The past decade has been driven by rapid technology innovation and immense business pressure to increase revenue and improve customer and employee experiences. Yet McKinsey research shows 70 percent of digital transformation efforts during that era failed to meet their goals.

Why did so many of these initiatives fail? Did leaders set the wrong goals?

Over the past decade, CIOs and CTOs focused on the right business priorities, but the expected business outcomes, employee productivity goals, and customer and employee experience improvements ultimately fell short. For many of these failures, analytic insights weren’t adequately prioritized or utilized.

To make matters worse, the digital transformation era also caused a significant skills shortage. Companies implemented innovative technologies such as personalized recommendation engines in e-commerce, applications for supply chain visibility, and more. These innovations require support from data engineers, data scientists, and other data roles, and there aren’t enough skilled data professionals to fill the jobs. The U.S. Bureau of Labor Statistics projects a nearly 36 percent growth rate in jobs requiring data science skills by 2031, so this problem will be around for a while.

[ Also read Digital transformation: 3 guiding principles for 2023. ]

Today, we’ve entered the fourth industrial revolution, or the era of intelligence, which promises to give us more powerful and innovative technology for successful transformation. This new phase is possible because computing power and storage are more affordable and accessible than ever, and companies recognize the power of data and why speed to insight is critical to success.

With countless AI-driven technologies and intelligent systems available, companies are prioritizing the democratization of data and analytics across departments and teams. Because Time to Insight (TTI) is now the strategic business transformation imperative and the skills shortage still exists, data scientists and data analysts can’t keep up with the exponential data growth and business demand to accommodate.

This seemingly perfect storm creates an opportunity for IT to expand its influence as operational leaders by understanding and deploying the key components of the modern data stack to impact broader business outcomes through analytics.

Data literacy starts with IT

Data literacy is the key to harnessing these intelligent systems, and IT leaders must help train people to use those systems and the data within them effectively. The first step starts with IT and building their own data literacy skills.

Taking the following steps will help ensure that IT teams have the necessary skills to share knowledge and drive change throughout their companies:

1. Assess the team’s skillset

IT covers an ever-increasing range of technical expertise. Data engineers will require different training than IT desk support, so it’s important to personalize a curriculum that corresponds with each employee’s skillset while giving them the time and flexibility to learn.

2. Teach people the foundation

Learning to use an analytics tool will not automatically mean your employees can transform any data into valuable business insights. Instead, they need a solid understanding of the problem and available data to ensure they are deriving valuable insights.

3. Provide easy-to-use self-service tools

Data and analytics do not have to be complex. Self-service, no-code/low-code platforms allow individuals to close the skills gap and achieve greater insights on their own.

4. Leverage automation

With new AI and Large Language Model (LLM) technologies entering the market like ChatGPT, teams need to understand how to uncover valuable insights from these tools. They must be able to ask the right questions, implement the right data techniques, and yield beneficial outcomes.

5. Use the cloud to democratize access

Make it easy for your team to access the tools they need by using cloud-based solutions. This also frees them from setting up on-premise solutions so they can devote more time to learning.

6. Make it fun

Gamify the experience and incorporate hands-on activities like datathons. Compared to learning modules, these efforts make upskilling more engaging and incentivize team members to continue their learning journey.

7. Treat upskilling as an investment

Checking the boxes on a training program should not be considered a business expense. Upskilling creates a more inclusive workplace and supports broader business goals by allowing individuals to make an impact.

8. Make it easy enough for anyone

In the intelligence era, data literacy needs to expand beyond IT. Companies and their IT leaders must not focus solely on upskilling data engineers who know R or Python to create code-based data analysis. Instead of waiting for the talent market to catch up, we must make it so easy that all employees can turn into citizen data scientists and data workers who can make data-driven decisions.

Siloed data that’s available to only a few people or departments within an organization will severely hinder that organization’s transformation aspirations. A truly data-driven culture means investing in the democratization of a company’s data and giving employees the ability to make data-driven decisions with the right tools. Cloud-based solutions, like a cloud data warehouse, enable IT employees to govern data while getting it into the right hands across all teams. Get IT up to speed on the cloud components of a modern data stack, and you will see dividends across the company.

The IT skills shortage is no longer a macro condition – it is within our control. If we don’t have data-literate employees, IT leaders have not done their job of empowering them with the right skills.

Company leaders who recognize this will lead the charge by upskilling and reskilling all employees – from IT to sales, accounting, marketing, and more – and creating a culture of data literacy where anyone can leverage data for strategic decision-making without relying on skilled data workers. Those who fail to take these steps will quickly fall behind the competition.

[ Learn the non-negotiable skills, technologies, and processes CIOs are leaning on to build resilience and agility in this HBR Analytic Services report: Pillars of resilient digital transformation: How CIOs are driving organizational agility. ]