How to Foster Collaboration Between Data Teams and Stakeholders

Analytics teams thrive when they’re aligned with clear business goals.. Without that alignment, even the best data can lead to confusion instead of actionable insights. To make sure your team and the business are on the same page, here are five essential steps to keep in mind: 1. Define ↳ Start with crystal-clear goals. ↳ Know what success looks like for the business and how analytics can support it. 2. Collaborate ↳ Alignment is an ongoing process, not a one-time task. ↳ Stay connected with stakeholders to refine priorities as needs evolve. 3. Communicate ↳ Transparency is everything. ↳ Regular updates and open communication build trust and ensure the team is always working toward the right objectives. 4. Clarify ↳ Everyone’s role must be well-defined. ↳ When responsibilities are clear, progress becomes faster and smoother. 5. Celebrate ↳ Don’t skip the wins! ↳ Shared victories not only build morale but also strengthen the bond between analytics and the business teams. For analytics teams, the journey to alignment is all about building strong relationships and keeping the big picture in focus. ➔ Ask the right questions ➔ Listen ➔ Deliver value And remember, collaboration turns insights into action and results into IMPACT. Which of these steps resonates most with your team right now? #teams #analytics #innovation #data #ai #entrepreneurship #leadership #value #impact

I've spent over 4,000 hours in stakeholder requirement-gathering meetings! Save hours of your life by asking these questions: 1. What do they plan to use the data for? 1. What initiative are they working on? 2. How will this initiative impact the business? 3. Is this for reporting or optimizing existing workflows? Understanding the purpose of the data helps you define its impact. 2. How do they plan to use the data? Will they access it via SQL, BI tools, APIs, or another method? 1. Do they have a workflow to pull data from your dataset? 2. Do they just do a `SELECT *` from your dataset? 3. Do they perform further computations on your dataset? This determines the schema, partitions, and data accessibility needs. 3. Is this data already present in another report/UI? 1. Is this data already available in another location? 2. Do they have parts of this data (e.g., a few required columns) elsewhere? Ensuring you're not recreating work saves time and avoids redundancy. 4. How frequently do they need this data? 1. How frequently does the data actually need to be refreshed? 2. Can it be monthly, weekly, daily, or hourly? 3. Is the upstream data changing fast enough to justify the required latency? Understanding frequency helps you determine the pipeline schedule. 5. What are the key metrics they monitor in this dataset? 1. Define variance checks for these metrics. 2. Do these metrics need to be 100% accurate (e.g., revenue) or directionally correct (e.g., impressions)? 3. How do these metrics tie into company-level KPIs? Memorize average values for these metrics; they’re invaluable during debugging and discussions. 6. What will each row in the dataset represent? 1. What should each row represent in the dataset? 2. Ensure one consistent grain per dataset, as applicable. 7. How much historical data will they need? 1. Does the stakeholder need data for the last few years? 2. Is the historical data available somewhere? Ask these questions upfront, and you'll save countless hours while delivering exactly what stakeholders need. - Like this post? Let me know your thoughts in the comments, and follow me for more actionable insights on data engineering and system design. #data #dataengineering #datastakeholder

When the data doesn’t fit the data model: Is it the data’s fault or the data model’s? Yesterday I had a fascinating conversation with my friend Dan Gschwend about a scenario that might sound all too familiar to data engineers: A team had a table in the data model that relied on a single identifier—let's call it a BatchID. Everything worked fine with internal data, but when external data was added, the assumptions broke down. The BatchID wasn't unique anymore. So, the data engineer took action, creating a composite key to make it work. Problem solved, right? Not quite. By forcing the data to fit the model, rather than re-evaluating the model itself, the team was about to create multiple downstream issues. The pipeline was green, but the meaning of the data was wrong. Applications would have started to receive data where they would need to  make arbitrary decisions—pick the max, min, random —you name it. Ultimately, this would have led to incorrect insights and bad business decisions. How did we get here? 1) Siloed team structures: The data modeling team worked independently of the data engineering team. They didn’t collaborate on sourcing or truly understanding the data. 2) Static assumptions: The model was designed for internal data but didn’t account for the evolving reality of external data sources. 3) Lack of communication: There wasn’t a safe space for the data engineer to raise questions or challenge the assumptions baked into the model. So what can we do differently? 1) Encourage collaboration: Data modeling and data engineering should go hand in hand. The people designing the model need to understand the data they’re working with. 2) Create a safe space: If something doesn’t look right, team members should feel empowered to raise their concerns—even if the pipeline is “green.” 3) Acknowledge shortcuts and debt: Not every solution will be perfect, but it’s crucial to document decisions and trade-offs so they can be revisited later. The best shortcuts balance near term needs while leaving a clean path to the ideal representation. At the end of the day, data and knowledge work takes a village. It’s not just about moving data or building models—it’s about fostering a shared understanding and creating systems that can evolve as reality changes. This is an example of why we need to invest in semantics and knowledge. Have you faced a similar challenge? How do you ensure collaboration between data modeling and engineering teams?

Too many teams accept data chaos as normal. But we’ve seen companies like Autodesk, Nasdaq, Porto, and North take a different path - eliminating silos, reducing wasted effort, and unlocking real business value. Here’s the playbook they’ve used to break down silos and build a scalable data strategy: 1️⃣ Empower domain teams - but with a strong foundation. A central data group ensures governance while teams take ownership of their data. 2️⃣ Create a clear governance structure. When ownership, documentation, and accountability are defined, teams stop duplicating work. 3️⃣ Standardize data practices. Naming conventions, documentation, and validation eliminate confusion and prevent teams from second-guessing reports. 4️⃣ Build a unified discovery layer. A single “Google for your data” ensures teams can find, understand, and use the right datasets instantly. 5️⃣ Automate governance. Policies aren’t just guidelines - they’re enforced in real-time, reducing manual effort and ensuring compliance at scale. 6️⃣ Integrate tools and workflows. When governance, discovery, and collaboration work together, data flows instead of getting stuck in silos. We’ve seen this shift transform how teams work with data - eliminating friction, increasing trust, and making data truly operational. So if your team still spends more time searching for data than analyzing it, what’s stopping you from changing that?

In the work of data and AI professionals, the career can be full of technical tasks. But, these technical tasks may not produce desired outcomes if the stakeholders do not engage with the products or outcomes. Data and AI professionals, one key thing you can do with your work is to build relationships and get to know your stakeholders better. If you build products YOU like, you may build products your audience doesn't like. Don't build for you, build for outcomes and your audience. How can you engage with your stakeholders better and how can you build relationships with them? Here are tips to help you in your work: - 15 Minute Breaks: Set up a coffee/tea break with those you want to network with and don't talk business, get to know your audience. - Improve Communication: Learn how your audience likes to communicate. Is it over Slack, Teams? Email? or do they like live conversation? - Data and AI Skills: Understand your audiences skill-level. Are they technical? Beginners? Where are their skills? - Feedback: Seek feedback from your audience. Proactively listen to their ideas and thoughts. - Celebrate Wins: Celebrate wins with our audience. Cheer them on. - Shadowing: Sit with your audience and learn about their roles and work. Yes, you may have a full-plate of work but what if that work isn't adopted? Put in the time and effort to get to know your stakeholders and help your work be more successful. Stay nerdy, my friends. #dataliteracy #AILiteracy #data #AI

So many data teams are scared to talk to their upstream engineering teams. "They haven't listened to our concerns about the data they provide." "They don't have time to work on things outside the product." "They have put all of our bug fix requests in their backlog." Thus, the top question I ask anyone interested in data contracts is, "What's your relationship with your engineering colleagues?" If the relationship is good, the next step is to start building a case for being included in their CI/CD workflows. This requires actually bringing the eng team to the table and discussing how further constraints would benefit them. If the relationship is bad, we must first build relationships between stakeholders across the data supply chain instead. While you shouldn't use top-down methods to add constraints, having leadership buy-in is critical in prioritizing meetings to build the missing relationships. The core of data contracts is to facilitate change management amongst teams with conflicting priorities. Not putting relationships as the foundation is a recipe for disaster!