Innovative, artificial algorithms, such as pattern matching, anomaly detection and cognitive learning, are transforming the global analytics market, according to new research from Frost & Sullivan.
While the influx of new data streams and unstructured data is compelling end users to seek analytical platforms to convert the data into actionable insights, the rise in advanced algorithms and progressive analytics is driving new business models and enhancing connectivity, control, and convergence.
To stay competitive, manufacturers must invest in specialist personnel and become early adopters of rapidly changing industrial analytical technologies.
Global Data Analytics for Industries Report, 2017-2023, recent research from Frost & Sullivan’s Industrial Automation & Process Control Growth Partnership subscription, analyzes market forces, challenges, and future trends in global data analytics across process, discrete and hybrid industries.
The study covers vertical industries such as oil and gas, transportation and manufacturing, and provides the competitive profiles of innovative companies such as Teradata, Riverlogic, and TrendMiner.
“The fusion of data analytics with innovative industrial technologies, such as virtual 3D modeling or smart wearables, is spurring a parallel and fresh growth curve for niche analytic applications in the industrial environment,” said Frost & Sullivan Industrial Automation & Process Control Research Analyst Sharmila Annaswamy. “With several new start-ups on the horizon specializing in patented reinforcement learning and artificial algorithms, large participants will need to engage in merger and partnership efforts to develop internal organizational solutions that deliver distinct end-user results.”
The future industrial data analytics market is expected to foster project partnerships, engaging in a continuous cycle of planning, execution and value improvement, and providing mutual benefit for the customer and the supplier.
Key growth prospects for industrial data analytics are: data socialization, self-healing machines, analytics in augmented reality, and virtual factories.
“With cybersecurity challenges increasing daily, analytics will need to foray into guarding machinery and process insights,” noted Annaswamy. “Going forward, data-sensitive industries will drive high demand for analytics with multiple parallel modules handling security and analytics in real time.”