“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.

Hence, the reason why cloud services such as Amazon’s AWS and Microsoft Azure have become so popular. While one could theoretically employ a “supercomputer” with a high degree of RAM capacity to conduct analysis locally – the costs of doing this can quickly become extortionate depending on the size of your organization. Moreover, the solution is simply much less scalable.

Therefore, the cloud is allowing you to harness the power of several computers to run your analysis remotely. In this regard, it allows access to a much higher degree of computing power at a much lower cost.

However, if you’re going to enact a cloud strategy – do it right off the bat. Storing everything locally and then trying to switch to the cloud can quickly prove a logistical nightmare.

3. Know how you wish to harness data science

Saying “I wish to do some data science” is like saying “I wish to play some music”. What instrument? What genre? It’s no good learning the guitar if you ultimately plan to produce house music!

In much the same way, start with end in mind and choose the tools from there? What problem do you wish to solve specifically with data science?

For instance, if you are undertaking an internal scenario analysis or a risk assessment for a particular aspect of your business, then much of your analysis will be focused on probability and statistics, e.g. Monte Carlo Simulations. Depending on the size of your dataset, it would not make sense to employ more complex machine learning algorithms when a simple statistical analysis gets the job done.

On the other hand, let us say that your organization is building a complex application, e.g. one that uses voice and facial recognition as part of its service. In this scenario, you will find yourself employing a far greater deal of techniques from the machine learning side, and the resources required to process this data will be greater.

Conclusion

Ultimately, how you choose to use data science is up to you and your organization. However, experience has taught me that it’s important to start with the end in mind when embarking on any data science project. Implementing one solution and then realising you need another costs time and money. In a sense, being a great data scientist also means being a great data architect. It’s not enough to be a pro in Python and R – one must understand how these data science technologies fit into the wider strategic framework of what a firm is trying to achieve.