Why the need for data upskilling?

In the past few years, the growth of AI solutions and applications across industries has been meteoric and almost become a necessity to gain an edge in any industry. However, there has been growing unease among the workforce with the eternal question – will AI/ML or the use of bots replace human effort?  

In the face of this reluctance of teams to adopt data analytics, global business leaders are faced with a dilemma – how to retain employees without giving up on AI, data analytics, automation, or robotics? 

This is where upskilling your workforce is important, especially in AI and data analytics. 

As per a recent Gartner report, only 50% of enterprise strategies list data analytics as a critical skill set for delivering enterprise-level value. A Deloitte survey verifies that only 20% of executives believe their institutions have the skills to operate in an AI-enabled digitized world. 

So how do you go about making your team ‘Data Fluent’? Here is how you can get started with data upskilling in your organization.  

Stage 1: Assessing the current and identifying the desired future state. 

Let’s understand with the example of a Canadian Crown Corporation (CCC) which assessed the skill gaps it needed to fill. Here are five steps to make a well-rounded assessment of your team’s current state and future needs.  

Stage 2: Invest, develop, and deliver effective training 

Before the pandemic, learning was usually confined to classroom environments. Since then, new forms of learning have gained acceptance. Employees have more options available. These include self-studying, informal learning, and e-learning through self-paced online courses. The benefit of using such non-classroom environments is that learners can participate whenever, wherever, and however they feel like it.

Practical application is essential to the entire learning process. It allows us to acquire and retain new knowledge. The Kolb Model of Experiential Learning has consistently helped learners using a cyclic pattern. The following figure shows us how the experiential learning model works. 

The focus in experimental learning should be on practice, real-world application, and repetition. Institutions and organizations can expect to derive long-term value from their investments in data upskilling their workforce. In fact, experiential learning tends to create self-motivation among learners. Organizations can further incentivize upskilling.  

For e.g., Deloitte has provided financial benefits to professionals who obtain upskilling certificates. LinkedIn offers badges, thus allowing learners to showcase their skills. Anheuser-Busch InBev and Labatt use rotation programs to instil a sense of continuous learning among learners/employees. New recruits are rotated through different functions, so they develop a broader understanding of the business and pick up vital skills. 

Stage 3: Making learning ‘stick’ 

Now that your workforce is learning some new AI-related skills, how do you make sure this is sustainable? To solve this problem, we must look at our respective organizations from three perspectives – people, technologies, and processes. 

People – Is your organization ready for AI and the AI-powered workforce? 

• A growth mindset about AI applications is necessary at all levels of an organization, be it leadership, executives, or employees. Teams need to understand the relevance of AI in a changing market. For this to happen, AI usage needs to be demystified.  

• Leadership must mentally be prepared since the process of decision-making will change once AI is integrated. Decisions will no longer be made based only on intuition or experience but on data. At the same time, human intervention is crucial. One might say the new leadership will include humans and AI working together. 

• Interdisciplinary teams are important because AI can no longer be confined to the domains of tech experts and data scientists. For a well-functioning team, insights and ideas need to come from business specialists, tech experts, and product specialists. What organizations need is contextual AI and this can happen only with interdisciplinary teams. 

• If new technology is a characteristic of the digital age, so is skill obsolescence. Therefore, organizations need to nurture an appreciation for life-long learning so their workforce can adapt to changing needs. 

Technology – Does your organization have the infrastructure to support AI talent and transformation? 

• Having the correct technology and data infrastructure is key if you want to benefit from upskilling your workforce in AI. ‘Plug-and-play’ technology can take you so far. It is much more advisable to invest in data infrastructure.  

• Additionally, as a business leader, you and your peers may have to rethink some of your organization’s policies to smoothen the data transformation process. 

Process – Are you ready to embrace a new way of working? 

• A significant change that comes with adopting AI is speed and imperfection. Organizations cannot afford to wait for error-free fully formed solutions. Instead, they will have to work with a ‘test and learn’ mentality, continually refining and tweaking for a better solution. 

• Organizations will also have to rethink their levels of accountability as well as the measures and performance indicators. 

At a glance 

Here are some of the things to keep in mind as a business leader if you intend to upskill your team/workforce. 

References: 

• https://mitsloan.mit.edu/ideas-made-to-matter/a-new-study-measures-actual-impact-robots-jobs-its-significant 
• https://www.pwc.com/gx/en/issues/upskilling.html 
• https://hbr.org/2022/04/6-strategies-to-upskill-your-workforce 
• https://www.datacamp.com/blog/why-data-upskilling-is-the-backbone-of-digital-transformation 
• https://www2.deloitte.com/content/dam/Deloitte/ca/Documents/Analytics/ca-en-deloitte-analytics-upskilling-aoda.pdf 
• https://www.gartner.com/en/newsroom/press-releases/2019-10-23-gartner-says-traditional-data-and-analytics-strategies-cannot-satisfy-digital-business-demands 

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