The early stages of startup development are a time of experimentation, speed and intuition. Data? Yes, it's important, but it's often scattered, treated in an ad hoc manner, used for investor pitching or initial retention analyses. In such a setting, it is possible to operate. Until.
At the point of scaling, the data that used to be “enough” begins to beguile. There are problems with quality, availability, processing speed. That's when data stops supporting development and starts blocking it.
Key data challenges in scaling organizations
1. Infrastructure unprepared for growth
An increase in the number of users and operations generates a spike in data volume. Classic databases, non-scalable APIs, manual exports from CRMs are no longer sufficient. Processing times lengthen, data flows stop working properly, and reports are no longer available in real time.
2. Data quality blurs into chaos
As data increases, inconsistencies appear. Different sources, different formats, divergent definitions of metrics are a recipe for errors and mistrust. Without clearly defined data owners and validation procedures, quality quickly deteriorates.
3. Increased analytical needs
Scaling comes with a greater appetite for data: cohort analysis, predictions, customer value metrics, churn rates, segmentation, partner reports. Flexibility, automation and the ability to respond quickly to custom queries are needed.
4. People and competence become a bottleneck gardłem
Many startups rely on one or two people who know “how it works.” At larger scale, this becomes unsustainable. There is a lack of documentation, automation, data owner roles, engineering competencies.
5. Lack of a data-driven culture
If data is difficult to use or inaccurate, teams won't use it. Decision-making reverts to intuition, and that's the way backwards. A culture of working with data must be rooted in the organization's structure and processes.
Scripit 1: Investor on board and sudden need for transparency
Startup raises funding. Fund expects monthly reports, cohort analysis, retention charts, forecasts. Problems begin:
- data must be manually combined from multiple sources,
- definitions of metrics are inconsistent,
- report preparation takes days.
The result? Tensions within the team, investor frustration, risk of losing credibility. Instead of being a data-driven organization, it becomes a spreadsheet-driven organization.
Scripit 2: Growth boom or big customer and data paralysis
A sudden increase in the number of customers or the acquisition of a large B2B partner overturns the existing order:
- the data infrastructure is overloaded,
- reports are delayed or contain errors,
- customer service doesn't have access to the information it needs,
- customers report discrepancies.
Scaling without a proper data foundation ends up putting out fires. This leads to chaos, technology debt and loss of trust, including internally.
How to counter this?
Successfully addressing data chaos requires a strategic approach and a focus on solid foundations. Start with the basics:
- Implement data platforms (e.g. Snowflake, BigQuery, Databricks) - scalable data warehouses allow rapid access, integration and analysis of growing volumes of information without manual processing.
- Data flow automation (dbt, Airflow, Dagster) - automated processes eliminate the risk of errors, speed up operations and ensure repeatable results.
- Data monitoring and quality control (Great Expectations, Monte Carlo) - ongoing data monitoring allows us to quickly detect deviations and anomalies before they affect business decisions.
- Data catalog and accountability management - clear documentation of sources, metrics definitions and assigned roles enables effective collaboration between teams and increases confidence in the data.
- Service model: Data Team as a Service - flexible analytical and engineering support available on demand, without the need to build your own team from scratch.
And most importantly, build a culture of working with data. Technology alone is not enough. You need a change in attitude throughout the organization - from the CEO to the customer service specialist. Everyone should have access to reliable data and know how to use it.
Summary
Scaling a startup isn't just about more users or a larger team, it's also about new data requirements. Organizations that fail to prepare for this moment will stumble painfully sooner or later. It's worth taking care of data before it becomes the biggest bottleneck.
If you're at such a point, or feel you're about to be, it's better to start taking action today. Want a data readiness checklist for scaling? Or a pitch to an investor showing you have it under control? Let us know. - get in touch with us!
