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Leverage Big Data Analytics to Enable Digital Business Transformation

Big data must be contextualized to be useful. The right platform is capable of executing this in real time — but that’s only the beginning.

Big data has always been a part of the manufacturing industry, albeit limited to certain aspects of the decision-making process. “Manufacturers were using data mostly to understand and optimize their operations,” explains Julien Calviac, Strategy Director for NETVIBES EXALEAD, Dassault Systèmes, adding, “But if we want to increase our ability to manage the unexpected, we need to raise our expectations of what’s possible.”

Before manufacturers take that step of expanding their decision-making process, three fundamental questions need to be addressed:

  • Do they have access to the right information data sets?
  • Do they have the right decision process in place to make the most beneficial decisions?
  • How long does it take before a business decision influences operations?

“Our customers realize that they can’t go back to normal and just catch up or do things faster,” says Julien. “We need to shift to a brand new way of envisaging industrial innovation, enterprise agility and resilience.”

Context Makes Big Data Effective

Even though the implementation of smart technologies like the Industrial Internet of Things (IIoT) have exponentially increased, the rate of data collection, analysis and decision-making quality is still sub-optimal. “The data being collected is still missing contextual information, and that limits the manufacturers’ ability to get the right answers,” explains Julien. “Where is the data coming from, what are the data sources that you need to connect to? If you want to engage in a true digital transformation, you have to get this part right.”

Getting it right involves a three-step process:

  • First, it involves collating and contextualizing all the decisions made in the past. “The decisions that you’ve made in the past will help add context, facilitate effective analytics and guide future decision-making,” explains Julien.
  • Next, it involves connecting multiple data sources — from multiple disciplines — and mapping these data sets to form a common knowledge base that’s accessible to all stakeholders.
  • Finally, a digital platform is needed to connect the stakeholders to this knowledge base. The results generated from this collaborative process will inform and guide business decisions in the future.

It is not only about capturing real-time data and information but also about discovering patterns from data that has already been captured. To help our customers address all these different points and successfully execute their digital transformation, we have developed a new collaborative data intelligence capability.

A New Way of Fostering Collaborative Innovation

Collaborative data intelligence is purpose-built to connect multiple data sources while managing and manipulating data at scale. In this fully featured collaboration environment, multidisciplinary stakeholders can work together to accelerate innovation.

“The foundation of collaborative data intelligence is the 3DEXPERIENCE® platform,” explains Julien. “The key outcome of this is the virtual twin experience of your operations based on a combination of input models — 3D simulation data, behavioral models or even physical models — to help contextualize real-world data and help inform better business decisions.”

Having access to the same knowledge base across every stage of the product development process allows stakeholders to access the same source of truth and streamline information sharing. “For example, product engineering and service teams have access to the same description of the products and the same description of services that can be applied to those products,” explains Julien.

Here’s how collaborative data intelligence works in the real world using a scenario that involves multidisciplinary teams who are co-developing a digital camera:

EXAMPLE 1

By observing real-world usage of the digital camera, the product engineering team gains valuable insight that will guide the revised model's design. At the same time, they will also gain data to improve the revised model’s serviceability. This will lead to:

  • Better engineering data that can help the service team to develop new SOPs for the servicing and maintenance routines of the revised model
  • Higher definition renderings of the repair process for the revised model that improve the actual servicing of the revised model

EXAMPLE 2

A from-the-ground-up collaboration between the sourcing and product engineering teams at the very start of the product development process can result in a revised model that’s more cost-efficient and modular in design.

Because of the controlled sourcing of raw materials, the revised model can be manufactured sustainability. This has the potential to open a new revenue stream and marketing opportunity in the long run.

Collaborative data intelligence is not a one-and-done affair. It is a continuous process that creates a real-time bridge between the real world and design, engineering and manufacturing. “With the power of the 3DEXPERIENCE platform, we can enable continuous learning and collaboration across multiple disciplines,” explains Julien. “This will enable our customers to continuously improve the performance, usability, quality, reliability and serviceability of their products.”

By combining big data with contextual information, manufacturers can anticipate future decisions based on past results. With the 3DEXPERIENCE platform, manufacturers can now make it happen in real time. This is the secret to an agile and resilient supply chain — and a sustainable competitive advantage.

Unlock the Full Value of Big Data

How can manufacturers fully leverage the hidden potential of big data? How can they enable better, faster decision-making? Get the answers to these questions — and more — in this exclusive interview with Julien Calviac, NETVIBES EXALEAD strategy director, Dassault Systèmes.

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