Why Startups Need A Solid Data Science Strategy

“The most valuable resource in the world is no longer oil – it’s data.”
– The Economist

In many ways, data science as a technology has been a great equaliser between startups and larger companies. Previously, only larger organizations would have the resources to extract the maximum value from their data – or at least make a realistic attempt to do so.

Today, cloud computing and open-source technologies have allowed companies of all sizes to leverage data science to solve some of the most pressing business intelligence issues today. Have no doubt – the next revolutionary technology or product will be driven by data science in one form or another.

If you’re a startup about to unleash the next big invention, then having a data science strategy in today’s world is essential. Your organization simply cannot function without it. So, how can you make sure you are capturing the maximum benefit from this technology?

1. Choose your tools to suit the analysis

Using the wrong tools to undertake a data science project is like trying to cook your dinner in a dishwasher! There’s nothing wrong with the tool itself, but it’s simply being used for the wrong task.

Are you planning on using your data to conduct heavy statistics research? In this case, you will find that the R Statistical Environment is the best fit for the job – the packages with the in-built statistical functions are already there, and there is no need to try to “reinvent the wheel”, so to speak.

On the other hand, are you planning on getting heavy with Machine Learning or looking to integrate your algorithm more seamlessly with other programming languages for production purposes? In such a scenario, you may be better off going with Python and scikit-learn.

2. Make the cloud your friend

A particularly good article from DataCamp explains the importance of the cloud in managing big data.

Ever noticed that the more populated your Excel spreadsheet gets, the more likely your system is to freeze up? Simply put, the size of the dataset is too large for that computer’s RAM to process, and therefore the CPU will either take a painstakingly long time to conduct computations – with the more likely scenario being that the operation will fail altogether.