Software company Progress aims for cognitive-first strategy with its acquisition of DataRPM for Industrial IoT (IIoT).
The company formerly known as Progress Software has ‘progressed’ far enough (by its own estimation) to drop the Software tag from its moniker. Like Sting, Madonna, Bono, Björk and Cher before it, the new and perhaps more snappily named Progress continues in its work to rebrand and reposition itself.
Where it was once an enterprise integration plus data interoperability firm, it then became a platform-as-a-service (PaaS) specialist, only to now brand itself as a cloud software application development and deployment specialist.
The latest machinations from the shape-shifters at Progress have seen the company complete its acquisition of DataRPM, a player in the in cognitive predictive maintenance for the Industrial IoT market (although hopefully that won’t become its own acronym: CPM4IIoT, right?)
Cognitive-first, who knew?
This acquisition is part of Progress’s openly stated strategy to provide a platform to build and deliver what it defines as cognitive-first applications.
“Progress has always provided the platform for building and deploying mission-critical business applications [and] the future of applications is cognitive-first,” said Yogesh Gupta, CEO of Progress.
“Our customers and partners already use many of the key product capabilities from Progress necessary for this approach – front-end application development tooling, mobility, back-end application services and data connectivity. With the acquisition of DataRPM, we now have leading predictive analytics capabilities to round out our cognitive apps platform.”
Read more: The rise of the IoT ‘megatrends’
What is cognitive predictive maintenance?
So what does DataRPM’s cognitive predictive maintenance tooling do? In essence, this is software that automates predictive modeling to create maintenance workflow strategies, by using the company’s proprietary so-called ‘meta learning’ capabilities. The idea, obviously, is to be able to increase the quality, accuracy and timeliness of IoT-connected equipment failure predictions.
Customers are said to include Jaguar, Samsung and Mitsubishi Heavy Industries, all of which use DataRPM to predict and prevent asset failures as they attempt to increase yield and efficiencies.
“Predictive analytics is very important to QAD’s vision of [what we call] ‘the effective enterprise’, where we partner with our customers on a continued journey of efficiency and agility,” said Tony Winter, chief technology officer at QAD, a company known for its cloud-based manufacturing software.
- DataRPM technology detects random and unknown failures using a combination of unsupervised and semi-supervised learning techniques to prevent failures of critical assets.
- DataRPM hopes to solves the ‘data science talent crunch’ by teaching machines to automate data science using a technique called ‘meta learning’, that learns from experience and feedback.
- The platform has been proven to horizontally scale to monitor and track any number of industrial machines.
“We had anticipated that Progress would need to acquire machine learning and predictive analytics functionality to deliver its cognitive apps strategy and DataRPM is a logical choice,” says Matt Aslett, research director for data platforms and analytics at analyst company 451 Research.
“The combination of application development, data connectivity and business rules management software with machine learning and predictive analytics makes sense in terms of providing a platform that will enable ISVs and enterprises to develop operational applications that take advantage of the intelligence being generated by big data and IoT projects.”
Still nascent, rapidly shifting
The fact that Progress changes its company name and the base color of its logo more regularly than most should not necessarily be taken as a negative – this is a still-nascent, rapidly shifting space and the company itself has been open about its moves to realign itself over the years.
When you hear about technology vendors striving for what they like to call the ‘democratization of machine learning’, then this is it. In other words, it involves a new degree of automation brought to bear upon application-based data analytics services designed to be specifically tuned to the needs of (in this case) industrial IoT machines. When this power is ‘automated’ as a component substrate of the total application layer’s inherent intelligence, then everyone (potentially) has more access to it.
Progress is by no means the only player in this field (pick any of the usual suspect IT behemoths and you’ll find they all have an offering in this space), but given that the predictive maintenance market is estimated to be worth $4.9 billion by 2021, the DataRPM acquisition certainly seems to make a lot of cognitive sense.