Companies are looking to their data in order to grow and be competitive. In fact, according to information released by Gartner, revenue growth in Business Intelligence and Data Analytics technologies is expected to reach $17.1 billion by 2016.
This growth is fueled by the increasingly important role that data and analytics now plays in companies, as business leaders search for more insight into their customers, core business process performance and sales pipelines.
However, not all companies are as far ahead in their use of data. While all companies will be using data for simple reporting, many are still at the initial stage of using data and analytics for decision-making.
The next step for developing this use of analytics is to get more sophisticated and will normally include more forecasting based on data. Rather than looking at sales targets and then working backwards to determine the sales volumes required to hit those targets, sales managers can instead look at their pipelines and future activities in more detail so they can build up a more accurate picture of forecast demand.
Another key aspect of this more advanced approach to decision making is the use of data for real-time analytics. Imagine a situation where you are selling goods, and one of your main raw materials suddenly jumps in price by 30 percent. How quickly can you factor that into your own sales strategy? Without good data, it can take a while for the sales team to factor this into their calculations. However, if you make use of analytics, this can be easily included within decisions around sales so that customers can buy and the company can be profitable.
The most sophisticated companies using data are extending their analytics to make more predictions around what is possible, based on a wide variety of factors. This predictive analytics approach is based on using data to give greater guidance to both internal and external teams and relies upon more advanced statistical analysis. This can be more complex than the basic analysis techniques that were previously used, so some experience and knowledge of statistics will be vital.
For companies looking at their journey around data, there are more opportunities to develop their use of BI and analytics as they grow. This is not something that can be put in place through simply applying tools; rather it does involve changing some elements of mindset and business process as well. By mapping it to a maturity curve, each company can progress at its own pace.
This growth of data analytics should be coupled with a greater use of visualization. While the analysis of data becomes more sophisticated, this has to be linked into also improving the ability for people to consume the information and take action too. This involves making it easier to display information in ways that are understandable to non-data specialists, while also keeping the ability to drill into data sources where required.
This combination will become more important as companies move to become more data-driven in their decision-making. Adding an adaptive user experience, along with more self-service and visualisation options will greatly extend the use of business analytics tools within companies, as they increasingly rely on data to underpin their strategic decision-making.