Today, it’s all about the data-driven enterprise. Companies are collecting more data than ever, and decision-making is now heavily based on insights from all of that data. Primarily, the person in charge of putting together these valuable insights has been the data scientist, driving up the demand for data scientists worldwide.
However, as machine learning and artificial intelligence continue to develop an increasing role in the workplace, there’s also a lot of talk about the role of the data scientist becoming obsolete. Bots are providing new, powerful ways to automate many of the data science-related tasks that are being performed today. But just much of the data scientist role will new AI technologies take over?
For the most part, it’s still too early to predict just how the role of the data scientist will really be affected. Technologies like AI and deep learning have been around for years, but they still have a long way to go. AI didn’t start gaining traction until recently when companies like IBM, Google, and Facebook were able to overcome some of its most significant technological barriers and bring it to the masses. These companies have since become pioneers of AI technology; however, its applications remain very narrow in focus.
The truth is, there are still significant limitations with AI and machine learning. And for the most part, the tasks organizations are performing and problems they are facing are not solved with facial recognition or food-ordering bots. Today’s organizations have much more complex problems to solve — and this is where data scientists come in.
Data scientists are the ones responsible for analyzing problems and developing data-driven answers. More importantly, they are the ones who are able to find answers where data is not yet available, or where data has not yet been taught to a bot.
Still, as technology advances, many are questioning whether or not bots have the potential to work faster and cheaper than data scientists in the near future. The simple answer is that only time will tell, but many believe AI will first have to overcome many challenges if it is ever to replace data scientists completely. For now, I believe it’s much more likely bots will instead complement the data scientist role in a way that will revolutionize the position and turn it into something even more meaningful for organizations. Here’s why.
Organizations still need human judgment
The process of converting raw data into data that can be easily digested and understood is known as data wrangling, or data munging, and is not something that AI bots can handle entirely just yet. The process still requires human judgment to turn raw data into insights that make sense for an organization and take all of an organization’s complexities into account.
While bots can help identify organizational trends, they cannot yet truly understand what specific data means for an organization and its relationships, or even the relationships between different, unconnected operations.
We certainly see strides to help automate many of these moving parts, as humans simply cannot keep up with the demand to process the enormous amount of data that organizations are generating. Bots can help automate lower-level steps in data interpretation and visualization, leaving humans to walk executives and decision-makers through what all of the data means.
But for the most part, humans are still needed to interpret the data. They are also needed to write the bot scripts that are taking over the more mundane, simple data science tasks before they could ever possibly replace them.
A path similar to programming roles
As computer programming languages advanced, the quantity of lower-level programmers did indeed decrease. However, as Rudina Seseri points out, the overall number of software developers needed increased as the world adapted to these new languages. Competition for programming jobs also increased due to new programmers coming into the profession with the knowledge of the newer, higher-level languages.
The data science field is already following suit, with bots automating lower-level tasks and leaving the more complex, problem-solving tasks to human professionals. As a result, the combination of automation with human problem-solving has actually empowered, rather than threatened, the jobs of data scientists. As Andrew Milroy, senior VP of Frost & Sullivan, claims, “The lack of manpower needed to enable the transformations that are expected will slow down technology adoption and automation. So, the argument that new technologies will only destroy jobs is nonsense. It will also create jobs. New higher skilled jobs will emerge together with the use of new, disruptive technology. The implementation of this technology is impossible without them.”
Safe for now, but need to adapt
Bots may be automating the process of collecting and cleaning data; however, uncovering insights from that data takes time and expertise. Currently, there is great demand for data scientists because AI is creating a new category for professionals who can understand the technology and turn it into something meaningful. Organizations are increasingly recruiting data scientists because they are so difficult to find and keep.
Instead of posing a threat to data science jobs, it’s much more likely bots will become incredibly smart assistants to data scientists, allowing them to run more complex data scenarios than ever before.
It’s predicted the role of the data scientist will also evolve from the broad and somewhat vague responsibilities many data professionals currently cover into much more specific roles. The benefits of AI in terms of automation will allow data scientists to focus their attention elsewhere in more creative and innovative roles that do not even exist yet.
Gartner forecasts citizen data scientists will become the new norm. Analytical skills will soon be required in many more traditional roles, and therefore, the need for professionals that can carry out more advanced analytical tasks will increase. This transition is expected to create a new class of data scientist, closing many of the gaps between business intelligence and strictly analytical roles.
Advances in AI only increase demand for talent
“The reality is that these recent advances have only created an unprecedented need for talent and a considerable gap between the demand and supply of data scientists, a highly trained segment of the workforce,” says Seseri, the founder and managing partner at Glasswing Ventures.
Forrester predicts that by 2025, the cognitive era will create 8.9 million new jobs in data science, robot monitoring, automation specialization, and content curation. Now more than ever, we need workers who understand the technology long before we can think about how they are going to be replaced by it.
Even with the rise in data scientists we see today, it is only the most recent graduates and data professionals who are receiving the appropriate training in advanced AI and machine learning technologies. This has created even more demand for data scientists who can understand and combine the work they’re doing with AI and machine learning tools, and I’m certain this demand will continue to exist well into the future.
Vivian Zhang is the founder and CTO of the NYC Data Science Academy and also adjunct professor at Stony Brook University.
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