Machinery and facilities are generating more data than ever before. Who controls that data will determine the winners and losers in the new data economy.
Manufacturing Blog: The New Industrial Data Economy
Oct 20, 2020
by Tim Lieuwen and Bobby Noble
These trends flow from the growing ability to extract valuable data and insights from operating industrial assets. The declining cost of sensors and data storage, coupled with growing computing power and analytic tools, opens up new insights into how facilities operate, when they should be taken offline for repairs, or insights into what can be done to improve performance and reduce operational costs.
This firehose of data spewing from every device presents a choice for individuals, companies, and even nations. Is it ultimately more valuable to horde data and control it, even if that leads to lost opportunities for creating synergies? Or should access be more open, even at the risk of letting more nimble competitors make better use of data you collected? To what extent should regulators intervene in this space, such as by compelling companies to share aspects of this data, or enforcing strict controls over data security in matters influencing delivery of critical services to the public sector, such as water or electric power?
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Insights flowing from data are worth big money. In their 2012 white paper, “Industrial Internet: Pushing the Boundaries of Minds and Machines,” Peter C. Evans and Marco Annunziata noted that even a 1 percent improvement in efficiency in the industrial sector could lead to enormous savings. Their calculations suggested that, over a 15-year period, the savings included $30 billion in aviation and $66 billion in gas fired power generation. Thus, expert advice to the plant operator is of high value.
These opportunities have led to the development of major facilities for aggregating, analyzing, and monetizing data from industrial sources. For example, General Electric operates a facility that live-streams 200 billion data points per second from over 5,000 power generating facilities in over 60 countries, analyzing it for trends that can be used to detect anomalies, increase reliability, and improve efficiency.
But the promise of big data, machine learning, and data analytics is predicated on data access. There are four distinct, but somewhat overlapping challenges at play in terms of access to data: data ownership, data nationalism, cybersecurity, and data privacy.
Data ownership—the question of what company or individual owns data continues to be an issue for the consumer Internet. The industrial internet faces somewhat different circumstances. Here data ownership is typically determined through contractual negotiations between private companies. However, these can be fuzzy, leading to a variety of actions to protect data, get access to data, and even actions from policymakers to “democratize” data.
Similarly, it has led to a variety of companies developing business models that give them access to data. Google’s Nest brand of smart home thermostats are a prime example. In addition to being a thermostat that does a lot of cool stuff, it also generates a lot of data about energy patterns—data that can be monetized by enabling insights into how to further improve energy performance or to suggest needed maintenance.
Data nationalism refers to actions that a country may take to keep data within its boundaries. This is harder than it sounds. When a passenger departs Atlanta Hartsfield-Jackson airport on a Delta Airlines flight on a Boeing airplane equipped with GE engines, and flies to Beijing Capital International Airport in China, there are multiple overlapping claims to the data generated during the flight.
Data nationalism is expanding with a growing number of countries imposing restrictions on the cross-border flow of data. Just as countries would not allow the export of some valuable mineral or other asset without compensation, so they are realizing that the data generated in-country has great value.
A related issue is cybersecurity. In an ideal world, distributed assets, such as an electricity distribution system, would be connected to a larger network, enabling experts to look for potential problematic behaviors or to identify opportunities to reduce costs. Such data would then be aggregated across many locations to sleuth out more subtle or lower probability issues.
However, such access creates vulnerabilities and several incidents have made it into the open press. For example, the Department of Homeland Security and the FBI issued a public alert in 2018 on Russian government cyber actors who gained access into US energy networks.
Indeed, it may not be possible to completely prevent suitably resourced hostile actors from accessing critical industrial infrastructure, leading to discussions of how to limit or manage risk. Such issues will be an inherent limiter of the ability to access data from industrial facilities.
Finally, data privacy issues come up when data sets come from individual consumers, such as those who own internet-connected cars or whose electricity use is monitored via smart meters. Connected devices promise to provide better services to consumers, such as reduce electricity bills. Sharing this data with companies has a downside. For example, in the wrong hands, data from smart meters could indicate energy use patterns yielding predictable times that a homeowner was away from their house. These privacy concerns have led to public backlashes against companies harvesting data.
Data privacy is an issue where national governments are intervening. The European Union has promulgated the General Data Protection Regulation, or GDPR, whose overall goal is to give primary control of personal data to the individuals from where that data originated. It’s likely that the world may be divided into regions with stiff rules on data privacy, such as Europe, and those with less regulation, such as China.
The combination of Big Data and advanced analytics is powerful. But just at the time that the internet promises democratization of information, it’s not inconceivable that the opposite will happen. The new monopolies will not be in delivery of commodities like oil, steel, or sugar, but in ownership and access to critical data.
It’s too soon to say which direction the industrial data economy will take: open and connected, or closed and monopolistic. But that choice will determine the way companies working in the industrial sector will look—and the business models they utilize—for decades to come.
Tim Lieuwen, P.E., is executive director of the Strategic Energy Institute at Georgia Institute of Technology. Bobby Noble is program manager for gas turbine systems at the Electric Power Research Institute.
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