Industrial Internet Insights report - for 2015

 
Industrial Internet Insights report - for 2015
Industrial Internet Insights Report
             for 2015
Industrial Internet Insights report - for 2015
According to new research from GE
and Accenture, executives across the
Industrial and healthcare sectors see
the enormous opportunities of the
Industrial Internet and in many cases
are deploying the first generation
of solutions. The vast majority
believe that Big Data analytics
has the power to dramatically
alter the competitive landscape
of industries within just the next
year and are investing accordingly.
Yet challenges around security,
data silos and systems integration
issues between organizations
threaten to delay Industrial Internet
solutions that could offer distinctive
operational, strategic and competitive
advantages. Spurred on by board-
level direction, surveyed executives
feel a sense of urgency in moving
more briskly toward the Industrial
Internet future.

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Industrial Internet Insights report - for 2015
Table of contents

Introduction                                                                            4
Tapping the potential value                                                             5
The formula for the Industrial Internet                                                 7
A sense of urgency                                                                      8
About the research                                                                      9
Moving to growth and value creation                                                    10
But are companies up for the challenge?                                                12
Moving from today’s implementation to tomorrow’s strategies                            14
Big Data analytics in the healthcare industry: A diagnosis                             16
   Positive outcomes                                                                   17
   Shaking up the healthcare space                                                     18
   Expectations are high and driven by the Board                                       18
   An urgency to deliver improved patient outcomes                                     19
   Talent issues                                                                       20
   Driving better patient outcomes                                                     21
Becoming an Industrial Internet value creator                                          22
   Invest in end-to-end security                                                       23
   Break down the barriers to data integration                                         24
   Focus on talent acquisition and development                                         26
   Consider new business models needed to be successful with the Industrial Internet   31
   Actively manage regulatory risk                                                     32
   Leverage mobile technology to deliver analytic insights                             32
Conclusion                                                                             34
About GE                                                                               36
About Accenture                                                                        36
About GE and Accenture Alliance                                                        36

                                                   3
Industrial Internet Insights report - for 2015
Introduction
How big is the economic power of the Industrial
Internet? Consider one analysis that places a
conservative estimate of worldwide spending at $500
billion by 2020, and which then points to more optimistic
forecasts ranging as high as $15 trillion of global GDP
by 2030.1
The Industrial Internet—the combination of Big Data
analytics with the Internet of Things (see sidebar)—
is producing huge opportunities for companies in all
industries, but especially in areas such as Aviation,
Oil and Gas, Transportation, Power Generation and
Distribution, Manufacturing, Healthcare and Mining.
Why? Because, as one recent analysis has it, “Not all Big
Data is created equal.” According to the authors, “data
created by industrial equipment such as wind turbines,
jet engines and MRI machines … holds more potential
business value on a size-adjusted basis than other types
of Big Data associated with the social Web, consumer
Internet and other sources.”2

1. David Floyer, “Defining and Sizing the Industrial Internet,” Wikibon, June 27, 2013; Peter C. Evans and Marco Annunziata, “General
Electric: Industrial Internet, Pushing the Boundaries of Minds and Machines,” November 2012.

2. http://wikibon.org/wiki/v/The_Industrial_Internet_and_Big_Data_Analytics:_Opportunities_and_Challenges.

                                                                                      4
Industrial Internet Insights report - for 2015
Tapping the                                    61 percent of those surveyed noted
                                               that analytics is their top-ranked
                                                                                            surveyed. Strong board-level support
                                                                                            can also be seen in industries such

potential value                                priority; this number drops to about 30
                                               percent or less for industries such as
                                                                                            as Manufacturing (67 percent noted
                                                                                            the Board as the primary influencer),
                                               Power Distribution (28 percent), Power       Aviation (61 percent) and Rail (60
Executives of industrial companies
                                               Generation (31 percent), Oil and Gas         percent).
are well aware of the potential power
and source of value of the Industrial          (31 percent) and Mining (24 percent).
Internet, according to new research            (See Figure 2.)
from GE and Accenture. (See “About             This prioritization of spending
the research.”) For example, according         becomes clearer when we look at
to our survey, 73 percent of companies
are already investing more than 20
                                               who is supporting Big Data initiatives.
                                               Simply put, it’s no longer the usual
                                                                                            Across the industries
percent of their overall technology
budget on Big Data analytics—and
                                               suspects such as the CIO or COO.             surveyed, 80 to 90
                                               Indeed, 53 percent of all survey
more than two in 10 are investing              respondents indicated that their Board       percent of companies
more than 30 percent. Moreover,
three-fourths of executives expect that
                                               of Directors is the primary influencer
                                               of their Big Data adoption strategy—
                                                                                            indicated that big data
spending level to increase just in the
next year. (See Figure 1.)
                                               more than those citing the CEO (47           analytics is either the
                                               percent), the CIO (37 percent) or
                                               a business line P&L executive (15            top priority for the
Across the industries surveyed, 80 to
90 percent of companies indicated              percent). (See Figure 3.)                    company or in the top
that Big Data analytics is either the
top priority for the company or in the
                                               This board-level influence particularly      three.
                                               stood out in several industries such
top three. This finding is especially          as Mining, where the Board is the
strong in the Aviation industry, where         top influencer for 73 percent of those

                       Figure 1:
Figure 1: InvestmentsWhat
                      in Big Data analytics are strong
                          portion of your overall technology expenditure is              Do you expect this to increase, stay the same
                       made in analytics Big Data?                                       or decrease over the next year?

1. What portion                          3%                             2. Do you expect
of your overall                                                         this to increase,
                                                     22%
technology                                                              stay the same or
expenditure is                24%                                       decrease over             24%
made in Big Data                                                        the next year?
analytics?

                                                                                                                          76%

                                               51%

                         More than 30%                                                      Increase
                         21%-30%                                                            Stay the same
                         10%-20%                                                            Decrease (0%)
                         Less than 10%

                                                                   5
Industrial Internet Insights report - for 2015
FigureFigure
        2:     2: Big Data analytics is one of the top corporate priorities
How important is Big Data analytics relative to other
      How important is Big Data analytics relative to other priorities in your company?
priorities in your company?

               Aviation      61%                                                                          29%                                  10%

                  Wind       45%                                                       45%                                                         6%        3%

     Power Generation        31%                                  63%                                                                                   6%

     Power Distribution      28%                                56%                                                                   16%

             Oil & Gas       31%                                  56%                                                                         9%             3%

                   Rail      40%                                              47%                                                            10%             3%

        Manufacturing        42%                                                 45%                                                          9%             3%

                Mining       24%                          55%                                                                   18%                          3%

                             Top/highest priority    Within the top three priorities     Within the top five priorities     Not a priority

    Figure3:3: Support for Big Data analytics initiatives is coming from the top of the organization
    Figure
     Please rate the level of influence of each of the following in setting the strategy for Big Data analytics adoption
     in your company.

                     Figure 2
          Our Board of Directors      53%                                                                           32%                                           11%         1%   3%

                          The CEO     47%                                                                 39%                                                       9%        2%   2%

                          The COO     28%                                    46%                                                                   19%                        4%   2%

     Business line P&L executive      15%                 47%                                                                   31%                                           4%   2%

                          The CIO     37%                                                    35%                                              21%                             5%   2%

                          The CFO     22%                             36%                                                 29%                                           7%    5%

          Mid-level management        10%           25%                                 44%                                                              15%                  6%

                  Our customers       17%                  23%                                 31%                                           17%                        11%

                                       2%

                                                                                               6
Industrial Internet Insights report - for 2015
The formula for the
Industrial Internet
The Industrial Internet can be described as a source of both
operational efficiency and innovation that is the outcome of a
compelling recipe of technology developments:
• Take the exponential growth in data volumes—that is, “Big
  Data”— available to companies in almost every industrial
  sector, primarily the ability to add sensors and data
  collection mechanisms to industrial equipment.
• Add to that the Internet of Things, which provides even
  more data—in this case about equipment, products,
  factories, supply chains, hospital equipment and much
  more. (Cisco predicts that by 2020 there will be 50 billion
  “things” connected to the Internet, up from 25 billion in
  2015.3) With new technologies such as data lakes, the
  ability to capture and process such data is now a reality.
• Then add the growing technology capabilities in the area of
  analytics—the ability to mine and analyze data for insights
  into the status of equipment as part of Asset Performance
  Management (APM), or the delivery of healthcare, and then
  even to predict breakdowns or other kinds of occurrences.
• Finally, add in the context of industries where equipment
  itself or patient outcomes are at the heart of the
  business—where the ability to monitor equipment or
  monitor patient services can have significant economic
  impact and in some cases literally save lives.
The resulting sum of those parts gives you the Industrial
Internet—the tight integration of the physical and digital
worlds. The Industrial Internet enables companies to use
sensors, 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, value-added services.

3. “The Internet of Things,” Cisco, http://share.cisco.com/internet-of-things.html.

                7 7
Industrial Internet Insights report - for 2015
A sense of urgency                                 do not adopt a Big Data analytics
                                                   strategy in the next year risk losing
                                                                                             There is risk in not taking action now,
                                                                                             according to surveyed executives.
A striking finding from our survey                 market share and momentum.                Asked to name their top three fears
was the sense of urgency felt by                                                             if they are unable to implement a Big
                                                   Executives are looking over their         Data strategy in the next few years,
respondents in implementing Industrial             shoulders at competitors as well.
Internet solutions. This is driven in part                                                   the number one answer was, “Our
                                                   Seventy-four percent said that their      competitors will gain market share at
by the impact being felt at an industry            main competitors are leveraging
level as well as the competitor level.                                                       our expense.” The second top answer
                                                   Big Data analytics proficiencies to       was the concern that investors will lose
For example, 84 percent of those                   differentiate their capabilities with
surveyed indicated that the use of                                                           confidence in their company’s ability to
                                                   clients, investors and the media.         grow. (See Figure 4.)
Big Data analytics “has the power to               New entrants are also coming into
shift the competitive landscape for                their industries, according to 93
my industry” within just the next year.            percent of respondents, and these
A full 87 percent believed it will have            newer entrants are leveraging Big
that power within three years. Eighty-             Data analytics as a key differentiation
nine percent say that companies that               strategy.

Figure 4: Companies are aware of the risks of not implementing a Big Data strategy soon
   Figure 4:
If Ifwe are unable to implement our Big Data strategy in the next one to three years, my top three fears are:
      we are unable to implement our Big Data strategy in the next one to three years, my top-three fears are:

       Our competitor(s) will gain market 28%
                   share at our expense
                                          66%

       We will not be able to recover and 18%
                   “catch up” if we delay
                                          45%

      We will start to lose qualified talent 18%
                            to competitors
                                             48%

  Our investors will lose confidence in our 17%
   ability to effectively grow our business
                                            61%

      Our product(s)/solution(s) cannot be 9%
                      competitively priced 52%

          I don't believe there will be any 3%
                                     impact 3%

       We have already implemented our 5%
                       Big Data strategy
                                         5%
                                                                                             Top
                       None of the above
                                           2%                                                Top 3
                                           2%

                                                                      8
Industrial Internet Insights report - for 2015
About the research
Recognizing that Big Data analytics is the foundation of the
Industrial Internet, GE and Accenture fielded a survey in China,
France, Germany, India, South Africa, the UK and the United
States that explored the state of Big Data analytics and how
it is being viewed across eight industries. Sectors surveyed
were Aviation, Wind, Power Generation, Power Distribution,
Oil and Gas, Rail, Manufacturing, and Mining. A similar survey
was conducted for the US Healthcare industry and results
are integrated into this report. 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, and
the sample also included vice presidents and directors from
information technology, finance, operations and other cross-
functional management areas.
This study adds to the growing portfolio of thinking and reports
on this topic–one of those being the recent Accenture report,
Driving Unconventional Growth through the Industrial Internet of
Things (IIoT), which explored the opportunity the IIoT represents
and implications for the workforce. The report also outlines
seven steps companies can take to overcome challenges they
may encounter as they prepare to use and analyze the vast
amount of data they have at their disposal to generate new
revenue-producing products and services. The report is available
at http://www.accenture.com/us-en/technology/technology-labs/
Pages/insight-industrial-internet-of-things.aspx.

         9
Industrial Internet Insights report - for 2015
Moving to growth                                             Predictive maintenance of assets is
                                                             one such area of focus, saving up to
                                                                                                                     flight time by using full flight data, “tip
                                                                                                                     to tail,” as well as using performance

and value creation                                           12 percent over scheduled repairs,
                                                             reducing overall maintenance costs
                                                                                                                     analytics to combine an aircraft’s flight
                                                                                                                     data, weather, navigation, risk data
Industrial companies are at varying                          up to 30 percent, and eliminating                       and fuel operation, can result in direct
stages of adoption of Big Data analytics                     breakdowns up to 70 percent.5 For                       bottom-line savings.
and, as with all new technologies,                           example, Thames Water Utilities Limited,
                                                             the largest provider of water and                       Smart buildings are another prevalent
a maturity curve is emerging that                                                                                    type of Industrial Internet solution. The
delineates the early adopters from those                     wastewater services in the UK, is using
                                                             sensors, analytics and real-time data                   city of Seattle, for example, is applying
who are at a more foundational level.                                                                                analytics to building management data
One of the first stages in that maturity                     to help the utility respond more quickly
                                                             to critical situations such as leaks or                 to optimize equipment and related
curve is connecting operating assets                                                                                 processes for energy reduction and
and performing monitoring and problem                        adverse weather events.6
                                                                                                                     comfort requirements. The software
diagnosis. Industrial companies are                          Another example comes from the                          identifies equipment and system
focused on moving from this type of                          Oil and Gas industry, where one of                      inefficiencies, and alerts building
asset monitoring to areas of higher                          America’s premier regulated energy                      managers to areas of wasted energy.
operational benefits. By introducing                         providers, Columbia Pipeline Group, has                 Elements in each room of a building—
analytics and more flexible production                       placed a particular focus on pipeline                   such as lighting, temperature and
techniques, manufacturers, for instance,                     operations and safety. Using existing                   the position of window shades—can
could boost their productivity by as                         asset data integrated with digital                      then be adjusted, depending on data
much as 30 percent.4                                         visualizations, analytics and shared                    readings, to maximize efficiency.7
Industrial companies are addressing                          situational intelligence, pipeline operators
                                                             can respond to potential events even                    Although improving existing operations
their needs for better efficiency and                                                                                through Big Data analytics is highly
profitability in two major categories:                       faster. This helps prioritize maintenance
                                                             tasks, resource allocation and capital                  valuable, it is, relatively speaking, low-
asset and operations optimization.                                                                                   hanging fruit. Moving up the maturity
In the area of assets, it’s clear that                       spend more effectively based on risk
                                                             assessment.                                             curve are solutions that go beyond
Industrial companies are progressing                                                                                 being proactive to being predictive. For
in creating financial value by gathering                     The movement toward more                                example, a petrochemical producer
and analyzing vast volumes of machine                        sophisticated use of analytics is also                  can rely on predictive maintenance
sensor data. Additionally, some                              exemplified by fuel consumption                         to avoid unnecessary shutdowns
companies are progressing to leverage                        solutions in the airline industry. Fuel is              and keep products flowing. Apache
insights from machine asset data to                          typically the largest operating expense                 Corporation, an oil and gas exploration
create efficiencies in operations and                        for an airline; over the past 10 years,                 and production company, is using
drive market advantages with greater                         fuel costs have risen an average of 19                  this approach to predict onshore
confidence.                                                  percent per year. The ability to reduce                 and offshore oil pump failures to help
                                                                                                                     minimize lost production. Executives

Determine how your company compares with others in your industry along the
Industrial Internet maturity curve by engaging with the Industrial Internet Evaluator*
(gesoftware.com/IIEvaluator). See how others in your field are leveraging Big Data
analytics for connecting assets, monitoring, analyzing, predicting and optimizing
for business success.
* Note: Use of the Industrial Internet Evaluator is subject to the terms at the website referenced here.

4. “Industry 4.0: Huge potential for value creation          5. G. P. Sullivan, R. Pugh, A. P. Melendez and W. D.    7. “Accenture Analytics and Smart Building
waiting to be tapped,” Deutsche Bank Research, May           Hunt, “Operations & Maintenance Best Practices: A       Solutions are helping Seattle boost energy efficiency
23, 2014.                                                    Guide to Achieving Operational Efficiency, Release      downtown,” Accenture, http://www.accenture.com/
                                                             3.0,” Pacific Northwest National Laboratory, US         SiteCollectionDocuments/PDF/Accenture-Analytics-
                                                             Department of Energy, August 2010.                      and-Smart-Building-Solutions.pdf.

                                                             6. Press release, “Accenture to Help Thames Water
                                                             Prove the Benefits of Smart Monitoring Capabilities,”
                                                             March 6, 2014.

                                                                                        10
“We’d like to take a more holistic approach to
 asset maintenance—to look at an asset from
the point at which it went into service across
the entire lifecycle, leveraging analytics to
 improve operations.”
Matt Fahnestock, Vice President IT Service Delivery,
NiSource Inc., Columbia Pipeline Group

“If you generate a small savings on each
flight, it translates to big savings at the end
of the year. Even a 1 percent savings can
translate into millions of dollars.”
Jonathan Sanjay, Regional Fuel Efficiency Manager,
Air Asia Berhad

“Using Big Data analytics can be powerful. It
moves us beyond being reactive and allows
industries to predict and prevent. There
are challenges with disparate data sources
and varying levels of quality. At Johnson
Controls, we are building an infrastructure
to standardize and manage information.
Ultimately, we want to be able to leverage
predictive analytics to prevent and solve
problems, while continuously improving
processes.”
Craig Williams, Vice President, Quality,
Johnson Controls Power Solutions

          11
at Apache claim that if the global Oil
and Gas industry improved pump
                                                          throughput metrics. The hospital
                                                          was able to achieve shorter lag              But are companies
performance by even 1 percent, it
would increase oil production by half
                                                          time between discharge and patient
                                                          pickup, with most ambulatory patients        up for the
a million barrels a day and earn the
industry an additional $19 billion a
                                                          on their way in about 30 minutes.
                                                          These achievements are all the more          challenge?
year.8                                                    impressive given that the hospital’s         Are companies ready for more
                                                          census continues to rise: the average        predictive and innovative kinds of
The Healthcare industry provides                          patient age is 74 years, and 85 percent
another example. Technology tools are                                                                  value-creating solutions? The answer
                                                          of admissions come through the ED.10         here is mostly “Not yet,” but they are
enabling providers to collect health data
in real time and then use advanced                        As companies master these higher-level       actively positioning themselves for
predictive analytics techniques to help                   capabilities they can also move into         such solutions. Asked to describe their
uncover what will likely happen next.                     innovative, revenue-generating services.     current capabilities in Big Data analytics,
By proactively measuring, monitoring                      For example, a global energy company         almost two-thirds of respondents (65
and managing this data, providers                         is leveraging analytics to analyze tens      percent) are focusing on monitoring—
can improve care management and                           of thousands of data points in a wind        the ability to monitor assets to identify
address risk factors and symptoms                         farm every second. With this data,           operating issues for more proactive
of chronic disease early and provide                      the company can use data science             maintenance. Fifty-eight percent
positive reinforcement in new and more                    to direct a set of performance dials         have capabilities such as connecting
effective ways.                                           and levers (speed, torque, pitch, yaw,       equipment to collect operating data and
                                                          aerodynamics and turbine controls) to        analyzing the data to produce insights.
One leading US health system’s work                       fine-tune a wind turbine’s operation and     However, only 40 percent can predict
in infection, or sepsis, management                       help enhance its energy production. By       based on existing data, and fewer still
is an example of the power of                             having this level of control with a single   (36 percent) can optimize operations
predictive analytics to improve clinical                  turbine and leveraging this technology       from that data. (See Figure 5.)
delivery. Recognizing that sepsis is a                    across a farm, energy providers can
leading driver of in-hospital mortality,                                                               This finding is validated by other
                                                          gain up to 5 percent improvement             responses. When asked about their
the medical center defined early                          on power output. With an integrated
leading indicators of sepsis and used                                                                  progress in managing business
                                                          approach to providing fuel-powered           operations, only one-fourth said they
technology tools to monitor patients to                   energy as well as renewable-produced
drive earlier diagnosis and intervention.                                                              had predictive capabilities and only 17
                                                          energy, analytics can contribute directly    percent indicated the ability to optimize.
The new initiative saved hundreds of                      to new revenue streams with positive
lives and has saved millions of dollars                                                                In a separate query, when asked about
                                                          bottom-line impact.                          capabilities on the analytics spectrum
for the health system.9
                                                          Intelligence-based services are likely       from connect to monitor to analyze
In another case, a leading Florida-                       to be the answer to “What’s next?” in        to predict to optimize, answers were
based hospital and medical center                         the realm of information technology          primarily on the lower end of that
used real-time tracking and analytics                     and the Industrial Internet. One of          spectrum. Only 13 percent indicated the
to optimize patient flow, cutting                         the executives we surveyed as part           ability to optimize, and 16 percent said
emergency department (ED) wait times                      of our research offered an insight           they did not fall on the spectrum at all.
by 68 percent. Upon admission, an ED                      behind the urgency to implement more         (See Figure 6.) However, connecting,
patient receives a tag. A dashboard                       innovative kinds of Industrial Internet      monitoring and analyzing are necessary
then begins monitoring the patient’s                      solutions based on Big Data analytics:       precursors to development of predictive
journey, combining patient Real-Time-                     information technology needs to move         models and optimization capabilities,
Location System (RTLS), interfaces                        from the era of automation-based             so the advancement of industrial
and bed placement timestamps to                           savings alone to an era of intelligence      companies along the maturity curve
evaluate and display real-time patient                    and value creation.                          positions them to take advantage of
                                                                                                       more sophisticated Big Data analytics.

8. Scott MacDonald and Whitney Rockley, “The
Industrial Internet of Things,” McRock Capital.

9. “Better Than a Crystal Ball: The Power of Prediction
in Clinical Transformation,” Accenture, http://www.
accenture.com/SiteCollectionDocuments/PDF/Accenture-
Cystal-Ball-Power-Clinical-Transformation.pdf.

10. “Losing the Wait,” GE Healthcare.

                                                                             12
Figure 5: Current Big Data analytics capabilities are stronger in the areas of monitoring and connecting equipment than in
               Figure 5:
              predicting issues and optimizing operations
               In my company, our current capabilities around Big Data analytics include the ability to: (Multiple responses.)
               In my company, our current capabilities around Big Data analytics include the ability to (multiple responses):

                      Monitor equipment/assets to identify operating
                                                                          65%
                             issues for more proactive maintenance
                                Connect equipment/assets and collect
                                                                          58%
                                                     operating data

                               Analyze data to provide useful insights    58%

                          Gain operational insights that lead to better
                                                                          57%
                                                             decisions
                Consolidate and correlate data from various sources
                                            to feed analytic insights     48%

                                        Predict based on existing data    40%

                                 Optimize (e.g., operations, workforce
                                                                          36%
                                     efficiencies, business decisions)

                                                                 Other    0%

                                                    None of the above 2%

              Source: GE Insights, September 2014                                                Base: Total sample; n=254
                                                                                Q10

              Figure 6: Companies’ Big Data capabilities are strongest in the area of analysis
Figure 6:
On averageOn  average
           across       across where
                  the company, the company,    where do
                                     do your company's Bigyour
                                                           Datacompany’s    big data
                                                               analytics capabilities fallanalytics  capabilities
                                                                                           on the spectrum below? fall on the spectrum below?

                                                                                  35%

                                                           19%
                       18%
                                                                                                                                16%
                                                                                                    13%

                    Connect                                 Monitor             Analyze          Predict                     Optimize

Source: GE Insights, September 2014

                                                    Figure 5                            13
Moving                                      However, when asked about future
                                            plans, respondents stated their

from today’s                                priorities in more ambitious plans
                                            for analytics: increasing profitability

implementation                              (60 percent), gaining a competitive
                                            advantage (57 percent) and improving

to tomorrow’s                               environmental safety and emissions
                                            (55 percent) represent more pervasive,

strategies                                  cross-functional and sophisticated use
                                            of analytics.
What does the roadmap of surveyed           Breaking this out by business line,
companies look like in the next one         individual strategies emerge for
to three years? Understandably, initial     particular industries. As shown in
investments are focused in areas            Figure 7, survey respondents were
representing the connection and             asked what their top three priorities
monitoring of assets for improving the      were for the next one to three years.
ability to react and repair and move        The shaded areas indicate the
toward zero unplanned downtime.             highest-ranked priorities, by industry.

Figure 7: Top business priorities, by industry

   Highest-ranked priorities

                                                    Power             Power
 Priorities: 1-3 years         Aviation   Wind      Generation        Distribution   Oil & Gas   Rail   Manufacturing Mining
 Increase profitability        61%        71%       56%               59%            56%         67%    58%           55%
 through improved resource
 management
 Gain a competitive edge       58%        55%       53%               69%            50%         50%    76%           48%

 Improve environmental         39%        61%       50%               75%            59%         43%    52%           58%
 safety and emissions
 Gain insights into customer   58%        61%       47%               56%            38%         60%    70%           39%
 behaviors, preferences and
 trends
 Gain insights into            55%        48%       34%               56%            47%         73%    67%           39%
 equipment health for
 improved maintenance
 Drive operational             42%        48%       41%               72%            44%         53%    55%           64%
 improvements and
 workforce efficiencies
 Create new business           45%        61%       34%               53%            47%         40%    52%           58%
 opportunities with new
 revenue streams
 Meet or exceed regulatory     32%        39%       41%               63%            50%         33%    39%           39%
 compliance

                                                                 14
“In the ’60s through the ’90s, railroads
focused on manpower reduction by
automating processes. But we have greatly
exhausted that. Now the focus is on better,
more informed and intelligent decisions. This
is coming from several directions, but top
down more so than bottom up.”
Fred Ehlers, Vice President - Information Technology,
Norfolk Southern Corporation

 The impact of unplanned downtime on industrial
 companies is real and significant. See how
 unplanned downtime impacts industries from
 Aviation, to Oil and Gas, to others. Learn more.
 (gesoftware.com/the-power-of-ge-predictivity)

           15
Big Data analytics in
the healthcare industry:
A diagnosis
Among the healthcare organizations we surveyed as
part of our Industrial Internet research, an overwhelming
majority acknowledged the critical role of analytics
in driving improved clinical, financial and operational
outcomes. These organizations feel that analytics will
have the power to dramatically improve patient outcomes,
even in the next year. However, challenges around system
barriers between departments, budgetary constraints and
organizational obstacles are impeding implementation of
their analytics initiatives.

“In the past, decision-making was more
straightforward—doctors simply made a decision based
on knowledge of the domain, personal experience, and
evaluation of the patient's physical signs and symptoms
plus relevant diagnostic laboratory data. Now there is
much more data available to the clinician that requires
additional expertise and having the right people with
the right skills to interpret the data—biostatistics,
epidemiology, health informaticists, other health
professional clinicians, and so forth. Caring for patients
is now a team activity, and learning to work in teams is
an important skill for physicians to acquire.”
Christopher C. Colenda, MD, MPH
President and Chief Executive Officer,
West Virginia United Health System

                                         16
Big Data analytics in the healthcare industry: A diagnosis

Positive outcomes                                  profitability as an important business
                                                   impact of analytics.
                                                                                             focused respondents (compared
                                                                                             with 25 percent of clinical-focused
More than half of the healthcare                                                             respondents) looked to the outcome
                                                   In the next one to three years, these     of predictive analytics that provides
executives surveyed believe that
                                                   healthcare organizations see Big          actionable insights into opportunities for
analytics can drive a variety of positive
                                                   Data analytics driving several kinds      operational improvements.
outcomes for their organizations,
                                                   of positive results in particular. The
including improved diagnostic speed
                                                   top outcome named (by 44 percent          Asked to name their best current
and confidence (named by 54 percent
                                                   of respondents) was the ability           capabilities around analytics, top
of respondents); reduction in patient
                                                   of analytics to integrate a view of       answers focused on improving
wait times and length of stay (56
                                                   clinical, financial and operational       patient flow (56 percent); tracking and
percent); and better clinical outcomes
                                                   data—data that is currently spread        monitoring the utilization of healthcare
and patient satisfaction scores (59
                                                   across multiple disparate systems.        equipment (49 percent); tracking
percent). Fifty-seven percent of
                                                   The second outcome cited was              clinical, financial and operational
respondents overall—and 66 percent
                                                   the ability to provide unified patient    measures (46 percent); and managing
of operations-focused respondents
                                                   records (38 percent). Perhaps not         workforce productivity and engagement
—named improved healthcare system
                                                   surprisingly, 34 percent of operations-   (46 percent). (See Figure 8.)

 Figure 8: Healthcare companies’ strongest capabilities in Big Data analytics
In my organization, our current capabilities around analytics include the ability to:
(Multiple responses)
                                                             56%
                                                             59%
                                    Improve patient flow     58%
                                                             49%
       Track and monitor the utilization and location of     51%
                                  healthcare equipment       46%
                                                             46%
        View/track clinical, financial, and/or operational   43%
                                  performance measures       48%
                                                             46%
      Manage workforce productivity and engagement           47%
                                                             50%
                                                             38%
                                   Streamline workflows      41%
                                                             36%
                                                             36%
 Optimize clinical procedures to drive improved patient      31%
                                             outcomes        40%
                                                             36%
       Connect information across the care continuum         37%
                                                             40%
                                                             33%
     Consolidate and correlate patient data for analytic     29%
                                                insights     38%
                                                             31%
Proactively manage defined patient groups/populations        33%
                                                             32%
                                                             31%
                         Optimize reimbursement rates        31%
                                                             30%
                                                             31%
      Benchmark performance against my peers across          27%
clinical, financial, and/or quality performance measures     34%
                                                                                                                       Overall (n=61)
                                                             26%
                               Minimize payment cycles       27%                                                       Clinical (n=51)
                                                             24%
                                                                                                                       Operations (n=50)

                                                                    17
Big Data analytics in the healthcare industry: A diagnosis

Shaking up the                                     leveraging analytics as a key strategy
                                                   over the past two to three years.
                                                                                             Expectations are
healthcare space                                   The threat of analytics-based             high and driven
Big Data analytics is both a                       competition is on the minds of
                                                   healthcare executives, especially if
                                                                                             by the Board
promise and a threat to healthcare
organizations. Three-fourths of                    they are unable to integrate analytics    Overwhelming percentages of
those surveyed believe that the use                into clinical and operating processes     healthcare respondents are already
of analytics has the power to drive                in the coming years. Seventy-four         seeing positive outcomes from their
a productivity transformation in                   percent of respondents named “losing      analytics investments. Ninety percent
healthcare in the next year, and 87                qualified talent to competitors” as one   or more of healthcare organizations
percent believe it will do so over the             of their three greatest fears; another    lay claim to having successfully
next three years.                                  70 percent were concerned that their      implemented analytics related to
                                                   financial performance would cease         improving patient outcomes and to
Eighty-four percent of respondents                 to be competitive in the marketplace;     driving improvements in operational
agree that healthcare providers who                two-thirds feared that other healthcare   efficiency.
adopt an analytics strategy in the                 providers would make greater
next one to three years will outpace               headway in providing the highest-         Relative to their competitors,
their peers in the marketplace. And                quality care. (See Figure 9.)             about one-third of healthcare
74 percent have seen competitors                                                             organizations (31 percent) claim
                                                                                             that they are significantly ahead of
                                                                                             the game in the area of analytics,
                                                                                             with another 39 percent saying they
                                                                                             are at least somewhat ahead of the
Figure 9: Top concerns of companies if they are unable to effectively integrate              competition. Only 2 percent of those
analytics capabilities                                                                       surveyed claim to be lagging behind
                                                                                             competitors in this area.
If we are unable to integrate analytics into our clinical and operating
process in the next one to three years, my top three fears are:                              A look at investment levels reveals
                                                                                             that healthcare companies are
     We will start to lose qualified
                                       74%                                                   not investing to the same level as
                                       75%                                                   industrial companies, but percentage
              talent to competitors    74%
                                                                                             of budget is still significant. About
                                       70%
                                                                                             half of all healthcare organizations
        Our financial performance
                                       69%                                                   surveyed (clinical, 53 percent;
       will cease to be competitive
                in the marketplace
                                       74%                                                   operations, 50 percent) are investing
                                                                                             from 11 to 20 percent of their
                                       66%                                                   overall technology budget on Big
   Other healthcare providers will     65%
make greater headway in providing      68%
                                                                                             Data analytics. About one-third are
          the highest-quality care                                                           investing more than 20 percent; by
                                                                                             contrast, 73 percent of industrial
                                       61%
  We will not be able to effectively   63%                                                   companies are investing at that level.
   integrate our operations across     60%                                                   (See Figure 10.) Asked to name their
             the continuum of care
                                                                                             top three challenges in implementing
                                       2%                                                    analytics solutions, the number one
          I don't believe there will   2%
                      be any impact                                                          response was “budget constraints
                                                                                             are slowing our analytics initiatives.”
                                       2%
    We have already implemented        2%
           our analytics strategy      2%
                                                           Overall (n=61)
                                              7%           Clinical (n=51)
                None of the above            6%
                                             6%            Operations (n=50)

                                                                       18
Big Data analytics in the healthcare industry: A diagnosis

Even if investment is not at the            Figure 10: Percentage of technology spend on analytics initiatives
same levels as industrial companies,
executive commitment is strong: Big        What portion of your overall technology expenditure is made in
Data analytics strategies are being        analytics initiatives?
driven from the very top of these
organizations. Eighty-two percent
of respondents said their Board of            Overall (n=61)                34%                    49%                      13%   3%

Directors is either a primary or strong
influencer of the analytics adoption
strategy at their company. Almost as          Clinical (n=51)             31%                     53%                       14%   2%
many (77 percent) named the CEO as
a driving force. By contrast, just 43
percent named the CIO as the strong        Operations (n=50)                34%                    50%                      12%   4%
or primary influencer and 52 percent
named the COO.

                                                                   More than 20%        11%-20%   5%-10%         Less than 5%
An urgency to
deliver improved
patient outcomes
Healthcare organizations, unlike
                                           Figure 11: Perceptions of competitors’ analytics capabilities
their industrial counterparts, have a
greater focus on how analytics can         My understanding of other healthcare providers with analytics capabilities is
drive better patient care rather than      that they: (Multiple responses)
as a means to achieve competitive
advantage. For example, the
perceived threat level posed by                                                   54%
                                                      Are gaining analytic
analytics-based competition appears                                               59%
                                                   insights in at least one
to be lower than among the industrial              area of their operations       58%
companies surveyed. There, recall,
74 percent said that their main                                                   52%
                                                       View analytics as a
competitors are leveraging Big Data             strategy to drive improved        51%
analytics proficiencies to differentiate                  patient outcomes        56%
their capabilities. Only 30 percent of
                                                    Have made significant         46%
healthcare executives had that fear.
                                                  progress in the ability to
                                                                                  49%
                                                   capture and store data
Instead, the power of analytics to
                                                     for analytic purposes        48%
improve patient outcomes was a
greater concern for respondents.                      Have publicly stated        44%
Over half (54 percent) stated that their            their intent to optimize      45%
competitors were already gaining                        their operations by
                                                       leveraging analytics       48%
analytic insights in operations and
52 percent thought their competitors                  Are leveraging their        30%
viewed analytics as a strategy to drive             analytics proficiencies
                                                            to differentiate      25%
patient outcomes. (See Figure 11.)

                                           figure 10
                                                           their capabilities     36%

                                                                                  7%
                                                  Have no visible plans to                              Overall (n=61)
                                                       leverage analytics         4%
                                                                                                        Clinical (n=51)
                                                                                  6%
                                                                                                        Operations (n=50)

                                                                   19
Big Data analytics in the healthcare industry: A diagnosis

The top three capabilities targeted for
development in the next three years
                                                     Talent issues                         shortfalls. Asked to rank their top three
                                                                                           challenges, only 23 percent named
indicate that healthcare organizations               What kinds of talent and              “Talent acquisition is impacting our
are planning on building out the                     competencies will be needed in the    ability to understand and realize the
infrastructure required for leveraging               analytics area in the coming years    potential from our collected data.”
analytics. Forty-four percent said they              to help healthcare organizations
are planning to build an integrated                                                        Perhaps related to this last point is
                                                     succeed? Two-thirds of respondents
data source for patient information;                                                       the fact that healthcare executives are
                                                     named patient-care analytics as the
43 percent noted plans to create an                                                        more open than their industrial peers
                                                     most important competency; 51
analytics platform for managing large                                                      to using external analytics providers.
                                                     percent cited software development/
volumes of data; and 41 percent                                                            Sixty-one percent intend to pursue
                                                     engineering. Far fewer (38 percent)
claimed to be developing analytics                                                         relationships with providers who are
                                                     cited the need for data scientists.
capabilities that drive improvements in                                                    experts in their industry and who
clinical performance. (See Figure 12.)               The surveyed executives seemed        can quickly translate their needs into
                                                     relatively unconcerned about talent   analytics solutions.

Figure 12: Top analytics capabilities targeted for development
Which of the following are you planning to build or grow over the next three years? (Multiple responses)

                      Integrated data source for    44%
                             patient information    45%
                                                    48%
                Analytics platform for managing     43%
                           large volumes of data    39%
                                                    48%
 Analytics capabilities that drive improvements     41%
                          in clinical performance   41%
                                                    44%
                  Automated tracking of patient     38%
                                 and staff flow     35%
                                                    40%
          Mobile capabilities for improved staff    34%
                     productivity/patient care      35%
                                                    36%
               Adoption of cloud-hosted model       34%
                         for analytics solutions    31%
                                                    36%
                   Automated external reporting     31%
             (e.g., CMS, Joint Commission, MU)      29%
                                                    30%
                                                    31%
             Tracking and reporting compliance      33%
                                                    34%
              Monitoring healthcare equipment       28%
                   to ensure greater availability   27%
                                                    32%
 Analytics capabilities that drive improvements     25%
                         in financial performance   24%
                                                    28%
 Analytics capabilities that drive improvements     25%
                      in operational performance    25%
                                                    22%
                                                    21%
                     Enterprise data warehouse      22%
                                                    20%                                    Overall (n=61)
                                                    18%                                    Clinical (n=51)
                 Automating manual workflows        16%
                                                    18%                                    Operations (n=50)

                                                                       20
Big Data analytics in the healthcare industry: A diagnosis

Driving better patient                              to realize their vision? With outcomes
                                                    such as better clinical results,
                                                                                                 “Being successful at Big
outcomes                                            improved profitability and improved          Data requires putting the
                                                    diagnostic confidence (see Figure
Healthcare organizations clearly                    13), it is unlikely that any provider will   foundational elements
understand the potential of analytics
to drive better patient outcomes
                                                    wish to lag in realizing the wide range      in place—allocation of
                                                    of benefits that analytics can deliver.
and operational efficiencies. With                                                               strategy, capital and
board-level support, will they be
able to translate that commitment
                                                                                                 mindshare.”
to budgetary support to build the                                                                David Hefner, CEO (retired), Georgia
right infrastructure, overcome system
                                                                                                 Regents Medical Center
barriers and attract the talent needed

Figure 13: Likely outcomes delivered by analytics capabilities
Outcomes most likely to be impacted by analytics in our organization include: (Multiple responses)

      Better clinical outcomes and patient   59%
               satisfaction/HCAHPS scores    61%
                                             62%
                                             57%
  Improved healthcare system profitability   55%
                                             66%
                                             56%
                 Reduction in patient wait
                                             55%
               times and/or length of stay
                                             54%
                                             54%
Improved diagnostic speed and confidence     57%
                                             54%
                                             51%
                   Increased clinician and
                                             53%
                    workforce productivity
                                             54%

     Reduction in operating costs through    49%
            improved asset management        51%
                                             54%

            Improved coordination across     48%
                     the care continuum      47%
                                             48%
                                             38%
             Reduction in cost of treating   39%
                         chronic disease
                                             38%

                Reduction in preventable     33%                                                             Overall (n=61)
               adverse events (e.g., HAIs)   31%                                                             Clinical (n=51)
                                             34%
                                                                                                             Operations (n=50)

                                                                      21
Becoming an
Industrial Internet
value creator
What can companies do today to start
advancing their Industrial Internet capabilities
on the maturity curve to more value-
creating activities? Based on our research
and experience, here are several actions to
consider.

                                            22
Invest in end-to-                                  have end-to-end security in place
                                                   against cyber-attacks and data leaks.
                                                                                                    • Enforce security throughout
                                                                                                      the supply chain. Incorporate

end security                                       This has enterprise-wide implications.
                                                   The tools and technologies
                                                                                                      robustness testing, and require
                                                                                                      security certifications in the
The value of the Industrial Internet               leveraged to protect company-                      procurement process to ensure
is being brought to bear due to                    wide assets would be appropriate                   vendor alignment.
the confluence of a multitude                      in the operational technology (OT)
                                                   environment as well. IT and OT leaders           • Utilize mitigation devices
of technology enablers such as                                                                        designed specifically for
cloud, mobility, Big Data and                      responsible for security policies
                                                   related to the Industrial Internet should          Industrial Control Systems
analytics. These technologies,                                                                        (ICS). Use the same effort given to
while innovative and even game-                    consider the following best practices:
                                                                                                      the IT side to ensure ICS-specific
changing, nevertheless can expose                  • Assess the risks and consequences.               protections against industrial
a company to security risks. Indeed,                 Use experts to evaluate and fully                vulnerabilities and exploits on the
the survey found that one of the top                 understand vulnerabilities and                   OT side.
three challenges to deliver on the                   regulations to prioritize the security
promise of the Industrial Internet                   budget and plan.                               • Establish strong corporate buy-
is security (35 percent). Security                                                                    in and governance. Gather internal
is of special concern in the Power                 • Develop objectives and goals.                    champions, technical experts,
Generation sector. (See Figure 14.)                  Set the plan to address the most                 decision-makers and C-level
                                                     important systems with the biggest,              executives to ensure funding and
Although these results are not                       most impactful and immediate risks.              execution of industrial security best
surprising, what is concerning is that                                                                practices.
less than half (44 percent) feel they

Figure 14: Percentage of companies, by industry, that named security as a top three challenge
Security concerns are impacting our ability to implement a wide-scale Big Data initiative

          Aviation     32%

             Wind      39%

 Power Generation      31%

Power Distribution     44%

        Oil & Gas      34%

              Rail     37%

   Manufacturing       27%

           Mining      39%

                     Security concerns are impacting our ability to implement a wide-scale big data initiative

                                                                         23
Break down the                                                enterprise. More prevalent are initiatives
                                                              in a single operations area (16 percent)
                                                                                                           Data management itself will be a core
                                                                                                           skill set. Industrial companies report

barriers to data                                              or in multiple but disparate areas (47
                                                              percent).
                                                                                                           that a vast amount of the time is
                                                                                                           consumed by accessing, cleansing,

integration                                                   The lack of an enterprise-wide analytics
                                                                                                           manipulating and consolidating
                                                                                                           machine data before data scientists
                                                              vision and operating model often             can examine the resulting datasets
Asked to name the top three                                   results in pockets of unconnected
challenges faced in implementing Big                                                                       to create predictive models. Breaking
                                                              analytics capabilities, redundant            down organizational, data and system
Data analytics initiatives, the answer                        initiatives and, perhaps most important,
most frequently appearing (36 percent)                                                                     silos will be both a requirement and an
                                                              limited returns on analytics investments.    outcome of companies implementing
was “System barriers between                                  New technologies such as data lakes,
departments prevent collection and                                                                         their Big Data strategies—at least
                                                              combined with Industrial Internet            the ones that hope to generate the
correlation of data for maximum                               capabilities, enable operators to funnel
impact.” In addition, for 29 percent of                                                                    greatest benefits from their efforts.
                                                              sensor data from various networked
executives, a top-three challenge was                         machines onto a single platform. From        Given these findings about data silos,
in the consolidation of disparate data                        there, massively parallel processing         it is not surprising that our survey data
and being able to use the resulting data                      capabilities analyze the data as a           shows a move toward centralization
store. (See Figure 15.)                                       unified whole rather than as a billion       of the analytics function in one
All in all, only about one-third of                           separate bits of information, each with      way or another. For 50 percent of
companies (36 percent) have adopted                           its own individual file path.11              executives surveyed, their companies
Big Data analytics across the                                                                              are moving toward either an overall

Figure 15: Top challenges in implementing Big Data initiatives, overall
Please rank the top three challenges your organization faces in implementing Big Data initiatives:
    Security concerns are impacting our ability to implement               14%
                             a wide-scale Big Data initiative              35%
             Consolidation of disparate data and being able to             13%
                      use the resulting data store are difficult           29%
System barriers between departments prevent collection and                 12%
                    correlation of data for maximum impact                 36%
                                                                           9%
     Quality and cost of collecting machine data are a barrier
                                                                           23%
               The integrity of the network for transmission of            9%
               massive amounts of data is not reliable enough              28%
 Talent acquisition is impacting our ability to understand and             8%
                  realize the potential from our collected data            26%
                        Organizational barriers prevent effective          7%
                            cross-departmental use of analytics            22%
                                                                           6%
            Poor data quality or lack of confidence in the data
                                                                           26%
                   Budget constraints are slowing our Big Data             6%
                                             analytic initiatives          26%
                                                                           5%
        Reliance on multiple vendors–no integrated solutions
                                                                           20%

                                                                Other      0%

                        We are not experiencing any challenges             10%                                               Top
                                    with our Big Data initiatives          10%                                               Top 3

11. Ideas Lab, “Trawling for Big Insight in the ‘Industrial Data Lake,’”
GE Ideas Lab Staff, August 15, 2014.

                                                                                 24
“We aren’t planning to just aggregate Big Data.
We are gearing up to re-engineer the business
 around this capability.”
Matt Fahnestock, Vice President, IT Service Delivery, NiSource Inc.,
Columbia Pipeline Group

“The biggest challenge was the data itself—
to get it to the right system and to the right
 person. It seems so easy for data to be
transferred, but it’s not—it’s much more
 difficult than people realize.”
 Jonathan Sanjay, Regional Fuel Efficiency Manager, Air Asia

          25
“We had more data                    centralized group to manage Big Data
                                     analytics initiatives or a coordinating      Focus on talent
than we could actually               group within the IT function. Half of
                                                                                  acquisition and
                                     these companies (49 percent) also
 use; it was good to
 have this information,
                                     intend to appoint a Chief Analytics
                                     Officer responsible for business and         development
                                     implementation strategies concerning
 but you need resources              analytics. (See Figure 16.)
                                                                                  Executives surveyed are aware of their
                                                                                  own talent shortfalls in the area of Big
to analyze the data,                 Bringing the OT and IT divisions             Data analytics and the critical nature
                                                                                  of sourcing and developing the talent
 derive best practices               together to deliver on the value of the
                                     Industrial Internet will be key to driving   needed to succeed in these areas.
 and generate new                    maximum benefits. Only 26 percent            About half of those surveyed note
                                                                                  that they have talent gaps in several
                                     are considering merging the IT and
 flight plans. There were            OT organizations to deliver analytic         critical areas including analyzing data,
                                                                                  interpreting results, and gathering and
 not enough resources                solutions. IT/OT convergence is an
                                     important objective because:                 consolidating disparate data. (See
to interpret the data.”                                                           Figure 17.)
                                     • Two of the top three challenges as
Jonathan Sanjay, Regional Fuel         discovered in the survey—system            Hiring talent with the expertise
                                       barriers between departments               needed is the most obvious remedy
Efficiency Manager, Air Asia
                                       (36 percent) and disparate data            to the talent gap issue, named by 63
                                       (29 percent)—would be greatly              percent of survey respondents. Yet
                                       mitigated by being addressed by            the fact is that there won’t be enough
“Once standards                        jointly held OT/IT responsibility. The     experienced talent to go around in this
 and practices are                     OT executives would have clear             burgeoning area of Big Data analytics.
                                                                                  Indeed, shortages in the number of
                                       visibility of operational processes,
 established, it                       data stores and usage, while the           data scientists are projected, as well
                                                                                  as the number of managers capable of
 becomes easier and                    IT executives would have the
                                       line of sight to new technologies          using Big Data analyses to make good
 more manageable                       that would help mitigate the data          decisions. Another option favored by
                                                                                  55 percent of executives is to partner
                                       integration issues.
to carry out higher                                                               with organizations such as universities
                                     • As other organizations leverage            to groom the talent needed. This is
 and higher levels of                  asset data outside of the Operations       an option being used at one of the
 IT/OT integration that                arena, such as in Finance for capital      aviation companies surveyed.
                                       management purposes, the need for
 go beyond time and                    consolidation and business-focused         The use of skilled external talent—
                                                                                  experts in an industry who can quickly
 costs savings to value                analytics to derive valuable insights
                                       will drive the OT/IT convergence.          translate business needs into analytics
 creation via data                                                                solutions—is also an option favored
                                                                                  by more than half of respondents (54
visibility and agile                                                              percent). (See Figure 18.)

 availability of data.”                                                           When it comes to retaining talent for
                                                                                  augmenting internal skills, industry
IT and Operational Technology                                                     knowledge is key to success (30
Alignment Innovation Key                                                          percent), exceeding analytics talent
Initiative Overview,                                                              alone (24 percent). However, it is those
Kristian Steenstrup,                                                              who hold both industry knowledge
                                                                                  and analytics skills who would be
Vice President and Gartner Fellow,
                                                                                  preferred by most industrial companies
Gartner Research, 26 March 2014                                                   (43 percent). (See Figure 19.)

                                                         26
Figure 16: Anticipated organizational changes to implement Big Data analytics
Which of the following organizational changes have occurred or do you expect will occur to support your company’s
use of analytics? (Multiple responses)

A centralized group will be formed that manages all Big
                                Data analytic initiatives    50%

          A group within our IT organization will lead all
                                      analytic initiatives   50%

            A Chief Analytics Officer will be appointed,
      responsible for our business and implementation        49%
                              strategy around analytics

         A group within our OT (operations technology)
              organization will lead analytics initiatives   29%

       Our IT and OT organizations will either merge or
                                                             26%
                        jointly lead analytics activities

                                      None of the above      3%

Figure 17: Talent gaps in the area of Big Data analytics
In which of the following areas do you have gaps in your talent? (Multiple responses)

                                         Analyzing data      56%

                                     Interpreting results    48%

            Gathering and consolidating disparate data       48%

    "Bridge" roles (people with ability to analyze data,
                                                             42%
                           interpret and tell the story)

                                             Storytelling    28%

                                                   Other     0%

                                We have no talent gaps       9%

                                              figure 16
                                                                   27
Figure 18: Strategies to fill talent gaps in Big Data analytics
Which of the following will your company pursue to ensure you have the talent needed to fulfill
your Big Data analytics strategy?

            Hire new talent with the expertise we need      63%

     Partner with organizations that have the talent we
          need (e.g., universities, specialty companies,    55%
                                     technology vendors)
       Work with Big Data analytics providers who are
      experts in our industry that can quickly translate    54%
                        our needs to analytic solutions

      Use existing in-house resources (provide training
                                           as needed)       46%

            Acquire companies with Big Data analytics
                                            expertise       39%

Work with Big Data analytics providers who have deep
expertise in the field of Big Data analytics and Big Data   33%
        processing (but not necessarily in my industry)

                                                   Other    0%

   When choosing an outside consultant for a Big Data Analytics initiative, I would choose
Figure 19: Desired qualities in outside consultants in Big Data analytics
   someone with:
When choosing an outside consultant for a Big Data analytics initiative, I would choose someone with:

               24%
                                                                     30%
                                                                                     Deep industry knowledge
                                      73%

       3%                                                                            Both industry knowledge and analytics
                                                                     43%             experience

   Analytics cross-industry experience                N/A, we do not hire outside consultants
                                                      for Big Data analytics initiatives

                                  figure 18
                                                                              28
“Talent acquisition is difficult in the
 maintenance-engineering world. Experience
 is a good thing, but Qantas has a relationship
with universities where we recruit double
 degree majors: aero engineering and
 computer programming. Personally, I feel
that this is the best combination of skills.”
Bertrand Masson, Manager Aircraft Performance
and Fleet, Qantas Airways Ltd.

         29
“When it comes to incident response, our
 regulators are requesting that we review
 and plan with so much more information
than before. It’s now in one data mart; all
 of the information is available and will
 play a large role in saving us time and
 money in the whole regulatory process.”
Matt Fahnestock, Vice President, IT Service Delivery,
NiSource Inc., Columbia Pipeline Group

                                                        30
Consider new                               Begin by asking: “What product-
                                           service hybrids beyond remote
                                                                                       It is also important to think about
                                                                                       tomorrow’s partner ecosystem.

business models                            monitoring and predictive asset
                                           maintenance resonate with our
                                                                                       Companies will work with partners
                                                                                       and suppliers to create and deliver

needed to be                               customers and our customers’
                                           customers? What product, service and
                                                                                       services as well as reach potential new
                                                                                       customers. Think of the partnering

successful with the                        value can we deliver to clients? How
                                           prepared are we to accelerate our
                                                                                       taking place among farm equipment,
                                                                                       fertilizer and seed companies, and

Industrial Internet                        move toward a services-and-solutions
                                           business model? How do we develop
                                                                                       weather services, and the suppliers
                                                                                       needed to provide IT, telecom,
                                           and add the talent we need to be            sensors, analytics and other products
To begin moving up the maturity curve
                                           successful?”                                and services. Ask: “Which companies
of Industrial Internet solutions, it is
                                                                                       are also trying to reach my customers
important to think boldly about the        Focus on rapidly progressing to             and my customers’ customers? What
new business models needed. As             predictive analytics and optimization       other products and services will talk to
noted earlier, digital services based      capabilities to derive the greatest value   mine, and who will make, operate and
on Big Data analytics capabilities         from operational insights.                  service them? What capabilities and
represent an important evolution of the    Asset Performance Management                information does my company have
Industrial Internet. Some companies        (APM), at its baseline level, delivers      that they need? How can we use this
are already converting products into       value to industrial companies by            ecosystem to extend the reach and
product-service hybrids—intelligent        monitoring availability and performance     scope of our products and services
physical goods capable of producing        of assets across the entire enterprise.     through the Industrial Internet?”
data for use in digital services. These    However, predicting equipment
services enable companies to create        outages for proactive action, before a
hybrid business models, combining          catastrophic event can occur, results
the benefits of operational efficiency     in significantly greater value with
with recurring income streams from         increased overall productivity. Taking it
digital services. These digital services   to the next level–optimization–will allow
will also enable firms in resource-        for greater insights that can be used
extracting and process industries to       for business trade-offs. For example,
make better decisions, enjoy better        with complete visibility into output from
visibility along the value chain and       a fleet of power generation plants, an
improve productivity in other ways.        energy trader can execute optimal
                                           transactions in the market, leading to
                                           greater profitability.

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