In the conservative culture of most manufacturing-based businesses, competitive advantage is usually perceived to lie in ownership, secrecy, control, and sometimes adversarial relationships with suppliers and partners. It goes without saying that such a culture does not blend well with the notions of openness, transparency, and trust.

THINK LIKE A CHILD

What’s the first thing kids are taught? Maybe you have children in pre-K or Kindergarten, or you can remember your own early years in school. When we look back, what sticks out most are lessons in sharing. “We share because we care” was the slogan our teacher used, and she made it concrete. We couldn’t eat a piece of candy unless we’d brought enough for the entire class. It would be wrong for Johnny to enjoy a chocolate bar in front of classmates who had nothing. Every child learned this.

Many of the really big truths in life go back to the simplest things. “Think like a child” people say when they want you to see things freshly. Some of the most truly innovative thinking has come from perceiving the painfully obvious—once the perceiver saw past her learned preconceptions. Think of Einstein sitting on a train and not knowing, for one crucial moment, whether it was his train or the train alongside him that was moving. That simple experience led to the Theory of Relativity, and changed everything.

“We share because we care” could easily be a foundational principal of data policy in the 21stcentury: We share data because we care about the welfare of our society.

We’re not necessarily talking about sharing “for free,” though much data, like that gathered with public funds, will continue to be shared without a fee because it’s already been paid for with our taxes. But whether we pay in advance or in arrears, whether the data is privately held or held in the Commons, our point remains the same. Innovation should make life better for all humans, not just for some.

To do that in this century means sharing data to further develop AI and machine learning, and to facilitate the lifelong learning that humans need to do to continue developing themselves alongside their machine servants.

WE HAVE HALF THE DATA WE NEED

Google, Facebook, Amazon and similar peers already 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 consumer Internet players with just about anything and everything in the universe they would ever want to know about the user. Maybe too much.

B2B alliance and ecosystem development for smart systems today looks nothing like the mobile and consumer Internet worlds. Why? The mobile phone business spent years looking for its killer application and, as things turned out, the killer application ended up being the ecosystem of application developers, exemplified by the rapid growth of the iOS and Android platforms.

Data and apps are the core value creation mechanisms within the Smart Systems and the IoT. But the B2B world that comprises so much of the IoT 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 they lack half the data needed to inform new application values and fulfill on artificial intelligence and machine learning opportunities. How should B2B players think about creating equivalent value with data?

Given the environment they’re in, we at Harbor have long maintained that B2B players should create data ecosystems.

However, ecosystem development in B2B domains has been much slower in its evolution than in the consumer world. Product OEMs and machine builders work with software developers and solution players in a much more “command and control” mode and have largely forged only simple “hub and spoke” relationships with wireless carriers, enterprise applications or professional services providers.

In the conservative culture of most manufacturing-based businesses, competitive advantage is usually perceived, to one degree or another, to lie in ownership, secrecy, control and sometimes adversarial relationships with suppliers and partners. It goes without saying that such a culture does not blend well with the notions of openness, transparency and trust. Product OEMs, for the most part, fall far short of either sharing or caring about sharing.

An Ecosystem of Data

As B2C and B2B networked business models inch closer to each other in the marketplace, it is increasingly evident that the consumer Internet models provide many lessons for the “cloistered” B2B players. The benefits of large-scale collaboration, ecosystems, and developer communities in the B2B dimension of the Internet of Things arena are just beginning to be recognized.

What can the Internet of Things learn from this? What would B2B data ecosystems for the Internet of Things look like and what potential could they inform? How should innovative players, large or small, engage in new collaborative data relationships to drive market development? Are there fundamental barriers that need to be overcome? What maneuvers can players execute to address them?

Data will never come from a single unified source. What technology 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.

The collection of dull and dreary “solo” solutions—like equipment automation, meter reading, and fleet tracking applications—that comprise a significant percentage of the IoT world today are really simple applications focused on remote diagnostics or tracking/location services. This is largely because of technical complexities and business model challenges. These simple applications don’t really need to be “open for data sharing.”

Moving from such “simple” applications to “compound” applications—for example, monitoring and acting on all the services in a skyscraper—involves multiple collab­orating systems with significant inter­actions between and among devices, systems, people andvendors. No longer is the focus solely on the product supplier’s ability to deliver support for theirprod­uct efficiently. Rather, value is brought to the customer through new process-automation and systems optimization. However, open data sharing between and among B2B OEMs would best be described as a mythic future state. These players still push proprietary systems to their users in hopes of gaining monolithic account control.

Between the complexities of “compound” applications and the culture and behavior of B2B OEMs, it is difficult to imagine freely open and fluid data ecosystems. However, the need to combine and integrate diverse data and data sources will soon become the very air that business breathes. Product OEMs that do not find ways to participate in newly emergent ecosystems and to share data with partners and alliances will simply not survive. But how do businesses who become more connected, open and willing to share data change their underlying concepts of “ownership” and yet remain distinct and profitable?

DATA BROKERAGES: MERELY A STEPPING STONE?

Today, we have emergent innovators creating data brokering and exchange tools. Players like Terbine, geared toward smart-city applications, and Otonomo, which targets the connected vehicle and transportation arena as well as smart cities, are creating platforms for data brokerage. In the present moment, these systems and others are setting the stage for provisioning diverse data sources, data exchanges, and interactions. They provide a real and tangible means for everyone to think about how data ecosystems in the real-world might work.

Our caveat about these services is that they could easily turn out to be temporary and transitional—ventures that that get subsumed by more comprehensive information-architecture designs that we believe the future will demand.

The biggest hurdle to data sharing and ecosystems is the fact that information is not free—and we mean free as in “freedom,” not free as in “free of charge.” In fact, thanks to legacy information architectures, most data is not free to easily merge with other information, and thus to enable any kind of cumulative intelligence.

The traditional approaches to data discovery and systems intelligence have three failings:

1. They can’t provide a holistic view of diverse data types
2. The types of intelligence tools available to users are, at best, arcane and typically limited in use to “specialists” (how many guys in white lab coats are required to manipulate data sets)
3. The costs and economics of data management are still horrendous. Today more than half the cost of any data-science project is still just cleaning up single batch data sets

As hunters would say, “This dog don’t hunt.”

HOW DO WE GET THERE FROM HERE?

So, what’s really required to drive the data aggregation, sharing, and innovation that would inform new collaborative business models for Smart Systems? At Harbor we recommend changing the risk/reward formulas for data alliances and new relationships in the Internet of Things.

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 and provides 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 really scale entirely new value creation via data sharing and new services delivery systems.

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 data and equipment and IoT 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 leveraging imaging data, maps, traffic flow data and much more