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The Enterprise Scope of Product Big Data


Product data is at the core of what every electronics company needs to know to succeed. In a new research study Capitalizing on Big Data from Products, more than

half of respondents point to new product success and a broader portfolio of products as keys to improved business performance.


Product data is information about a product’s design intent, specifications, and performance throughout its lifetime, from initial product design to the customer’s experience in using the product. As such, product data is big data, and much of it is also non-relational – or even parametric. (Parametric data typically comes from tests or measuring the parameters of a product or the process that created it. This data is particularly tricky, since the meaning depends on what is expected from that parameter in that specific situation.)


Product data in Enterprise graphic.png

The key areas in the enterprise that depend on product data include:


  • Product design & development: Product engineering is a major originator of product data, but it also needs performance data from all stages in a product’s lifecycle to make improvements, variants, or new products.
  • Sales & marketing: Customer experience with products is a factor in determining what capabilities to promote or highlight as well as what to de-emphasize.
  • Supply chain: Product data must be detailed enough to trace a product’s history through manufacturing and delivery as well as every supply tier. This level of understanding can pinpoint supplier quality and delivery issues.
  • Quality & regulatory: Product data from suppliers, testing, plus end-to-end genealogy is essential to compliance and contributes to a company’s ability to improve quality.
  • Manufacturing & distribution: The production team clearly needs as-designed product data for quality sampling and testing. Data from the field can also drive continuous improvement efforts.
  • Service: A detailed history of a product from design, through test, revision, customer experience, and repair is invaluable to customer and field service for resolving product issues or failures.
  • Warranty & finance: Historical data drives resource allocation for product improvements, new product development, and maintaining adequate warranty reserves. It can also help point toward high margin product mix.

In short, data about a product and its performance can be a critical tool in making informed operational, tactical, and even strategic decisions.

The caveat: To be useful in making business decisions, product data needs to be accurate, timely, complete and readily accessible in useful formats for decision-makers. This is not easy to achieve.


A complex electronics product may have hundreds of parts and dozens of suppliers across the globe, so the task of collecting, correlating and analyzing product data is challenging for many organizations. In the new report Capitalizing on Big Data from Products, we found that that many respondents identified external data sources as “problematic”, such as:


  • Supplier data (38%)
  • Outsourcing partners data (34%)
  • Unstructured data (27%)
  • Distributor data (22%)

Much product data is unstructured: design and CAD data is typically not relational; and test results are often parametric. Some other data from suppliers, partners, and distributors may be in database form. Still, incompatible formats can require extensive effort and time by IT to convert. So this external data effectively presents the same problems for capture, cleansing, context and analysis as unstructured data.


This is not a trivial problem. For example, while 13% of companies experienced delays of weeks to acquire genealogy records, a shocking 57% of respondents were unable to get historical product data at all, compounding the difficulty of responding to product problems or customer issues.


Without accurate and timely information decision-makers are literally making uneducated guesses to find solutions. As a result, even the best efforts have the potential to damage enterprise profitability or tarnish the company’s brand.


Fortunately, the research also shows that a few leaders have found solutions to these product big data challenges. They are still in the minority, but as with everything else in the electronics industry, we suspect things will change quickly.


About the author:


With over 30 years of passion and experience as an industry advisor and researcher specializing in people, process and software systems for manufacturing and production companies, Julie Fraser is Founder and Principal of Iyno Advisors Inc.  Fraser is renowned for her ability to synthesize disparate and technical data into clear business value messages.  She also understands what production businesses and the people who run them need to succeed and flourish, and helps build bridges of understanding between the buyers and sellers of solutions.