According to a recent Wall Street Journal, companies barraged with data from the Web and other sources want employees who can both sift through the information and help solve business problems. As the use of analytics grows quickly, companies will need employees who understand the data. A May 2011 study from McKinsey & Co. found that by 2018, the U.S. will face a shortage of 1.5 million managers who can use data to shape business decisions.
Keeping pace with the fast changing business world the wings of business analytics have been spreading!
Business Analytics is the use of modern data mining, pattern matching, data visualisation and predictive modelling tools to produce analyses and algorithms that help businesses make better decisions.
This very specialisation, having formal origin in the recent past, seeks to extend big data analytics for all business professionals, even including those with no prior analytics experience. The very purpose is to have insights into how data analysts describe, predict and inform business decisions in the specific areas of marketing, human resources, finance, and operations, among others. That is to say, the basic data literacy coupled with analytic mindset helps make strategic decisions based on data and as such the acquired skills help interpret a real-world data set leading to arriving at appropriate business strategy recommendations.
It has been a fact that data about our browsing and buying patterns are everywhere. From credit card transactions and online shopping carts, to customer loyalty programmes and user-generated ratings/reviews, there exists a staggering amount of data that can be used to reflect past buying behaviours, while at the same time predict future ones, and prescribe new ways to influence future purchasing decisions. An overview of key areas of customer analytics: descriptive analytics, predictive analytics, prescriptive analytics, and their application to real-world business practices help arrive at higher business targets / profitability with human touch.
The benefits are obvious -- how data collected can continuously help arrive at business decisions, predict customer behaviour and identify the appropriate uses for each tool to communicate key ideas about customer analytics. Descriptive Analytics help the business in question to learn what data can and can't describe about customer behaviour as well as the most effective methods for collecting data and deciding what it means - locating the critical difference between data which describes a causal relationship and data which describes a correlative one as one explores the synergy between data and decisions, including the principles for systematically collecting and interpreting data to make better business decisions. A solid understanding of effective data collection and interpretation helps the entrepreneur use the right data to make the right decision for the company / business.
Prescriptive Analytics help one turn data into action. Prescriptive analytics provide recommendations for actions one can take to achieve one's business goals -- how to optimise for success, how quantity is impacted by price, how to maximise revenue and profits, and how to best use online advertising. Clearly, the requirement is to define a problem, define a good objective, and exploring models for optimisation taking competition into account, so that the company can finally write prescriptions for data-driven actions that create success for the company or business over space and time.
Operations analytics speaks of the way one thinks about transforming data into better decisions. Obvious enough, recent extraordinary improvements in data collecting technologies have changed the way firms make informed and effective business decisions -- how the data can be used to profitably match supply with demand in various business settings temporally, spatially, functionally and hierarchically. Surely, one has to take into account the risks and uncertainty aspects related to the probable real-world business challenges such as future demand uncertainties, predicting the outcomes of competing policy choices and choosing the best course of action in the face of risk and uncertainty.
Prescriptive analytics and high uncertainty wing introduces decision trees, a useful tool for evaluating decisions made under uncertainty. Side by side, Prescriptive Analytics, Low Uncertainty deals with how to identify the best decisions in settings with low uncertainty by building optimisation models and applying them to specific business challenges.
Then comes the People Analytics [P A] -- a data-driven approach to managing people at work. As of now, there is some development in the field of business world, business leaders can make decisions about recruitment and select their people based on deep analysis of data rather than the traditional methods of personal relationships, decision making based on experience, and risk avoidance. A lot of weightage is attached not only to recruit people, but retain efficient people also. P A explains how data and sophisticated analysis is brought to bear on people-related issues [such as recruiting, performance evaluation, leadership, hiring and promotion, job design, compensation, and collaboration] -- how and when hard data is used to make soft-skill decisions about hiring and talent development.
The Wharton School of the University of Pennsylvania has nicely opined that accounting analytics (that explores how financial statement data and non-financial metrics can be linked to financial performance) should not be lost sight of. For that matter one has to learn how data is used to assess what drives financial performance and to forecast future financial scenarios. While many accounting and financial organisations deliver data, accounting analytics deploys that data to deliver insight and this accounting data provides insight into other business areas including consumer behaviour predictions, corporate strategy, risk management, optimisation and more. As financial data and non-financial data interact to forecast events, optimise operations and determine strategy, the emerging roles of accounting analytics must be taken into account so as to make business decisions and create strategy using financial data.
In order to target today's business to become more successful, firms have to rely on using data to create cutting-edge, customer-focused marketing practices. It is thus possible to apply customer analytics to marketing, starting with data collection and data exploration toward building predictive models and optimisation and continuing all the way to data-driven decisions. The most innovative and effective data-driven practices, in turn, paves the way for reaching and staying at the top position. .
So, nothing to get very frustrated while not being able to keep up with the changes that are going around as it is better to keep struggling to find direction and a path for success. "Best Practice" is fundamentally about continuous improvement and is at its most useful when it informs growth opportunities and new areas of business or income.
The proverb is to be respected: want to make changes … think of the effects.
Dr B K Mukhopadhyay is Professor of Management, ICFAI University, Tripura, India.
