Every company says they are data-driven, but very few have a solid internal data strategy behind that claim. Most of them still have a document in their shared drive called “Final report v6.1 REALLY final_FINALONE.xlsx”. It's okay, we used to be one of them too.

Running your business on spreadsheets works when you're working with one, or perhaps two people sitting next to each other. It stops working the moment you have multiple teams, clients, projects, and tools that all speak slightly different languages. The more people who touch the same sheet, the higher the risk of late reports, changing definitions, messy formatting, and inaccurate forecasting.

Our internal data journey went through several phases. At a certain point, the spreadsheets became the work. We spent hours hunting for the right version, fixing formulas, and trying to line up numbers that never quite matched. Every one of these pain points pushed us toward the same conclusion: we needed a proper business intelligence platform. We needed Looker.

1. When spreadsheets stopped being enough

Spreadsheets were fine when we were tiny. Once we grew, they became a bottleneck. Too many versions, too many broken formulas, too many shifting definitions. Manual updates made it impossible to trust the numbers. The more processes we added, the harder it became to maintain any level of data quality or governance inside a spreadsheet-only setup. 

This is the kind of problem Looker solves by design: one semantic layer, one version of the truth, and one place where metrics stay consistent.

2. When early automation revealed deeper issues

We attempted automation early with Google Cloud Platform (GCP) Dataprep. It helped, but only in small pockets. The underlying logic and definitions remained in scattered sheets across teams, which highlighted a bigger issue in our internal data strategy: without a central model or consistent business logic, no amount of automation was going to give us reliable BI. Automation only moved the problem around; the gaps in our internal data strategy were still exposed.

Looker gave us that central model. It forced clarity, alignment, and consistency across the entire data pipeline.

3. When dashboards looked good, but the data didn’t

Using DataStudio gave us nicer-looking dashboards, but the underlying architecture was still fragmented. We had visuals, not a data model. It looked polished on the surface, but everything sat on top of a patchwork of spreadsheets and inconsistent definitions. Without proper data governance and a semantic layer, nothing stayed aligned for long.

Looker changed that completely. Looker’s semantic layer is where definitions live, stay consistent, and apply across the entire business. That gave us actual BI, not just dashboarding.

4. When reporting needed to become our operating system

Looker was the turning point. It shifted us from disconnected reporting tools to an actual business intelligence platform with one semantic layer and unified definitions.

Many people think reporting is the final step. Build the work, then report on it. However, reporting is the operating system of the business. It is where people and projects across sales, marketing, human resources, and operations come together, and it is how we track what is happening, forecast what is next, and make decisions based on data. Looker became the place where all this data finally came together in a consistent and structured way by connecting our operational metrics with our commercial KPIs, so every team sees the same reality in real time. It has become the source of truth we make decisions from every day

5. When centralisation became a non-negotiable (a major data strategy mistake)

This was the moment our business intelligence stack finally came together. Looker finally gave us real centralisation and real-time reports that update themselves instead of waiting for someone to manually refresh or piece things together manually. With everything connected in one place, planning time dropped from hours to minutes. Occupancy became accurate to the hour. Pricing decisions became grounded in reality. For the first time, every single person in the company was looking at a single source of truth… and we will never go back!

To Looker and Beyond

Looker is now the foundation for our next stage of transformation. It is the platform that will power our AI-assisted forecasting, predictive insights, and more reliable forward-looking KPIs. We are also pulling operational metrics closer to commercial KPIs, bringing new data sources into Jira, and modernising parts of our pipeline with cleaner, event-driven components. 

Application Integration is becoming a key part of our data stack because it replaces older pipelines and enables stable, consistent data flows into Looker, which strengthens the entire BI layer. As more of our data flows into Looker, including Jira and other operational data from internal systems, the platform becomes even more capable. Every new source deepens our forecasting models, improves planning accuracy, and strengthens the connection between day-to-day operations and strategic decision-making.

We are our own test case. We build business intelligence data platforms for clients, and we run our own company on the same foundations. When we talk about scalability, trust, or visibility, it is not theoretical. It is our lived experience. If your organisation is facing similar challenges, we can help you avoid the same mistakes and build a stronger internal data strategy foundation from the start.