Startups are comprised of small, early-stage teams full of risk-takers who want to change the world and are ready for anything. This results in a dynamic, determined, fast-growing organization that’s all about testing hypotheses and learning incredibly quickly.
However, Startups are not sustainable. “Startup” is a phase, through which newly born businesses must pass - but it’s transient. Startups need to quickly evolve and grow into larger, more structured businesses in order to reach profitability.
This evolution usually happens at the cost of their initial speed and growth rate, that will inevitably decrease as their life expectancy grows.
In a way, you could compare startups with childhood. Children grow quickly and are able to learn and absorb new things fast. They’re full of potential as they evolve at lightning speed, but they are very fragile and ignorant at the same time.
Over the course of their transition into “adulthood,” Startups and their employees face all sorts of dilemmas and crossroads, one of them being what I call the “Startup Data Dilemma.”
The first part of this dilemma is that startups need data to grow into their next stage:
The second part of the dilemma is that startups need to be growing already to get data:
To summarize, startups need data to grow, BUT to get data they need to be growing already.
This sounds like a chicken-egg problem to me.
Startups should to focus on what they are good at, which is speed.
Their choice in data tooling and processes should meet the following criteria:
Let’s dive into each of these below.
To deploy a data culture quickly, you need to show business impact quite fast with a small budget (time and money).
Initial results will help create the momentum to convert the startup into a data-driven organization later on.
This means that you should focus on selecting a solution that:
Internal users at a startup need to move quickly to keep up their competitive advantage, so you want to aim for “self-service” solution that anyone can use. You’ll want to optimize for adoption at first.
That means that you should find a solution on which people can easily:
In the future, you’ll have more and more questions that require answers. And more and more people will join the company as it grows.
Hence, you should start setting certain “standards” right away to minimize the time to adoption for future teammates.
Get a Data Warehouse from the start - BigQuery, Snowflake or Redshift, not a “black box” solution on which you don’t own
→ Data Warehouse are the most common installation options of data tools (connectors, transformation tools, quality control, catalog, BI, ML, etc.), where all your data can be easily centralized in one place. By investing in a Data Warehouse from the early days, you’re doing yourself a favor for tomorrow, to serve your future needs.
Push for SQL usage whenever possible
→ SQL is the “lingua franca” of Data. It will be mastered by your new hires and by the tools you’ll implement in the future, and training for SQL is widely available. All logic written in SQL can be easily migrated from tool to tool, and maintained by future coworkers.
Get a solution that has “governance” features baked in
→ When people start working with a lot of data, different numbers will pop up, and some sensitive data will emerge (finance, for example). These problems are solved through strong data governance which can be achieved by centralizing the definition of metrics, giving the proper user access, etc. Your tools will need to adapt, and it’s best to opt for a tool that is strong in this field by design right away, to prepare for your future growth.
Solving the “Startup Data Dilemma” is hard, and it’s getting increasingly difficult as Data hires are becoming more and more expensive. Data competency is scarce, while the demand is higher than ever before.
However, as the success of recent Startups have shown us (AirBnb, Uber, Facebook) - if you solve this data dilemma early on and make it a priority, the future of your startup is bright!