Among all the buzzwords startups use when pitching investors and in their marketing, “data-driven” is nearly at the top of the pile. But what does being data-driven really mean?
Investments are slowing down and VCs are tightening their purse strings. Previously trending tech startups in fields like BNPL, crypto and the delivery market are struggling to show the growth and returns they promised in their initial funding rounds.
Smaller startups with more modest goals can entice VCs looking for safer, smaller deals, but approaching an early-stage venture with a data-driven strategy is a one-sided approach — one that often disadvantages startups.
Simple but necessary shifts in mindsets can change the way startups and investors look at data when making major investment decisions. Here are a few tips:
Using raw, unfiltered data is common at startups that donʼt know how to properly filter their information, and they often end up offloading data irrelevant to their company and mission.
For example, donʼt show investors the total visits to your webpage without also showing the average duration of those visits — veteran investors will pick up on this.
Instead of simply showcasing growth, show off your growth against the backdrop of the funding you’ve raised.
Unfiltered data can skew toward biases and cause more harm than good. Many fast-evolving AI programs have unintentionally developed racial or gender biases based on the unfiltered data fed to them. Understanding how to filter data to properly tell a companyʼs story is critical to understanding where a company shines and where thereʼs room for improvement.
To avoid this, segment your data and use outliers to your advantage.
Filtering data to accurately depict operations and performance ensures that you’re comparing apples to apples. Unfiltered data creates a series of inaccurate comparisons, highlights the wrong aspects of the business and muddles critical outliers that VCs look for.
Source @TechCrunch
