All businesses generate data. Even the smallest business has access to hundreds, if not thousands, of interesting data points that they could explore. But it is not uncommon for business owners to think their data is small, inferior and not yet worth analysing. This is where they are wrong every time. Starting small is the best thing you can do, so we say, the time to start your first data science projects is now.

Big data projects are expensive, time-consuming, and - by some estimates - carry a shockingly low success rate. Investing in such big data projects is unwise unless you have a strong foundation of data science competency and a culture of appreciation for AI and its benefits. Redman and Hoerl suggest that instead of waiting for big data to be available, you implement a series of "small data" projects, to build this foundation and tap into the benefits of data analysis right away.

The benefits of these small data projects are far greater than you may expect. They cite a much higher success rate, lower costs because of smaller teams and reduced time requirements, and - at the bottom line - an annual financial gain of $10,000 - $25,000. Perhaps more key than any of these benefits though is the impact on company culture. Involving your staff in small analytical projects builds skills, confidence, and appreciation for the benefits of automation and AI. Not to mention, they can be really fun!

What should your first small data project be? Ask your staff what would benefit them - what do they want to know? Identify a business process that you want to streamline, automate or speed up, and gather a team to work towards the goal. Even when working with small datasets, work on building the right skills by taking a disciplined approach and not skipping steps. You may also need to provide relevant and hands-on training to speed up skill development.

"Start small" is a resounding instruction, appearing in many guides to preparing your business for big data and AI . As Redman and Hoerl so nicely put it, it "build[s] organisational muscle" and fosters data literacy. There's much to learn from the data you already have, and with a small dataset you can get started right away.

If you start your data science projects while your data is small, your big data projects will be more likely to succeed, and you will have benefited from all the insights you gained along the way.

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