Making the most of analytics: How to move beyond simple reportingby
Business intelligence and data analytics technologies continue to attract huge investment from companies; Gartner’s latest Magic Quadrant for BI noted that this market reached over $14 billion in spend last year. Companies are looking to their data in order to grow and be competitive, and this amount of spend is not going to go down. In fact, Gartner expects growth to continue for the next three years by 7% annually.
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, organisational performance and sales pipelines. As this use of data becomes more strategic, companies are also appointing chief data officers and data specialists to improve their management and governance of data.
However, not all companies are as far ahead in their use of data. While all companies will be using data for operational reporting, many are still at the initial stage of using data and analytics for decision-making.
This level of reporting can include basic querying of data and drill down into the numbers. However, it tends to be more ad hoc and often delivered by use of spreadsheets, with all the issues around security and consistency of information that entails. The other tendency is to look only at historical data when it comes to trends.
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. Essentially this includes using data to determine targets, rather than taking the traditional approach of simply adding 10% to last year’s numbers.
Another key aspect of this more advanced approach to analytics 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%. 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 own calculations. However, if you make more use of analytics, this can be more easily included within decisions around sales so that customers can buy and the company can be profitable.
A good example of how BI can provide more value back to the organisation is when sales teams are selling a finite commodity with variable pricing based on availability. An example is advertising space – price goes up as supply diminishes and therefore it’s important for sales to have real-time access rather than data (say) 24 hours old. Using ‘fresh’ information in this way means the sales team can sell the product at the most profitable rate, and can even implement real-time decision-making support for sales as well.
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 can be vital here.
The most obvious uses of predictive analytics are around sales and marketing. By looking at data, it becomes easier to justify questions like “How much should I spend to acquire customers? Where are the hold-ups in the sales process? What happens if I invest in activity X rather than Y?”
These are important business management questions that would normally be answered through a mix of gut feel, reporting and experience. Predictive analytics does not replace that; instead, it provides a more nuanced and detailed way to support that decision-making process. By looking at data in this more detailed way, it is also possible to ensure that investment of time and resources is made in the most efficient way possible.
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.
Looking into the future, this growth of data analytics will be coupled with greater use of visualisation. 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 more self-service and visualisation options will greatly extend the use of analytics within companies, as they rely on data for their strategic decisions.
David Gray is vice president international at Birst.