A data-driven approach to scaling your company

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 – 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Register today!

There is no fool-proof plan when it comes to scaling; issues will occur, pivots may be necessary, and different industries and social dynamics require different solutions. Only half of startups make it past the first five years, and one out of every 200 (or 0.5%) become scaleups.

Yet there are also decisions startups can make early on, especially around data, that can increase their likelihood of scaling and making the journey at least somewhat more predictable. My advice is to embrace a data-driven scaling process. I’ve noticed that founders who overlook a data-driven process early on often fail in the long term. Implementing data-driven processes lets you base decisions on facts from the beginning and can support pivots that are often necessary.

Here are three tips for future-proofing your startup by embracing data:

1. Consider hiring a Chief Data Scientist

While data scientists are seasoned professionals, many organizations should consider hiring a Chief Data Scientist (CDS) early on. Around 92% of firms report that the pace of their investments in data and AI projects is increasing, and it’s no wonder, with data-driven firms 23 times more likely to acquire customers and 19 times more likely to be profitable. Yet the transformation to becoming a data-driven company requires sound judgments vis-a-vis the right tools and strategies and ongoing expertise in implementation and maintenance. Elevating data decisions to the highest level of a company’s decision-making process early on will most likely prove to be a significant advantage. It ensures that when data teams need to be built out and overseen, there’s an expert decision maker at the helm with the ear of the other executives.

In my company’s field — approving loans for foreign buyers — shortening underwriting cycles is paramount. We can quickly, simply, and efficiently underwrite a loan, whereas traditional methods are time consuming, requiring lots of manual work. Our data-driven process is only possible with dedicated guidance and the kind of strong field expertise that a CDS can provide.

2. Allow CTOs and CDSs to focus on their respective expertise

In a data-driven company, the role of the CDS is to bridge the gap between business managers and data teams, guiding both sides to a mutual understanding of what can be accomplished with data. The CTO, by contrast, is more focused on product development and the resources necessary to achieve product-specific goals. Each role requires a separate, distinct, set of tools, a fact that is often overlooked. Treating the CDS as a “sidekick” role or putting the data scientists under the purview of the CTO fosters shortcomings vis-a-vis data-based decisions and deep AI and ML expertise. Having both roles clearly defined, however, creates a solid data infrastructure with accessible tools to extract meaningful insights and business intelligence results. Decoupling the data and ML pipelines from the customer-facing research and development has empowered our company to develop a collaborative partnership between the two departments, which enables the teams to focus their expertise and hone their strategies, working together rather than in friction with one another.

3. Invest in data infrastructure or pay for it later on

Having a rockstar CTO and an incredibly savvy Chief Data Scientist is a key starting point, but the right people and strategy must always be paired with action. One of the greatest steps companies can take to become scalable is investing in data infrastructure. In particular, data warehousing is key because it eliminates the constant back and forth between DevOps and backend engineering departments by incorporating data from multiple sources into a single source of truth that is easily extractable. The next investment should be expanding that accessibility beyond the data team by embracing a data mesh approach and purchasing software that empowers marketing, customer success, and other groups to leverage data effectively on their own.

Adopting these three tips may seem easy, but implementation comes with its fair share of challenges. Entrepreneurs who remain undaunted and work hard to achieve them will build the foundations for a thriving business well into the future.

Tim Mironov is Chief Data Scientist at Lendai.

DataDecisionMakers

Welcome to the VentureBeat community!

DataDecisionMakers is where experts, including the technical people doing data work, can share data-related insights and innovation.

If you want to read about cutting-edge ideas and up-to-date information, best practices, and the future of data and data tech, join us at DataDecisionMakers.

You might even consider contributing an article of your own!