predictive analytics

New Predictive Analytics Application:Managing Your Wardrobe

I see the new ‘data intensity’ business model getting traction in new sectors and creative applications are emerging. Check out Stylitics, a new analytics platform to help manage your wardrobe.

http://mashable.com/2012/02/03/stylitics/

Note the reference to linking branding, couponing and so on. Most importantly, note the clear reference to Mint.com which I and many others view as a “flagship” data intensity business model.

Working with Sante Corporation, a new healthcare/analytics venture, it is clear to me providers can add high value to consumers through next generation analytics, carefully crafted to deliver insights to consumers and provide significant public benefit. The key points here – information aggregation and retrieval are yesterday’s business and commoditizing. Real opportunity now is going deeper, developing “data intensity” models, identifying the “unknown unknowns” providing real value, using powerful, creative predictive analytics to create sustainable value, and developing high value partnerships using electronic couponing, machine readable packaging and other new tools to deliver real value to consumers and create exponential shareholder value growth.

Given Mint.com’s successful business strategy, we can expect to see the data intensity business model trend accelerating. Very exciting developments are coming in this high potential market space.

Paul B. Silverman

Author: Worm on a Chopstick : Understanding Today’s Entrepreneurial Age: Directions, Strategies, Management Perspectives http://paulbsilverman.com/books/

Chief Executive Officer
Sante Corporation
Creating Next Generation Personalized, Simple Solutions to Improve Personal Health Management

Email:      paul@paulbsilverman.com
Linked in:  Paul Silverman
Twitter:     globalbizmentor

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Next Gen Ad Analytics:’Finding the Significant Few Among the Trivial Many’

The online advertising market is estimated at more than $30 billion in 2011  growing at 22 percent annually based on Internet Advertising Bureau (IAB) statistics. What we are seeing is explosive growth of predictive-analytics based tools and applications to drive the creation of new targeted ad services.

Look over today’s announcement  http://tinyurl.com/7tmmvsp that predictive analytics firm eBureau is spinning off its online advertising targeting business into a new company called TruSignal(TM) offering targeted advertising using proprietary predictive analytics and other tools.

All companies are interested in finding what we call the ‘significant few among the trivial many’ – I foresee many exciting developments and issues emerging here as we pursue this goal:

  • Expect other online advertising companies to create separate, specialized analytics driven service entities – analytics technology is driving this trend, is highly specialized, and this is moving very quickly
  • Different skills sets are needed as ad business moves to even more advanced analytics and visualization technologies- think of the implications for the online advertising sector looking for creative and ‘analytics-savvy’ candidates – new skill sets are needed now to secure and retain industry leadership
  • Expect to see more analytics spin-offs in other sectors- the same model is occurring in the health care, financial services and others

Always important to look at how major companies respond to these changes (think response of Barnes & Noble vs Borders to the e-book revolution). How does a major ad firm, well entrenched in traditional print, TV, radio media, address these new trends- most are obviously committed to the social media revolution but new predictive analytics tools are changing the rules of the game, helping ‘find the significant few among the trivial many’ in ways not possible today.

As these services accelerate, and they will, I expect to see major firms ramp up internal efforts to develop competitive analytics services organically. These powerful services are evolving very quickly and I expect to see major industry leaders seeking alliances with creative innovation leaders in the predictive analytics market.

 

Paul B. Silverman is a Lecturer in the Robert H. Smith School of Business in the University of Maryland. He also serves as CEO of Sante Corporation, an early stage personal health care management company, and Managing Partner of Gemini Business Group, a new venture development and advisory services firm. He can be reached at paul@paulbsilverman.com or via Twitter at @globalbizmentor

 

Paul B. Silverman writes about entrepreneurship, healthcare, analytics, and strategy management and serves as Advisor, Speaker, Educator, and Managing Partner of the Gemini Business Group, LLC, a new venture development firm, and author of “8 Building Blocks To Launch, Manage, And Grow A Successful Business.” He also serves as Adjunct Professor in the School of Business at George Mason University. See more at Paul B. Silverman Blog and sign up for Entrepreneurship Today! email updates to track latest new venture developments.

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Understanding ‘Unknown- Unknown’ Information Drivers Addresses ‘Sea of Data’ Issues and Creates Opportunities

Understanding The ‘Unknown- Unknown’ Information Drivers Addresses ‘Sea of Data’ Issues and Creates Opportunities

I posted comments on Fast Company article discussing ‘Sea of Data’ issues Fast Company Article “Avoiding Short-Term Thinking In A World of Big Data”   http://tinyurl.com/7gaepbl

I shared my vision that predictive analytics is ‘raising the bar’ in how we manage the ‘sea of data’,  and offered comments on new directions I see in manufacturing and health care. Here is a copy of my comments posted on the Fast Company site:

The article makes the point that “…in a sea of data, how can we make sure that we’re not just reacting to the information in front of our face, but rather analyzing every possible input.”

One solution to the problem, not mentioned in the article, is the need to develop new analytics to identify key drivers which create the data ‘outcomes’. Predictive analytics enable us to identify these ‘unknown-unknown’ drivers that can only be found by analyzing data, looking for relationships and new rules that emerge developed by analyzing the data. Contrast this to today’s ‘deductive’ approach using expert opinion and well-defined rules.

This ‘data-driven’ analysis to create new rules is an inductive (rather than deductive ‘expert opinion’ based approach) and from my perspective holds great promise to radically change current business processes, improve productivity and improve our quality of life.

This may sound bold, but as the former CEO of an early stage predictive analytics company and also looking at new opportunities in analytics, I see exciting potential here.

Some possibilities:

Look at manufacturing. If a “supplier’s supplier” has a problem, supply chain management ensures quick notification, before it impacts the assembly line. Predictive analytics engines ‘raise the bar’ here by analyzing historical performance and risk data, often real time, defining future risk and performance drivers, and enabling management to optimize performance and mitigate risk.

Going beyond traditional data mining, these new predictive analytics tools analyze industry reports, government filings, trade press, and other sources to assess supplier “health,” pending regulations, and other “unstructured” data sources. Seamlessly integrating with other data, we can use these to more accurately gauge supplier and production line risk and improve performance.Driving new rules,  providing real time early warning signs that impact future supplier and business performance are the new management tools to harness ‘the sea of data’.

Look at health care, my primary focus, where PA techniques hold great promise to help our current health care system. Consider the benefits of these new capabilities which are only a small sample of what lies ahead here:

•    Tracking  Medical Diagnoses, Treatments, Medications, Outcomes, Costs,Reimbursements, and Relationships

ICD or International Classification of Disease Codes , classifies diseases on health records.CPT or Current Procedural Terminology codes developed by the AMA describe services provided by medical practitioners. Medicare employs a similar system, using ‘HCPCS’. Tracking and examining relationships among these metrics, looking at patient data, identifying processes, and key cost and patient health drivers, you can develop ‘best practices’ to improve the health
care process.

•    Identifying Adverse Drug Analyses – assessing underlying drivers to more effectively identify “at risk” patients

•    Optimizing clinical trials (candidate selection and monitoring) – predicting higher risk clinical trial candidates and assessing the key risk drivers

•    Developing directional indicators to predict the underlying drivers for treatment of chronic disease to understand how medication protocols impact treatment plans and patient outcomes

The new predictive analytic-based tools now emerging in all sectors are helping companies cope with the sea of data problem, and  “raising the bar” in how leading firms optimize business performance in today’s  dynamic global markets.

Paul B. Silverman

Paul B. Silverman writes about entrepreneurship, healthcare, analytics, and strategy management and serves as Advisor, Speaker, Educator, and Managing Partner of the Gemini Business Group, LLC, a new venture development firm, and author of “8 Building Blocks To Launch, Manage, And Grow A Successful Business.” He also serves as Adjunct Professor in the School of Business at George Mason University. See more at Paul B. Silverman Blog and sign up for Entrepreneurship Today! email updates to track latest new venture developments.

Email:      paul@paulbsilverman.com
blogs:       http://paulbsilverman.com/blog/
Linked in:  Paul Silverman
Twitter:     globalbizmentor

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