As in almost every industry, those in the financial services sector hold swathes of data about every aspect of their organisation. In an ideal world, all departments within a business should have easy access to this data to help guide intelligent decisions. The reality, however, is totally different. Any data that is available is either hard to access or in a state that cannot be easily interpreted. This means organisations are potentially missing out on useful insights.
Dealing with the data deluge
One issue is the sheer quantity of data being analysed. As data volumes double every 18-36 months, traditional business intelligence and analytics solutions are simply failing to keep pace.
Analysis is often measured in days, rather than seconds, minutes or hours. Financial services organisations, therefore, face a stark choice: restricting themselves to narrow subsets of data in order to receive limited insight in a timely fashion, or performing a more in-depth analysis that cannot necessarily give insight at the speed the business needs.
Another issue is who can perform the analysis. Most FS providers have a significant pool of staff with deep expertise in different areas of the business. If these subject matter experts could analyse business data directly, they could drive enormous additional value and revenue. So why do many organisations never achieve this?
Data analysis has a reputation for being difficult. Most traditional BI and analytics tools require specialist skills – including in-depth knowledge of mathematical modelling, an understanding of machine learning techniques, and coding languages such as R or Python. It’s not hard to see why this might be off-putting to someone whose expertise lies in logistics or purchasing.
As a result, most FS organisations rely on trained data scientists to perform analysis. This can increase the time analysis takes and reduce the organisations’ agility. After all, these trained specialists will probably lack detailed knowledge of specific business units – meaning any insight will need to rely on a back-and-forth with business specialists, or risk insights that are incomplete at best, and misguiding at worst.
So how can banks and other FS companies empower subject matter experts to analyse data directly?
The first and most obvious step in terms of encouraging wider engagement with analytics is to put in place the right tools. Most organisations understand that speed and power is critical to digest enormous amounts of information in near-real-time, with no pre-filtering. Yet technology that opens up accessibility and offers step-by-step support for the non-professional is just as important. Any such platform should make the common functions easy – with templates for complex tasks, as well as tutorials, dashboarding and easy-to-use visual modelling.
Even so, as analytics can seem intimidating to the uninitiated, you’ll need to address employee ‘fear factor’. In truth, all that FS users really need to get started is an understanding of spreadsheets and basic maths, and a willingness to give it a try – something the sector definitely doesn’t lack. The trick is in helping them to recognise this and encouraging them to engage with the data available to them.
The first step is identifying those individuals who are willing to try using data to their advantage. Businesses should also investigate who, whether existing staff or a new hire, can bridge the worlds of data and the business. This person can help employees identify the questions they need to ask to get the insight they need from the available data. This won’t be an overnight process. Employees need time to recognise the benefits of engaging with data – to the point that they start to become internal advocates and spread their message within the organisation.
To succeed organisations will also need to engage with their IT department to ensure that data is not exclusively ‘controlled’ and ring-fenced. Taking up data scientists’ and IT professionals’ time to help other employees access data directly should be framed in terms of the clear long-term benefits. Ultimately, it will free up your data scientists and IT professionals to do more valuable, skilled tasks – in other words, a win-win.
A brave new analytical world
The financial services industry knows better than others about how important analysis can be, which is why they should jump at the chance of opening data to employees throughout the organisation. The sales team in a bank, for example, can use data to identify opportunities to cross-sell products, such as credit cards, based on live purchasing information. Data can be used within product development teams to assess the likely reception of new investment offerings based on previous launches. The possibilities are truly endless. However, in an industry which can be slow to accept change, organisations must push from within to make the cultural shift that is required to benefit from open access to data.