Leaning on business alytics

By Dr B K Mukhopadhyay

*    Alyze, prepare reports and present to Leadership team on a defined frequency

                                                       or

*    Lead multiple alytical projects and businesses planning to assist Leadership team deliver business performance.

According to a recent Wall Street Jourl, 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 alytics 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 magers who can use data to shape business decisions.

Keeping pace with the fast changing business world the wings of business alytics have been spreading!

Business Alytics is the use of modern data mining, pattern matching, data visualisation and predictive modelling tools to produce alyses and algorithms that help businesses make better decisions.

This very specialization, having formal origin in the recent past, seeks to extend   big data alytics for all business professiols, even including those with no prior alytics experience.  The very purpose is to have insights into how data alysts describe, predict, and inform business decisions in the specific areas of marketing, human resources, fince, and operations, among others. That is to say the basic data literacy coupled with alytic 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 in everywhere - from credit card transactions and online shopping carts, to customer loyalty programs and user-generated ratings/reviews, there exist a staggering amount of data that can be used to reflect past buying behaviors, while at the same time predict future ones, and aids prescribing new ways to influence future purchasing decisions.  An overview of key areas of customer alytics: descriptive alytics, predictive alytics, prescriptive alytics, and their application to real-world business practices help arriving at higher business targets / profitability with human touch.

The benefits are obvious - how data collected can continuously help arriving at business decisions, predict customer behavior and identify the appropriate uses for each tool to communicate key ideas about customer alytics. Descriptive Alytics help the business in question to learn what data can and can’t describe about customer behavior 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 -  how data is used to explore a problem or question as well as how to use that data to create products, marketing campaigns, and other strategies. A solid understanding of effective data collection and interpretation helps the entrepreneur to use the right data to make the right decision for the company / business.

Prescriptive Alytics help one turn data into action? Prescriptive alytics provide recommendations for actions one can take to achieve one’s business goals - how to optimize for success, how quantity is impacted by price, how to maximize revenue, how to maximize profits, and how to best use online advertising.  Clearly, the requirement is to define a problem, define a good objective, and exploring models for optimization taking competition into account, so that the company can filly write prescriptions for data-driven actions that create success for the company or business over space and time.

Operations alytics speaks of the way one thinks about transforming data into better decisions. Obvious enough, recent extraordiry 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, functiolly and hierarchically.  Surely, one has to take into account the risks and uncertainty aspects related to would be-real-world business challenges - future demand uncertainties, how to predict the outcomes of competing policy choices and how to choose the best course of action in the face of risk and uncertainty.

Prescriptive alytics and  high uncertainty wing introduces decision trees, a useful tool for evaluating decisions made under uncertainty -  how optimization, simulation, and decision trees can be used together to solve more complex business problems with high degrees of uncertainty. Side by side, Prescriptive Alytics, Low Uncertainty deals with how to identify the best decisions in settings with low uncertainty by building optimization models and applying them to specific business challenges.

Then come the People Alytics [P A] - a data-driven approach to maging people at work. As of now there is some development in the field of business world - business leaders can make decisions about recruitment and selectiontheir people based on deep alysis of data rather than the traditiol methods of persol relationships, decision making based on experience, and risk avoidance. A lot of weightage is attached not only   to recruit people, but retain great people also. P A explains how data and sophisticated alysis 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 alytics – that explores how fincial statement data and non-fincial metrics can be linked to fincial performance – should not lose sight of.  For that matter one has to learn how data is used to assess what drives fincial performance and to forecast future fincial scerios. While many accounting and fincial organizations deliver data, accounting alytics deploys that data to deliver insight and this accounting data provides insight into other business areas including consumer behavior predictions, corporate strategy, risk magement, optimization, and more. As fincial data and non-fincial data interact to forecast events, optimize operations, and determine strategy , the emerging roles of accounting alytics must be taken into account so as to make own business decisions and create strategy using fincial data.

To target today’s business to become more successful the firm / farm have to rely on the talk of the town - using data to create cutting-edge, customer-focused marketing practices.  It is thus possible to apply customer alytics to marketing, starting with data collection and data exploration, moving toward building predictive models and optimization, 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 on 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 Mukhopadhyay, a noted Magement Economist and an Intertiol Commentator on Business and Economic Affairs, can be located at m.bibhas@gmail.com