How Organisations Leverage Analytics To Build A Data-Savvy Workforce

Do you see data science and analytics training as a functional piece?
How Organisations Leverage Analytics To Build A Data-Savvy Workforce

Can data create a competitive advantage in business today? At Jigsaw Academy’s virtual roundtable discussion on ‘Analytics As A Driver Of Talent Transformation,’ Ashish Gupta, the Head of Programs at Manipal Jigsaw, discussed how to build a data analytics or data-savvy workforce and various strategies/methods to upskill employees. 

The session kickstarted with a keynote speech from Sarita Digumarti, the co-founder and CEO of Jigsaw Academy where she underlined the importance of honing data skills across teams, and its impact on organisational performance and the future of work.

The virtual round table panel consisted of industry thought leaders, learning and development (L&D) heads, and chief human resource officers, including UnitedHealth Group’s Aalok Ajay Purohit, CGI’s Anil Santhapuri, Accenture’s Aniruddha Ray, Birlasoft’s Deepak Kumar Arora, Mindtree’s Manoj Karanth, GlobalLogic’s Neeru Mehta and Telstra’s Rohin Radhakrishnan

You can watch the full recorded session here.

Data as a competitive moat

Ashish emphasised the need for teams to become more data-savvy via training and upskilling programmes. “Do you see data science and analytics training as a functional piece?” he asked to kickstart the discussion. 

While Anil stressed the importance of conducting internal surveys to understand the learning requirements of employees before evaluating the learning partners, Birlasoft’s Deepak highlighted the need for contextualising learning apropos the job roles. 

“Everyone cannot be a data scientist or an analyst. So, we have to look at the right fit and where it starts. We can not start from the point where the candidates join the organisation. It should start within the campus or colleges,” said Deepak. 

Mindtree’s Manoj has a different take. “There is nothing called a data problem. There is a business problem, and how best we make a decision from it becomes crucial,” he said. 

He said it is all about getting comfortable with making data-driven decisions and suggested three key pointers:

  1. Excel is still a potent tool
  2. Arm yourself with at least one way to inquire on data where you can solve the given problem.
  3. Ask questions.

Explaining more, he said, “Today, we ask questions and get an answer, and that leads us to more questions. That is how the differential capability of an organisation grows.”

Meanwhile, Telstra’s Rohin is all for a top-down approach, where companies need to make sense of data at every layer of the organisation. “That is where the problem lies. So the way you create an ecosystem that consumes data and uses it to provide relevant skills becomes important,” said Rohin.  

GlobalLogic’s Neeru said they are shifting towards a data-driven ecosystem by setting up data science and data engineering academies within the organisation. 

“The data and analytics savvy workforce is going to create the competitive advantage in the market for most companies, whether they are fortune 1000 companies or startups,” said Accenture’s Aniruddha, stating, a majority of successful companies in the last few years have analytics as a differentiator. 

“We really can’t afford to do anything without data,” said UnitedHealth Group’s Aalok. He said it’s not about using R or Python or any other powerful BI tools but having a data-friendly mindset and how you interpret and analyse data. 

You can watch the full recorded session here.

How to build an effective data-savvy workforce 

In the second half of the session, Ashish spoke about the nuts and bolts of building an effective data-savvy workforce. “In a world full of distraction, how do we make learning effective, and what are the different approaches it takes to build an effective organisation,” Ashish threw the question to the expert panel.

In response, Anil said his company had incorporated a DNI strategy to develop a clear roadmap, alongside building a career framework to adopt new technology and advanced tools. “However, when it comes to developing a culture fit, there exists a confirmation bias. I think culture plays a big role where you need to be accurate, and should also see one or two areas where you can make some inroads,” he added.

Explaining the three pillars of building an effective data-savvy workforce, Deepak said the HR fraternity or L&D team should lead the data analytics operations within the organisation. Secondly, employees across various experience levels should hone their data skills. Finally, the third pillar is about using these skills to solve real-life problems. “I think the central ecosystem where all these components are aligned is very important,” Deepak added. 

Manoj said Mindtree had implemented various e-learning courses, both 1-on-1 (personalised) and workshops, to upskill their employees across different experience levels (from entry, mid, to senior levels). 

“When people come into data science roles, they believe that applying an algorithm is the ultimate nature of it. However, getting to appreciate data by doing the hard work of setting and slicing the data is crucial,” said Manoj. Mindtree addresses this effectively by conducting coaching and case study sessions to help employees understand the nuances of solving data science problems.

Rohin said companies should tap into an existing talent to build a culture of fun and learning across the organisation. He said Telstra is working to help people elevate their skills through a collaborative approach. “Most organisations follow multimodal approaches, blended learning programmes, etc. But, what I have not seen in my limited experience is organisations, tapping into resources and ensuring the fungibility of skills within the workforce.” 

Drawing an interesting analogy between learning and insurance, Neeru said learning is a continuous process. “The biggest trend that I see is how you get people to learn by learning together. The whole individual learning from the e-learning model is not working. So, that is what we have been focusing on right now, making sure there is a lot of community learning and collaboration,” she added. 

Aniruddha Ray, on the other hand, said Accenture provides individual learning platforms for employees — bite-sized learning experiences based on their interests and build a community of champions and scale accordingly. 

Aalok said the success of any model specifically in these times has multiple parameters. For example, he said UnitedHealth Group is using a model to make sure their workforce is future-ready. “The moment we decided that these two are different kinds of approaches, the whole model changed. It is now working very well for us,” said Aalok.

You can watch the full recorded session here.

More Great AIM Stories

Amit Raja Naik
Amit Raja Naik is a seasoned technology journalist who covers everything from data science to machine learning and artificial intelligence for Analytics India Magazine, where he examines the trends, challenges, ideas, and transformations across the industry.

More Stories


8th April | In-person Conference | Hotel Radisson Blue, Bangalore

Organized by Analytics India Magazine

View Event >>

30th Apr | Virtual conference

Organized by Analytics India Magazine

View Event >>

A beginner’s guide to Spatio-Temporal graph neural networks

Spatio-temporal graphs are made of static structures and time-varying features, and such information in a graph requires a neural network that can deal with time-varying features of the graph. Neural networks which are developed to deal with time-varying features of the graph can be considered as Spatio-temporal graph neural networks. 

Yugesh Verma
A guide to explainable named entity recognition

Named entity recognition (NER) is difficult to understand how the process of NER worked in the background or how the process is behaving with the data, it needs more explainability. we can make it more explainable.

Yugesh Verma
10 real-life applications of Genetic Optimization

Genetic algorithms have a variety of applications, and one of the basic applications of genetic algorithms can be the optimization of problems and solutions. We use optimization for finding the best solution to any problem. Optimization using genetic algorithms can be considered genetic optimization

Yugesh Verma
How is Boolean algebra used in Machine learning?

Machine learning model with Boolean algebra starts with the data with a target variable and input or learner variables and using the set of rules it generates output value by considering a given configuration of input samples.

3 Ways to Join our Community

Discord Server

Stay Connected with a larger ecosystem of data science and ML Professionals

Telegram Channel

Discover special offers, top stories, upcoming events, and more.

Subscribe to our newsletter

Get the latest updates from AIM