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.


The scale also is easier to implement for an organisation with a dozen employees rather than in the thousands, making it easier for the data focused mindset to permeate into the company culture.


Openness to new ideas, flexibility, and out-of-the-box thinking, as well as a creative and exploratory mindset are hallmarks of startup culture.


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, this concept of validated learning is very important to steer the business model and the product in the correct direction.


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.


This ingrained practice and "knowledge" can be very risky, due to the fact that customers and markets change, and if the business doesn't adapt it might fail.

That is why the phrase 'I know' is dangerous, because we can only have certainty within doubt, and with data we can prove an hypothesis and then say 'Our data shows that...'.

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).


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 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.


Even by just implementing some simple concepts and tools, a data scientist can improve drastically the data gathered and processed in the company, as well as guiding others and instructing on the best practices when handling data, many of these tools are free and with great community support.


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, 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 Giant Tech companies, 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.


Now more than ever, startups and small companies have the technology available to thrive where many older, larger companies are faffing about — the question is, will they take advantage of it? If you're ready to take advantage, read more about how a data science platform can help, and how a consultancy can help you start this journey.


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