December 2009 Archives

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.

Today Aha! (http://www.ahasoftware.com/insight.html) presented to the BBBT.  Aha! is a provider for "a new generation of software for business to compete with collaborative, closed-loop, predictive analytics.".  Looking to capitalize on the gap between analytics and the operational processes that could best use them, Aha! proposes to close the loop just as Six Sigma and TQM have advocated for years.

 

What to Like:

• I liked the fact that a SaaS company was moving to link strategy analytical models with operational processes.  This is a bold approach for an organization with data access challenges outside the corporate data center.... Then again SalesForce.com was a bold challenge to move the sales funnel outside the data center as well.
• I also liked the fact that Aha! views analytics as being much more than something that you can accomplish in MS Excel or with the SUM() function in SQL.  Along the continuum of analytics, Aha! looks at the highest levels of optimization and predictive analytics and how to put those analytics to use within the above mentioned operational business processes.... Not just models that "operate" in isolation among the guys/gals in the "white lab coats".
• Finally, I really appreciated that not only does Aha! take the improvement concepts of Six Sigma and TQM to heart in their preamble... Aha! brings that into their stated methodology to link the modeled results to their brand of collaborative analytics.

 

 

What not to Like:

• The demo provided lots of good information to the user.  However, I would have expected a more robust dashboard environment.  But this is a slight disagreement with the presentation layer.  It was agreed at the BBBT that it is/was much more important to have the data accurate than pretty.  In this Aha! provides the confidence that the numbers presented are accurate and faithful to the model.  Enhancing the 'human factors' is a relatively easy exercise.

 

In Summary:

I really like the features and functions of the Aha! SaaS Axel Services Platform and the methodology of the Aha! implementation.  In combination, they represent a good proposition for many organizations.  However, there are refinements to the human factor aspects and the overall high level messaging that Aha! need to make before the Axel platform can really move from the niche opportunities, that it has been filling, to a mainstream product offering that moves beyond those niches.

But again, SaaS pioneer Salesforce.com must have seemed like a niche player to many at its start to link sales performance with operational data.  While Aha! may not be in SalesForce.com's class in the future; Aha! is making strides to take advanced analytics from the lab to the frontline.

Today, we got to hear from Aha! at the Boulder BI Brain Trust. 

Once I disconnected my brain synopses that were connecting the company name to the 1980’s song and innovative video “Take On Me”, I wrapped into the ideas they were presenting.   They are taking on the “magical, mystical, overloaded” term of “analytics” - a market expected they expect to grow 10% next year.  They are targeting the “93%” of business users that don’t use analytics today.  Aha! defines their space as the “Business Embedded Analytics to operationalize predictive and other analytical models.”  This 4-year-old company is part of the growing trend of software-as-a-service providers.

After spending some time defining analytics, which remains elusive, Aha!’s definition is “Analytics are models and model-based decision making.”  Companies can differentiate themselves with the output of analytics.

They are attempting to bring, to pull a title of a talk I used to give, “data mining to the masses.”  This is a noble idea and has been pursued for quite a few years.  Microsoft, for one, has made great strides here.  The idea relates to the concept of “Attention economics”, which is an approach to the management of information that treats human attention as a scarce commodity, and applies economic theory to solve various information management problems (from its Wikipedia entry.)  

I have said this before, but it bears repeating here where Aha’s form of analytics is like the “data mining” definition we have used and they are addressing these problems with data mining.

“Data mining has long been a means to attaining high business value from a warehouse.  As the means of automating discovery to explore and identify new business insight, it stands alone as an access method.  Interactive Query or OLAP presents the measures of the business organized around their logical dimensions.  Hierarchies in the dimensions allow for organized grouping and lead to drilling up and down in the data to find what you’re looking for.  Mining, however, makes you aware of situations that may represent new market opportunities or business problems that have yet to surface to the level of notice through standard interaction methods.

However, much of data mining has been relegated to the domain of a special breed of expert, often holding a Ph.D. in a statistics discipline.  The mining process currently deployed in many organizations is not only time consuming due to the challenge of the tools and the semantic gap between the front line and the statisticians, it is also non-iterative in nature.  Discovered nuggets flow from the miners to the front line and are only selectively interesting and actionable.  The feedback loop is missing.  It’s like having a luxury car but keeping it parked in the garage at all times.  Mining tools that are interactive, visual, understandable, well-performing and work directly on the data warehouse/mart of the organization could be used by front line workers for immediate and lasting business benefit.

The advanced techniques deployed in a lot of mining programs are generally well beyond the understanding of the average business analyst or knowledge worker.  If this advanced level of analysis is reserved for the few, instead of the masses, the full enablement of the warehouse in the organization cannot be realized.  If those whose analytical interests stay well within the complexity of computing sales commissions are shut out of mining, mining is not nearly as effective as it could be.”

Cut to Aha!’s take on this problem… via a long-awaited demo…

In the healthcare example, they created a large flat file pulled out of the client’s databases, to assess propensity to disenroll.  In the example, they delivered improved retention rates at a measurable NPV of $43 million.

Customers of Aha! (product: Axel) are able to interact with a model until it is perfected.  The model runs on continual updates of data sent to Aha!.  Overall, including some things seen under NDA, it’s impressive.  Some of the challenges include that the collaboration is occurring only while the customer is reviewing the output, though the metrics are numerous and interesting and the interface obscures some of the value.

Aha! is bringing together an old world that needs change - data mining/predictive analytics - and the new world of software-as-a-service.

Aha! Software of Colorado presented with Mark Teflian, CEO & Founder, Bruce Bacon, VP Product & Founder, Tom Holloran, VP Delivery, and Peter Gallanis, Chief Architect & Founder. When Mark was asked about what differs their company in a very crowded analytics marketplace, he replied, "The theme is embedding analytics into business process. 93% do not use analytics in their day-to-day jobs. We fuse management science and operational research into practical business applications." Their customers are in healthcare, telecommunications, travel and transportation, such as Qwest, Coventry Health Care and Deltacom.

Mark pointed out that the analytics will be driven by aligning and integrate it into the business fabric. Their market segment is "Business Embedded Analytics" that he estimates to be $2 B to $3 B in 2010, or about 10% of the total Analytics marketplace. He offered the chart at the right as a definition and range of analytics. He elaborated that analytics is analytics when it is driven by a "model". Analytics is a model-driven decision making. Needs to close the loop with actionable information whose impacts/effects are measured to become part of the next iteration.

Tom did a demo on Coventry Healthcare to analyze and manage the "dis-enrollment" of clients, which has reached 21% churn rate. One situation noted by Tom was when a high dis-enrollment of new clients was questioned, which lead to changes in call center for verifying new policies. Note the drop in rates from 10% to 2%. It was actually a third-party service partner who had a broken script who was dis-enrolling clients as fast as they were enrolling. The point was that a holistic perspective on KPIs could see strategies impacted by faulty operations.

Claudia asked about the meaning of "collaborative analytics" in this business situation since the three user group shared the same data but do not interact during the process. Their tools does support event management that allows users to interact on KPIs over specific durations.

My Take

Aha Software is touching a critical part (embedded analytics) within BI industry practices. This was a great session that surfaces several fundamental issues for the evolution of business intelligence. In some ways, the issues have not changed (simple versus complex, depth versus breadth). In other ways, the issues become intertwined into hard organizational and management problems.

Balancing the simple with the complex...How do you simplify and embed complex analytics into biz processes, AND the results are valid? Simple for simple sake is easy! How valid is the generic model to the specific business situation?

Achieving actionable analytics... Actionable should imply that the results can be easily mapped to business actions with interfaces into business process management systems for workflow creation/monitoring.

Preserving organizational attention... A wealth of information creates a poverty of attention, by Herbert Simon. See the Wikipedia entry on Attention Economy and the full quote from Simon. This challenges that more information is better, the special case is... Is more analytics better? Under what conditions?

Defining predictive analytics... Where is the boundary for analytics between "rear view mirror" historical and "forward looking" predictive? Is a linear trend line on a Excel chart predictive? Does "What-If" case a necessary part of predictive analytics?