Economic Development Institute Kansas City 2010 Predicting the Future Presented by: Dean Whittaker, CEcD
Learning Objectives Broaden your understanding of the use of Business Intelligence in economic development. Be capable of applying new information resources and tools to predict future events. Share knowledge among participants.
Economic Development – Making the Pie Bigger
Economic Development Context Changing nature of work – work moves, workers stay put Globalized, automated, outsourced, and off-shored Increasing concentration of wealth in the hands of a few
The History of Prediction
Business Intelligence Using information to make better decisions Reduce risk and predict outcome Focus your effort – “biggest bang for the buck” Brains vs. Muscle Abundance of information, scarcity of attention
The Information Cycle Data Organization Collection Information Action Analysis Application Knowledge
The Role of Information Reduce risk Increase certainty Predict outcome
Seeing the Future Look to the past Repetitive pattern Cycles 1,000,000 year 1,000 year 300 year 100 year
Measure Change Over Time to Predict the Future Barometric Pressure Length of Daylight Solar Flares Waves Light Sound
Linear Thinking in a Cyclical World Event Time
The Long Tails of the Black Swan Frequency Time
Exercise I: Build a Community Economic Barometer Identify five “real time” factors that could be measured to create a real time view of the local economy Identify five “leading” indicators that would forecast future economic performance.
Economic Barometer Measuring the Economy Usage of Resources Electricity Gas People Raw materials Transportation Movement of goods and people
Types of Cycles Tipping Point Precipitating events Outliers The long tail Cycles Repeating pattern
Predictive Analytics What is it? Why should you care?
Predictive Analytics Predictive analytics is an area of statistical analysis that deals with extracting information from data and using it to predict future trends and behavior patterns. The core of predictive analytics relies on capturing relationshipsbetween explanatory variables and the predicted variables from past occurrences, and exploiting it to predict future outcomes. Source: http://en.wikipedia.org/wiki/Predictive_analytics
Statistics Correlation vs. causal Pattern recognition Knowledge mapping Predictive Analytics
Ranking Predictive Model Use of Weighted Matrix Control Group Variables Weighting
Examples of Predictive Models Corporate Behavior Sporting Events Return on Investment Loan Repayment
Business Intelligence Predicting Corporate Behavior Behavior linked to history Accuracy - function of data available Giving Information Meaning Data…information…knowledge…action Assumptions Rational Behavior? Cyclical
Economic Cycle Boom/Bust 36,000 year economic cycle Generational Baby Boomers Generation X, Y, and Z Corporate life cycle Product life cycle
Product Life Cycle
Predicting Corporate Behavior Probability to close or have major layoff Probability to relocate, expand, or consolidate facilities
Exercise II: Business at Risk An Early Warning System Identify five “leading” factors in their business environment and five within the company that would indicate a company is at risk of closing or laying off a significant number of employees. Weight each of the factors by its relative importance in being predictive.
Exercise III: Targeting Businesses for Attraction Identify five “leading” factors in their business environment and five within the company that would indicate a company that is likely to relocate or expand Weight each of the factors by its relative importance in being predictive.
Why Target Companies? Focus energy and effort Limited resources of time and money Maximize results Brains vs. muscle
Why do Companies Relocate or Expand? Internal changes Ownership Leadership Others External Changes Business Environment Regulatory Environment Others
How to Target Companies Creating a target company profile Target industry Size Location Corporate Changes/Events Others
Where to Find Company Information? Primary Company Information Secondary Information
Google http://www.google.com http://www.new.google.com http://finance.google.com/finance http://labs.google.com/ http://google.com/trends
People Linkedin.com Facebook.com Others
Places www.ZipSkinny.com – a zip code-based demographic comparison tool www.earth.google.com – a bird’s eye view of earth and beyond ZoomProspector.com – a geographic information site selection tool
Fee-Based Internet Resources www.Hoovers.com - excellent source of company information www.OneSource.com – broad multiple sources of news and company information www.Nexis.com - news sources for events
Web 2.0 – The New Media Social Networks www.LinkedIn.com www.Facebook.com www.MySpace.com Streaming Video www.YouTube.com Podcasting
Websites of Interest www.factcheck.org www.wikipedia.com www.whittakerassociates.com www.ceoexpress.com
Summary Context Role of Information Information Cycle Predictive Analytics Predicting Behavior Weighted Matrix For More Information
Thank you for your participation Dean Whittaker Dean@whittakerassociates.com 616-786-2500 www.whittakerassociates.com