I am working on a new BOOK called The Predictive Generation, which will be written over the next 12 months with 4 blog entries per month written on Monday morning. Guess you know what I am doing every weekend. I will explore the following themes and look forward to the journey with my readers.

Month #1:      The History and Roots of Predictive Analytics

Month #2:      The Mix of Art and Sciences enabling Predictive

Month #3:      The Rise of Machines: Going Beyond Quantum Physics

Month #4:      The Rise of the Predictive Generation

Month #5:      Value of Predictive in Business & Industries

Month #6:      Value of Predictive in Sales & Marketing

Month #7:      Value of Predictive in Talent Management

Month #8:      Developing a Data Sciences “Tuned in” Organization

Month #9:      Predictive Performance Metrics – Managing Risks.

Month #10:    How to Capitalize Predictive or Simply Die as an Old Fossil.

Month #11:    Beyond Predictive Analytics: The Rise of Predictive Discourse

Month #12:    Top 10 Predictions for Predictive Advantage

I may change these themes as I get going, but for now these are my guide posts, welcome your thoughts on these themes as well.


The Rise of Predictive Analytics – When Did This All Get Rolling?

Most of the world believes that predictive analytics started in the last fifty years. Well if you thought this, you are wrong. The roots actually go back to 1689, over 337 years ago. While, it is true that computing science disciplines helped to fuel the advancement of predictive, with relational databases, faster CPUs, Hadoop, Mongo, R, etc. the roots actually go back to sea voyages.

One of the very first applications of predictive analytics was in underwriting with Lloyd’s of London. How this worked was that the financial bankers would accept the risk on a given sea voyage in exchange for a premium and they would write their names under the risk information that was written on a Lloyd’s slip created for this purpose.

Edward Lloyd had established the Lloyd’s coffee house in 1689, which became popular with sailors, merchants, and ship owners because he delivered reliable shipping news that assisted the community in discussing deals, including insurance. Eventually the place became so popular that after his death, they carried on the arrangement and eventually formed a committee that became The Society of Lloyd’s. For the next two centuries, the Society of Lloyds would be primarily engaged in the dissemination of information across the industry and become the world’s leading market for specialist insurance.

Although relational databases, faster CPUs, and new tools have paved the way to bring predictive analytics to the masses, it’s important to remember the history of predictive analytics.

Predictive is not a new science. It has been here for a few hundred years now.

Predictive analytics is the practice of extracting information from existing data sets in order to determine patterns and predict future outcomes and trends. Predictive analytics does not tell you what will happen in the future. But they do serve as guide posts on odds or risks, given past patterns of statistical relevance.

As we look forward, it is important to appreciate humans have been analyzing patterns for centuries – just not as fast as we can now. What is vastly different is the speed for pattern detection and the human element although still involved – is increasingly becoming marginalized as the rise of machines intelligence grows.

But as of 2016, like the sailors, merchants, and ship owners before, experts in your business must be tapped for their knowledge and experience to validate your predictive models, whether you’re underwriting risk or analyzing your sales forecasts, or examining the propensity of customers to purchase or customer churn risks. Without validating that the models, mistakes will be made. But what’s exciting is that the models can already get to levels of 85% to 95% predictive accuracy without humans, its the last 15 % that requires human reviews/reflection and in time the deep learning of Artificial intelligence  will underlie all human interactions. Some are worried this is the end of Mankind. I prefer to think that this will simply take the human form to another level of performance when man and machine will eventually merge.


Next week’s blog, I will summarize the different approaches used in Predictive Analytics and explore the predictive science definitions more to help ensure the roots are more clearly understood.

Why am I investing the best years of my life into Predictive Analytics?

Bottom Line: Predictive Analytics will infect every business process world-wide and eventually all Smart AI Algos will be connected into deep learning networks. We may think that the internet is the backbone of the predictive generation; it is simply the road to travel on. The Sense Making of all the data signals inside the internet and outside in every tapestry of life will create data nuggets for us to mine. Data will be in my smart walls, my smart car, my smart phone, my smart clothes, my smart appliances, my smart shoes, my smart watch, my smart business products, my smart processes, my smart DNA, wired up everywhere is fast becoming a reality. We will experience more change in the next 25 years than in any other time in human civilization. This is an exciting time for each of us as our Predictive Planet emerges rapidly. Are you ready? Most CEO’s are not, and most Board Directors are not, and most CRO’s and CFOS are not. Time to innovate or be crushed as this WAVE is a Tsunami covering the earth at an unprecedented rate.


Follow us on Twitter @SalesChoice_Inc or visit our Predictive World Resource Center. Want a Demo of Predictive Analytics to predict your sales forecasts or predict your pricing to see more to win more, book here.


Dr. Cindy Gordon, CEO SalesChoice Inc.