In the age of IoT, large simulation datasets and product lifecycle management tools, INENDI will help companies find the answers they need in their growing dependence on big data.
The technology behind the launch was acquired by ESI Group from French startup Picviz Labs and its Picviz Inspector technology. Although Picviz was mostly known for cyber security such as plowing through log files to find hackers, in an April 1, 2015, announcement ESI Group mentions how it would like to capitalize on “Picviz Labs’ big-data visual analysis capability to develop a disruptive machine-learning solution dedicated to industrial clients whose product development processes deploy more and more computer-controlled systems, including product line-embedded electronic systems and robots.”
Does That Sound Like IoT to You?
“For ESI Group's core market, Picviz Labs visual solutions add a powerful capability to smartly navigate the ocean of data generated by increasingly large and complex digital models and to reveal the wealth of knowledge deeply buried and consequentially often undetected and wasted,” said Alain de Rouvray, CEO of ESI Group. “Picviz Labs’ technology constitutes a remarkable and exceptionally clever innovation that strengthens operational intelligence by visually identifying important and hidden issues.”
The technology is based on security information and event management software. This software would traditionally alert users based on patterns found in deep investigations of big data. However, INENDI is a lot more interactive, allowing for humans to guide the search for the information they need.
“Deep investigation of large amounts of logs is required,” said Philippe Saadé, director of the big data analytics business unit at ESI Group. “Today, companies need IT infrastructures that demonstrate a human’s ability to discover the unknown. INENDI makes this approach successful.”
Perhaps one of the biggest opportunities for this new software lies with the big data collected from the vast amount of data in IoT. By collecting and assessing this data, designers can better comprehend how their customers are using their products. This software can help highlight design flaws and areas of improvement for their designs. But what is perhaps even more useful to the design team, the software can help them to determine the ways in which their product is being used that they never had expected before. With such data-reinforcing design decisions, engineers can better ensure that future versions of the product adapt more quickly to their markets.