1. Speak Their Language – Avoid technical jargon. Convert data insights into clear business narratives that resonate with stakeholder priorities. 2. Start with the Business Question – Understand what stakeholders are trying to solve before diving into the data. Let business needs guide the analysis. 3. Collaborate Early and Often – Involve stakeholders from the start. Co-create dashboards, KPIs and definitions to ensure alignment and ownership. 4. Visualize for Impact – Use simple, intuitive visuals to highlight trends, risks and opportunities — not just numbers. 5. Tell a Story, Not Just a Stat – Wrap your findings into a story with context, action points and outcomes that support smarter decisions.

When your data team and stakeholders are on different pages, bridging the communication gap starts with creating a shared language. Avoid jargon and translate technical insights into clear, actionable business terms. Regularly align on goals and expectations, ensuring everyone understands how data supports decision-making. Foster collaboration through visual reports, dashboards, and storytelling to make data relatable. Encourage open dialogue where questions are welcomed, and feedback flows both ways. Building trust and clarity between teams turns data into a powerful tool that drives informed, unified action.

Stakeholder sez: "My Marketing Spend Is ALL WASTED!" CDAO (channeling his inner Jerry Maguire) Help me, help you. In all seriousness, this is probably half the job of whatever C-something is in charge of data. Talk data sense into the stakeholder. It's basically all of Step 1 of the CRISP methodology. And it's crucial to get their buy-in for deployment at the end of the cycle as well.

We need to make it simple. 1.Regular meet up and feedback session. 2.Simple Language which can be understandable .no need of jargon words. 3. Work towards goal.

This can be a big issue, my recommendations would be the following. - Ensure the project objectives are developed by stakeholders. - Have regular check ins with stakeholders based on these objectives and KPI’s - Do regular show and tell’s, this in my experience makes a massive difference. Stakeholders need to visually see progress. Take feedback on board and make amendments for the next session

Aligning on goals is essential—starting with a meeting to clearly define and agree on the project’s objectives and success metrics is the best approach. It’s the right way to bridge gaps, foster teamwork, and build a true success story together.

One of the biggest disconnects I see is around knowledge base management. The data team knows poor content hurts AI and self-service, but the business often underfunds it. Paying a bill? Simple on the surface, but full of nuance—and if the KB is outdated or incomplete, your bot fails. To bridge the gap, speak their language: show the cost of poor data (escalations, handle time, lost trust). Use tools to flag content gaps, but most importantly—assign ownership. KB upkeep should sit with the business, not be an afterthought.

Bridging the communication gap between data team and stakeholders is crucial for delivering valuable, actionable insights. Below strategies we can use: 1. Make sure everyone means the same thing when they say things like "active user." 2. Use a simple roadmap so everyone knows what’s coming. 3. Focus on actionable insights by answering “So what?” and “Now what?” 4. Don’t just show charts, explain what’s happening and what to do next. 5. Meet regularly to stay on the same page. 6. Explain data in simple business terms and avoid tech talk. 7. Help non-data folks understand reports and dashboards. 8. Pick someone from each team to connect business and data sides. This helps everyone work together better and turn data into real business value.

Our data team built a brilliant dashboard—but stakeholders weren’t using it. Why? It was too technical, too complex. So we stepped back. We held a goal-alignment session, rewrote key metrics in plain language, and redesigned the dashboard with simple visuals. Weekly check-ins helped us catch confusion early. Within a month, engagement doubled—and decisions got faster. The takeaway? Data only drives value when it’s understood. Speak the language of your audience, not just your code.