Computer Center turns Data Center; Computer Science turns Data Science

(This article, written by Prof. S. Sadagopan, was originally published in Analytics India Magazine)
There is an old “theory” that talks of “power shift” from “carrier” to “content” and to “control” as industry matures. Here are some examples

  • In the early days of Railways, “action” was in “building railroads”; the “tycoons” who made billions were those “railroad builders”. Once enough railroads were built, there was more action in building “engines and coaches” – General Electric and Bombardier emerged; “power” shifted from “carrier” to “content”; still later, action shifted to “passenger trains” and “freight trains” – AmTrak and Delhi Metro, for example, that used the rail infrastructure and available engines and coaches / wagons to offer a viable passenger / goods transportation service; power shifted from “content” to “control”.
  • The story is no different in the case of automobiles; “carrier” road-building industry had the limelight for some years, then the car and truck manufacturers – “content” – GM, Daimler Chrysler, Tata, Ashok Leyland and Maruti emerged – and finally, the “control”, transport operators – KSRTC in Bangalore in the Bus segment to Uber and Ola in the Car segment.
  • In fact, even in the airline industry, airports become the “carrier”, airplanes are the “content” and airlines represent the “control”

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It is a continuum; all three continue to be active – carrier, content and control – it is just the emphasis in terms of market and brand value of leading companies in that segment, profitability, employment generation and societal importance that shifts.
We are witnessing a similar “power shift” in the computer industry. For nearly six decades the “action” has been on the “carrier”, namely, computers; processors, once proprietary from the likes of IBM and Control Data, then to microprocessors, then to full blown systems built around such processors – mainframes, mini computers, micro computers, personal computers and in recent times smartphones and Tablet computers. Intel and AMD in processors and IBM, DEC, HP and Sun dominated the scene in these decades. A quiet shift happened with the arrival of “independent” software companies – Microsoft and Adobe, for example and software services companies like TCS and Infosys. Along with such software products and software services companies came the Internet / e-Commerce companies – Yahoo, Google, Amazon and Flipkart; shifting the power from “carrier” to “content”.

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This shift was once again captured by the use of “data center” starting with the arrival of Internet companies and the dot-com bubble in late nineties. In recent times, the term “cloud data center” is gaining currency after the arrival of “cloud computing”.
Though interest in computers started in early fifties, Computer Science took shape only in seventies; IITs in India created the first undergraduate program in Computer Science and a formal academic entity in seventies. In the next four decades Computer Science has become a dominant academic discipline attracting the best of the talent, more so in countries like India. With its success in software services (with $ 160 Billion annual revenue, about 5 million direct jobs created in the past 20 years and nearly 7% of India’s GDP), Computer Science has become an aspiration for hundreds of millions of Indians.

With the shift in “power” from “computers” to “data” – “carrier” to “content” – it is but natural, that emphasis shifts from “computer science” to “data science” – a term that is in wide circulation only in the past couple of years, more in corporate circles than in academic institutions. In many places including IIIT Bangalore, the erstwhile Database and Information Systems groups are getting re-christened as “Data Science” groups; of course, for many acdemics, “Data Science” is just a buzzword, that will go “out of fashion” soon. Only time will tell!

As far as we are concerned, the arrival of data science represents the natural progression of “analytics”, that will use the “data” to create value, the same way Metro is creating value out of railroad and train coaches or Uber is creating value out of investments in road and cars or Singapore Airlines creating value out of airport infrastructure and Boeing / Airbus planes.
More important, the shift from “carrier” to “content” to “control” also presents economic opportunities that are much larger in size. We do expect the same from Analytics as the emphasis shifts from Computer Science to Data Science to Analytics. Computers originally created to “compute” mathematical tables could be applied to a wide range of problems across every industry – mining and machinery, transportation, hospitality, manufacturing, retail, banking & financial services, education, healthcare and Government; in the same vein, Analytics that is currently used to summarize, visualize and predict would be used in many ways that we cannot even dream of today, the same way the designers of computer systems in 60’s and 70’s could not have predicted the varied applications of computers in the subsequent decades.
We are indeed in exciting times and you the budding Analytics professional could not have been more lucky.

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About Prof. S. Sadagopan
Professor Sadagopan, currently the Director (President) of IIIT-Bangalore (a PhD granting University), has over 25 years of experience in Operations Research, Decision Theory, Multi-criteria optimization, Simulation, Enterprise computing etc.
His research work has appeared in several international journals including IEEE Transactions, European J of Operational Research, J of Optimization Theory & Applications, Naval Research Logistics, Simulation and Decision Support Systems. He is a referee for several journals and serves on the editorial boards of many journals.

Which sorts of analytics are capable of anticipating what might occur in the future?

Descriptive analytics explains what happened previously. Diagnostic analytics can assist you in figuring out why something happened the way it did in the past. Predictive analytics is a type of data analytics that uses historical data and analytics techniques like statistical modelling and machine learning to make predictions about future events. Predictive analytics is capable of generating future insights with a high degree of accuracy. In most cases, historical data is utilized to create a mathematical model that captures key patterns. The predictive model then gets used with the current data in order to forecast what will happen next or to propose actions to take in order to get the best results.

What kinds of data analytics do businesses generally opt for?

Prescriptive analysis is commonly used by businesses. Prescriptive analytics takes a step further by identifying correlations within data and recommending the best course of action for a specific business problem. This kind of investigation shows what steps should be performed. This is the most important type of analysis since it generally yields rules and suggestions for further steps.

What are the tools used for data analytics?

Many job opportunities have arisen as a result of the rising demand for and relevance of data analytics in the industry. Shortlisting the best data analytics tools is a little more difficult because free-source solutions are more popular, user-friendly, and performance-oriented than commercial versions. Many free-source technologies, such as R programming in data mining and Tableau public, Python in data visualization, don't require much/any coding and give better results than commercial versions. Some other useful tools are RapidMiner, KNIME, Power BI, and QlikView.

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