Small Company, Big Data Science

Much of the focus and attention on data science, machine learning (ML), and artificial intelligence (AI) falls on huge enterprises and the sweeping, innovative gains they’ve made in the space. But the reality is that data science is critical to any business and can prove extremely valuable even when executed at a smaller scale.

Whether your company has 10 employees or 10,000, chances are you’re facing the exact same challenges in terms of customer acquisition cost, churn, sales forecasting, logistics, or capturing market share — it’s just a matter of making it happen with fewer resources.

And though startups or small companies may not be capturing the same volume of data as large enterprises, the variety and velocity is often the same. Making use of that data quickly with lean resources becomes perhaps even more imperative to compete with larger competitors in the space.

In fact, smaller companies even have some advantages when it comes to leveraging data science, ML, and AI. For example:

Small Companies Are More Agile

The advantage that startups and small companies have over the big guys is that most are founded by younger employees who are not as set in the traditional ways of doing business. Openness to new ideas, flexibility, and out-of-the-box thinking (and sometimes ping pong tables and beer taps in the office) are hallmarks of startup culture.

Startup and small company out-of-the-box thinking can give them an edge when it comes to data science.

A more agile approach to handling data means being able to iterate on ideas and models, fail quickly, and put working models into production easily in order to see real business value faster.

Small Companies Can Be Data-Driven from the Start

Since most small companies and startups are inherently thinking about their business differently, this means that with the right tools at hand, they can be data-driven from the start. Many large companies (surprisingly, and to their detriment) still operate on a mix of gut instinct, and the whims of upper management with decades of ingrained habits from traditional business practices.

Using data to drive decision making effectively from the start can be a huge competitive advantage for smaller, newer businesses as well as a foundation for future growth (instead of being a painful transition, like many older companies are facing).

According to a recent article in Wired Magazine by David Siles, VP of Worldwide Field Operations at DataGravity:

As smaller companies gain the capability to collect transactional, social interaction and customer sentiment data, they increasingly need the capability to process and analyze that data so it becomes useful to the business. The organizations that manage to do that come out ahead in terms of competitive edge, customer happiness and financial performance.”

Small Companies Don’t Need a Data Science Team to Do It

The reality is that data scientists are hard to find, expensive to hire, and hard to keep around. This makes the barrier to entry appear to be nearly impossible for a growing company that (as we’ve seen) faces the same challenges as larger ones and in often crowded markets.

The good news (and the secret) is this: you don’t need a large data science team to get value from your data (or to do ML or even AI). The key is having the right technology in place that properly leverages the skills of business analysts, who may or may not be able to code, to contribute in a meaningful way to impactful data projects.

You don't need a large data science team to get value from your data.

That means allowing them to connect to data easily, prepare and clean it quickly, and even produce or iterate on machine learning models for predictive analysis. The final piece is having a reliable way to put those models into production (side note: why is production important?), ideally with only a few clicks with a visual interface to reduce friction.

While being a startup or small company may seem like a disadvantage in a world of Amazons, the fact of the matter is that with the rights tools and mindset in place, it’s really an advantage. Data science is not an elite realm reserved for those with deep pockets and infinite resources.