Predictive analytics lacking among businesses despite growing urgency to adopt

Less than one-third of companies have predictive analytics capabilities although big data analytics is a top priority for 88 per cent of executives.

A new global study, Industrial Internet Insights for 2015, from GE and Accenture reveals there is a growing urgency for organisations to embrace big data analytics to advance their Industrial Internet strategy. However, less than one-third (29 per cent) 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 per cent) 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. 

According to Gartner's Kristian Steenstrup and Stephen Prentice, "Few technology areas will have greater potential to improve the financial performance and position of a commercial global enterprise than predictive analytics.''

Two-thirds (66 per cent) 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 per cent already seeing new market entrants using big data to differentiate themselves, 88 per cent of the executives stated that big data analytics is a top priority for their company. 

Nearly half (49 per cent) 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 per cent 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," said Matt Reilly, senior managing director, Accenture Strategy, 

Reilly, however says 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.

''He says, 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 realisation. More than one-third of the executives (36 per cent) 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 per cent) 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. 

He says, ''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.

• Prioritisation: Aviation executives (61 per cent) most often placed a higher priority on big data analytics as compared to about 30 per cent or less for industries such as power distribution (28 per cent), power generation (31 per cent), oil & gas (31 per cent) and mining (24 per cent).
• Adoption: Railroad (40 per cent) and power generation (38 per cent) 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 per cent) said they plan to use big data analytics to help them create new business opportunities with new revenue streams.  Railroads (73 per cent) were most likely to plan to use big data analytics to gain insights into equipment/asset health for improved maintenance. Mining (71 per cent) most often planned to use it to achieve increased profitability through improved resource management.