Bob Witte

Boost Your Business Results by Applying Data Analytics to Design and Test

Blog Post created by Bob Witte Employee on Nov 15, 2017

Data analytics is an emerging technology that is getting a lot of attention in the press these days. As with many exciting technologies, there is a mix of real opportunity surrounded by a lot of hype. While it is sometimes difficult to separate the two, I am among those who believe data analytics can make your business run better. As with any technology development, though, a positive outcome starts with a clear definition of the question you are trying to answer.


We can start with this one: Which tangible business results can come from data analytics? For most technology-intensive companies, one key driver is getting the right new product to market quickly and efficiently. The benefits are faster time-to-market, reduced risk and lower costs. In addition, topline results will improve when data analytics is used to optimize product plans and customer engagement.


Deloitte posted an article that suggests many companies are finding value in data analytics but, because these are the early days, there’s more insight yet to come. One early conclusion: the key benefit of analytics is “better decision-making based on data” (Figure 1).

Figure 1. Summary of findings from Deloitte’s Analytics Advantage Survey (pg 4, The Analytics Advantage: We’re just getting started)


Drowning in data, grasping for scalability

Companies that create electronic products are part of the overall trend toward data analytics. In a recent report, Frost and Sullivan sees growing demand for big data analytics applied in the test and measurement market. Keysight is part of this ecosystem, and our design and test solutions generate critical data from measurements of product performance.


We see many of our customers swimming in this data, and some are drowning in it. There are so many data sources that it is easy to accumulate a bunch of disjoint files that are poorly organized and difficult to manage.


This is typical, and it is why most large data analytics projects currently involve way more investment in data collection than in actual analysis. It is estimated that 80% of the effort goes into data collection and “data wrangling.” To me, “data wrangling” is the perfect phrase because it conjures up images of a cowboy tossing a rope around a spreadsheet in hopes of subduing it.


Many electronics firms have created homegrown solutions, ranging from simple collections of Excel files to complex software systems coded in C. Spreadsheet wrangling can work well for small, localized datasets—but it won’t scale up because data is isolated among individual users or test stations, perhaps spread across multiple sites. Revision control may be weak and it can be difficult to ensure that you have the most recent data. What’s worse, it usually turns into lost productivity as key technical staff spends time fiddling with all those spreadsheets. Over time, this maps to lost velocity towards finishing and shipping the product.


One alternative is reaching out to the IT department to create a more robust system. The resulting solution will be more scalable and supportable, but it also has internal costs. For one, changes fall to the IT team, robbing resources from other priorities. This is workable as long as all ongoing IT projects are fully supported and staffed.


Taking baby steps toward better data for better results

The actual analytics required can often be very basic. Sure, we’d like to turn on some machine-learning application that derives brilliant insight from our manufacturing test line and then feeds it back into the next design revision.


More likely, we are just trying to look at product performance in a consistent way so we can tell if the design is performing correctly. This is especially true in the prototype phase, when there are fewer devices to evaluate and the actual specification is still in flux. Later, in the manufacturing phase, we usually have plenty of data but it may still be siloed at the various production line stations or stored at another site, perhaps at a contract manufacturer.


Getting better at clarifying the target problem

As you apply the ideas discussed above, you will get better at defining the business problem you want to solve using in-hand design and test data. It may be improved time-to-market, lower R&D cost, better production yield, or something more specific to your operation. The next crucial step is creating or acquiring scalable tools that enable you to get your data under control.


My questions for you: Do you see this challenge in your business? What sources of data feel a bit disorganized or maybe completely out of control? Which tools have been most useful? We will be exploring these ideas in future blog posts, so stay tuned for additional insights.