I admit, I am a weather geek. As a boy, I was fascinated by the weather. By age 11, I was recording the daily weather in a small diary. Back then, predicting the weather seemed to be more about luck than science, based on personal observations and the Farmer’s Almanac.
But even if tools for weather prediction were not yet perfect, there were still many tools in place to enable monitoring and measuring. [In fact, I can’t write a post about weather without mentioning that Italians invented some of the most important weather monitoring tools: an early thermometer (Galileo Galilei in 1592) and the barometer (Evangelista Torricelli, 1643).]
The data monitoring and measuring would eventually allow scientists to create the models that now enable the weather forecasting tools in use today. And while predicting the weather is obviously still not perfect, we are much closer than seemed possible 40 years ago.
I can’t help but see the similarities between this and the business world. Companies have been collecting internal data for years now, and most are quite good at explaining what happened to them. Today, with the advance of tools and technologies able to capture what was once uncollectable, companies are getting better at using it for decision making and forecasting. Here, I am obviously talking about unstructured information. And while companies have rich information sets available to them, I believe that there is still a lot of information left on the table, unused.
Today information can be collected because there are repositories available, (i.e. CRM systems, social media, market research verabatim, etc.), and, thanks to semantic technologies it can be understood and structured in a format that may be used to support innovative predictive models.
Using the example of internet content, all of the things embedded in text—topics, tone, style, relationships between concepts, etc.—as attributes/variables that can be associated to each piece of text to describe a phenomenon. A predictive model can take all of this information into consideration in a number of ways that can have a huge bottom line impact. For example understanding if a new product can be successful in order to understand how to avoid churn in your customer base, or even more simply whether it is time to hire a new CEO, are complex matters, but the return on the investment could be huge for whomever will make progress in developing effective systems.
The good thing is, you don’t need a major capital investment to put such systems in place. And, if you are successful, this model can be a significant competitive advantage that can help pave the road to success.
Today, we really do have a level playing field, more than any other time before. And everyone has the opportunity to be the next Apple (or a weather geek, perfecting the art of always knowing what to pack for the next trip).
[...] RT @scagliarini: A new age (and better weather) for predictive modeling #semantics #textanalytics http://t.co/Ni12VdPc… [...]
product models, product model, best product models in bangalore.
[...] Forecasting Is Not Just for Weather [...]