As companies continue to invest in big data, the issue is becoming less about the data itself, but rather how the data is used to create competitive products and services. According to Gartner, “Companies will be valued not just on their big data, but on the algorithms that turn that data into actions and impact customers13”. Python is the fundamental tool for this purpose, serving as a common language for the multi-disciplinary field of data science. It allows data scientists to interrogate data from disparate sources, developers to turn those insights into applications, and systems engineers to deploy on any infrastructure, whether on-premise or in the cloud. With Python, companies are able to get the most ROI out of their existing investments in big data.
Python, companies are able to get the most ROI out of their existing investments in big data. Companies are not only maximizing their use of data, but transforming into “algorithmic businesses” with Python as the leading language for machine learning. Whether it’s automatic stock trading, discoveries of new drug treatments, optimized resource production or any number of applications involving speech, text or image recognition, machine and deep learning are becoming the primary competitive advantage in every industry.
The time is now for companies to get started on data science initiatives if they have not already. Introducing Python into their technology stack is an important step, but companies should consider factors such as support requirements, staffing plans, licensing compliance and security. By addressing these needs early on, data science teams can focus on unlocking the power of their data and driving innovation forward.
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