For many companies, the concept of studying business data for insight to guide their decision-making is not a new one. In fact, business intelligence programmes have been in place in some organisations for two decades or more.
But with the developments of a more digital age, these processes are increasingly unable to meet the evolving needs of enterprises. In order to compete in today’s data-rich, fast-moving environment, newer solutions will be required, which is why businesses should be looking at big data.
So how can companies recognise when they may benefit from an overhaul of their analytics processes to handle this and improve their operations to become more data-driven?
Technology has moved on greatly since business intelligence processes first emerged. It is estimated that around 90 per cent of the digital data in existence has been created in the last two years, and traditional business intelligence solutions have not been able to keep up with this exponential growth.
In fact, technologies in this area have reached a plateau, with many deployments struggling as they come up against the limits of what they are capable of. One of the biggest issues with these types of solutions is they are only capable of taking in transactional data and giving businesses a reactive overview of what has already occurred within their organisation.
But in today’s fast-moving world, this will not be enough to ensure success. Businesses that are solely relying on what has already happened to guide their decision-making will not be able to respond to new opportunities quickly enough, whereas their more agile competitors will be able to act as soon as opportunities emerge.
The need for a new approach
Therefore, adopting big data analytics will be an important step for companies that are trying to break free from this business intelligence stagnation. Technologies that are able to take in data from a wide variety of sources and deliver fast, relevant results can greatly help businesses when they are looking to move beyond reporting-based intelligence to solutions where they can accurately forecast events, rather than react to them.
Being able to do this effectively is one of the key factors that separate the most successful organisations from the also-rans. The insight gained from predictive analytics can enable companies to get ahead of the competition and greatly improve their overall performance.
Making the transition
A key part of moving from BI to big data will be in how you approach your analytics and what information you expect to receive. Essentially, organisations need to be moving away from reports that only give insight into events that have already occurred, and towards reports and dashboards that integrate future forecasting.
Part of this will also involve increasing the speed at which these reports are compiled. It is no use getting forecasts on future trends that, by the time they are finalised and delivered to the right people, will already be outdated. Fast data processing is central to this, and the ability to conduct real-time or predictive analytics depends on having the right hardware and software to cope with such high computational demands.
Having this infrastructure will be a critical step if businesses are to successfully overhaul their business intelligence solutions into modern, effective big data analytical tools. However, it must be remembered that as well as new technology, new ways of thinking will be required. It will therefore be important for staff to understand the differences between the two and how they will need to adapt their processes and the questions they ask in order to generate business appropriate results.