As enterprises increasingly adopt artificial intelligence (AI) to drive business processes, employees across all industries and in many diverse roles are revisiting the age-old question, “Will I be replaced by automation?”

In most scenarios, the answer to that question is likely no; however, for data analysts, the answer is a bit more complicated.

The Perceived Threat of AI to Data Analyst Jobs

There are several reasons why data analysts are right to regard increased AI adoption with a certain amount of apprehension.

Globally, there is a shortage of skilled workers across all professions, but data scientists are in particularly short supply. Because of the nature of the work, data scientists need to possess an intricate mix of both highly technical and non-technical skills. This makes finding the right professional for the job challenging even during the best of times.

Add to the equation the enormous volume of data being generated through enterprises’ increasing reliance on the Internet of Things (IoT) and Industry 4.0 practices. These technologies are making it easier to use data to drive decision-making and shape business outcomes, but they also make the lack of available data analyst talent even more critical.

The human brain is very good at doing a lot of things, but scaling isn’t one of them. There are definite limitations on humans’ ability to process the massive quantities of structured and unstructured data that are being collected and aggregated from many disparate sources these days. This is where the artificial intelligence versus human data analysts debate gets interesting.

The Current and Future State of Artificial Intelligence

Once the purview of sci-fi novels, artificial intelligence has become ubiquitous in almost every aspect of our lives—both personal and professional. From a business perspective, AI is being used to supercharge analytics in a wide variety of industries, but adopting and implementing this technology is not without its share of challenges.

For example, the transportation and logistics industries are embracing AI as a means to reduce costs, increase safety, and improve operational efficiency.

Applying machine learning algorithms to everything from inventory management to delivery routes to traffic patterns helps organizations save money by accurately estimating demand for inventory, optimizing cargo loads and driver time, and reducing fuel costs.

AI can also be used in place of humans for high-risk or repetitive factory and warehouse work. By analyzing historical and real-time data, AI algorithms can predict maintenance needs, which will reduce equipment and fleet breakdowns and help ensure availability of critical systems.

AI and machine learning are powering the Industry 4.0 movement, which focuses on bringing connectivity, automation, and cyber-physical systems to the manufacturing and industrial sectors.

In addition to automating repetitive processes and preventing equipment breakdowns, AI is enabling Industry 4.0 data analysts to collect, compile, and assess sensor data from multiple systems. This information is then used to create 3D visualizations that make it easier to understand how to optimize supply chains, forecast supply and load, and identify other efficiencies that will save the business time and money.

Applying AI technology to analytics in the healthcare and biotech sectors is more arduous than in other industries. Because of the amount of personal data involved in the analytics process, data protection regulations and compliance mandates dictate how data can be collected, stored, and transported. These requirements and restrictions add a significant amount of complexity to the process.

Looking ahead, it is clear that AI will play a central role in data analytics. Smart factories and warehouses are becoming more common, driverless delivery via autonomous vehicles and drones is losing its “futuristic” feel, and no-code AI is making the technology accessible to the masses.

Will AI Replace Human Data Analysts?

Although there is no cut-and-dried answer to this question, the general consensus is no. 

Automation with its dynamic decision-making capabilities will always outperform a single algorithm approach to analytics. AI also can analyze high volumes of unstructured data quickly and identify patterns, outliers, and trends within the data that humans cannot.

However, there are also data analysis tasks that are better accomplished using human intelligence and gut instinct, such as manually troubleshooting certain performance issues and mentally working through a problem. 

Rather than fully replacing the role of data analyst, it’s more likely that AI will work in concert with data scientists. With AI functioning as an intelligent assistant, data scientists will be able to run increasingly more complex data simulations on even larger data sets and create richer visualizations than they would be able to without the help of AI.

Artificial intelligence and machine learning are the present and the future of data analytics. Watch the on-demand webseminar “ML Model Explainability Using 3D Visualizations” to find out how integrating machine learning and human data analyst capabilities will provide your organization with the best of both worlds.