Predictive Analytics: Moving beyond the buzzword to the action
by Ajay Ohri
Predictive Analytics has been around for some time, championed by the likes of SAS, SAP, IBM, among others. While data science and big data analytics bring hope, joy, despair and confusion to CIOs, it is acknowledged that predictive analytics is a tested and mature industry. Then by extension we should see predictive analytics in most industries and corporations around us. But do we? Is predictive analytics even close to reaching its full utilization and potential? The answer is a resounding “no”. Yes, awareness of analytics is increasing and more and more people are exploring it.
The true benefits predictive analytics-led decision making imparts to an enterprise are many and are getting better. The reason for this is quite simple – data storage has become much more inexpensive, data pipelines have increasingly been digitized from end to end and basic blocks for predictive analytics including business reporting and baseline metrics already exist for a majority of organizations.
What predictive analytics can do is give you a lift over traditional decision making including historic planning and naive forecasting paradigms. An added emphasis is not just on increasing revenue but on decreasing costs. An enterprise with a high profit margin realizes a greater advantage when it decreases a single dollar in costs than in increasing a single dollar in revenue. This is because cost decreases go straight to profit enhancement. A more methodical way of utilizing predictive analytics is key to good Return on Investment.
We can now predict which of your employees are going to leave and reduce employee attrition slightly, reduce litigation costs by analyzing our legal expenses, tie in website and social media analytics to text mining to social network models to analyze interactions and relationships, click stream models for enhanced digital revenue, use life-time value modeling for customer revenue, and using recency frequency and monetization for segmentation.The decreased costs in hardware thanks to the cloud computing paradigm, and the added increase in software capability to handle big data using distributed paradigms like Hadoop have further ushered in a golden age of analytics.
It is not enough to do analytics though, one must model the right data and question your assumptions constantly and refine these models. Some critical missteps in predictive analytics are ignoring basic infrastructure like data quality, master data management, expensive capital outlays due to legacy reasons, and risk aversion to both cloud computing hardware and inexpensive open source software. An added point is not keeping adequate test and control champion and challenger strategies that lead to inaccurate baselining. Peter Drucker said, “Culture eats strategy for breakfast”. With a predictive analytics strategy one needs an analytical culture as well, where data driven questioning and investigation is both encouraged and rewarded.
How do you measure the Return of Investment on Predictive Analytics? What is the way to measure and analyze which software in my analytics suite of choices is going to give me better ROI? In a forthcoming post we will discuss, how the analytics industry can provide for even greater depth and breadth of analysis, in addition to implications for customer data.
About- Ajay Ohri is the author of R for Business Analytics.
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