
Monetising compliance: the business case
The majority of CEOs would say that the best way to achieve organic growth is to become a ‘data-driven company’. However, many C-Suite executives cannot afford to invest enough in this transformation as they struggle to prove its ROI. Traditionally, companies have taken data from wherever possible to fill a data lake and then turn to data scientists in the hope that they can derive actionable insights from it. But there is potential for this approach to encourage a culture of ‘garbage-in garbage-out’, causing leaders to question where the real value lies.
Creating this value involves setting standards and putting processes in place to ensure data is controlled and accurate before applying analytics – which sits squarely in the field of data governance and compliance.
Fortunately, life after the General Data Protection Regulation (GDPR) has seen FS organisations already begin to change how they think about data. While the FS industry is more accustomed to stringent regulation than some, banks, investment funds, insurance companies and many more businesses are now tasked with managing their data even more closely to avoid fines and reputational loss from failing to comply with the regulation.
By ensuring that data processes are GDPR compliant, companies can, in turn, generate high quality data that is relevant, complete, accurate, consistent, meaningful and understood.
Data management for predictive analytics
Success in predictive analytics is the result of making data useful through strong data management strategies. This involves shaping and supplementing the data with ‘metadata’, discovering and defining what is important and wrapping the data with context.
By utilising Redundant Obsolete and Trivial (ROT) methodologies, FS organisations can focus on the data which counts, remove ambiguity, reduce the risk-footprint of breaches, focus security efforts, and introduce a data lifecycle. This not only reduces storage costs, but also enables companies to hold onto the quality data which supports predictive analytic decisions.
Creating a data-driven culture
While implementing data management and compliance processes is essential for maximising the use of predictive analytics, the benefits can be lost if not combined with the right company culture.
To create a data-driven culture, the gap must be bridged between the IT operations teams that are responsible for keeping systems and storage running, and the those in charge of the wider business strategy. This depends on at least one person feeling empowered to have one foot in each camp – by failing to do so, progress will be slow or non-existent.
Using data ethically
Using compliance to drive the management of data is a fundamental step in the right direction, but it is only a starting point.
Organisations also need to think about how the data can be deliberately or accidently misrepresented. Can your data scientists model accurate predictions for customer preferences or even sales for a certain demographic? Inaccurate data, or data that has been misrepresented, can reflect bias or stereotypical opinions rather than reality.
Businesses across all industries have a higher responsibility to manage their data beyond the GDPR to ensure that it is not misrepresented, misused or misunderstood.
Boosting business outcomes
Although the benefits of using compliance to successfully apply predictive analytics are clear, they are difficult to put an exact monetary value on, raising questions among FS institutions about measurement.
In fact, ascertaining this value can be compared to the concept of ‘goodwill’. On any company report, there is a line entry for ‘goodwill’ with a monetary value attached, indicating that the company has worth because it is well-known. For example, in 2018 Facebook estimated its goodwill at $18 billion. This number is not tangible – you cannot touch or sell it – but people accept it as an important asset. In a similar way, companies should have a line entry for their databases given that the locked-up potential value of data is huge, especially when this data is managed correctly.
By investing in optimised data management driven by compliance, FS organisations can effectively increase the quality, and in turn, value of their data. This not only saves them pouring time and resources into making sense of exploding datasets further down the line, but also creates an environment where teams can efficiently deploy analytics to make informed decisions.
If predictive analytics is the ‘holy grail’ for boosting business outcomes, then compliance is an essential component. And looking ahead, it will be a major driving force behind the development of modern, ethical, data-driven financial organisations.
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