The Hidden Skill Every Data Team Needs: Effective Stakeholder Communication

Getting AI algorithms to produce reliable, scalable results is hard, very hard.

Building high-quality data pipelines is tedious. Styling and iterating on dashboard visualizations with stakeholders takes time. All these investments cost time, money and require efforts during our time as data engineers, data scientists or business analysts.

However, what we experience very often is that these efforts are not recognized by the rest of the company – or worse, are denied by senior management. This is something I’ve been hearing a lot in conversations with peers (you can also check out my AI Impact Live session where I spoke a lot about this).

How could this happen?

In my opinion, these problems arise with a lack of suitable communication. Not only by techies, though. Communication, the transmission of information, always depends on both the sender and receiver to be clear, understandable, and actionable.

Let’s unpack where things break down and what to do about it.

Why business stakeholders don’t (want to) understand technical jargon

LinkedIn & Co often say it’s bad to use technical language to communicate with business stakeholders because they can’t understand the tech language. However, many of our senior management executives have a tech background (especially in Diplom Germany) and actually do understand what we’re talking about. They also care that the output is improving and that we’re delivering on our target metrics.

However, what really drives results is the information about the impact we as data teams are generating. Formulating this impact in business terms (and not in “easy language”) is what really counts. Here, our senior stakeholders can directly act upon the information we’re transmitting, which allows an efficient steering of the company.

To improve the communication, do not fully omit all the technical information. Just tie it logically to the business outcomes, because these help you to coherently explain how your work impacts the bigger business. As a nugget, it also allows you to see clearer and make sure that your daily doing is aligned with the desired strategic direction of the company.

Ineffective communication example:
“Our model precision is up 12%” CFO replies: “Great…but did we make more money or just feel smarter?” (Antonios Angelakis, World Data Summit 2025)

‍Effective communication example:
“We improved our model precision, which helps the sales team better identify high-value leads.”

Stakeholders care about outcomes.

If communication does not connect to business impact, it’s not directly actionable for our business stakeholders. Any effort from their side to build the connection increases the chances that our (valuable!) work is not perceived as helpful.

Why "just build it" is a red flag

Ever had a stakeholder say, "Can you create this new column in the data mart for me"?

Is this a problem? I would say, it depends.

Our business stakeholders mostly have deep knowledge and experience of their domain, so just following the order and quickly resolving the ticket might be reasonable. Just delivering output is ok. If you know your stakeholder well, you trust their skills and you know that your change is being used, it’s a very efficient way to generate value.

However, we also often observe that requested data is unused, dashboards are never looked at, and new tools are mainly used by early adopters and not the broad, expected audience. If this is happening, we should question the “service” orientation in the communication with our business partners. Here, having a better alignment and collaboration with the business on the desired solution, understanding the underlying user needs, and creating a real product can be very helpful.

To achieve that, we need to collaborate closely with our internal customers and this involves a tight, but friendly communication, which aims to get buy-in of the business stakeholders. Instead of pushing back with a hard "no," reframe the conversation.

Instead of saying:
"I need more information before I can build this."

Try:
"I understand you need this quickly. To make sure we deliver exactly what you need, let’s take five minutes to align so we get it right the first time."

The shift is subtle but important.

It changes the perception of the data team from being a blocker to being a problem-solving partner. By increasing the focus on the actual problem, aligning closely with the stakeholder, and building a better solution, we improve the “product-market-fit” of our product.

Here, communication isn’t about slowing things down or denying ideas, but it’s about making sure the solution is right, and the problem is worth solving. Now, this requires effort from us in framing the right discussions. But it should also lead to changes in the communication from our counterparts: business stakeholders should learn to approach us with the right problems at hand and trust our skills and experience as data experts to solve their problems in an efficient way.

How to get stakeholders on board

Beyond talking to our internal stakeholders in business language and educating them to tell us all about their problems, really getting them on board and pushing them towards a proactive collaboration is cumbersome.

But why should they still invest?

Because successful data products require ongoing engagement, not just a one-time request. Because it’s the business themselves who leverage the value from data. Now, it’s our task to communicate this value and convince our stakeholders to take this (often long) way together.  

For that, we need to build up a good relationship with our stakeholders. It’s all about influence, trust, advocacy, and just technical or social value.

For that, you need to build relationships beyond the immediate request:

• Get to know stakeholders outside of formal meetings. Learn their priorities, frustrations, and biggest challenges.
• Make the business impact explicit. Do not just deliver a model but explain how it will help them achieve their goals.
• Show progress early and often. Products are long-term projects. Make sure to communicate early value gains and talk about the bigger picture to get their conviction.

Once you’ve convinced them to work with you, to actively contribute to the joint development, make sure that they don’t hop off quickly. A tiny, wrong number in an important executive deck can create massive turbulence.

And mistakes happen.

Again, make sure that you communicate clearly: where did this error come from? What went wrong? How can you avoid it in the future? Which mitigations do you plan? Here, solving for the mistake and talking about how exactly you want to do it helps to foster trust, even in the event of a mistake.

The lesson? Overcommunicate (mostly)

If there’s one key takeaway I’ve learned: try to overcommunicate.

Stakeholders should never ask you for status updates. You can avoid that:

• Report on progress proactively
• Tie your results to business impact to make them actionable
• Turn stakeholders into collaborators, not just requesters
• Keep the message concise and choose the channel wisely

Ultimately, the impact and recognition of your work depends not only on the quality of your models or platforms, but on how well you align with the people they’re meant to help.

Great communication builds trust. And trust is what turns data teams from order-takers into business partners.

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