Located in Denver, CO, Aha! Software was founded in 2006 to develop "a complete analytics management system". It's estimated that analytics have been adopted by only 7% of the potential user base, which tends to be the advanced analysts. They focus on embedding predictive analytics into business processes, which is predicted to be a $2 to $3 billion market in 2010. They feel that predictive models have not been operational, in that in today's environment they don't close the loop with a business outcome. To address this challenge, they have created a software-as-a-service (Saas) application that allows users to view statistical results organized into KPI's which target business processes.
What is analytics? According to Aha!, it is embracing the "actionability" of data and statistical based models to drive business decisions. They feel strongly that companies differentiate themselves based on the level of analytics being employed. (see the graphic below)
From my standpoint, the question is whether making it easier to do predictive analytics means that low level analysts will able create viable models? Can people create a sophisticated model without understanding the statistical analysis behind the models? This is one of the limitations that traditionally has kept analytic applications complex and the adoption rate low.
We were treated us to a demo of their product which was based on a case study from Coventry Health Care. Their customer wanted to reduce their churn rate and develop an integrated retention approach. According to Aha!, based on the project they were able to improve their member retention by 7.5%, versus their target of 3%. For the demo, they used a flat file of data that had previously been run through a predictive analytic engine.
The user interface provides both aggregate and detail information. The detail screens contain 'sparklike' graphs that provide a window into the performance of each particular KPI. The summary screens provide some basic statistics of the KPI along with a larger line graph, which represents the historical trending of the measure. The graphs, while not flashy, do build incrementally on the screen. The business metadata is fairly sparse, which implies a user base that truly understands their data and the metrics being presented.
Unfortunately, I was unable to stay for the entire demo. From what I saw, I was impressed with the scope and organization of their application. Like most analytic applications, a great deal of the value in their application appears to be reliant upon the upfront development of the statistical models. While they don't solve all the upfront work, they are passionate about exposing complex analytics to the 93% of the market that is currently only being served by BI and reporting.
