The global recession has engulfed the world of technology. In the field of data science, layoffs are happening across many companies, and analytics professionals have concerns staying relevant in these tough times. Alongside, businesses are also strategising to cut costs to ensure continuity during this crisis.
The Background Behind The Layoffs
In this article, we will take a look at some of the factors behind the ongoing layoffs in data science. During the last five years, a lot of professionals had migrated towards data science from other job titles. We saw a lot of companies which started expanding their data science teams rapidly, but now many say the bubble has burst.
One of the key aspects where data science and analytics teams are hired is automating, improving customer experience or making businesses processes more efficient. But experts say businesses didn’t have a clear vision in mind, and just had a vague idea on strategising data science.
“The hype is still there, but it is slowly coming to the actual reality in terms of what is possible not, and what is not possible with data science. The last couple of years, the economy had been doing quite well and since every company wanted to join the AI race, they started pulling up these data science teams. But they didn’t do the due diligence in hiring. They didn’t have a clear vision in mind as in how their AI strategy is actually going to help,” says Dipanjan Sarkar, Data Science Lead at Applied Materials.
Lack Of Funding For Data Science Projects
Earlier, businesses could afford data science in terms of funding ensured by a race among businesses to leverage AI for different projects. Then, the pandemic happened which severely affected product sales, and getting funding for new AI projects became difficult. Companies have now started looking at the most important things, and the key business units which can deliver value, say experts.
Businesses certainly have been closely evaluating a large chunk of their job functions, trying to cut costs and focus on essential positions. We can take a look at the recent layoffs at companies like Zomato, Uber, Airbnb, Lyft and many others around the world. Many of the laid-off staff had skills in advanced technologies, including AI/data science. But, that’s no longer the only parameter for employment these days. The key is survival.
“If we remove the insights which data scientists are bringing, like forecasting sales, the business won’t be critically affected. That is where the layoffs are happening. Data science is a thing which is nice to have for business innovation, but it can be selective as well. For businesses, the main thing is how long can you sustain their operations and pay salaries,” tells Dipanjan.
Focus On Essential Job Functions
Organisations around the world are now focussing on the key roles in the company which are related to the essential functions. Many say data science now isn’t an essential function, and that is where a lot of data scientists have been some of the first ones to get laid off.
Dipanjan Sarkar explains, “The key thing is that data science as a position does not guarantee you job security, especially at this time unless you exhibit key skills and talent which is directly plugged into value creation. It doesn’t matter if you’re building a fancy deep learning model. You have to ask yourself how you can deliver value for the business.”
According to experts, a lot of people have entered into data science, but that doesn’t make them data scientists without learning the core expertise. Even though there is a rush towards AI projects, particularly using open source tools, it is difficult to quantify the value of such data-driven innovation. This was seen recently during the COVID-19 forecasting models, most of which failed to deliver any real-world value in mitigating the pandemic.
Businesses Management May Not Recognise The Value Of Data Science
Coming under the brunt of the economy, companies have been trying their best to cut costs to the bare minimum. COVID-19 has led to changing priorities that require quick turnarounds on deriving insight from data to make sound business decisions. Financial institutions are re-assessing strategies across their business and resources are limited.
“The perspective of data science jobs and the need for analytics is changing, and this will be critically analysed as we move forward. Where earlier 20-30 professionals were needed, in coming times, companies would hire 5-10 because much of the work related to data science will be automated,” tells Puesh Rajiv Ajmani, Global Head of Analytics & Insights at Square Panda
AI or machine learning is just one small part of the whole business. In addition to that, data scientists may face resistance because they want to change the existing processes in the business. Unless you are very sure about having supportive management, many data science projects will remain POCs for a long time, say experts. This particularly holds true for many companies which rely on legacy infrastructure.
“Companies may suspect that they’re not getting any tangible value from excessive data scientists. This can trigger a move towards cutting down the staff, which may be non-essential units. And that is where data scientist got hit really hard, especially in companies where data science is a supportive role or non-essential,” Dipanjan added.
Experts say that for companies to survive in the next coming months, they need to be able to pay their employees. The most essential things like supply chain and logistics will be prioritised.
Subhobroto Ghosh, Head – Data, Analytics & Actuarial at Allstate India tells, “We are at a major inflection point in the field of data science. While the pandemic will taper off, it will leave behind a very bad economy. So businesses are looking at containing costs, leading to layoffs.”