A new global study, “Industrial Internet Insights for 2015,” from GE and Accenture reveals there is a growing urgency for organizations to embrace big data analytics to advance their Industrial Internet strategy. However, less than one-third (29 percent) of the 250 executives surveyed for the study are using big data across their company for predictive analytics or to optimize their business.
But progress is underway. The majority of the companies (65 percent) use big data analytics to monitor their equipment and assets to identify operating issues and enable proactive maintenance. Sixty-two percent have implemented network technology to help gather vast amounts of data in dispersed environments such as remote wind farms or along oil pipelines.
"Few technology areas will have greater potential to improve the financial performance and position of a commercial global enterprise than predictive analytics,” according to Kristian Steenstrup and Stephen Prentice, Gartner.
Two-thirds (66 percent) of the executives surveyed across eight industrial sectors believe they could lose their market position in the next one to three years if they do not adopt big data, which the report suggests is needed to support their Industrial Internet strategy. Additionally, with 93 percent already seeing new market entrants using big data to differentiate themselves, 88 percent of the executives stated that big data analytics is a top priority for their company.
Nearly half (49 percent) of the companies represented in the study said they plan to create new business opportunities that could generate additional revenue streams with their big data strategy while 60 percent expect to increase their profitability by using the information to improve their resource management.
“The Industrial Internet, fueled by machine-to-machine data inputs, has the potential to drive trillions of dollars in new services and overall growth. But to reap those rewards, industrial companies will need to use insights about their customers and their customers’ use of industrial goods to build new offerings, reduce costs and reinvest their savings,” said Matt Reilly, senior managing director, Accenture Strategy. “To get there, many must work through a multitude of issues to use their machine data for more advanced forms of predictive data analytics, including sourcing the right analytics talent to ensure effective execution and scaling of analytics programs.”
Paving the Way to Adoption
Despite the sense of urgency, there are roadblocks to realization. More than one-third of the executives (36 percent) said system barriers between departments prevent collection and correlation of data. Twenty-nine percent said it is difficult to consolidate disparate data and to use the resulting data repository. Security also ranks high as a challenge with less than half (44 percent) reporting an end-to-end solution to defend against cyber-attacks and data leaks.
“The payoff from joining industrial big data and predictive analytics to benefit from the productivity gains the Industrial Internet has to offer is no longer in doubt,” said Bill Ruh, vice president, GE Software. “The tally of success for industry is evidenced by the greater visibility and speed-to-decision across operations and asset performance management. But data alone won’t generate value. To make information useful requires an investment in new capabilities and talent that will serve as a catalyst for extracting value quickly.”
Additional Industry Highlights
By and large, the executives surveyed acknowledged the importance of big data analytics, but their responses varied by sector.
- Prioritization: Aviation executives (61 percent) most often placed a higher priority on big data analytics as compared to about 30 percent or less for industries such as power distribution (28 percent), power generation (31 percent), oil & gas (31 percent) and mining (24 percent).
- Adoption: Railroad (40 percent) and power generation (38 percent) companies most frequently said their big data analytics capabilities had advanced to a level of maturity that includes predictive and optimization capabilities.
- Implementation: Wind energy companies most frequently (61 percent) said they plan to use big data analytics to help them create new business opportunities with new revenue streams. Railroads (73 percent) were most likely to plan to use big data analytics to gain insights into equipment/asset health for improved maintenance. Mining (71 percent) most often planned to use it to achieve increased profitability through improved resource management.
Recognizing that big data analytics is the foundation of the Industrial Internet, Accenture and GE fielded a survey in China, France, Germany, India, South Africa, the U.K., and the United States that explored the state of big data analytics and how it is being viewed across eight industrial sectors. They included the aviation, wind, power generation, power distribution, oil and gas, rail, manufacturing and mining industries. Companies represented had revenues in excess of $150 million, with more than half of them having revenues of $1 billion or more. More than half of the respondents were CEOs, CFOs, COOs, CIOs and CTOs, but the sample also included vice presidents and directors from information technology, finance, operations and other cross-functional management areas.
The study is an outgrowth of the strategic global alliance formed in 2013 by Accenture and GE with the aim of co-developing technology and analytics strategies and applications that will help industrial companies take advantage of massive amounts of data generated through machine operations.
Toward that end, the companies also recently launched the Intelligent Pipeline Solution, the first-ever Industrial Internet offering designed to help pipeline operators make better decisions concerning the condition of their critical machines and assets in the oil and gas pipeline industry. The offering combines Pipeline Management, a GE Predictivity software solution powered by the Predix platform, with Accenture’s digital technology and systems integration capabilities, to help customers make better, faster decisions on their pipeline operations to improve safety and prevent costly downtime.
This study adds to the growing portfolio of industry research and insights on this topic — one of those being the recent Accenture report, Driving Unconventional Growth through the Industrial Internet of Things (IIoT), which explores new topline growth opportunities in digital services enabled by the IIoT. The report also outlines seven steps companies can take to start preparing to take advantage of the vast amounts of data they have at their disposal to generate new revenue-producing products and services.