How to Skill Employees as a Digital-First Era Takes Root
A few months ago, I had what I thought was a simple request. I needed a Power BI dashboard to track some key metrics for my team. Nothing fancy, just a few graphs, a couple of slicers, maybe a trendline or two. Naturally, I turned to one of my colleagues in the tech team. He was brilliant with dashboards. I shot over a detailed brief, sat back, and waited.
And waited.
And then waited some more.
Fifteen days, three reminders, and two escalation emails later, I finally had my dashboard. It was good. But the delay made me realize something: maybe, just maybe, it would have been faster if I had learned Power BI myself.
And so I did.
As I stumbled through my first few dashboards (and later began building better ones), a larger trend started becoming obvious to me—we’re moving beyond “requesting dashboards”. We’re entering a new era where non-tech employees are expected—and enabled—to build their own insights. This shift isn’t accidental. It is the result of a larger push across organizations towards data democracy. And that leads to another significant question that needs immediate attention: how to skill employees in topics that are not their area of specialization?
Usability is the New Differentiator: The Rise of DIY Analytics
Organizations today aren’t just buying analytics platforms that look impressive on paper. They are buying platforms that feel simple, act smart, and invite usage from people who don’t necessarily know SQL from HTML. It is a subtle take on how to skill employees and requires proactive employee participation.
According to Gartner’s 2024 Analytics and BI Magic Quadrant, by 2026, 75% of organizations will standardize on self-service analytics platforms for all non-technical functions—a sharp jump from 40% in 2021.
Here are some real-world examples to support that trend:
- Walmart rolled out a self-service data platform called Data Café, where business users from merchandizing, marketing, and operations can slice and dice data themselves, reducing time-to-insight by 90% for many initiatives
- Netflix built an internal platform called Metacat to make self-service data access easier for non-engineers, empowering everyone from HR to product managers to make smarter decisions
- Colgate-Palmolive adopted Power BI globally, cutting dependency on central BI teams and empowering local country teams to create over 5,000 self-built dashboards in just 18 months
And the product vendors are catching on, too!
- Microsoft Power BI, Tableau, and Looker are embedding natural language querying
- ThoughtSpot lets users ask questions like “What are our top-selling SKUs in the Northeast this quarter?” to get instant, visual answers
- Sigma Computing brings spreadsheet-style interfaces to cloud-scale data warehouses like Snowflake
The days when you needed a PhD in SQL to answer basic business questions? Almost over.
Digital Transformation Courses
But the Workforce Resists
However, the question of how to skill employees and encourage them to embrace tech is ever present. Even with easier tools, many employees hesitate. Here’s what’s happening:
1. Lack of Willingness to Learn
According to a Forrester survey (2023), nearly 47% of business professionals said “learning new data tools” feels overwhelming.
2. Skill Gap
A Deloitte report found that only 18% of non-tech workers feel “very confident” interpreting complex data on their own.
3. Cultural Inertia
In many companies, “tech does tech” is still deeply ingrained. Shifting this mindset requires sustained effort.
The bottom line, therefore, when it comes to the question of how to skill employees is this: you can give people the tools, but if you don’t change their habits, you won’t change the outcome.
And giving up is not an option. Neither for the employees, nor for the organizations.
How to Skill Employees
These are the new must-have skills that the employees need (and how organizations can help).
- Data literacy: Understanding what data means, not just what it says
- Analytical thinking: Spotting patterns, questioning assumptions, and making data-driven decisions
- Prompt engineering: Crafting effective queries in natural language (especially with AI such as Copilot)
- Data storytelling: Turning numbers into compelling narratives that drive action
Organizations that are getting the “how to skill employees” mission right are treating data fluency like a core job skill. Let’s look at a few examples:
- McDonald’s created a global data literacy program across operations, marketing, and HR
- HSBC launched an internal academy offering data certifications, with over 7,000 employees participating within the first year
So, in the context of how to skill employees, what exactly works best?
- Mandatory onboarding modules for data tools
- Micro-credentialing (badges, certificates) is tied to internal career advancement
- Peer learning via communities of practice, Slack groups, “data hackathons”, and internal YouTube-style tutorial hubs
What This Entails
There’s no denying that this pathway is easier said than followed, but there is no other way. Because, when non-tech employees start pulling and analyzing their own data, magical things happen:
- Faster time to insight: Coca-Cola reported that decentralized dashboarding led to a 45% faster marketing campaign optimization cycle
- Better insights: Who better to find growth levers than the people closest to the customers, the markets, and the products?
- Increased innovation: Companies with higher data democratization scores are twice as likely to exceed revenue goals according to a 2024 Forrester report
The Risks
A McKinsey study found that 22% of self-service dashboards contain at least one “critical error” that could mislead a business decision. While this is the most common pitfall (and let’s admit, can be caused by the most adept analytics developer), some other issues sneak in. These include the following:
- Data hallucination: Misinterpreting correlations as causations
- Junk data: Poor tagging, inconsistent filters, and wrong formulas sneaking into dashboards
- Overanalysis: “Paralysis by dashboard”, where too much slicing and dicing leads to no real action
The Solution
Building Smarter Guardrails
To reap the benefits without falling into the pitfalls, companies must build smarter frameworks across the data value chain:
- Expert-managed ingestion and governance: Good data in = good insights out
- Tiered access models: New users get basic access; power users earn broader permissions
- Prompt engineering bootcamps: Teach employees to ask better questions to AI copilots
- Dashboard review boards: Before any dashboard is widely shared, have a peer or expert check it
Best-in-class companies like Amazon and Spotify run quarterly “data audits” on dashboards to prune outdated, duplicated, or misleading views.
For me personally, what started for me as a frustrating 15-day wait turned into a personal data empowerment journey. And that’s the reality for most workplaces today. The future of data isn’t top down; it’s bottom up. Shaped by not just the specialists in the IT department, but everyone.
The best organizations will be the ones who recognize this shift early, not by forcing dashboards down employees’ throats, but by handing them the keys and teaching them to drive.
NOTE: The views expressed in this article are those of the author and not of Emeritus.
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