Last month Connected World Editorial Director Peggy Smedley interviewed Twisthink CEO Dave Moelker about the importance of contextual data for OEMs (original-equipment manufacturers). In this conversation she asks Moelker to highlight Twisthink’s strengths around strategy, teamwork, depth of talent, human-centered design, data architecture, workflow integration, and digital product acceleration when it comes to helping clients move from point A to point B.

CW: How should OEMs prioritize which connected-product features to build first?

DM: It is all about user value. Understand where your connected product creates tangible value and how it impacts the day-to-day workflows of your users. In many cases, the best early features are not the most technically advanced ones. They are the ones that help someone make a better decision, avoid a service event, train a new operator, reduce errors, or plan work more effectively. With that understanding in place, you can focus your efforts on the features that will have the most impact.

CW: What’s the right operating model for scaling connected solutions across engineering, service, and product teams?

DM: The most important thing is to be cross-functional and continually engaged throughout the entire lifecycle of the solution. Organizations cannot view connected product design, development, and ongoing support as a sequential process with handoffs. Engineering, service, and the product need a common set of outcomes, a shared operating cadence, and clear ownership for what happens after launch. Importantly, delivery is not complete when the product is launched. Launch is when delivery really starts for connected products.

CW: How do OEMs turn fragmented data systems into unified, actionable data architecture?

DM: Start with the business problem you are looking to solve. Take time to understand the data you need, where it resides, and what the quality of the data is. From there, look to create a data model and integration layer that is based around entities (customers, assets, etc.) rather than the systems that store the data (ERP, CRM, MES, etc.).

The simplest way to summarize it is that organizations need to start building data products. By that, I mean trusted, reusable data assets that have a clear owner, a defined user, and a specific business purpose. You need to think about your data in the same way that you think about the design, features, and value creation for physical products. It starts with establishing clear product owners, understanding the value creation, and then incrementally building toward those outcomes.

CW: What are the biggest mistakes OEMs make when launching digital services, and how do you avoid them?

DM: Many of the OEMs that we work with struggle to build and launch incrementally. First generation services are described as MVPs (minimum viable products) but are actually large monolithic solutions with large-scale launches. Coupled with this idea is the assumption that once the product is launched the hardest part is complete. If we are embracing the MVP approach, the initial launch is the start of the learning, and the digital service should rapidly evolve from that point forward. The way to avoid the mistake is to define the smallest useful release, get it into the hands of real users, measure adoption, and create a clear cadence for improving it after launch.

CW: How should OEMs measure ROI (return on investment) for connected products beyond uptime and alerts?

DM: OEMs should measure connected-product ROI through multiple value metrics, not a narrow operational dashboard. Uptime and alerts tell you whether the system is functioning. They do not tell you whether the business is improving.

The better ROI model looks at factors such as revenue, service cost, customer retention, and product learning. The key is to connect each metric to a business outcome. If connectivity reduces downtime, what is that worth to the customer? If remote diagnostics avoids a truck roll, what is that worth to the service organization? If field data improves the next product release, what is that worth in warranty reduction, margin, and customer trust?

CW: What organizational capabilities separate OEMs who succeed with digital transformation from those who stall?

DM: Strong product management and product ownership is one key factor we see that impacts success. Having the right people who can navigate the blend of user needs, business value, and technical constraints pays dividends throughout the lifecycle of digital transformation. As mentioned earlier, this applies not only to connected products in the physical world but also to data and data products.

Another key capability is decision discipline. Successful OEMs are clear about who owns the roadmap, who funds the platform, who makes tradeoffs, and how value is measured after launch.

At an even higher level, the organization’s ability to work cross functionally across disciplines is critical. Digital transformation cuts across all aspects of the business, and if it is viewed as a single department’s initiative it will fail. The ability to build broad alignment and collaboration is critical for moving initiatives forward.

CW: How can OEMs redesign dealer and service workflows to use predictive insights?

DM: OEMs should focus on the actionability of the prediction. It is not enough for a predictive insight to be a dashboard alert. It should automatically trigger a coordinated workflow: triage the risk, create the service case, check parts availability, guide the technician, contact the customer, schedule around planned downtime, and capture the repair outcome.

At Twisthink, we push OEMs to design the workflow from the customer moment backward. What does the customer need to know? What action should the dealer take? What information does the technician need? What data should be returned to engineering? If those handoffs are not designed, predictive insights will create noise instead of value.

CW: What’s the right balance between building in-house vs. partnering vs. buying digital capabilities?

DM: Build the capabilities that drive your differentiation, buy the capabilities that are mature and non-differentiating, and partner where speed, specialization, or experience are required.

If the capability shapes the customer experience, the connected-product roadmap, the service business model, the product data model, or future revenue streams, you need to own it. If the capability is important, but the organization lacks speed or expertise, you should partner.

As you partner, it is important to be pragmatic and establish a hybrid model where you are not outsourcing your future. Ensure you have knowledge transfer points with your partner, and they are helping to establish your internal capabilities along the way. In this way you can accelerate learning, reduce risk, and help the organization scale.

CW: How do OEMs accelerate from pilots to scalable, revenue-generating digital offerings?

DM: Accelerating from pilots to revenue-generating digital offerings requires treating the pilot as the first version of a business, not a technical proof of concept. This means starting with a monetizable customer problem, defining the commercial model early, assigning persistent product ownership, enabling the dealer and service channels, and measuring adoption, revenue, renewal, and customer outcomes.

At Twisthink, we push OEMs to design for revenue-generation from the beginning. A pilot should answer three questions: Does the customer value it? Can the OEM deliver it repeatedly? And can the business make money from it? If the answer is yes, then the work expands from proving the technology to building the operating system around it.

CW: What does a modern connected-product roadmap look like for the next 3–5 years?

DM: I recommend that roadmaps move through three stages. First, establish the foundation: connected assets, trusted data, secure architecture, remote diagnostics, and basic customer visibility. Second, convert that foundation into operational value: predictive service, dealer workflow integration, technician enablement, parts readiness, and closed-loop learning. Third, enable digital business models: software-enabled features, premium analytics, service subscriptions, and outcome-based offerings.

The roadmap must be more than just a technology roadmap. It must also include the use cases, value unlock, and outcomes delivered over time as technology is deployed and adopted.

Connected World

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