Is Data Science Truly Dead?
By: Brandon Cosley
Tags: Data Science, Artificial Intelligence, Enterprise Technology, Industry, Machine Intelligence

The Ever Evolving/Expanding Toolkit

If there is one thing that data scientists are good at, it’s catching a buzz (and a few new tools along the way). The concept of data science itself is a buzz term that many professionals with any statistical understanding in business attached to themselves in order to improve their marketability, and to good effect. Why should we expect the building wave of artificial intelligence to be any different? As the concept of data scientist has evolved, so too have the tools associated with it, and thus the professionals in this field have been caught in a constant race to remain relevant by exposing themselves to the newest tools being made available. Although the rate of change has been near to overwhelming, those who have survived and been able to demonstrate competence around the core functionality of these data science technologies are well poised to take advantage of the tools of artificial intelligence. Thus, the data scientists who learn to evolve will learn how to rebrand themselves as practitioners of artificial intelligence. But to be able to convince others of this rebrand, such professionals will need to continue to expand on their toolkits. Whereas the early 2000’s brought us Hadoop, NoSQL, IoT, Python’s scikit-learn, Tensorflow, and Spark, the next generation will be leveraging AI-as-a-Service, cloud computing, intelligent automation, and containerization for analytics. This means that data scientists must continue to learn how to leverage API calls, architect cloud environments that support data science, and deploy analytics to expose API endpoints.

Image by Ed Zilch from Pixabay

The Importance of the User Experience

As you can see from above, statistical tools are not the only tools that will help data scientists to survive in this quickly changing landscape. Artificial intelligence is not merely statistical technologies but rather it is the embedding of those statistical technologies into user experiences. Thus, the savvy data science survivalist will identify opportunities to solve problems using embedded statistical analytics. Such efforts will require a greater understanding of software programming concepts, which the data scientist is already well-poised for through the acquisition of open source scripting tools, and the ability to work more closely with application development teams. There are many ways to tackle the user experience problem from both a technical as well as a theoretical (see our previous blog post as one example) perspective and what works will always depend on satisfying the user but the key is to identify strategies whereby statistical models improve the user experience. In this way data scientists will need to continue to evolve their approach to problem solving. Where once we focused on using cutting edge modeling techniques to extract insights from data, we now need to focus on their utility within an application.

Image by Gerd Altmann from Pixabay

Evangelizing a Trade

And finally, because the true test of our data science products depends on the user’s ability to get value from them, we must be prepared to take our specialized understanding of these AI-enabling technologies and empower the citizen data scientist rather than pontificate over the sacredness of our special anointed knowledge. Despite the apparent ease-of-use promised by the onslaught of automated data science products, citizen data scientists will still lack understanding of their application. As one Reddit user so elegantly put it, “most people can barely use Excel, and even most data/business analysts have a hard time understanding anything beyond basic aggregation and statistics.” Thus, businesses will look to data scientists to train the citizen data scientist of the future to use those tools as use cases permit. The reason that data scientists will be required is because data science is not a tool but rather it is a way of thinking and tackling problems. Tools certainly enable new ways of thinking, but people need to be trained on how to think about the tool in order for the tool to change their approach to solving problems. In short, we must evangelize the tools that enable the artificial data scientist. In this vain, data scientists become the hub of both artificial and human data science products within an organization and the citizen data scientists the spokes.

Image by Comfreak from Pixabay

From Data Scientist to AI Practitioner

In conclusion, the data scientist is not dead, or dying for that matter, but is, instead, in need of a coming evolution. Those who are most successful in continuing to expand their tool kits to leverage AI services, expose results to and interact with applications, and impart their way of thinking to enable others will be the most confidently poised to meet the coming needs of the AI practitioner for the future of digital enterprise.

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