Archive for the ‘Integration’ Category

Theme 6: Delivering the prediction at the point of decision is critical

[tweetmeme source=”atripathy” only_single=false]In 2007, my wife worked as a hospitalist in Charlotte.  Around that time, I noticed a strange pre-work ritual she followed. She took printouts of a few pages from the Wal-Mart website before going to work.  The behavior was surprising and one day I asked her about the mysterious printouts. It turns out that she was printing out the list of generic drugs that were covered in Wal-Mart’s $4 prescription plan.

Like most physicians, she used to struggle with the fact that some of her patients were not complying with their medications as they were not able to afford the medicines she was prescribing. Wal-Mart had introduced a plan, where they sold some common generic drugs at $4 per prescription.  A lot of patients were able to afford the Wal-Mart medicines and it was popular amongst the doctors to prescribe from the Wal-Mart covered drugs. However there was one issue. The Wal-Mart covered drug list was not integrated with the Epocrates system, the mobile clinical decision support software that most doctors use to verify drug dosage and interactions prior to writing the prescriptions.  At the time of writing the prescription, the doctor did not know whether the specific drug was covered within the Wal-Mart plan, unless she chose to make the extra effort and carry a printout of covered drugs and refer to it prior to writing every prescription.  A great idea suddenly became less attractive to act upon, as the right information was not made available at the point of decision.  I refer to this as the last mile decision delivery problem of predictive analytics projects.

Most of the effort in analytics projects is spent on defining the problem, aggregating data, building and testing models. Getting the information of the model to the decision maker at the point of decision is at many times an afterthought.  However, the benefits of the project are dependent on solving this critical last mile problem.

In my experience decision delivery is challenging as it requires cross-organizational coordination. Successful analytics projects are a partnership between the analytics, business and IT groups.  The analytics group needs to work very closely with decision makers or the end users to put the analysis results in context of the decision maker’s workflow.  The actual delivery of the information is done through a mobile handheld device to a distributed sales force, CRM system integration for call centers or executive dashboard delivery through reporting system integration. All of them require close collaboration with the IT group which has to take the results of a predictive model and integrate it with the relevant front end or reporting infrastructure.  Then there is end-user training to ensure the end-users know what to do with the new information. The program management effort required to execute such a cross-organization initiative is significant and very often not anticipated or planned by the project sponsors.

A good program manager is critical to most complex predictive analytics projects. He/she is able to coordinate the various stakeholders to align on problem definition, outcome format, technology integration and training to drive user adoption of predictive analytics solution. Something to keep in mind as you plan your predictive analytics initiatives.

Have you seen predictive analytics projects getting derailed due to lack of coordination  between various groups within the organization or under investment in program management resources?

PS: Last year Wal-Mart solved its last mile problem and integrated the covered $4 prescription drug list into the Epocrates application.

Previous parts of the series are available here: part 1, part 2, part 3, part 4, and part 5

Cross posted on TheInformationAdvantage.com


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[tweetmeme source=”atripathy” only_single=false]I believe that the next wave of productivity improvement in companies is going to come from investment in decision support tools.

In our economy almost all workers are now knowledge workers. However unlike the workers of the industrial era, we still do not have the right set of tools to support our knowledge workers. We live in an era of information overload where employees increasingly need to make faster and more complex decisions using large amounts of available data. Under such circumstances, making informed, let alone optimal decisions is simply not humanly possible.

This creates a need for a range of new tools for the employees. Tools that will guide the decision making process and where ever appropriate automate them. To create such tools, companies will need to create expertise in four foundational areas:

1. Data: Identifying, collecting, managing and curating the data within the company and relevant third party sources
2. Analytics: Creating a scalable process to turn data to relevant insights and recommendations
3. Visualization: Presenting the insights within the appropriate context to support a decision
4. Integration: Bringing all of the pieces together to make the recommendation/insight available at the point of decision in the workflow of the employee

Companies will become better at bringing together the four foundational areas. We will also see increased activity in the vendor space. The established ones becoming more active in acquiring companies in the value chain. And a range of startups who will rush into the space to fill the gap.

For now, the blog is about tracking this trend.

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