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Lou Agosta

Greetings and welcome to my blog focusing on reengineering healthcare using information technology. The commitment is to provide an engaging mixture of brainstorming, blue sky speculation and business intelligence vision with real world experiences – including those reported by you, the reader-participant – about what works and what doesn't in using healthcare information technology (HIT) to optimize consumer, provider and payer processes in healthcare. Keeping in mind that sometimes a scalpel, not a hammer, is the tool of choice, the approach is to be a stand for new possibilities in the face of entrenched mediocrity, to do so without tilting windmills and to follow the line of least resistance to getting the job done – a healthcare system that works for us all. So let me invite you to HIT me with your best shot at LAgosta@acm.org.

About the author >

Lou Agosta is an independent industry analyst, specializing in data warehousing, data mining and data quality. A former industry analyst at Giga Information Group, Agosta has published extensively on industry trends in data warehousing, business and information technology. He is currently focusing on the challenge of transforming America’s healthcare system using information technology (HIT). He can be reached at LAgosta@acm.org.

Editor's Note: More articles, resources, and events are available in Lou's BeyeNETWORK Expert Channel. Be sure to visit today!

Recently in predictive analytics Category

Predictive analytics has been Page One news for many years in the business and popular press; and I caught up with Predixion Software principals Jamie McLennan (CTO) and Simon Arkell (CEO) shortly prior to the launch earlier this month. Marketers, managers, and analysts alike continue to lust after golden nuggets of information hidden amidst the voluminous river of data flowing across business systems in the interest of revenue opportunities. Competetive opportunities require fast time to results and competition for clients remains intense when they can be enticed to engage.

 

Predixion represents yet another decisive step in the march of predictive analytics in two tension-laden but related directions. First, the march down market towards wider deployment of predictive analytics is enabled by deployment in the cloud for a modest monthly fee. Second, the march up market towards engaging and solving sophisticated predictive problems is enabled by innovations in implementing advanced algorithms from Predixion in readily usable contexts such as Excel and PowerPivot. Predixion Insight offers a wizard-driven plug-in for Excel and PowerPivot that uses a cloud-based predictive engine and storage.

 

The intersection of predictive analytics and cloud computing forms a compelling value proposition. The initial effort on the part of the business analyst is modest in comparison with provisioning an in house predictive analytics platform. The starting price is reportedly $99 per seat per month - though be sure to ask about additional fees that may apply. The business need has never been greater. In survey after survey, nearly 80% of enterprise clients acknowledge having a data warehouse in production. What is the obvious next step, given a repository of clean, consistent data about customers, products, markets, and promotions? Create a monetary opportunity using advanced analytics without having to hire a cadre of statistical PhDs.

 

Predixion Insight advances the use of cloud computing to deliver innovations in algorithms in advanced analytics. Wizards are provided to analyze key influencers; detect categories; fill from example; forecast; highlight exceptions; prediction calculator; perform shopping basket analysis. While the Microsoft BI platform is not the only choice for advances in usability, those marching to the beat of the usability drum acknowledge the leadership in ease of deployment wizards and implementation practices.

 

Finally, notwithstanding the authentic advances being delivered by Predixion and its competitors, a few words of caution about predictive analytics. Discovering and determining meaning is a business task not a statistical one. There are no spurious relationships between variables, only spurious interpretations. How to guard against misinterpretation? Deep experience and knowledge of human behavior (customer), the business (service), and the market (intersection of latter two). The more challenging (competitive, immature, commodity-like, etc.) the business environment, the more worthwhile are the advantages attainable through predictive analytics. The more challenging the business environment, the more important is planning, prototyping, and perseverance. The more challenging the business environment, the more valuable is management perspective and depth of understanding. Finally, prospective clients will still need to be rigorous about data preparation and quality. Much of the narrative about ease of use and lowering the implementation bar should not obscure the back-story that many of the challenges in predictive analytics relate to the accuracy, consistency, and timeliness of the data. You cannot shrink wrap knowledge of your business. However, the market basket analysis algorithm and wizards that I saw demoed by Predixion come as close to doing so as is humanly possible so far.


Posted October 5, 2010 7:02 AM
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