How to Leverage Data Science for your new Business

data analytics

When you look at the most successful companies of the past decade, it will be impossible to find one company that is not using data science in a meaningful way. It is the key to competitive advantage in most cases but at the same time, you could have the best systems in the world but make no money due to inefficient business practices, let us look at how to solve this issue.

Data Scientist often has a STEM degree focusing on technical education vital for creating or maintaining the key product of a business. But when it comes to the actual running of a business, they often fall short. Countless useful products have failed spectacularly because of a bad business plan. When it comes to MBA or BBA graduates, they will often be unable to provide an actual product. To succeed in today’s VUCA (volatile, uncertain, complex, and ambiguous) world potential entrepreneurs need to be well versed in both technical and business aspects of their company.

When looking at specifics, data science refers to the analytics of the company, including operations, employee and customer feedback, etc. It doesn’t seem to be overly technical but the entire process needs to automated efficiently. You don’t actually need an engineering degree to do this but you do need to know how to do it. Similarly, you don’t need an MBA to run a business but you need to have the knowledge of an MBA graduate. The easiest way to mix both is to hire one or the other but these days most technical degrees will have a small focus in business aspects and vice versa.

To implement this in your business, first look at the business side of things and take all the key aspects into account. Second, identify available data to support the main issues, also check if the data is reliable and complete. Then prepare the data in a structured format that can be easily analyzed by the machine learning algorithm. Next, the machine learning algorithm has to applied to the structured data. Before deploying the results have to evaluate, it has to match business objectives. Finally deploy the results into the operations, via software update or new mobile app, etc.

By implementing this data effectively businesses can perform objectively better. It allows them to tackle problems as they come up while driving their initiatives forward.


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