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2018 Test Industry Trends to Watch For: Data Science and Test Management

Technology is evolving faster than ever with more intelligence, more data, more connectivity… more everything. The test industry is no bystander to these effects and must adapt to the challenges of designing, validating, and diagnosing increasingly complex devices. So what’s on the horizon for 2018? Find out what we at Wineman Technology predict will have the biggest impact on test lab managers and system integrators:

Data Science

Engineers by nature are problem solvers. So when it comes to designing a test machine, the team will dive straight into nailing down the design requirements without really thinking about big picutre questions --

Providing Insight through Electromechanical Test

When someone asks what we do at Wineman Technology (WTI) it is often hard to explain.  The simplest answer is we build test equipment; although this statement is true, it fails to convey the most valuable service we provide to our customers.  Test equipment describes a wide range of machines and hand-held devices performing design validation, quality control, end-of-line testing, and diagnostics. A wide variety of products use these machines and devices.  Test equipment ranges from simple to complex and is in R&D labs, factory floors, and local service departments.  How can we describe the niche we have carved out in this market and capture the unique value we bring?  When I put this question to the engineering managers at WTI, one of them came back with the phrase “we provide insight through electromechanical test”, and when he said it we knew we had defined what is special about WTI.

Identifying a Customer's Big Questions

In a previous post , I wrote about how data science can be an important part of systems integration. In this post, I will outline a few more details about how that happens by explaining a graphic that our Raleigh office has developed. 

Data Science and Systems Integration

 

Data Science. Systems Integration. For many systems integrators, the two seem to lie at an ideal hand-off point with nothing in common. The systems integrators generate the data and store it. Once that is done, the systems integrators have finished the job. The customers do what they like with the data, problem solved. I would argue, however, that there are significant opportunities for helping customers at a higher level (higher, meaning not only further away from hardware but also higher up in the customer's management hierarchy) in the realm of data science.