Issue link: https://iconnect007.uberflip.com/i/1542458
JANUARY 2026 I SMT007 MAGAZINE 31 costly last-minute changes. This keeps the lines running and the product shipping, a win for both the customer and the EMS provider. There's an understanding in software develop- ment: Give a user a new application, and they'll find ways to use it that the designer never antic- ipated. That will be true when providing data as a service. By informing your customers, they will think to ask meaningful questions they never thought to ask before. This is a good thing. Competitive Advantage and Revenue Impact The service being sold may be different than what an EMS company is accustomed to, but data prod- ucts are a service just as assembly itself is a ser- vice. Data products are commonly packaged and sold in the following ways: • Data products can be offered on subscription models such as tiered analytics, API access, premium predictive services. These pro- grams create higher-margin, recurring reve- nue that sits outside traditional manufactur- ing margins. • These offerings differentiate sales position- ing. Sellers can shift customer conversations from price-only to "how our analytics will reduce your yield loss, shorten your NPI time, and give you forecast confidence." OEMs with analytics integrated into their internal workflows develop dependency on EMS pro- vider data services, making switching more difficult. When you're integrated into the cus- tomer's MES, providing real-time data they can use to plan and respond, from their per- spective, you look like an internal team member, not a vendor. • While using analytics internally is not new, packaging it externally forces additional data discipline, system integration, and consis- tent measurement. That diligence typically yields internal efficiency gains as a side ben- efit. Keep in mind that data products typically carry a gross margin in the 40–70% range, which can help stabilize the margin profile. Investment Priorities and Capital Allocation So, how do you get started? In general, you start by developing data collection and integration for internal analytics. The next phase includes taking the infrastructure public, introducing customer-fac- ing tools and interfaces. Data engineering and integration are at the heart of this product offering, but that expertise is also required for internal process digitization. Generally speaking, allocate 30–40% of your initial invest- ment here. This includes staff (data engineers), platform licenses (cloud data warehousing, integra- tion tools), and connectors for MES, ERP, test, and inspection data. Target internal use first, with an eye toward productizing. For analytics, allocate 25–30%. This budget includes new talent as well as machine learning tooling and model training infrastructure. All this is distinct from the data engineers, who oversee the gathering and storage of the data. The analyt- ics engineers mold the data into actionable infor- mation. This component drives the product value proposition for your customers. Next, allocate 20–25% for APIs, dashboard development, UI design, documentation, and por- tal infrastructure. Like analytics, this initiative begins as an internal project, eventually expand- ing into customer offerings. A frictionless customer experience is critical for adoption, and to get there,