Author: Manoj Madhusudanan, Managing Director, InsightBee 

As the hype surrounding big data increases, so too does the number of organisations investing in high-end data analytics. Although big data can offer clear-cut benefits to some organisations, others may be far better suited to utilising already existing information – according to a study by Harvard Business Review entitled “You may not need big data after all”.

Already businesses have been adapting to changing data trends. Firms know to look beyond their internal data – ‘small data’ – as it is not sufficient enough to provide valuable business insights. In our research, we found that half of business executives say that they spend more than 10 hours a week seeking business insights derived from external data.

However, not all of the data sourced from external research is actually useful. Our Insight Crisis report reveals the damage that inefficient and unguided research can have on an organisation, with ineffective research predicted to cost the UK economy £14 billion a year – hardly small change.

The impact of inefficient research is not purely pecuniary. Astonishingly, 1 billion work hours are predicted to be wasted each year, with almost two-thirds of employees feeling as though their research is unreliable and their time wasted. Whilst the loss of revenue is a highly visible issue, the further effect on employees is a less observable pitfall; businesses need to alter their approach to data gathering to improve productivity and staff wellbeing.

The main factor in why Financial Institutions, in particular, fail to efficiently gather data is the sheer volume available to them. An estimated 2.8 zettabytes, or 2.8 trillion gigabytes, of data, is generated and replicated each year worldwide.[1] A third of this data could be valuable analysed, yet over three-quarters of it remains untouched. Casting a wide net into this ocean of raw data no longer works, and drowning in it can be a real detriment to the operation of your business.

Slipping down the proverbial rabbit hole that is big data is not the only problem facing financial institutions regarding research. With developments in technological infrastructure come new opportunities – to those who can access them. The recent MiFID II postponement was due to the banks’ inability to adjust their IT systems in a reasonable time frame, illustrating the technological disparity that exists in the market, and the stresses it can place on both the market and businesses.

Technological developments are changing how research data can add value to an organisation. Through digital networks and streamlined new techniques of gathering unstructured data, the focus has shifted from quantity to quality – sourcing large amounts of data is no longer the goal of research, rather consolidating the insights gained are.

Even with advanced IT infrastructures, some financial institutions still do not know where to look. Business intelligence can help companies serve existing clients in a better way and gather information on companies, industries or specifics. However, it is imperative that the business intelligence is gathered in a timely and cost-effective manner, as financial institutions may not be able to comply with the data requirements of regulations like MIFID II or seize new business opportunities, due to untimely research.

The data needed to develop valuable business insights and the modern data collection methods to gather it exist, yet the two do not necessarily marry up, as our survey on employee confidence in research findings revealed.

Cloud-based start-ups such as ours are now aiming to close this ‘insight gap’. Many firms have already begun to source insights externally using services specifically designed to identify trends. Financial institutions are fast looking for efficient ways to gather actionable insights, seize new opportunities, and react to market opportunities more quickly.

The lesson is clear here. Obtaining high-quality insights on customers and opportunities is vital for financial institutions of any size, but big data is not always the answer.