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The Innovation Curve & The Hype Cycle
Why So Many B2B Data Ecosystems Fail - 06 Sept 2022
Why So Many B2B Data Ecosystems Fail

the five basic requirements for creating
data-driven ecosystems

B2B hardware, services and equipment players have been trying to create ecosystems for quite some time, but the track record has been abysmal. It’s logical to assume that B2B companies will eventually learn how to build data-driven ecosystems that generate high-margin recurring-revenue businesses and create distinct competitive advantages, but why do B2B ecosystem development efforts fail? Our consulting work and related analysis point to five basic requirements. Succeeding at one or two of these requirements, but not all five, may get some players to first base, but getting to second, third and home base is where real value is created for customers and a sustaining ecosystem is established.

Several of the world’s most valuable companies have built their dominance upon data-driven ecosystems. Google, Microsoft, Meta, Amazon and other large technology players have leveraged ecosystem development to control customer interactions and strategic control points with search, messaging, commerce and advertising.

Data ecosystems organized by consumer Internet players have demonstrated that they can create enormous value. These B2C platform players and similar peers have a unified usage and data relationship with their respective users—so much so that they don’t require additional data sources to create value within their business models. Mobile phone data-feeds come from virtually everyone today and provide these tech players with just about everything they would ever want to know about their users.

Beyond the consumer and mobile Internet arena, ecosystem development in B2B domains has progressed at a much slower pace due to several factors:

  • B2B typically requires deeper domain expertise to develop new networked solutions as well as the customer relationships required to capture value and monetize them;
  • Extracting value from data from diverse multi-vendor equipment systems is much more difficult; and,
  • Most B2B players do not have access to even half the data they need to inform new application values and fulfill on machine learning and AI opportunities.

Given this, how should B2B players think about creating equivalent value with data and ecosystems?

FUNDAMENTAL ECOSYSTEM TYPES

Fundamental Ecosystem Types

source: Harbor Research

THE ERA OF FLYING SOLO IS REALLY TRULY OVER

Over the last decade or more, Smart Systems and IoT technology developers have largely focused their core development work and innovations on primarily serving OEMs and related service providers and intermediaries.  Digital and IoT technologies are driving many new growth opportunities and efficiencies for OEMs based on new data collection, management and analytics tools that provide a deeper understanding of a connected product or machine’s performance and usage. Because of immediate returns on efficiencies and the new applied values these systems can generate, OEMs have been the dominant adopters of new Smart technologies and, like “Typhoid Mary,” have carried these innovations into end customers where their presence and impacts are expanding like a disease spread pattern.

However, the collection of dull and dreary “solo” solutions that comprise a significant percentage of the Smart Systems today—like equipment health, meter reading, and fleet tracking applications—have not evolved beyond simple applications focused on alerts, alarms and remote diagnostics. The reason the market has been slow to adopt more robust systems is largely due to the technical complexities required to deploy networked systems and the challenges for many suppliers that come with the shift to services-focused business models.

As end customers in factories, hospitals, buildings and more become more familiar with digital and Smart Systems capabilities, they are realizing these technology innovations will push the boundaries of how data from products, systems and equipment can be utilized to manage and optimize processes within their operations which, in turn, has increased pressure on equipment manufacturers and services providers to embrace data management and analytics tools.

As software tools mature and new hardware technologies increase performance and open up new use cases, applications based on deeper peer-to-peer interactions between sensors, machines, data, systems and people will drive more compound and dynamic value streams. These innovations are powering more complex applications in a variety of industries, from predictive maintenance to optimizing equipment systems and to synchronizing multi-tiered support. However, the challenges of gathering machine data and integrating diverse data types have been big adoption hurdles, particularly in industries where the range of brands and equipment types number in the hundreds.

With that said, for at least twenty years, Harbor Research has been telling our clients that “the era of flying solo is over.” But this is hard advice to accept. Compared to flying in a group, flying solo is easy. You have near-complete control over everything.

Of course, most businesses insist that they’re not flying solo. They depend upon many relationships; they’re part of complex value chains. Yes, but those excuses miss the point. Their relationships aren’t organic ecosystems. They’re command and control hub-and-spoke arrangements, and the business in question is always the hub.

You want us to collaborate and share our data,” we often hear from the C-suite. “But our data’s the new path to profitability.” Systems suppliers that have come from a legacy of “solo” solution delivery resist fundamental change. But change they must.

The healthcare sector illustrates the B2B dilemma and the many challenges players face trying to create ecosystems. Today, the average 200+ bed hospital has over 250 brands of equipment and devices which causes the typical hospital patient to interact with over 75 devices per day. If every device and machine has its own embedded intelligence and monitoring scheme, how should healthcare CIOs respond to hundreds of equipment OEMs showing up on their doorstep proclaiming that they have the most superior digital and remote data collection capabilities?

The simple truth is optimization of any system or resource illustrates the value of shared systems and data. Customers want to integrate data from diverse devices, machines and equipment systems to enable new and novel ways to solve operational and business problems. Remote connectivity alone may help the manufacturer of a machine provide more efficient service and support, but it does not allow the end user to leverage very much intelligence across myriad brands, suppliers and diverse systems.

ORCHESTRATING SUCCESSFUL B2B ECOSYSTEMS

Given the many aspects that must be addressed from the end customer’s standpoint, alliances between users, OEMs, value added service providers, software developers and related channel partners represents the only sane approach to address the challenges of leveraging data across differing brands of equipment and also create maximum value for all parties involved.

ECOSYSTEM EVOLUTION - FROM CLOSED SOLO SOLUTIONS TO SHARED DATA

Ecosystem Evolution – From Closed Solo Solutions To Shared Data

source: Harbor Research

As a result, specification and adoption of digital and IoT enabled equipment and systems is beginning to shift towards a “shared” set of roles between and among suppliers. Given the complexities driven by diverse multi-vendor systems, we believe this shift in the way new Smart Systems are organized and deployed is setting the stage for the development of B2B ecosystems. A critical question remains, how far and how fast will B2B ecosystems evolve and what’s required to unlock the value of data?

Generally, ecosystems divide into two broad categories: solution development and delivery ecosystems and transaction-based ecosystems and marketplaces. While these two broad groups of ecosystems each have unique characteristics, they share many common development and governance challenges. We believe the following five factors are the most critical considerations:

Ecosystem Value Proposition and Rationale: Many ecosystems fail because they did not address a customer challenge significant enough to justify the high investment cost and time required to organize ecosystem roles and enlist partners. An ecosystem’s rationale and value proposition are a function of the scale of the challenge, the ecosystem’s ability to address that challenge and the customer’s ability and willingness to pay. Based largely on Amazon’s early success, many players tried to organize B2B marketplaces for components and products in areas such as power transmission equipment, electrical gear, auto parts and similar.  These marketplaces failed because the translatability of the high transaction costs that Amazon and others addressed in B2C markets were not as significant in B2B segments.

Ecosystem Design: Ecosystem design must effectively address the required roles, participants, interactions and incentives. For companies looking to take the lead in developing a new ecosystem, the role of the orchestrator is critical. Defining governance, standards and links to coordinate ecosystem participants and resolve conflicts are the core elements in any ecosystem’s design. Solution development and delivery ecosystems often fail when either the incentives to participate are not enough to motivate the intended players or when participants don’t naturally align with one another. The Sony e-reader campaign, which was introduced into the market before Amazon’s Kindle, did not develop into an effective ecosystem. Sony’s design required users to manually upload their purchases to the e-reader. Because of the open upload mechanism, publishers worried about copyright issues and did not join the ecosystem. nPhase, who lead the design of Amazon’s Kindle delivery system, developed a seamless integrated eBook purchase and delivery experience that was considerably simpler and more effective in driving publisher participation.

Value Creation and Monetization: Determining how to price and whom to charge are the core enablers of monetization. Ecosystem developers must balance key trade-offs including how to divide the economic pie; enabling key participants ability to make money while capturing value as the orchestrator. Balancing these trade-offs as well as potentially subsidizing elements within the ecosystem at key times during development are key success factors. IBM’s original personal computer design became an industry standard, but IBM failed to create any mechanism to economically capture that value. ARM Holdings, the designer of the processor contained in 90% of smartphones, developed a very successful ecosystem with two value capture mechanisms – an upfront licensing fee to gain access to its processor designs and a royalty for each device that contained ARM’s IP.

Early Momentum and Eventual Scale: Enabling early catalytic maneuvers to drive momentum and attaining critical mass which informs increasing scale are critical to success in ecosystem development.  Under investing during early staging of new ecosystems is a sure path to failure. This is particularly true in system and solution delivery ecosystems that are based on a specific technical standard. JVC made the specifications of its VHS format openly available to other manufacturers and locked up more pre-recorded content to defeat Sony’s Betamax, despite Beta’s earlier entrance and technical superiority

Ecosystem Evolution and Defense: Once an ecosystem is established, they require defense mechanisms. Threats to ecosystem harmony include disintermediation, multiple overlapping ecosystems, poor on-going governance and more. These can be prevented with effective ecosystem design that addresses participation rules, incentives, switching costs or compliance but, once an ecosystem is established, the orchestrator must find ways to iterate and evolve its core contributions and differentiation.  Players in the agriculture and smart farming sector who initially provided farmers with precision location data and services to increase crop yield have evolved to providing aggregated datasets and insights to their customers based on the experiences of ecosystem participants who are sharing planting data, acreage information and related crop condition data.  While some of the knowledge gained by ecosystem players needs to remain private, key learnings can be shared between partners to help sustain the differentiation of the overall ecosystem.

B2B DATA ECOSYSTEM OPPORTUNITIES

A fundamental shift is occurring across B2B domains as players come to recognize the impacts data and analytics can enable as well as the potential to monetize data values. However, in the B2B world monetizing a single company’s captive data on the open market isn’t worth as much as when it is pooled and combined with other datasets where the cumulative data value creates unique and compound value that would not be possible otherwise.

Data will never come from a single unified source. What B2B players looking to leverage data collaboration—or benefit from connecting diverse “smart products” to the Internet—need to understand is that we have entered a phase in the marketplace where data with real practical value can originate from just about anywhere. It simply needs to be better organized, facilitated and orchestrated across diverse multi-vendor environments.

Adoption of data-driven ecosystem opportunities will vary by industry with the pace of adoption largely determined by multiple interrelated factors. We believe the vertical industries and applications with the highest potential for shared data will demonstrate some combination of the following characteristics:

  • Deep sustaining relationships coupled with an in-depth understanding of the end customer’s operations and processes.
  • Behavior in target accounts is sophisticated and users tend to have higher skill levels (often invest early in new capabilities such as predictive maintenance).
  • Target accounts show a propensity toward adopting new technologies that connect equipment and machines to the Internet earlier than peer industries.
  • Process technology is complex, heterogeneous and tightly interrelated, with many varied types of complimentary equipment and systems.
  • The capital cost of the equipment, as well as the economic impact of the equipment and systems “in-use,” is high (and corresponding cost of downtime is high).
  • There is a “mixed” maintenance and support environment that drives frequent interactions between and among first level in-house maintenance staff and OEM equipment service staff, as well as third party services, including traditional out-sourced maintenance and specialty services (e.g. vibration analysis services, etc.).
  • Access to a very large installed base of equipment capable of generating high-quality real-time data as well as access to historical data from existing equipment that enables modeling of processes and the development of machine learning algorithms.
  • The provider’s equipment, product and/or services are primary value adding elements within the customer’s operations.

Even though segments with the characteristics described above tend towards more sensor use, instrumenting and automation, customers in these segments suffer the same challenges that many B2B businesses face. Existing systems, including CMMS/asset management, condition monitoring, remote equipment services, reliability services and related automation and control technologies, are not fully integrated and the information they accumulate cannot be easily accessed across differing vendors systems.

The B2B world doesn’t have the same unified sources or monolithic usage tracking and analytics that the consumer world utilizes to make money. Reliable sources estimate that B2B players lack half or more of the data needed to inform new application values and fulfill on artificial intelligence and machine learning opportunities.

RE-THINKING EVERYTHING

So, what’s really required to drive data aggregation, sharing, and innovation to inform new collaborative business models and ecosystems for Smart Systems? We believe changing the risk/reward formulas for data alliances and new roles and relationships. This involves three interrelated elements:

  • A vision for how data collaboration networks will drive “catalytic” innovation to help focus participants;
  • An architecture to organize value creation with data which provides leverage to reduce the investment and effort participants need to get access, as well as providing tools and easier ways to fuse and use data;
  • Relationship enablers and economic incentives which persuade participants that the ecosystem developer is serious and can scale value creation via data sharing and new services.

If we accomplish these things, then in most complex domains—smart cities, smart agriculture, smart grid, connected transportation, etc.—we will have the capabilities for a sane future. For example:

  • Agricultural companies will be able to develop new services to help farmers predict and optimize crop yields that could fuse weather models, forecasts, and real time weather conditions with geolocation and crop condition data.
  • Logistics companies will use external data to predict disruptions in their retail customers’ supply chains based on locations, inventories, social media, data from suppliers and more.
  • Smart-city emergency response services will get to accidents or disaster sites and on to hospitals as quickly as possible by leveraging imaging data, maps and traffic flow data.

Though no one can predict precisely when new innovations will mature, the process by which they will be delivered is now becoming clear. Transforming B2B businesses and domains with new digital tools is creating the potential for visionary players to step into the important role of “data ecosystem orchestrator”—that is, to become the enablers of new digital innovations and the facilitators of new alliances and relationships based upon information-sharing and co-creation of new solutions.

Architecture-driven data orchestrators and ecosystem developers will need to stand in their prospective partners’ shoes and work through the logic of how these collaborative systems get designed, procured and deployed. It won’t be a classic linear product development cycle.  Old-fashioned “customization” gives way to configurable tools and systems, while the technology itself makes everything more re-programmable.

Orchestrating innovations for complex adaptive systems creates an opportunity for a new generation of players to reach out to ecosystem participants and build empathy with potential partners and innovators. Data ecosystem orchestrators will have the potential to help facilitate changes in how complex systems are specified and developed, helping to synchronize diverse players and innovations.

This role of data ecosystem orchestrator will require unique knowledge of diverse technologies, a deep understanding of how networks, data and analytics will enable a new generation of applications as well as how to foster collaboration.  Developers and investors are ideally positioned to help users and customers as well as technology development organizations to solve tangible challenges, enable a step-function increase in digital capabilities, and enable new innovative applications. ◆

Please fill out the form below to download our Growth Strategy Insight, “Data Ecosystem Orchestration.”

Data Ecosytem Orchestration

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