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87% of enterprises believe big data analytics will redefine the competitive landscape of their industries within the next three years. 89% believe that companies that do not adopt a Big Data analytics strategy in the next year risk losing market share and momentum.

These and other key findings are from a Accenture and General Electric study published this month on how the combination of Big Data analytics and the Internet of Things (IoT) are redefining the competitive landscape of entire industries. Accenture and GE define the Industrial Internet as the use of sensor, software, machine-to-machine learning and other technologies to gather and analyze data from physical objects or other large data streams, and then use those analyses to manage operations and in some cases to offer new, valued-added services.

Big data analytics now seen as essential for competitive growth

The Industrial Internet is projected to be worth $500bn in worldwide spending by 2020, taking into account hardware, software and services sales according to Wikibon and previously published research from General Electric. This finding and others can be found on the home page of the Accenture and GE study here: How the Industrial Internet is Changing the Competitive Landscape of Industries.

The study also shows that many enterprises are investing the majority of their time in analysis (36%) and just 13% are using Big Data analytics to predict outcomes, and only 16% using their analytics applications to optimize processes and strategies. Moving beyond analysis to predictive analytics and optimization is the upside potential the majority of the C-level respondents see as essential to staying competitive in their industries in the future.

A summary of results and the methodology used are downloadable in PDF form (free, no opt in) from this link: Industrial Internet Insights Report For 2015.

Key take-aways from the study include the following:

• 73% of companies are already investing more than 20% of their overall technology budget on big data analytics, and just over two in ten are investing more than 30%. 76% of executives expect spending levels to increase. The following graphic illustrates these results:

• Big data analytics has quickly become the highest priority for aviation (61%), wind (45%) and manufacturing (42%) companies.  The following graphic provides insights into the relative level of importance of big data analytics relative to other priorities in the enterprises interviewed in the study:

• 74% of enterprises say that their main competitors are already using big data analytics to successfully differentiate their competitive strengths with clients, the media, and investors. 93% of enterprises are seeing new competitors in their market using big data analytics as a key differentiation strategy.  The single greatest risk enterprises see from not implementing a big data strategy is that competitors will gain market share at their expense.  Please see the following graphic for a comparison of the risks of not implementing big data strategy.

• 65% of enterprises are focused on monitoring assets to identify operating issues for more proactive maintenance. 58% report having capabilities such as connecting equipment to collect operating data and analyzing the data to produce insights. The following graphic provides an overview of Big Data monitoring survey results:

• Increasing profitability (60%), gaining a competitive advantage (57%) and improving environmental safety and emissions compliance (55%) are the three highest industry priorities according to the survey. The following table provides an analysis of the top business priorities by industry for the next three years with the shaded areas indicating the highest-ranked priorities by industry:

• The top three challenges enterprises face in implementing big data initiatives include the following: system barriers between departments prevent collection and correlation of data for maximum impact (36%); security concerns are impacting enterprises’ ability to implement a wide-scale big data initiative (35%); and  consolidation of disparate data and being able to use the resulting data store (29%), third. The following graphic provides an overview of the top three challenges organizations face in implementing big data initiatives: