The Story of a failed analytics professional: A Short Sci-Fi story

The year is 2029.
Alice rushes towards the elevator, trying to balance the coffee and her bag as she makes the run. Watching her rush, a colleague blocks the elevator door, allowing Alice to get into the elevator. She thanks the person with a smile. Ever since she has joined WonderLand Inc., she has been impressed by the friendly culture at the company.
After getting off at her floor, as Alice settles down in her work station, she asks her voice assistant to highlight the key metrics “MadHatter” captured yesterday, while she is still unpacking.
MadHatter-is the name WonderLand Inc.’s Data Science team gave to this Machine learning algorithm whose sole purpose is to plan and keep track of the end to end Supply Chain flows. It was developed by a team of external advanced analytics consultants and the internal Data Science team took the ownership after transition. It is the brain behind the digital supply chain of the organization.MadHatter tracks the supply chain performance based on metrics defined, runs analytics and identifies trends, risk areas, opportunities etc. based on what it had “learned” over the years. It has not let down its creators and in last one decade, it has become really smart. It not only churns out operational analytics data but also recommends strategic insights and suggestions like SKU rationalization, customer segmentation strategy (from a supply chain perspective), consolidation and expansion opportunities and many other “smart things”.
Alice’s manager Bob walks by her desk and sees her interacting with her voice assistant and listening to insights provided by MadHatter. As Bob grabs his coffee, he starts thinking about the era when he was a Supply Chain Planning Manager. He managed a team of many analysts,each assigned to analyze data from a specific system or group of systems. Now this damn piece of algorithm was everything combined into one-forecasting, demand planning, transportation, S&OP, Network Optimization-all being managed by this virtual brain. The Digital Brain behind the Supply Chain.He still remembers the intense project where he partnered with an external consulting company to launch the beta version. Was it 2020 or 2021? He can’t remember exactly. As he crossed Alice’s workstation to move towards his office, Alice smiled and waved to him. He smiled back-life was much simpler now with “MadHatter” managing the intricacies of the Supply Chain analytics madness. Much of WonderLand’s iconic rise in last few years can be tied to such smart algorithms.A few blocks from the towering WonderLand Inc. building was an old apartment building. In one of the studio apartments in this building (which had only one window that allowed some sunlight into the apartment, but with a view of three large garbage bins), Kumar was trying to drag himself out of the bed. He has been awake for some time now, staring at the ceiling, his attention moving from one bad patch in the ceiling to another. One area looks scary-he should probably complain? But with the rent he could afford, this was all he could get.

Times were not always bad. As he lay on the bed, he started to think of the golden era. Almost a decade ago, Kumar used to be a very successful Supply Chain Advanced analytics professional, his expertise was helping clients develop Supply chain models, both prescriptive and predictive. It was the era of Data Science Boom. He was valued, well paid and the future looked bright. Working with some of the smartest minds, he helped companies automate analytics.

Then over the years, the algorithms and tools that he helped develop took over. The best in class tools he had designed evolved into “MadHatters”. Suddenly, Kumar realized that he was not as much in demand anymore. Obviously tools like “MadHatter” needed support and continuous development/learning. There were two aspects to it-technical and functional. Technical support was provided by data engineering team that were trained on the algorithm. Functional “learning” for these tool came from functional managers, who helped the tool learn what needs to be flagged, what are the benchmarks etc. etc.

Why couldn’t he see that?-Kumar thought as he walked into the shower. It was the writing on the wall. Most of the analytical tools were leveraging some level of Machine learning aspects. They were slowly taking over the actual” modeling” and yet he did not see this day coming? Not that the market of tools development was fully saturated but it kept on declining slowly and Kumar did not evolve his skill sets with the changing ecosystem.

The needs and ask of clients from analytics consulting evolved!

There was now one specific area where the demand peaked. Companies now needed to train their new workforce-The Digital Workforce. This was about “Reskilling”. Resources like Alice-who were not true advanced analytics professionals but needed skills and expertise to leverage the machines optimally as well as train these machines using a best in class approach. With more and more functional managers taking ownership of these advanced analytics tools, there was this huge demand in the market for people who could help train these functional managers beyond rudimentary analytics.

As Kumar walked outside his apartment, he looked at his watch. He can still get on the 9:45 bus. He ran across the street to the bus stop to join the crowd waiting there. As he waited for his bus, his mind started lamenting about the past again.

The skill set required to train or educate someone requires few more aspects as compared to skill set required in modeling. Some “soft skills” come into play, like developing the relationship with the folks you are trying to train, understand their thought process and expertise level and the most important of it all, translate the true advanced analytics part of the tool/algorithm into a lingo that can be understood easily by the functional practitioners.

Some of these soft skills, unlike pure coding and model development were not easy to acquire. But human brain is amazing and can be trained for anything. With his skill sets, he could have worked hard on developing the traits of a best in class Analytics trainer. He could have still been in the game, doing good professionally. This thought of missed opportunity haunted him day and night.

The bus screeched to a halt at the stop and that derailed Kumar’s train of thoughts. He hopped on the bus and found an empty seat in the very first row. It took few minutes for all the passengers to settle down before the bus got into motion again. Maybe today will be the start of a new phase in life-Kumar was headed to a reskilling initiative being run by the government. He would learn the skillets required for shop floor manufacturing operators in this digital age. He was hoping that once he understands what their pain point and challenges are, he will then work on skills required to train such professionals in the digital age. He had worked hard to build his career once, he could do it again.

As he looked outside to the horizon, the clouds moved from the city skyline and he could see the brightness from the sun again.

Views expressed are my own.

The story above is Sci-Fi today but part of it will be reality in 2029.

With  focus currently on “Reskilling” of shop floor talent to align with Digital Manufacturing and automated warehouses, a key area that is under the radar is the “reskilling” of  While collar talent.

Years from now-there will be a major market for training functional managers to take ownership of Machine Learning algorithms. Such training requires evolved expertise, more in line with analytics consulting, but with very specific skillsets. You will have to train the power users to help them understand the best in class approaches to “train” these machines. There will be a huge demand for resources trained to train these power users of planning algorithms.

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