Peter Pop, SVP Financial Services, HCL Technologies

Big Data was the phrase on the lips of many business leaders last year, with the concept already moving past the Peak of Inflated Expectations in Gartner’s Hype Cycle. The current Big Data landscape is one of increasing maturity. We are seeing some benefits being accrued by early adopters, while others look to lay firm foundations by reigning in control of their data sprawl to create a central data lake, opening up access and insight.

Insurance leads the way

In the financial services sector, insurers have been leading the way when it comes to Big Data. Many of them are already using it throughout their businesses, from making sure the appropriate product is offered to customers, mitigating risk through predictive modelling and reserving through to identifying potentially fraudulent claims.

Many insurers are also becoming increasingly innovative when it comes to capturing and using customer data. For example, Aviva’s in-car app monitors the driving style of its customers, offering a 20% discount on car insurance for careful drivers. While in contrast, the banking sector has been much slower to get on board with Big Data, we are now increasingly seeing banks embark on a Big Data journey to support their back-office operations.

To a large extent insurers are seeing Big Data as a means to overcoming their historic problems with customer visibility and then using this improved customer transparency to drive their business forward without the need for lengthy MDM initiatives or significant legacy transformation.

What’s the driver?

There are three main drivers for banks to adopt analytics of customer data: customer retention, increasing market share and capturing a larger share of the customer’s wallet.  By using customer data, banks can analyse the probability that a customer will take its business to a competitor.  Early intervention can reduce this loss. There are success stories where banks have been able to significantly increase customer acquisition through careful analysis of existing customer behaviour, from both on-line and off-line sources. This data is then used to feed into a CRM solution, giving the call centre more targeted leads.  The data can be fed back into the teams building the customer touch points like mobile and web applications to improve the customer experience and personalisation when using these channels. Customer data analytics has been successfully used to target customers with products that were more relevant to their individual requirements. The take up rate from these targeted campaigns is significantly higher than the typical scatter gun approach used without applying data analytics.

Barriers to adoption

By far the most significant reason for the slow uptake of Big Data projects in banks is the siloed nature of data held in their existing legacy systems and data repositories. This often means that data is not shared across the enterprise, and when it is, it often only covers some areas; limiting the field of vision and making it difficult to gain a 360-degree customer view. A lack of internal skillsets is also proving to be a challenge for financial services firms. Data management is a brand new skillset beyond what already exists in the sector, with expertise required in statistics, mathematics and programming to make it a success. On the other side of the coin, people must also be trained to interpret this data, or it is pointless producing it in the first place. As a new discipline, it is hardly surprising that there is a significant shortage of people with the right skillsets to be able to successfully implement Big Data projects and drive benefits for the organisation.

Another thing to take into consideration is the fact that a lot of time is required to analyse large amounts of data. It also requires huge amounts of processing power and storage, which cost significant amounts of money, meaning Big Data must be a priority within a business in order for it to be a success. At the start the cost of Big Data projects outweighed the benefits, but now this isn’t the case as the technology and its application has matured. Of course, there remains the potential risk that customer data could be breached as a result of Big Data analytics activity. For example, if sensitive data sets that have previously sat in isolated, high-security siloes are transferred into less secure databases for cross-referencing with other, less sensitive data at rest in a data lake, this could inadvertently lead to it being more vulnerable to a data breach. As a result, financial services firms will need to pay careful attention to the data they are using in analytics projects and ensure it has the appropriate security measures in place to protect it at all stages.

Battles being won

We are now seeing banks turning towards Big Data technologies in increasing numbers as many of these initial barriers are being overcome. Cost optimisation is now very much the name of the game; as such many banks are looking at the likes of Hadoop as their Big Data platform of choice, as it offers them greater flexibility at a significantly lower cost of other platforms on the market.

Big Data is also no longer the sole domain of data scientists, as the latest reporting and dashboard tools such as Tableau are putting digestible data directly into the hands of business users. The ability to present data to users in a way that anybody in the business can understand should not be underestimated, as it really starts to unlock the huge potential of Big Data within banking and to enable self-service Business Intelligence.

Wider drive for innovation

Change won’t happen overnight. Implementing and migrating data to new platforms takes time; a number of years as opposed to months. At first glance that could seem overwhelming, but bringing in the right support and expertise will lead to long-term benefits. Making an up-front investment of time will ensure that banks are set up to effectively manage and organise both existing and new data in the future.

Understandably, banks have so far concentrated their Big Data efforts on their back-office functions. However, there is a great opportunity for banks to drive deeper engagement with customers than they ever have before. A number of banks are already investing in innovation labs as they look to capitalise on the Big Data opportunity. Ultimately, banks must be able to walk before they can run – it is vital they take the time to lay the firm internal foundations so that the cost savings and improved customer experience will follow.