One of the big challenges facing the Business Analytics industry is the historical complexity of business intelligence and analytics tools. For years companies have had to rely on their BI experts to do just about anything useful; it isn’t that companies do not see value in putting analytics in the hands of business people, it is that the products needed a Diploma in Statistics and intimate familiarity with the technology behind the tools.
However the situation is improving. Products like Spotfire and Tableau have worked hard to deliver data visualization solutions that provide users with business-context easy to understand data, and suppliers of broader Analytics suites such as Oracle and IBM have been trying to improve other aspects of analytics usability. To be honest, IBM has been somewhat lagging in this area, but over the last year or so it is giving clear indication that it has woken up to the advantages of providing such tools as predictive analytics and decision management in a form that the wider business user community can access.
The recent IBM announcement of SPSS Analytic Catalyst is another proof point along the journey to broader access, usage and value. This exciting new development may have been named by a tongue-twisting demon, but the potential it offers companies to create more value from corporate information is huge. In essence, the tool looks at this information and automatically identifies predictive indicators within the data, expressing its discoveries in easy-to-use interactive visuals TOGETHER WITH plain language summaries of what it has found. So for example, one SPSS Analytic Catalyst (really rolls off the tongue, doesn’t it) page displays the ‘Top Insights’ it has found, such as the key drivers or influencers of a particular outcome.
The combination of simple visuals with associated plain language conceals all the statistical complexity underneath, making the information easily consumable. The business users can quickly identify the drivers of most interest related to corporate key performance measures, for example, and then drill down to gain a deeper insight. Removing the need for highly trained BI experts means that the wider business community can create substantially more value for the company.