How do we make the world a better place? It’s a question that’s tickled many a worthy brain over the centuries – and now the answer is closer than ever. Data can make the world a better place. Data is why we don’t live in caves anymore, why we moved from fields to factories to silicon chips, from plague doctors to CAT scanners.
Data benefits everybody. Look at the finance industry. Maybe you’re an angel investor on the lookout for your next investment. Data can tell you what stocks and shares are looking hot right now. Or maybe you’re working in an emerging economy and your department hasn’t got the two proverbial pennies to rub together. Data can help you plan access to basic financial services. Data may "discriminate" when it makes decisions about who can and can’t have a loan, but it’s not discriminatory about being useful.
In finance, data can make the world better not only by improving the accuracy of risk models and fraud prevention but also by improving banks’ customer relationships, helping them build a strong understanding of customer needs and delivering the tools to meet them.
Banks must build and deploy analytical models that help them build a deeper relationship with their customers. The aim should be to provide a better service based on data-driven understanding – not working out how to influence people into spending more money.
Changing the banking climate
However, too many banks have operated on the latter model for too long. As a result, many now have a serious image problem.
That’s not really a new insight, though – banks know they need to improve their customer relationships; customers know they should have better relationships with banks. The provider that wins the customer will be the one that delivers the best user experience based on the information it has. It’s a classic question of supply and demand – can you provide what your customers want?
Data is the key to achieving that positive change and meeting evolving customer needs. Rather than being fearful of Big Brother, we need to understand that data can be a tremendous force for good. Banks must demonstrate that they’re using data responsibly under an ethical framework, such as FATE (fair, accountable, transparent, explainable) or the 2018 Data Ethics Framework, to build trust and improve their customer service.
The motto for data in banking should be "do unto others as you would have them do unto you." In other words, don’t sell data to third parties without permission, and don’t use it as leverage over potential sales targets. Instead, banks must focus on how they can use data to fuel and foster a better customer relationship, out of which at certain points there will be opportunities to transact.
Data without analytics is value not yet realised
However, even if a bank is committed to this positive vision of data analytics, concrete action is needed for customers to see the benefit. Data alone doesn’t drive change – decisions do. You need the means, method and motivation to analyse data, or you won’t be able to move forward.
Unfortunately, according to SAS research, fewer than 50% of analytical models built get deployed. Companies are spending lots of time and effort building models but then not using them. That’s a massive missed opportunity. Even the models which do see operational use are taking longer to come to light than they should. 90% of models take three months to deploy – by which time they’re already out of date.
Time is money when it comes to analytics. Spinning your wheels in research and development is not the same as making change. Not only that – many banks are struggling to recruit data scientists. What’s the likely outcome if they spend months developing a model only for their hard work to be left by the wayside? It will push them away to other industries or companies.
In short, banks that fail to deploy their analytical models in time (or at all) don’t just risk their customer relationships – they risk losing the only people who could help them fix the problem.
Jumping over barriers
Deploying models quickly to access useful insights is the key to developing stronger, more valuable relationships with customers. That being the case, there must be reasons why more companies aren’t working this way. What can banks do to overcome those obstacles?
First and foremost, they must recognise the need for deeper connection internally. For many front-line teams, the biggest frustration comes when they want to use more analytics but their tech teams can’t – or won’t – give it to them. Usually, that’s not because they can’t build the model – it’s because they’re not fully brought into the project. Where tech used to be the main obstacle to progress, now it’s culture. That means that you have to get cultural buy-in to analytics projects. Culture is famously hard to shift quickly, but the effort pays dividends in the end.Unfortunately, according to #SAS research, fewer than 50% of #analytical #models built get deployed. Companies are spending lots of time and effort building models but then not using them. Click To Tweet
Right data, right people, right time
In all of this, the guiding question should be: "How do we get the right answer to the right people at the right time through the right channels?" Banks recognise that contextual communication is important for customers (i.e., putting the right information in front of the right people at the right time). Analytics projects should be guided by the same principle. If you provide the right people with the right data at the right time for the right reason on the right device – that’s when you generate value for the whole company and the customer.
How do we make the world a better place? One step at a time. By trying to do our best for customers. By using the right technology to meet their needs. By bringing the whole company behind that effort. And by behaving ethically with people’s personal data.