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There is no fool-proof plan when it comes to scaling; Problems will arise, pivots may be needed, and different solutions are needed for different industries and social mobility. Only half the startups spend the first five years of it, and every 200 (or 0.5%) becomes a scaleup.
However, there are decisions that startups can make early, especially around data, that can increase the likelihood of their scaling and travel being at least more predictable. My advice is to adopt a data-based scaling process. I have noticed that founders who ignore the beginning of the data-driven process often fail in the long run. Implementing data-driven processes allows you to make decisions based on facts from the outset and support pivots that are often needed.
Here are three tips to make your startup future-proof by accepting data:
1. Consider hiring a Chief Data Scientist
While data scientists are experienced professionals, many organizations should initially consider hiring a Chief Data Scientist (CDS). About 92% of companies report that their investment in data and AI projects is accelerating, and not surprisingly, data-driven companies are 23 times more likely to get customers and 19 times more likely to be profitable. However, transformation into a data-driven company requires the right tools and strategies and constant mastery in implementation and maintenance. Raising data decisions to the highest level of the company’s decision-making process as soon as possible will prove to be a significant advantage. It ensures that when it comes to building and overseeing data teams, there is an expert decision maker at the helm with the ears of other executives.
In my company’s field – approving loans for foreign buyers – shortening the underwriting cycle is paramount. We can underwrite loans quickly, easily and efficiently, while traditional methods are time consuming and require a lot of manual work. Our data-driven process is only possible with dedicated guidance and strong field expertise that CDS can provide.
2. Allow CTOs and CDS to focus on their respective skills
In a data-driven company, the role of CDS is to bridge the gap between business managers and data teams, guiding both parties to a mutual understanding of what can be accomplished with data. The CTO, by contrast, focuses more on the resources needed to achieve product development and product-specific goals. Each role requires a separate, distinct, set of tools, a fact that is often overlooked. Treating CDS as a “sidekick” role, or placing data scientists in the field of CTOs, promotes data-driven decisions and shortcomings compared to deep AI and ML skills. However, after clearly defining both roles, meaningful insights and business intelligence build solid data infrastructure with accessible tools for achieving results. By deciphering data and ML pipelines from customer-facing research and development, our company is empowered to develop a collaborative partnership between the two departments, enabling teams to work together rather than focusing on their expertise and confronting their strategies. . .
3. Invest in data infrastructure or pay for it later
Being a Rockstar CTO and an ultra-smart Chief Data Scientist is the key starting point, but the right people and strategy should always be linked to action. One of the biggest steps companies can take to become scalable is to invest in data infrastructure. In particular, data warehousing is key because it eliminates the constant back-and-forth between DevOps and backend engineering departments and can be easily extracted by incorporating data from multiple sources into a single source of truth. Further investment should extend accessibility outside the data team by adopting a data mesh approach and purchasing software that enables marketing, customer success and other groups to effectively leverage data on their own.
These three tips may seem easy to adopt, but implementation comes with its fair share of challenges. Entrepreneurs who stay calm and work hard to achieve them will lay the foundation for a prosperous business in the future.
Tim Mironov is the chief data scientist at Landai.
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