When Your Talented Data Team is Stuck in Second-Gear
You did everything right. You recognised the limits of spreadsheets, secured the budget, and invested in a talented data team. You’ve adopted the modern data stack with dbt, a cloud warehouse such as Snowflake or BigQuery along with a self-service BI tool such as Looker and yet … something is off.
The quick wins you expected are slow to materialise. Your team seems to be constantly busy, but they’re stuck in a cycle of maintenance and firefighting. Stakeholders are starting to lose faith in the data, and your team’s morale is dipping. Instead, you’re left asking why your investment in data is delivering the value you’d expected?
This is a common and painful stage in a company’s data journey. It’s the moment you realise that having a data team and having a high-performing, strategic data function are two very different things. The problem isn’t your people or your technology; it’s the organisational operating system that connects them.
The five symptoms of a stalled data team
When a data team is struggling to transition from a support function to a strategic one, a familiar set of symptoms almost always appears:
A “Flat” or “Directionless” Team : Your daily stand-ups feel like a list of chores, not a strategic huddle. The team is reactive, fixing broken pipelines and responding to ad-hoc requests, with little time or energy left for the innovative, proactive work that truly drives the business forward. This reactive cycle is a major driver of low morale.
No Single Version of the Truth : Despite having a modern data warehouse, different teams still present conflicting numbers in meetings. The warehouse has grown organically into a confusing landscape of redundant tables and inconsistent metric definitions. This erodes the most valuable commodity in data analytics: trust.
An Analytics Queue That Never Shrinks : The data team has become a bottleneck. Simple requests for new metrics or data cuts take weeks to fulfil, frustrating business stakeholders. This forces them to either make decisions without data or create their own “shadow IT” in spreadsheets, exacerbating the data trust problem.
Dashboards Without Decisions : The team is shipping dashboards, but they don’t lead to action. There’s a disconnect between the technical work and the core business questions. The dashboards are technically correct but fail to provide the strategic insights your leadership team actually needs to make better decisions.
BI Tools That Are Anything But “Self-Service”
You invested in Looker, Cube or one of the other self-service BI tools built around a semantic layer, but it’s either locked down by complex governance or has become a “wild west” of unreliable, user-generated reports. Instead of empowering business users, it’s either a bottleneck or another source of untrustworthy data.
If this sounds familiar, you’re not alone. We recently partnered with Pleo, one of Europe’s leading FinTech unicorns, as they navigated this exact stage of their data journey and you can read more about their story in our latest case-study on our website.
Their remarkable success and hyper-growth had created a new challenge: the data infrastructure and processes that had served them well during their start-up phase needed a strategic overhaul to match their future ambitions.
It’s Never Just a Technical Problem
Our belief is that modernising a data function is never just a technical project. Traditional consulting that focuses only on implementing the next tool often fails because it ignores the human element. Real, sustainable transformation is built on three pillars: Technology, Process, and People.
Our approach is built on a proven, three-step methodology designed to address all three in tandem.
Step 1: Confront the Hard Truths (The Diagnostic)
Before you can build a better future, you must have an unflinchingly honest view of the present. We begin every modernization engagement with an intensive discovery sprint where we embed ourselves within the client’s organization.
With Pleo, this meant conducting in-depth interviews with leadership and engineers, attending daily stand-ups to observe real-time dynamics, and performing a deep analysis of their existing documentation. This diagnostic phase revealed a talented, deeply committed central data team that was in need of a new operating model to support the company’s rapid growth. It allowed us to move forward with a shared, evidence-based understanding of the core challenges.
Step 2: Create the Change (The Blueprint)
With a clear diagnosis, we partner with the client’s team to co-create a new foundation. This is where we architect the technical and procedural solutions that will solve the root problems.
For Pleo, this involved a multi-faceted effort:
Co-Creating the Pleo Analytics Warehouse (PAW): We collaborated to design a new, structured data warehouse architecture and a formal five-phase analytics development lifecycle. This brought consistency and accountability to their data product development.
Streamlining with a Unified dbt-Looker Monorepo: To reduce friction and improve delivery speed, we helped create a unified repository that brought dbt models and Looker assets into a single development environment. This made their workflow faster, more reliable, and easier to govern.
Step 3: Manage the Change (The Handover)
This is our key differentiator and the most critical phase. A new platform is useless if the team reverts to old habits. We actively manage the human side of the transformation to ensure new ways of working become the new standard.
This means we don’t just deliver a solution and walk away. We work side-by-side with our clients’ teams, providing hands-on mentorship, overhauling team rituals, and helping to establish a culture of clear ownership. Over time, as we did with Pleo, our role shifts from hands-on building to coaching and support, as the internal team grows in confidence and takes the lead.
The Outcome: A Strategic, High-Performance Data Function
By focusing on the holistic system of people, processes, and technology, we were able to help Pleo accelerate the delivery of their new analytics warehouse, increase the capabilities of their internal data team, and build a more robust, scalable platform to support their continued growth.
“Working with Rittman Analytics has been great. They have been a very helpful extension of our own Data team, rolling up their sleeves and building with us. That partnership was key to leveling-up our data foundations, which lets our team focus on what they do best: using data to drive innovation faster.”
— Pri Nagashima, VP of Data, Analytics and AI at Pleo
Interested? Find Out More!
Rittman Analytics is a boutique data analytics consultancy that partners with successful organisations to transform their overwhelmed data teams from a reactive cost centre into a proactive, strategic asset. We deliver more than technology; we deliver the organisational change that makes it work.
You can read more about our approach to modernising data teams and data platforms on our website, where you can also take our diagnostic assessment and receive a custom playbook with targeted advice for your specific challenges.
And of course full details on the Pleo story and many others are on our Case Studies page.
So if you’re looking for some help and assistance scaling your data capabilities or would just like to talk shop and share your thoughts on what’s going on in your organisation and the wider data analytics world, contact us now to organise a 100%-free, no-obligation call — we’d love to hear from you!
When Your Talented Data Team is Stuck in Second-Gear was originally published in Rittman Analytics Blog on Medium, where people are continuing the conversation by highlighting and responding to this story.