What is the prediction on Predictive Analytics?


Its not just a play on words, but a truly intriguing topic that’s on many top executives’ minds.

In the past, predictive analytics was more prevalent in big companies and mostly used to create insights of customer churn, adjust product pricing, improve marketing campaigns and other knowledge elements from big data. Very quickly many small companies across all markets started seeing the value of predictive analytics.

In order to make all this possible, complex modeling techniques are developed, validated and modified. This requires deep technical skill, knowledge of the popular modeling platforms, understanding which attributes are relevant, are the co-relations statistically significant, and so on. Hence today’s much desired need for data scientists.

As we evaluate today’s requirements for analytics, the business leaders in large companies and small firms, are in dire need of such actionable business insights to aid them in day to day operations and critical decision making. Its important to differentiate the need for analytics that improve and refine the day to day business and process, from the insights that can help solve big problems and generating revolutionary ideas, which requires digging into embedded patterns, and focus on bigger-picture and potentially new and incremental opportunities.

The SMAC (Social, Mobile, Analytics, Cloud) environment has made multiple data sources more relevant to the big data, thus evolving a multi dimensional view of the end customer.

We have moved from closed systems of communication to open platforms. We have moved from physical systems to wireless systems that makes data be on the move. We have moved from verbal and structured content to intelligent processing of multiple sources of content, and from location-specific data centers to use-as-you-go forms of usage, communication and interaction. We know more about the knowledge user now than ever before.

So, how is technology and new tools in the market help a business user to be able to define the business problem at hand, and be able to design a model to give him/her insights? How do the decision scientists pair up with such business users for maximum leverage? How can we stand up a data science department to be a revenue generator and not cost center?

It all starts from the top. First we focus on Evangelizing Data-Driven Business decisions, which is best done top down with a sound understanding of organizational priority. Then create a strong infrastructure where the data is physically stored and processed, identify software tools that are needed to process and consume the insights generated. Then establish a well understood data governance to monitor Data quality, Data security, Data Management and Data compliance. Hence the tight partnership between CIO and Digital executives to drive the IT department and all other functions to work with data centric decision making. Finally, create accountability to all levels of the organization that will use the analytics, that can monitor performance and trends real time.

Today’s executive is much more empowered than ever before to make the best holistic decisions for his/her business

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