COVID-19 has made life tough on both sides of the money divide–while businessmen are worried over not just demand and supply but vagaries of production and availability of labour, salaried professionals across-the-board are staring at salary cuts, delayed payments and bans on bonuses and increments. Or worse, job losses.
According to the Centre for Monitoring Indian Economy (CMIE), one in four employees in India lost their jobs in just the last two months, as the coronavirus induced scare led to a nationwide cessation of economic activity. That’s a whopping 12.2 crore people we are talking about!
And the worse may be yet to come—a snap poll was done among the CEOs by the industry chamber CII earlier this week showed that almost half of them believed they may have to fire up to 30 per cent of their staff.
“In the long term, there could be staff (cuts) in the blue-collar and grey-collar, and reduced demand in the entry-level segment, as discretionary spending gets muted,” feels Nilabh Kapoor, Head of Revenue, OLX People (Horizontal Business Unit). Certain industries may have been hit so badly that recovery may be very difficult, for example, hospitality and airlines. Many start-ups, too, may not survive. However Analytics and Data Science Skills are still listed as the most sought after under the present circumstances.
“However Analytics and Data Science Skills are still listed as the most sought after under the present circumstances”
Two uncertainties could make the job market bleaker for both the salaried as well as new job aspirants. One is if the COVID-19 graph keeps rising, not just in India but across the world. India already has the unenviable task of restarting business even while grappling with a disastrous bureaucratic approach to lockdown which has seen migrant workforce flee to their villages, or even worse, struck in cities waiting for the first chance to go back home.
While getting the labour back, clearing up the issues with raw materials, transport and supply chain is in itself worrisome; a bigger pain would be the expected dip in consumer demand. Almost two months of the shutdown has already seen consumer spending dropping to new-lows, and future uncertainty might just prompt them to continue keeping their purse strings tight. That does not augur well for an economic revival, and in correlation, the employment scene.
So what can the salaried as well as the new job aspirants do? The writing is up there on the wall. Or the blackboard, in this case — upskill.
“Even before the pandemic, it became increasingly evident that the young generation who are just entering the workforce would need to constantly upskill themselves,” points out Payal Kumar of BML Munjal University. “The concept of education has transformed from school certificates and university degrees to life-long learning, especially through online courses. Now more than ever youngsters would have to be more agile, adaptable and would need to constantly upskill their knowledge and skills.”
Supplements Jayant Krishna, senior fellow, Center for Strategic & International Studies (CSIS) & executive director (public policy) at Wadhwani Foundation, “Develop a beginner’s mindset, inculcate critical thinking and acquire an attitude for solving complex problems. Such a skillset would be extremely useful in post-COVID-19 India and hold them in good stead.” His tip? Check out online seminars and talk shows to evolve as better professionals, and also, acquire new skills through online certification courses.
And of course, there is still light at the end of the tunnel, feel the experts. “While private jobs are more likely to be affected than government jobs, many sectors in the former segment are expected to pick up once the lockdown is lifted,” thinks Nilabh Kapoor of OLX People. He feels that once businesses resume and government pumps in stimulus packages for MSMEs, it could be “a shot in the arm for industries to ramp up hiring.”
Expanding your job search across areas faring better in the present scenario, from health, tech, fintech, e-commerce to logistics, as also looking for ‘gig economy’ freelance work and in the informal sector would also be a smart move.
10 Trends Driving the Need to Upskill for Data Concepts
1. Data and analytics are ubiquitous
Over the last decade, big data was largely viewed as a by-product of digital applications and platforms. Companies focused primarily on how to store, clean, and manage data securely.
Now companies are focusing on data as “the new oil”. They view it as an asset that must be further broken down and refined through data science and analytics to generate profits.
The challenge is that data is everywhere, and the volume of data is skyrocketing as the graph to the right from IDC shows.
We’re only at the tip of the iceberg when it comes to the volume of data that will be created and which will need to be analyzed for business purposes.
Currently, firms are just scratching the surface of what can be done with all this data. WEF found that in 2019, in the US, some 89% of companies planned to adopt user and entity big data analytics. More data skills will be needed to profit from the pervasive volumes of data.
2. Artificial Intelligence
The World Economic Forum (and just about everyone else) made it clear that artificial intelligence would be a huge factor, if not the biggest, driving the need for upskilling the workforce.
Not only will a significant part of the workforce be displaced by AI (think Amazon warehouse packers), but AI will also become part and parcel of corporate, business unit and functional strategy and operations. This includes new business model teams to the legal department and right down to the manufacturing floor.
3. Data-driven performance
“Healthy data cultures empower people to make better decisions, create open discussions, and ultimately leads to the kind of superior innovation that is necessary for businesses to remain competitive and agile.” – Helena Schwenk, Market Intelligence at Exasol.
A recent study from analytics database company Exasol, found that 65% of data teams have experienced employee resistance to the adoption of data-driven methods. This is primarily because employees and managers lack a fundamental understanding of data and the positive impact that data contributes.
The literal bottom line is that upskilling employees to understand data concepts pays. It may even be a requirement for long term survival.McKinsey data shows that these “laggard” companies, whose employees and managers are lacking education on data concepts, are falling behind on performance. High performing organizations attribute at least 20% of their EBIT to the use of data analytics. Furthermore, the gap in performance between companies with a data-educated workforce and those lacking in data skills growing wider every year.
4. The talent shortage
“The urgency for upskilling comes at a time when emerging skill sets (like data science) are scarce and the talent market is tight – making it prudent to keep people even if they don’t have the right skills now.”– Michael Hughes, Managing Director, West Monroe Partners.
Companies currently face numerous challenges operationalizing the innovative, consistent use of data, and are looking to develop more data analytics and intelligence capabilities. They currently face a challenge finding the necessary talent to achieve their objectives.
Although organizations are trying to become more data-driven, data science and analytics initiatives are being held back by a shortage of data talent, whether it be due to a hyper-competitive job market, high salary demands or prolonged time to fill data-related roles.
What’s more, even with efforts made by educational institutions to churn out new graduates armed with analytics degrees, it seems insufficient to fill the talent gap.
According to Josh Bersin, universities underestimate the demand for technical skills by 300% and they are twice as confident about the workforce skills they are generating as employers. There’s a continued disconnect between talent creation through education and the technical talent employers actually need.
The result is that demand for data science and analytics talent still outstrips supply. Upskilling and reskilling employees can help companies build the internal pool of data science and analytics talent they require and make them less reliant on expensive, hard to hire, talent.
5. The skills gap
The figure below, taken from a 2019 KPMG CIO survey shows that data and analytics skills remain in chronic shortage. Likewise, a Teradata survey found that 50% of business decision makers face a data skills gap and are calling for more training in data and analytics.
One reason for the skills gap is that to do the job of a Data Scientist or other analytics professional, or, to work with business analytics as a functional employee, a wide variety of skills is needed. The ideal person for these roles is often described as having “T shaped” skills
This means that an analytics professional ideally has highly specialized skills in one or two areas, such as data science, programming, or domain expertise, and also adjacent skills, such as critical thinking, communication, and visualization skills. In the current job market, this is a challenging profile to find. “The good news is that more often and with increasing frequency, we are seeing alot of the required skills being learnt and assimilated by
In perhaps the most significant acknowledgment of the chronic technical skills gap, Amazon announced in 2019 its Upskilling 2025 Pledge to spend $700 million retraining non-technical employees for data, analytics, and technical roles such as healthcare analytics, machine learning, and computer science. working professionals with little or no experience in Analytics” reports Subhra Bishnu of Oranget\Tree Global, a leading provider of Data Analytics Training based in Kolkata.
6. The Internet of Things (IoT)
IoT is expected to generate more diverse and exponentially extensive datasets, creating an unprecedented challenge to companies seeking to unlock value from this new data in order to maintain a competitive advantage. Currently, the duty of unlocking value from huge datasets falls largely on the shoulders of highly specialized Data Scientists.
The volumes of future data generated by IoT will simply become too big for advanced analytics teams to shoulder alone.
In Retail, expenditure on IoT technology aimed at improving customer experience and logistics management is expected to grow 20% p.a. to reach more than $35 billion by 2020.
Likewise, increasing demands for transparency and traceability of products will see IoT solutions drive data generation, collection, and analysis throughout the entire manufacturing process and supply chain, resulting in a doubling of IoT technology expenditure by 2025.
There’s simply not enough data science talent out there to wrangle value out of all this data.
Some of the heavy lifting will have to be shouldered by business users, such as manufacturing equipment managers or regional sales teams. These employees will need to become comfortable dealing with data and thinking in terms of data analytics.
This brings us to the next trend.
7. Self-service data analytics
The market has responded to the lack of Data Scientists and data engineering talent with automated platforms that allow nontechnical business users to engage in the exploration of data and the discovery of data-based insights. Many of these platforms provide data science features that, without education on data concepts, may go unused or be misused.
To operate these platforms, business users will need to understand data concepts such as data visualization, correlations and clusters, and data discovery.
8. Cross-functional teams
Data science is now recognized as a team sport. It’s become very clear to most companies that in order to achieve data science objectives and enlist the diversity of skills that data project teams need, these teams must be cross-functional.
Everyone on the team needs to be conversant in data concepts, including business and functional team members.
9. Data literacy initiatives
“By 2020, 50% of organizations will lack sufficient AI and data literacy skills to achieve business value.” – Gartner, Inc.
It would appear that some companies have begun to notice Gartner’s warning. According to Metis and Burtch Works, demand for data-related skills and talent is exploding at the less “technical” end of data skills, otherwise known as “data literacy” skills.
These skills include knowledge of data terminology, understanding of use cases, data project scoping, data project lifecycle management, and so forth. Demand for data literacy skills could also mean, for example, adding Python skills to a functional area (such as sales) skill set in order to broaden their tool kit.
Whatever the role, the ability to speak the language of data will become integral.
10. The bottom-line cost
According to the WEF, the average cost of hiring a new employee is $4,425. Data from the Association for Talent Development shows the average cost of upskilling an existing employee is just $1,300.
Even if you were to double this figure to account for the technical complexity of skills related to data and AI, companies are likely to come out ahead financially by upskilling existing employees.
With the high costs of Data Scientist salaries, the extended time to hire Data Scientists, and the high turnover cost of a bad data science hire, it is likely much cheaper to retrain existing employees. The added benefit to retraining the workforce is that existing employees are already loyal, have domain and business knowledge, and fit into the company culture.
By retraining employees to be data literate, companies get more bang for their buck.
So What Now?
All evidence points to an explosion demand for data, analytics, and AI skills beyond 2025 and a chronic lack of data talent and skills that shows no signs of ending. The solution, according to many experts and thought leaders, is to begin upskilling your workforce now.
The question then is, who should be upskilled, when, how, and for what skills?
The answer to this question is just beginning to emerge in 2020. We’re starting to have conversations with HR and Data leaders on these very topics. We’ll address them in future articles.