I loved browsing our BI environment. It is like scrolling through Tableau Public. Beautiful visualizations, technically flawless execution, impressive performance optimization. We had even standardized within our CoE the company branded templates so everything looked professionally polished.
Combined, they probably cost north of $600k to build. Surely, they were delivering incredible business value..
I decided to check the usage metrics.
23 out of 47 dashboards hadn’t been opened once in the last six months. More than half a million dollars in technically perfect solutions that nobody is using.
I call this The Translation Gap.
The Real Problem
After 15+ years in banking, I have seen this pattern everywhere. Smart business people explain what they need, but not in terms that technical people can understand. Smart technical people build exactly what was requested, but miss what was actually needed.
Details get lost in translation. Requirements get filtered through multiple layers. By the time the project launches, technical team has delivered a complex and sophisticated solution that barely adds business value.
Business teams are not wrong for wanting better insights. Technical team is not wrong for building what was specified. But the gap between them creates expensive digital graveyards.
My Controversial Take
I know this will ruffle some feathers, but if you are calling yourself a “Data Analyst”, you are positioning yourself for obsolescence.
With AI making technical execution easier, your value will not be in writing complex SQL queries or building complex data pipelines. Those skills are becoming commoditized. Your value needs to be in understanding what business problems you are actually trying to solve.
Look at the recent layoffs, technical roles took the biggest hit. But companies are still hiring for roles that bridge technical capability with business insight. The job market is telling us something important about what skills will survive.
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The Title That Actually Matters
Start calling/promoting yourself a Business Analyst who specializes in data. It is not just the semantics. It is a fundamental shift in how you approach every project. Instead of asking “How can I analyze this data?” you ask “What business decision does this analysis need to support?”
Instead of showing your technical stack, show the business value you have delivered. Instead of talking about query performance, you talk about how your insights improved profitability.
This shift saved my career. Coming from a finance background, I naturally think about business impact first, technical implementation second. Every dashboard I built started with a simple question: “Does this add business value?”
What Business Analyst Thinking Looks Like
Traditional Data Analyst approach:
The data shows customer acquisition increased 15% QoQ.
Focus: Technical accuracy of the calculation.
Business Analyst approach:
Our customer acquisition costs are up 15%, which means we are $2M over budget and need to either cut marketing spend or find more efficient chanels by month-end.
Focus: Business decision that needs to be made.
The first is information. The second is actionable intelligence.
The Future of Data Work
AI will handle the technical heavy lifting. Query optimization, data cleaning, basic visualization, all these are becoming automated tasks.
What AI can’t do is understand your business. It can’t sit in a board meeting and translate between what executives need and what the data actually shows. It can’t make judgement calls about which metrics matter and which are vanity numbers.
That’s where the value is. As a result, that’s where the job security is as well.
What is scarier than making decisions on bad data?
Teaching AI to make those same bad decisions automatically at scale across your entire business.
Data quality is not optional anymore.
— #Data Exec (#@DataExec)
12:21 PM • Sep 14, 2025
Making the Transition
If you are currently a data analyst, start making this shift now:
Learn the business: Understand P&L statements, key performance indicators and how decisions actually get made in your organization.
Change your language: Stop talking about technical processes. Start talking about business outcomes.
Update your title: On LinkedIn, in meetings, in your head, you are a Business Analyst who happens to know how to use data tools.
The companies that will survive AI transition will be the ones that use data to make better business decisions, not the ones with the most sophisticated technical infrastructure.
Your job is not to build dashboards. Your job is to help businesses make smarter decisions.
That’s the difference between a data analyst and a business analyst. And now with AI, it is the difference between relevance and obsolescence.
Next week, I will dive into the difference between Enterprise CoE and Federated BI Solutions and why giving business users more control actually creates better outcomes (and why most IT departments hate this idea).