Big Data is ‘big’ enough!

How Predictive Analytics could cheer up SMEs!!

“Yesterday is a history, tomorrow is a mystery but today is a gift. That’s why we call it the present” – the wise turtle in Kung Fu Panda. Quoted from Eric Siegel’s ‘Predictive Analytics –The power to predict who will Click, Buy, Lie or Die.

How interesting is Predicative Analytics

Undoubtedly the Present is all about Big Data. “Big” is really becoming a big keyword these days. If someone is into technology domain; it would be foolish to assume that he/she hasn’t heard anything about Big Data. It seems Data is the new oil to the industry. If we search through Google; we would have thousand examples of how billion dollar conglomerates are using ‘Big Data’ for their strategic business decisions – which could range from generic prediction of business revenues, analyzing advertising performance to wildest dream of determining mass mood or employees’ loyalty. The list is really long and versatile in nature.

HP ran a predictive data model named as ‘Flight risk score’ to determine who is the next employee of HP thinking of resigning from the company. Ironically the two person who developed this Flight Risk data model were also among those who were likely to move out of HP’s more than 330,000 work force.

Target (A retail store) ran a pregnancy prediction data model to offer market relevant products for its female customers. Eventually a mass mood detection predictive model was also derived from different blog posts to determine stock exchange up-downs. How amazing it is!

Exploring the missing-links

But is big data Big enough to adopt? Or is it like only the multinationals could afford to run such a high cost associated predictive analysis to leverage their value chain or grow exponentially? The questions don’t end here. What about small scale industries? Or onshore-offshore based small medium enterprises, start-ups etc.? Could they think of leveraging the power of predictive data modeling too? Fortunately every industry uses Data in day to day business activities. Even knowingly or unknowingly we incorporate analytics to certain extent. Google Analytic is one of them and easily available. However; tactically we fail to adopt a particular machine learning model to analyze the raw data which could give a near definite predictive score over the stated objective. Though Google Analytic offers an extensive experience in terms of traffic, visits, who peep into the website, who pop out of the website etc. in a very legitimate way but it is not sufficient enough. SMBs need to dig down a lot to generate more data. ‘Heatmaps’ is a useful technology to determine the mouse movement on the website. Apparently it may sound like a child’s game but think about that if you are able to know whether your website visitors are looking at the product page rather than the service page or if the visitor is reading more on a certain type of App you developed or enterprise website you maintained.

SMBs need to be opinionated and adoptive

You are surrounded by Data. Bigger is the amount of data more perfect would be your predictive score. But too much data could kill the objective so you need to know where to stop. The landscape of data mining, big data or predictive analysis could vary on the scope and operational procedures. Maintaining a clear strategy is essential. Paying attention to the data source is important. Capabilities to understand Machine learning process makes a difference. There are machine learning tools available from IBM, SAP etc. which could provide extensive analytical techniques both in regression or machine learning. But adopting these systems will create hole in the pocket if you are running on limited budget. OpenSource machine learning models are also available. ‘R’ from Revolution Analytics is one of them. However ‘R’ needs extensive programming knowledge. Few SaaS based platform are also available which could offer machine learning models as low as $100 per month. For confidentiality issue I’ll not be naming them here. But you could always Google it? Above all, the company as a whole need to transform and influence everyone associated with it to adopt data and models which could yield better business decisions.

Predictive modeling is a conceptual science mastered by professionals called data scientists. If you are planning to explore the power of Data trough predictive modeling then please be sure that you have the right expertise in the team. Failure could be miserable here.