There was this interesting little advertisement in the Times of India that offered training in the use of spreadsheets which caught my attention for several reasons. As a young product manager in a pharmaceutical company, I had access to a fair amount of data. But I had to do a lot of the data analysis using calculators and large sheets of paper with columns and rows, making my own version of spreadsheets.
The year was 1983. IBM had launched their revolutionary new PC in August 1981 and a few had found their way to India. Remember, in the mid 80s companies had a ‘Computer Centre’ and one had to take off one’s shoes to enter this sanctum sanctorum. There was also the Electronic Data Processing (EDP) Manager, the ruling deity of the computer centre, with whom one had to plead with for getting any data analysis done.
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I convinced my then boss to sponsor me to this spreadsheet training workshop. I got to know how to use the precursor of MS Excel, the VisiCalc. It left me quite blown. Later that year, I experimented with the use of VisiCalc for preparation of the annual sales budget for the company, much against the wishes of the EDP Manager.
The experiment worked and my company started using PC based tools. The company had a ‘statistics’ department consisting of over a dozen clerks whose only job was to do statistical analysis of all the sales data that was coming in. The department was retrained when the company introduced desktop computers. And surplus staff were redeployed. The same happened in the sales depots, the finance and accounts department, purchase department etc.
Making sense of the data tsunami
Over the last three decades, the use of computers and simple data analysis using spreadsheets has become ubiquitous in all organisations. A graduate who wanted to apply for an entry level job, in any department, would not get past the security guard unless he included computer literacy as one of his skills [ability to use a desktop computer, MS Word, MS Excel and Powerpoint].
This is now being upended with the rise of data analytics. What is driving this rise?
Prof Sudha Ram knows Big Data. She is the Anheuser-Busch Endowed Professor of MIS, and Entrepreneurship & Innovation in the Eller College of Management, University of Arizona. At a seminar two years ago, she explained how Big Data can be seen through two simple filters. One, how Big Data has created a lot of data from thin air; for example by wearing a smart watch you are capturing a lot of data that always existed but was never captured [the number of steps you took, the stairs you climbed, your heart beat etc.].
Second, a lot of Big Data comes with time and geo stamp. Take the example of a smart phone in the hands of a sales representative making 40 calls a day. With the right app, the National Sales Manager sitting in Mumbai or Bangalore can get a minute by minute or day by day analysis of sales rep effectiveness. Something that would have been impossible without the capture of time and geo tags.
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Though a lot of data is being generated, most companies, till recently, allowed all this data to lie around in silos. Now, the effective use of platforms like SalesForce or ERP solutions like SAP has enabled companies to get a seamless look at the data, drawn from multiple sources.
Global corporations are waking up to the potential of using all this data and according to the #FutureOfJobs report 2020 put out by World Economic Forum, adoption of cloud computing, big data and e-commerce are today high priorities for business leaders. The report says that technology will radically transform tasks, jobs and skills by 2025.
Companies see big change coming; 43% are set to reduce work force, 41% expect to expand contractors for task-specified work [Gig Economy] and 34% plan to expand the workforce due to technology upgradation. Global employers are planning to rapidly digitize working processes and move up to 44% of their workforce to remote working.
Data analytics courses
The economic census of India done in 2013-14 [Business Standard, Aug 9, 2021] reported that 131 million Indians worked in 59 million non-agricultural establishments. The report also pointed out that 11 million jobs are in establishments that employed more than 100 workers. Chances are that almost all of them would be computer literate today.
What will happen to them in the next five years? Globally, companies expect 94% of their employees to pick up new skills, either sponsored by the company or on their own.
If we look at the rapid uptake of digital education, the most popular online technical course in the year 2020 in terms of enrollment on SWAYAM platform were courses like ‘Introduction to Machine Learning’ offered by IIT Madras [enrolled number 42,102], Python for Data Sciences offered by IIT Madras [enrolled number 41,982] and Programming Structures/Data Structures/Algorithms using Python by Chennai Mathematical Institute [enrolled number 41,543]. Even in the general category, the top courses were animation, cyber security and digital marketing [Times of India, August 15, 2021].
As we capture more and more data of varied types, the field of data science is brimming with promising opportunities according to Sarita Dugumarti of Jigsaw Academy. She feels that this broad field of data analytics will further get split into multiple sub-category jobs like data analyst, data engineer, data scientist, data architect, data visualizer and data communicator to name a few [Hindu Business Line, Aug 16, 2021].
Read more: How a low-income community in Bengaluru used data to demand a bus route
All jobs will undergo transformation in the coming five years and data will be at the center of it. If we take the example of the sales representative who today knows how to use a spreadsheet to do simple analysis, he may now have to do more complex data analytics with the sales data. What if he can model the behaviour of retailers using their past buying pattern? Can he automate some of the reordering process? What skill will he need to do this?
In some areas jobs may get automated and thus eliminated. As a friend once mentioned to me, we often expect the low end jobs to get eliminated, but that may not pan out the way we expect. For example, in a pathology lab whose job will get eliminated? That of the receptionist, or the person who takes out the blood sample, or the person who prepares the slide, or the person who reads the blood sample, or the person who prepares the report? He felt that contrary to expectations, the top job, the person who interprets the blood report may be the job that will be eliminated early. This of course will be hastened by adoption of machine learning and artificial intelligence.
When computers entered public sector banks in the late 1980s, some unions protested that their jobs were being taken away. But some bank employees rejoiced since they will now get an air-conditioned office [everyone knew that computer centers in all companies had excellent air-conditioning]. Banks could train their staff and manage a smooth movement from the good old ledger system to computers.
The algorithm tyranny
We are now at a stage where some of my friends have never entered their bank premises. They transact through mobile phones and laptop computers. Their parents probably visited their bank every week, if not to deposit or withdraw money, definitely to meet the bank staff. All that is now history.
Data analytics will become more and more important for every department, and employees will be expected to handle large volumes of data and draw insights from it. This may not be possible with just a spreadsheet and may call for more sophisticated analysis. And hence the likely boom in data analytics education.
As a young engineer explained to me, if you work above the basic algorithm you are going to be in great demand. If you are working below that algorithm, you are going to be at the mercy of the algorithm.
As a recent article on the plight of food delivery boys said, they are at the mercy of the algorithm; if the algorithm detects that they are slow in picking up and delivery they get less orders to execute and make less money. If on the other hand they understand how the algorithm works and modify their behaviour they may make a little bit more. But they will still be living below the algorithm
In this big-data driven world, it is the person who sits on top of the algorithm who is going to end up the winner. He or she will design algorithms, test them and improve them. These are the folks who will get the valuations they demand. No wonder courses like data structures and algorithm design are much in demand.
As Jaspreet Bindra, author and data evangelist, observed in a column he wrote in the Mint [Aug 20, 2021], “In India, for generations, knowledge of English was the passport for the best jobs. Soon, data science and digital coding could become the new English”.
The future of jobs is very data driven. Our success will depend on how well we are able to drive data to the right destinations. Sitting on top of the algorithm.