7 Data Strategies That Work – What the Best Data Teams Do Differently

Every data team wants to scale efficiently, reduce costs, and deliver real business value. But in practice, many struggle with siloed workflows, unreliable data, and costly inefficiencies.

Since recording Season 1 of the Data Matas podcast, I’ve reflected on the key levers these great teams are using to deliver value in their businesses and pulled together the seven of the biggest lessons. These aren’t abstract theories—they are practical, tested strategies from professionals who have made data work for their organisations.

What the Best Data Teams Do Differently

1. They Fix Broken Workflows Before Adding Complexity
Messy workflows are a hidden tax on productivity. Jessica Franks (Not On The High Street) shared how her team used Wardley Maps to get clarity on their data architecture, ensuring technical and business teams were aligned before scaling operations.

Key takeaway:

Fix inefficiencies before adding complexity. If your data workflows aren’t mapped properly, scaling will only multiply existing problems.

2. They Build Trust in Data
If leaders don’t trust the numbers, your reports are useless. Joe Wright (CitySprint) tackled this problem by consolidating multiple BI and ETL platforms into a single source of truth. This shift eliminated conflicting reports and gave teams confidence in their data.

Key takeaway:

Standardisation and consolidation build trust. A fragmented data ecosystem creates conflicting insights, slowing decision-making.

3. They Scale Efficiently Without Overcomplicating
Bigger isn’t always better when it comes to data infrastructure. Stéphane Burwash (Potloc) built a lean, cost-effective data stack using open-source tools and a data champions programme, proving that you don’t need a massive budget to scale effectively.

Key takeaway:

Start small, scale intentionally. Open-source tools and strategic process improvements can help you grow without unnecessary overhead.

4. They Know ‘Good Enough’ Is Better Than Perfect
Many teams over-engineer their data pipelines, trying to eliminate every inefficiency instead of focusing on impact. Bethany Lyons emphasised that chasing perfection slows teams down. Instead, high-performing teams build practical, scalable solutions that work for the business.

Key takeaway:

Don’t let perfect be the enemy of progress. The best data teams focus on value, not unnecessary complexity.

5. They Make Data Quality a Shared Responsibility
At MVF, Adam Dathi saw that unreliable reporting wasn’t just a data team problem—it was a company-wide issue. His solution? Breaking down silos and making every team accountable for data quality. This improved collaboration and ensured that data was more reliable from the source.

Key takeaway:

Data quality isn’t just an engineering issue. Cross-functional ownership of data improves accuracy and trust.

6. They Use Real-Time Data for Smarter Decisions
Static, outdated reports lead to poor decision-making. Nick Bromley explained how real-time transport data is transforming city planning, proving that businesses that rely on historical data alone will always be one step behind.

Key takeaway:

Real-time insights give teams a competitive edge. Businesses should explore ways to incorporate live data into their decision-making.

7. They Integrate AI With Purpose, Not Just Hype
AI is a powerful tool, but without proper guardrails, it can create more problems than it solves. Murtaza Kanchwala (Amplify Capital) outlined a practical approach to AI adoption, focusing on guardrails, validation, and compliance to ensure automation actually delivers value.

Key takeaway:

AI needs structure and oversight. Successful teams implement AI with clear business goals and governance in place.

The Common Thread: They Fix Problems Before Scaling

The biggest takeaway from this season? High-performing data teams don’t just collect more data—they make it work better. Instead of constantly firefighting, they put the right processes, governance, and automation in place to build a solid foundation that scales.

Want to learn from the best? Watch all the podcast episodes here.

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