Where the Compute Should Live: How an Industrial IoT Platform Provider Used Market Research and Strategy Advisory to Decide Where to Invest Between Edge and Cloud
Executive Snapshot
Client
Situation/Challenge
Objective
Constancy Researchers Solution
Impact
Client Outcome
The Situation / Challenge
Edge computing has become one of the more commonly requested architectural capabilities in industrial IoT platforms, but the underlying drivers of that demand are considerably more varied than the term suggests. Some customers genuinely require edge processing because their use case involves real-time control loops where cloud round-trip latency is unacceptable, or unreliable connectivity makes continuous cloud dependency impractical. Others request it primarily because of bandwidth cost concerns addressable through less disruptive means such as compression or filtering at the data source.
The client’s product team had been receiving edge requests with increasing frequency for two years, creating internal pressure to commit substantial resources to a comprehensive edge capability. What the team had not done was systematically examine whether each request’s underlying driver actually required edge processing, or whether a narrower set of customers had a genuine technical requirement while a larger group had a cost concern a different feature could address more efficiently.
Committing to a comprehensive build without this distinction risked a substantial misallocation — solving a small subset of genuine latency problems while spending disproportionate effort accommodating a much larger group whose concern could have been addressed far more cheaply.
Key Challenges
- No structured analysis distinguishing edge requests driven by genuine latency or connectivity requirements from those driven primarily by bandwidth cost
- No use-case-level market research establishing which industrial applications genuinely required edge processing as opposed to benefiting marginally
- No segmentation of the client’s own customer base by the actual underlying driver of their edge requests, despite two years of accumulated data
- Risk of committing substantial investment to a comprehensive build addressing a smaller genuine need while leaving a larger cost-driven need inefficiently served
- Product roadmap pressure building from request volume alone, without the driver analysis needed to determine the appropriate scope of response
- Leadership expectation that the next cycle’s edge investment be sized according to genuine market evidence rather than request volume alone
Customer requests for a specific technology capability frequently bundle together genuinely distinct underlying needs. Edge computing requests are a clear example: latency-critical control applications and cost-sensitive monitoring applications can produce an identical surface-level request while requiring fundamentally different solutions.
Constancy Researchers Solution
Constancy Researchers structured the engagement around disaggregating the edge demand the client had observed into its genuinely distinct drivers, then building an investment plan sized appropriately to each rather than treating all requests as one undifferentiated need.
Global Industrial Edge Computing Market Sizing & Use Case Driver Analysis
- Delivered a market research report sizing the global industrial edge market and disaggregating demand by driver, distinguishing real-time control, intermittent connectivity, and cost optimisation as separate categories.
- Identified that real-time control and intermittent connectivity represented a meaningfully smaller share of total demand than commonly assumed, with cost optimisation representing the larger share.
Latency Sensitivity Benchmarking by Industrial Application
- Researched actual latency sensitivity thresholds across major industrial application categories, establishing which had genuine requirements below cloud-achievable levels and which cloud processing could comfortably satisfy.
- Confirmed that the client’s existing cloud architecture comfortably met the requirements of the majority of its customer base, with genuine latency-critical needs concentrated in a specific process control subset.
Customer Base Segmentation by Edge Request Driver
- Applied the driver framework to segment the client’s customer base and accumulated request data, classifying each request by its genuine underlying driver rather than surface-level framing.
- Found that a clear majority of requests were driven by bandwidth cost rather than genuine latency requirements, with the smaller latency-critical segment concentrated among process manufacturing customers.
Alternative Solution Assessment for Cost-Driven Requests
- Assessed compression and intelligent filtering approaches for the cost-driven segment, evaluating their effectiveness without the full complexity of comprehensive edge processing.
- Found that a targeted compression and filtering feature could address the large majority of cost-driven requests at a fraction of the engineering investment required for comprehensive edge capability.
Phased Investment Roadmap & Product Strategy
- Delivered a phased roadmap allocating concentrated resources to genuine edge capability for the latency-critical segment, while addressing the larger cost-driven segment through the more efficient feature.
The engagement gave the product team what two years of accumulating requests had not — a clear distinction between customers with genuine architectural requirements and those whose concern could be addressed more efficiently.
Impact
- Market research confirmed bandwidth cost optimisation as the larger share of industrial edge demand relative to genuine latency-critical applications
- Latency benchmarking confirmed the client’s existing cloud architecture already met the requirements of the majority of its customer base
- Customer segmentation revealed most accumulated edge requests were cost-driven rather than latency-driven, redirecting the scope of investment
- The alternative solution assessment identified a considerably more efficient feature for the larger cost-driven customer segment
- The phased roadmap concentrated comprehensive edge investment specifically on the smaller, genuinely latency-critical segment
- The targeted edge capability launched for the latency-critical segment following the engagement
- The compression and filtering feature addressed the larger cost segment at a fraction of the engineering cost of comprehensive edge processing
- Engineering resources were allocated according to genuine market evidence rather than the volume of surface-level requests alone
Client Outcome
Investment Efficiency
Engineering resources were concentrated on genuine edge needs while the larger cost-driven segment was served through a more efficient feature.
Segment-Specific Solutions
Latency-critical process manufacturing customers received comprehensive edge capability while cost-driven customers received a targeted solution.
Resource Reallocation
Substantial investment that would have gone toward an undifferentiated build was redirected according to genuine demand evidence.
Customer Base Clarity
The client gained a precise understanding of what was actually driving two years of accumulated edge requests.
Faster Time to Value
The lower-complexity feature reached the larger customer segment considerably faster than a comprehensive edge build would have.
Architectural Discipline
Future investment decisions now distinguish underlying demand drivers rather than responding to surface-level feature requests.
Competitive Positioning
Concentrated investment in genuine latency-critical capability positioned the client more credibly than a diluted broad-based effort.
Roadmap Confidence
Product leadership gained confidence that the next cycle's edge investment was sized appropriately to actual market evidence.
Market Positioning
The client was repositioned as a provider making investment decisions through disciplined demand analysis rather than request volume alone.
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