The most useful (or not) insight I received this morning:
“Someone on LinkedIn viewed your profile.”
Not very helpful, I know.
But it made me wonder how often some of the decks, reports, dashboards, and emails we create as data professionals land similarly with our business partners.
We may have spent considerable time collecting, processing, analyzing, and presenting the information. The analysis may be accurate, the dashboard may include every requested metric, and the presentation may be visually impressive.
Yet if the recipient has to spend additional time figuring out what the information means, why it matters, what has changed, and what to do about it, we probably didn’t deliver as much value as we think.
Consider a report showing that revenue declined by 8%.
For someone managing the business, the percentage alone is of limited use. They would want to understand where the decline originated, whether it reflects a broader trend or an isolated event, which products, customers, or markets are contributing to it, and what actions could help address the situation.
Much of this requires business context, an understanding of the underlying problem, and the ability to connect the analysis with potential decisions.
I believe business literacy is becoming even more important than data literacy for data and analytics professionals.
We have spent years emphasizing the need for business users to become more data literate. While that remains important, I believe we have placed far too little emphasis on developing business literacy within data teams.
Understanding how the company makes money, serves customers, manages costs, competes in the market, and measures success should be fundamental to how data professionals approach their work.
Processing files, building pipelines, developing data models, and creating dashboards are important capabilities. But delivering these technical outputs does not automatically translate into business value.
With advances in AI, data teams can go much further by developing context-aware data products and insights that help business leaders spot emerging opportunities, anticipate risks, evaluate alternatives, and act faster to improve performance.
This also changes what we should expect from data teams.
Instead of waiting for business partners to define every requirement, there is an opportunity to become more proactive in identifying problems worth solving, connecting information across business functions, and bringing forward recommendations grounded in a deeper understanding of the business.
AI can accelerate analysis, identify patterns, generate explanations, and make insights more accessible. However, the relevance and usefulness of those outputs will depend heavily on how well we understand the business context in which they will be applied.
I have often encouraged data and analytics teams to spend more time understanding how their work will be consumed and used. This requires engaging with business partners beyond requirements gathering, asking better questions, challenging assumptions, and understanding the decisions the analysis is expected to support.
As AI makes it easier to produce reports, presentations, summaries, and visualizations, the volume of information flowing across organizations will only increase. That makes the ability to distinguish meaningful insights from routine information even more valuable.
Before sending the next report, dashboard, or presentation, ask whether the recipient will be better equipped to understand a situation, evaluate alternatives, or make a decision because of what we have shared.
Without adequate context, problem framing, and potential solutions, even technically excellent deliverables can create more work for the people consuming them.
The real opportunity for data teams is to combine technical capabilities with business understanding and AI to help organizations make better decisions, act faster, and deliver stronger business outcomes.
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