Chad Jackson

The Internet of Things (IoT) has unleashed a new wave of interconnected products. Almost every company is now switching from traditional, mechanical offerings to smart ones requiring a lot of software, electronics, and electrical systems. But design engineers must go further than simply integrating the right hardware and software into today’s products. As the world …

Big Data and the Engineer Read More »

Big Data and the Engineer

The Internet of Things (IoT) has unleashed a new wave of interconnected products. Almost every company is now switching from traditional, mechanical offerings to smart ones requiring a lot of software, electronics, and electrical systems. But design engineers must go further than simply integrating the right hardware and software into today’s products.

As the world becomes increasingly interconnected, products are collecting and using data to optimize performance. Yet, this isn’t the only source of big data relevant to engineers. There are many more that can have a dramatic impact on how products are designed in the future.

In this post, we identify the exciting ways that design engineers can leverage big data. We also explain how machine learning (ML) and artificial intelligence (AI) are driving this innovation.

Big Data: Where Does it Come From?

First, let’s explain which big data sources are relevant to design engineers, and where they come from.

Operating Products

Operating products are now a common and established source of data. When the IoT began, companies started to put sensors in their products. These sensors stream a tidal wave of data into our IoT platforms every time a product is used.

Design engineers can then access the data to gain valuable insights into the product. But the story doesn’t end there. The world moved on, and so did our big data sources.

Simulation and Test

Two new sources came into play.

The first source stems not from the physical product, but from simulation analyses run on computers. Computer-aided engineering (CAE) groups have built up results from myriad digital tests over the years. The resulting data is extensive and different, but still important and insightful.

The second new data source, physical prototypes, yield massive amounts of data. Sensors all over the prototype capture this data during testing verification and validation. This type of big data compliments data from CAE groups nicely, acting as a counterbalance to a purely virtual source.

Product Specifications

Product specifications are another key big data source. They contain information about a product’s deliverables and development. Collectively, that information represents years of knowledge and engineering work.

When you put these sources together, that’s a lot of big data. And it’s only increasing in scale and complexity. Physical sensors continue to collect real-world data, CAE analyses contain digital test results, and product specifications change as a product changes. All of this information continues to grow as a product is in development and commercial use.

Big Data: What Do You Do with It?

We’ve identified the key sources of big data. But how can you unlock that data? How can you get any value out of it?

Operating Products

Our first source, product operation, is the easiest to deal with, because it has been most widely associated with data collection platforms as well as AI and ML technologies.

There are now many IoT platforms available to collect data from operating products. Many of those solutions rely on ML and AI algorithms. And those algorithms can find anomalies in the data to help you make more accurate decisions.

There is a range of AI-enabled analytics tools that can help you gain valuable insights, quickly. Some of these tools use visualization to summarize key trends, allowing you to understand and communicate these insights with ease.

Simulation and Test

The technologies to work with big data from simulation and analysis are not as mature. The data exists in a huge range of formats. But a few pioneering companies are developing platforms to resolve compatibility issues.

These next-generation platforms can index, scan, and understand massive simulation or test data sets, even mixing the two. This has obvious time-saving implications. And, in the not-too-distant future, you may never have to run another structural analysis again. ML and AI tools can review past analyses and predict how your design will perform with a high degree of accuracy. It’s a fascinating area, and the innovations keep coming.

Product Specifications

The knowledge locked away in your product specifications is in yet another format. This is where semantic technologies can help. These tools understand text and context. And by using ML and AI, you can boost your analysis capabilities:

  • You can now understand which of your documents is talking about a specific topic.
  • You can gain a deeper level of understanding of your products.
  • You can start to author your own expert content on your products, producing more data.

As a result, your big data mountain grows a little more. But, with the right tools, you know where to look to get the most valuable insights.

ML + AI = Superior Big Data Analytics

Big data is everywhere. It comes from the sensors embedded in today’s products. It comes from simulated tests and specification documents. But it’s not easy to get value from this data, because today’s IoT platforms are diverse and still under development.

ML and AI can help. They can index, scan, and understand massive data sets. They can automate your analysis capabilities and make accurate performance predictions. Big data is one of the most important developments in the world of design engineering. If you can harness it in the right way, the sky’s the limit.

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