Skip to content

Posts tagged ‘Analytics’

10 Ways Analytics Are Accelerating Digital Manufacturing

  • 42% of manufacturers say big data and analytics as their highest priority in 2015.
  • 56% of power distribution providers rank big data and analytics within their top three priorities for 2015.
  • 61% of aviation companies consider big data and analytics their highest priority this year.

Bottom line: Digital manufacturing strategies are gaining ground as manufacturers adopt big data and analytics to improve operational effectiveness, time-to-market, new product development and increase product quality and reliability.

Analytics Are Fueling Digital Manufacturing Growth

Big data and analytics adoption by manufacturers is the first step many are taking to create a galvanized, intelligent digital thread that unifies every aspect of their value chains. For aerospace manufacturers whose supply chains are exceptionally complex, big data and analytics are revolutionizing value chains starting with suppliers and progressing through all operations.

The majority of manufacturers are relying on analytics to improve order accuracy, shipment & cycle time performance, and product quality. Those excelling at digital manufacturing strategies are gaining additional analytical insights into how they can make decisions more accurately, quicker and with lower potential costs and risks.

The manufacturing industry generates more data than any other sector of the global economy on a consistent basis.   The more complex a given manufacturers’ operations are, the more valuable the insights gained from big data and analytics. The following comparison of big data analytics priorities by industry from a recent speech given by Jeff Immelt, CEO and President of General Electric illustrates this point:

analytics customer survey

Source: GE Minds and Machines Presentation, Jeff Immelt, CEO & President, General Electric.

10 Ways Analytics Are Accelerating Digital Manufacturing 

The ten ways analytics is accelerating digital manufacturing adoption globally include the following:

  • Providing real-time operator intelligence (70%), remote monitoring and diagnostics (66%), and condition-based maintenance (59%) are the three most valuable areas for analytics GE customers mentioned in a recent survey. GE’s industrial customers are looking to tailor pre-built applications that can deliver the eight different functional areas shown in the graphic below.  Manufacturers are looking to asset performance management as an integral part of their digital thread’s analytics and insight.

industrial customer perspective

Source: GE Minds and Machines Presentation, Jeff Immelt, CEO & President, General Electric.

  • Using data modeling to improve production workflows is improving Earnings Before Interest & Taxes (EBIT) by 55% for a chemical manufacturer.  Using analytics and data modeling to make more accurate,  efficient decisions encompassing making or buying ingredients, choosing to substitute an ingredient or not, optimizing equipment usage and/or reliability and gaining incremental sales through increased production capacity is leading to a significant improvement in EBIT for a leading chemical manufacturer on a consistent basis.  The following graphic provides insights into the contributions of each factor in improving EBIT performance.

EBIT Growth

Source: Taming manufacturing complexity with advanced analytics. McKinsey & Company by Patrick Briest, Valerio Dilda, and Ken Somers February 2015. 

  • Planning-execution integration in production centers and real-time production integration are two areas where analytics are having the greatest impact on manufacturers’ operating expenses (OPEX). When analytics are integrated as part of a digital manufacturing strategy, supply chains benefit when Web-EDI (Electronic Data Interchange) and real-time order conformation are implemented and analyzed for continual improvement.

Digital initaitves impact

Source: Operational Excellence through Digital in Manufacturing Industries. Capgemini Consulting.

  • Optimization tools (56%), demand forecasting (53%), integrated business planning (48%) and supplier collaboration & risk analytics (46%) are being rapidly adopted by manufacturers today, setting the foundation for digital manufacturing growth.  Deloitte recently interviewed supply chain executives regarding the thirteen fastest-moving technical capacities they are using today and expect to use in the future. The following graphic provides an overview of supply chain capabilities current in use and what percent of each they expect to use in the future.

use of supply chain capabilities

Source: Supply Chain Talent of the Future Findings from the third annual supply chain survey. Deloitte.  2015.

  • Analytics is integral to making the vision of Industrie 4.0 a reality. Industrie 4.0 is a German government initiative that promotes automation of the manufacturing industry with the goal of developing Smart Factories. Analytics is extensively used in manufacturing centers who are in the process of reengineering their entire operations to attain Industrie 4.0 compliance. Manufacturing value chains in highly regulated industries that rely on German suppliers and manufacturers are also relying on analytics extensively to guide their Industrie 4.0 journey. A recent Deloitte study of Industrie 4.0 adoption found that research and development (43%) will see the greatest transformational contribution from Industry 4.0.

Industry 4.0 areas

Source: Industry 4.0: Challenges and solutions for the digital transformation and use of exponential technologies. Deloitte Consulting, 2015

  • Analytics is enabling manufacturers to also scale real time cloud-based operational intelligence, condition-based monitoring, monitoring & diagnostics and asset lifecycle management across global manufacturing centers.  Capturing, aggregating, analyzing and taking action on analytics across all production centers using the GE Predix Cloud will also accelerate digital manufacturing growth over time.  Integrating analytics, industrial and sensor data into a scalable series of data models and apps delivered as a Platform-as-a-Service (PaaS), GE will make this service commercially available in 2016.  The following graphic illustrates how complex manufacturers could use Predix Cloud to improve operational efficiency and quality.

horizontal capability controls

Source: Jeff Immelt Presentation on Pivot Strategy, December 16, 2014

  • Analytics is providing greater insights into product, process, program and service quality, forcing manufacturers to revamp existing production centers and make them more efficient.  Gaining greater insight into which production centers and factories are delivering the highest quality products and why is now possible.  The vision of unifying quality across an enterprise quality management and compliance (ECQM) framework is now a reality, driving greater digital manufacturing growth as a result. The following graphic from Tableau is an example of a manufacturing quality dashboard.

Mfg quality dashboard

Source: Manufacturing Analytics Quality Dashboard

  • Increasing production yields through the use of more effective supplier quality management and bill of material (BOM) planning integrated within production processes.  Analytics is extensively being used today for supplier audits, supplier quality management and traceability. Capitalizing on the full value of these analytics is a strong catalyst for manufacturers to move closer to digitizing their operations.
  • Using analytics to predict machine failures before they occur reduces downtime, production costs and increase customer satisfaction.  In highly regulated industries production equipment is periodically audited and reviewed for conformance to specific standards.  Integrating even the simplest sensor into production equipment can deliver valuable insights into what factors cause it to fail.  Analytics are providing Failure Mode and Effects Analysis (FMEA) in real-time today, providing manufacturers with a glimpse into which equipment and machinery will most likely fail when. Knowing this can save literally millions of dollars in lost production time.
  • Adopting Pareto Analysis to continually improve schedule, quality and cost performance to the cell or production center level is driving digital manufacturing adoption.  Determining which factors are enhancing or reducing product, process and program quality is now possible using advanced manufacturing analytics. Differentiating between the many symptoms of a quality problem and its root cause is now becoming possible, especially for companies pursuing digital manufacturing strategies.

Additional sources of information on the impact of analytics on digital manufacturing:


2015 Roundup Of Analytics, Big Data & Business Intelligence Forecasts And Market Estimates

  • NYC SkylineSalesforce (NYSE:CRM) estimates adding analytics and Business Intelligence (BI) applications will increase their Total Addressable Market (TAM) by $13B in FY2014.
  • 89% of business leaders believe Big Data will revolutionize business operations in the same way the Internet did.
  • 83% have pursued Big Data projects in order to seize a competitive edge.

Despite the varying methodologies used in the studies mentioned in this roundup, many share a common set of conclusions. The high priority in gaining greater insights into customers and their unmet needs, more precise information on how to best manage and simplify sales cycles, and how to streamline service are common themes.

The most successful Big Data uses cases revolve around enterprises’ need to get beyond the constraints that hold them back from being more attentive and responsive to customers.

Presented below is a roundup of recent forecasts and estimates:

  • Wikibon projects the Big Data market will top $84B in 2026, attaining a 17% Compound Annual Growth Rate (CAGR) for the forecast period 2011 to 2026. The Big Data market reached $27.36B in 2014, up from $19.6B in 2013. These and other insights are from Wikibon’s excellent research of Big Data market adoption and growth. The graphic below provides an overview of their Big Data Market Forecast.  Source: Executive Summary: Big Data Vendor Revenue and Market Forecast, 2011-2026.

Wikibon big data forecast

  • IBM and SAS are the leaders of the Big Data predictive analytics market according to the latest Forrester Wave™: Big Data Predictive Analytics Solutions, Q2 2015. The latest Forrester Wave is based on an analysis of 13 different big data predictive analytics providers including Alpine Data Labs, Alteryx, Angoss Software, Dell, FICO, IBM,, Microsoft, Oracle, Predixion Software, RapidMiner, SAP, and SAS. Forrester specifically called out Microsoft Azure Learning is an impressive new entrant that shows the potential for Microsoft to be a significant player in this market. Gregory Piatetsky (@KDNuggets) has done an excellent analysis of the Forrester Wave Big Data Predictive Analytics Solutions Q2 2015 report here. Source: Courtesy of Predixion Software: The Forrester Wave™: Big Data Predictive Analytics Solutions, Q2 2015 (free, no opt-in).

Forrester Wave Big Data Predictive Analytics

  • IBM, KNIME, RapidMiner and SAS are leading the advanced analytics platform market according to Gartner’s latest Magic Quadrant. Gartner’s latest Magic Quadrant for advanced analytics evaluated 16 leading providers of advanced analytics platforms that are used to building solutions from scratch. The following vendors were included in Gartner’s analysis: Alpine Data Labs, Alteryx, Angoss, Dell, FICO, IBM, KNIME, Microsoft, Predixion, Prognoz, RapidMiner, Revolution Analytics, Salford Systems, SAP, SAS and Tibco Software, Gregory Piatetsky (@KDNuggets) provides excellent insights into shifts in Magic Quadrant for Advanced Platform rankings here.  Source: Courtesy of RapidMinerMagic Quadrant for Advanced Analytics Platforms Published: 19 February 2015 Analyst(s): Gareth Herschel, Alexander Linden, Lisa Kart (reprint; free, no opt-in).

Magic Quadrant for Advanced Analytics Platforms

  • Salesforce estimates adding analytics and Business Intelligence (BI) applications will increase their Total Addressable Market (TAM) by $13B in FY2014. Adding new apps in analytics is projected to increase their TAM to $82B for calendar year (CY) 2018, fueling an 11% CAGR in their total addressable market from CY 2013 to 2018. Source: Building on Fifteen Years of Customer Success Salesforce Analyst Day 2014 Presentation (free, no opt in).

Salesforce Graphic

  • 89% of business leaders believe big data will revolutionize business operations in the same way the Internet did. 85% believe that big data will dramatically change the way they do business. 79% agree that ‘companies that do not embrace Big Data will lose their competitive position and may even face extinction.’ 83% have pursued big data projects in order to seize a competitive edge. The top three areas where big data will make an impact in their operations include: impacting customer relationships (37%); redefining product development (26%); and changing the way operations is organized (15%).The following graphic compares the top six areas where big data is projected to have the greatest impact in organizations over the next five years. Source: Accenture, Big Success with Big Data: Executive Summary (free, no opt in).

Big Data Big Success Graphic

Frost & Sullivan Graphic


global text market graphic


  • Customer analytics (48%), operational analytics (21%), and fraud & compliance (21%) are the top three use cases for Big Data. Datameer’s analysis of the market also found that the global Hadoop market will grow from $1.5B in 2012 to $50.2B in 2020, and financial services, technology and telecommunications are the leading industries using big data solutions today. Source: Big Data: A Competitive Weapon for the Enterprise.

Big Data Use Cases in Business

  • 37% of Asia Pacific manufacturers are using Big Data and analytics technologies to improve production quality management. IDC found manufacturers in this region are relying on these technologies to reduce costs, increase productivity, and attract new customers. Source: Big Data and Analytics Core to Nex-Gen Manufacturing.

big data in manufacturing

  • Supply chain visibility (56%), geo-location and mapping data (47%) and product traceability data (42%) are the top three potential areas of Big Data opportunity for supply chain management. Transport management, supply chain planning, & network modeling and optimization are the three most popular applications of Big Data in supply chain initiatives. Source: Supply Chain Report, February 2015.

Big data use in supply chains

  • Finding correlations across multiple disparate data sources (48%), predicting customer behavior (46%) and predicting product or services sales (40%) are the three factors driving interest in Big Data analytics. These and other fascinating findings from InformationWeek’s 2015 Analytics & BI Survey provide a glimpse into how enterprises are selecting analytics applications and platforms. Source: Information Week 2015 Analytics & BI Survey.

factors driving interest in big data analysis

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

Ten Ways Big Data Is Revolutionizing Manufacturing

quality1McKinsey & Company recently published How Big Data Can Improve Manufacturing which provides insightful analysis of how big data and advanced analytics can streamline biopharmaceutical, chemical and discrete manufacturing.

The article highlights how manufacturers in process-based industries are using advanced analytics to increase yields and reduce costs. Manufacturers have an abundance of operational and shop floor data that is being used for tracking today.  The McKinsey article shows through several examples how big data and advanced analytics applications and platforms can deliver operational insights as well.

The following graphic from the article illustrates how big data and advanced analytics are streamlining manufacturing value chains by finding the core determinants of process performance, and then taking action to continually improve them:

Advanced Analytics Big Data in Manufacturing

Big Data’s Impact on Manufacturing Is Growing

In addition to the examples provided in the McKinsey article, there are ten ways big data is revolutionizing manufacturing:

  • Increasing the accuracy, quality and yield of biopharmaceutical production.  It is common in biopharmaceutical production flows to monitor more than 200 variables to ensure the purity of the ingredients as well as the substances being made stay in compliance. One of the many factors that makes biopharmaceutical production so challenging is that yields can vary from 50 to 100% for no immediately discernible reason. Using advanced analytics, a manufacturer was able to track the nine parameters that most explained yield variation. Based on this insight they were able to increase the vaccine’s yield by 50%, worth between $5M to $10M in yearly savings for the single vaccine alone.
  • Accelerating the integration of IT, manufacturing and operational systems making the vision of Industrie 4.0 a reality. Industrie 4.0 is a German government initiative that promotes automation of the manufacturing industry with the goal of developing Smart Factories. Big data is already being used for optimizing production schedules based on supplier, customer, machine availability and cost constraints. Manufacturing value chains in highly regulated industries that rely on German suppliers and manufacturers are making rapid strides with Industrie 4.0 today.  As this initiative serves as a catalyst to galvanize diverse multifunctional departments together, big data and advanced analytics will become critical to its success.
  • Better forecasts of product demand and production (46%), understanding plant performance across multiple metrics (45%) and providing service and support to customers faster (39%) are the top three areas big data can improve manufacturing performance.   These findings are from a recent survey LNS Research and MESA International completed to see where big data is delivering the greatest manufacturing performance improvements today. You can find the original blog post here.

LNS Graphic

  • Integrating advanced analytics across the Six Sigma DMAIC (Define, Measure, Analyze, Improve and Control) framework to fuel continuous improvement.  Getting greater insights into how each phase of a DMAIC-driven improvement program is working, and how the efforts made impact all other areas of manufacturing performance is nascent today. This area shows great potential to make production workflows more customer-driven than ever before.
  • Greater visibility into supplier quality levels, and greater accuracy in predicting supplier performance over time.  Using big data and advanced analytics, manufacturers are able to view product quality and delivery accuracy in real-time, making trade-offs on which suppliers receive the most time-sensitive orders.  Managing to quality metrics becomes the priority over measuring delivery schedule performance alone.
  • Measuring compliance and traceability to the machine level becomes possible. Using sensors on all machinery in a production center provides operations managers with immediate visibility into how each is operating. Having advanced analytics can also show quality, performance and training variances by each machine and its operators.  This is invaluable in streamlining workflows in a production center, and is becoming increasingly commonplace.
  • Selling only the most profitable customized or build-to-order configurations of products that impact production the least.  For many complex manufacturers, customized or build-to-order products deliver higher-than-average gross margins yet also costs exponentially more if production processes aren’t well planned.  Using advanced analytics, manufacturers are discovering which of the myriad of build-to-order configurations they can sell with the most minimal impact to existing production schedules to the machine scheduling, staffing and shop floor level.
  • Breaking quality management and compliance systems out of their silos and making them a corporate priority.  It’s time for more manufacturers to take a more strategic view of quality and quit being satisfied with standalone, siloed quality management and compliance systems.  The McKinsey article and articles listed at the end of this post provide many examples of how big data and analytics are providing insights into which parameters matter most to quality management and compliance. The majority of these parameters are corporate-wide, not just limited to quality management or compliance departments alone.
  • Quantify how daily production impacts financial performance with visibility to the machine level. Big data and advanced analytics are delivering the missing link that can unify daily production activity to the financial performance of a manufacturer.  Being able to know to the machine level if the factory floor is running efficiently, production planners and senior management know how best to scale operations.  By unifying daily production to financial metrics, manufacturers have a greater chance of profitably scaling their operations.
  • Service becomes strategic and a contributor to customers’ goals by monitoring products and proactively providing preventative maintenance recommendations.  Manufacturers are starting to look at the more complex products they produce as needing an operating system to manage the sensors onboard. These sensors report back activity and can send alerts for preventative maintenance. Big data and analytics will make the level of recommendations contextual for the first time so customers can get greater value.  General Electric is doing this today with its jet engines and drilling platforms for example.

Additional sources of information on Big Data in Manufacturing:


84% Of Enterprises See Big Data Analytics Changing Their Industries’ Competitive Landscapes In The Next Year

NYC Skyline87% of enterprises believe Big Data analytics will redefine the competitive landscape of their industries within the next three years. 89% believe that companies that do not adopt a Big Data analytics strategy in the next year risk losing market share and momentum.

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

Big Data Analytics Now Seen As Essential For Competitive Growth

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

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

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

Key take-aways from the study include the following:

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

Figure 1 big data investments

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

Figure 2 industry overview

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

Figure 3 Unable to Implement

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

Figure 4 big data monitoring

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

Figure 5 industry priorities

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

Figure 6 challenges for big data analytics


The Top 100 Enterprise Analytics Startups Of 2014

public-cloud-computing-forecast-2011-2016With the potential of removing legacy IT silos and freeing up valuable data to gain greater insights into their operations, enterprises continue to invest heavily in analytics.

Providing powerful analytics tools to business analysts who can get to work immediately on complex challenges instead of having to wait for ITs’ often over-committed resources is also driving analytics market growth. The better a business unit or division gets at understanding their own business, the faster they change their future.

Analytics Are Streamlining Industry Value Chains

Across every area of an enterprise, from supply chains, quality, manufacturing, marketing, services and pricing, analytics are making an impact daily.  IDC forecast that the advanced and predictive analytics software market will grow from $2.2B in 2013 to $3.4B in 2018, attaining a 9.9% compound annual growth rate (CAGR).  Wikibon’s excellent analysis of the Big Data market projects a $28.5B market in 2014, growing to $50.1B in 2015. For additional forecasts please see my post Roundup Of Analytics, Big Data & Business Intelligence Forecasts And Market Estimates, 2014.

Admit It: Analytics Startups Are the Sexiest Of All

The field of analytics is proliferating with entirely new approaches to solve very challenging, difficult problems, removing the barriers that held business analysts and the divisions they work for from accomplishing more.

That’s what makes analytics startups the sexiest of all. With the insights these companies are capable of delivering you can completely change your approach to marketing, selling, service, supply chains, pricing, service and over time reach an entirely new level of performance. There are many excellent startups in this arena and I’ve been tracking many of them out of personal interest for years.

Tracking Analytics Startups

Having seen just how much pain there is in enterprises trying to get the data they need to better manage their business units, divisions and departments, I’ve tracked many of the analytics startups mentioned in the list below.  Using manually-based methods to track their funding rounds and momentum in the market proved incomplete.

To gain a greater insight into analytics startups I signed up for a free first month trial of Mattermark (opt in).  Mattermark uses a combination of artificial intelligence and data quality analysis to provide insights into over 500,000 companies, over 125,000 with employee data, and over 90,000 funding events.  It’s a fascinating company that has created many new metrics for tracking momentum of startups on specific metrics and key performance indicators (KPIs) including their own Mattermark score.  This score is not meant to provide guidance on which startup to invest in.  Rather it’s a measure of momentum across the metrics and KPIs that Mattermark measures.   Their service is easy to use, powerful in the insight it delivers, and produced the following list of 100 enterprise analytics startups, ranked by total funding in the table below.  You can download the table here in Microsoft Excel format as well.

Top 100 Enterprise Analytics Startups Infographic


Roundup Of Analytics, Big Data & Business Intelligence Forecasts And Market Estimates, 2014

NYC SkylineFrom manufacturers looking to gain greater insights into streamlining production, reducing time-to-market and increasing product quality to financial services firms seeking to upsell clients, analytics is now essential for any business looking to stay competitive.  Marketing is going through its own transformation, away from traditional tactics to analytics- and data-driven strategies that deliver measurable results.

Analytics and the insights they deliver are changing competitive dynamics daily by delivering greater acuity and focus.  The high level of interest and hype surrounding analytics, Big Data and business intelligence (BI) is leading to a proliferation of market projections and forecasts, each providing a different perspective of these markets.

Presented below is a roundup of recent forecasts and market estimates:

  • The Advanced and Predictive Analytics (APA) software market is projected from grow from $2.2B in 2013 to $3.4B in 2018, attaining a 9.9% CAGR in the forecast period.  The top 3 vendors in 2013 based on worldwide revenue were SAS ($768.3M, 35.4% market share), IBM ($370.3M, 17.1% market share) and Microsoft ($64.9M, 3% market share).  IDC commented that simplified APA tools that provide less flexibility than standalone statistical models tools yet have more intuitive graphical user interfaces and easier-to-use features are fueling business analysts’ adoption.  Source:
  • A.T. Kearney forecasts global spending on Big Data hardware, software and services will grow at a CAGR of 30% through 2018, reaching a total market size of $114B.  The average business expects to spend $8M on big data-related initiatives this year. Source: Beyond Big: The Analytically Powered Organization.
  • Cloud-based Business Intelligence (BI) is projected to grow from $.75B in 2013 to $2.94B in 2018, attaining a CAGR of 31%.  Redwood Capital’s recent Sector Report on Business Intelligence  (free, no opt in) provides a thorough analysis of the current and future direction of BI.  Redwood Capital segments the BI market into traditional, mobile, cloud and social business intelligence.   The following two charts from the Sector Report on Business Intelligence  illustrate how Redwood Capital sees the progression of the BI market through 2018.

redwood capital global intelligence market size

  • Enterprises getting the most value out of analytics and BI have leaders that concentrate more on collaboration, instilling confidence in their teams, and creating an active analytics community, while laggards focus on technology alone.  A.T. Kearney and Carnegie Mellon University recently surveyed 430 companies around the world, representing a wide range of geographies and industries, for the inaugural Leadership Excellence in Analytic Practices (LEAP) study.  You can find the study here.  The following is a graphic from the study comparing the characteristics of leaders and laggards’ strategies for building a culture of analytics excellence.

leaders and laggards2

  • The worldwide market for Big Data related hardware, software and professional services is projected to reach $30B in 2014.  Signals and System Telecom forecasts the market will attain a Compound Annual Growth Rate (CAGR) of 17% over the next 6 years.  Signals and Systems Telecom’s report forecasts Big Data will be a $76B market by 2020.  Source:
  • Big Data is projected to be a $28.5B market in 2014, growing to $50.1B in 2015 according to Wikkbon.  Their report, Big Data Vendor Revenue and Market Forecast 2013-2017 is outstanding in its accuracy and depth of analysis.  The following is a graphic from the study, illustrating Wikibon’s Big Data market forecast broken down by market component through 2017.

Big Data Wikibon

  • SAPIBMSASMicrosoftOracle, Information Builders, MicroStrategy, and Actuate are market leaders in BI according to Forrester’s latest Wave analysis of BI platforms.  Their report, The Forrester Wave™: Enterprise Business Intelligence Platforms, Q4 2013 (free PDF, no opt in, courtesy of SAS) provides a thorough analysis of 11 different BI software providers using the research firm’s 72-criteria evaluation methodology.
  • Amazon Web Services, Cloudera, Hortonworks, IBM, MapR Technologies, Pivotal Software, and Teradata are Big Data Hadoop market leaders according to Forrester’s latest Wave analysis of Hadoop Solutions.  Their report, The Forrester Wave™: Big Data Hadoop Solutions, Q1 2014 (free PDF, no opt in, courtesy of MapR Technologies) provides a thorough analysis of nine different Big Data Hadoop software providers using the research firm’s 32-criteria evaluation methodology.
  • IDC forecasts the server market for high performance data analysis (HPDA) will grow at a 23.5% compound annual growth rate (CAGR) reaching $2.7B by 2018.  In the same series of studies IDC forecasts the related storage market will expand to $1.6B also in 2018. HPDA is the term IDC created to describe the formative market for big data workloads using HPC. Source:
  • Global Big Data technology and services revenue will grow from $14.26B in 2014 to $23.76B in 2016, attaining a compound annual growth rate of 18.55%.  These figures and a complete market analysis are available in IDC’s Worldwide Big Data Technology and Services 2012 – 2016 Forecast.  You can download the full report here (free, no opt-in): Worldwide Big Data Technology and Services 2012 – 2016 Forecast.

big data analytics by market size

  • Financial Services firms are projected to spend $6.4B in Big Data-related hardware, software and services in 2015, growing at a CAGR of 22% through 2020.  Software and internet-related companies are projected to spend $2.8B in 2015, growing at a CAGR of 26% through 2020.  These and other market forecasts and projections can be found in Bain & Company’s Insights Analysis, Big Data: The Organizational Challenge.  An infographic of their research results are shown below.

Big-Data-infographic-Bain & Company

potential payback of big data initiatives

Cloud Predictive Analytics Most Used To Gain Customer Insight

AnalyticsUsing analytics to better understand customer satisfaction, profitability, retention and churn while increasing cross-sell and up-sell are the most dominant uses of cloud-based analytics today.

Jim Ericson and James Taylor presented the results of Decision Management Solutions’ cloud predictive analytics survey this week in the webinar Predictive Analytics in the Cloud 2013 – Opportunities, Trends and the Impact of Big Data.  The research methodology included 350 survey responses, with a Web-based survey used for data collection.  The survey centered on the areas of pre-packaged cloud-based solutions, cloud-based predictive modeling, and cloud deployment of predictive analytics.  You can see a replay of the webinar at this link.

Key takeaways of the study results released during the webinar include the following:

  • Customer Analytics (72%), followed by supply chain, business optimization, marketing optimization (57%), risk and fraud (52%), and marketing (58%) are the areas in which respondents reported the strongest interest.
  • When the customer analytics responses were analyzed in greater depth they showed most interest in customer satisfaction (50%) followed by customer profitability (34%), customer retention/churn (32%), customer management (30%), and cross-sell/up-sell (26%).
  • Adoption was increasingly widespread and growing, with over 90% of respondents reporting that they expected to deploy one or more type of predictive analytics in the cloud solution.
  • Industries with the most impact from predictive analytics include retail (13% more than average), Financial Services (12%) and hardware/software (4%). Lagging industries include health care delivery (-9%), insurance -11%) and (surprisingly) telecommunications (-33%).  The following graphic illustrates the relative impact of cloud-based predictive analytics applications by industry.

Adoption of Cloud-based Predictive Analytics by Industry

  • The most widespread analytics scenarios include prepackaged solutions (52%), cloud-based analytics modeling (47%) and cloud-based analytic embedding of applications (46%).  Comparing the 2011 and 2013 surveys showed significant gains in all three categories, with the greatest being in the area of cloud-based analytic modeling.  This category increased from 51% in 2011 to 75% in 2013, making it the most likely analytics application respondents are going to implement this year.

Comparison of Analytics Applications Most Likely To Deploy, 2011 versus 2013

  • 63% of respondents report that when predictive analytics are tightly integrated into operations using Decision Management, enterprises have the intelligence they need to transform their businesses.

Impact of Predictive Analytics Integration Across The Enterprise

  • Data security and privacy (61%) followed by regulatory compliance (50%) are the two most significant concerns respondent companies have regarding predictive analytics adoption in their companies.  Compliance has increased as a concern significantly since 2011, probably as more financial services firms are adopting cloud computing for mainstream business strategies.

Concerns of Enterprises Who Are Using Cloud-based Predictive Analytics Today

  • Internal cloud deployments (41%) are the most common approach to implementing central cloud platforms, followed by managed vendor clouds (23% and hybrid clouds (23%). Private and managed clouds continue to grow as preferred platforms for cloud-based analytics, as respondents seek greater security and stability of their applications.  The continued adoption of private and managed clouds are a direct result of respondents’ concerns regarding data security, stability, reliability and redundancy.

Approach To Cloud Deployment

  • The study concludes that structured data is the most prevalent type of data, followed by third party data and unstructured data.
  • While there was no widespread impact on results from Big Data, predictive analytics cloud deployments that have a Big Data component are more likely to contribute to a transformative impact on their organizations’ performance.  Similarly those with more experience deploying predictive analytics in the cloud were more likely to use Big Data.
  • In those predictive analytics cloud deployments already operating or having an impact, social media data from the cloud, voice or other audio data, and image or video data were all much more broadly used as the following graphic illustrates.

Which Data Types Deliver The Most Positive Impact In A Big Data Context

Making Analytics Pay In The Enterprise

global-analytics-300x2001With analytics and big data being so heavily hyped today, it is ironic the majority of business analysts often lack access to data and tools they need.

But things are changing with the next generation of analytics software coming to market.  A recent study by The Economist, “Big Data and the Democratisation of Decisions,” shows the severity of the big data analytics problem and which departments need the most support: customer service, human resources, marketing, strategy and business development.  The following is an infographic based on the study’s key findings. To be clear, all companies mentioned in this post are not and never have been clients of mine or companies I have worked for.

Unleashing Greater Insight in the Enterprise

The real analytics payoff in the enterprise begins when business analysts can achieve customer and market insights faster than their competitors.  In the consumer packaged goods industry, every week counts in a new product launch and product lifecycle.  In healthcare, lag times in customer service lead to patients seeking more responsive treatment alternatives.  The net result in each is lost revenue.

Analytics applications and platforms are increasingly being designed for self-service and the needs of business analysts first.  Instead of having to rely on IT for analytics, big data and advanced statistical analysis support, business analysts need to be able to complete projects on their own. Analytics applications are advancing quickly on this self-service dimension, making it possible for business analysts to get complex projects done in a fraction of the time it would have taken IT to staff and complete them.

Alliances and partnerships between analytics software providers are focused on getting business analysts the tools they need so they don’t have to rely on IT so much to get their work done.  The recent partnership announced between Alteryx and Revolution Analytics puts R-based predictive analytics directly in the hands business analysts is a case in point.

What’s noteworthy about this partnership above all others is the option it gives enterprises to integrate big data and other 3rd party sources into a common system of engagement. Business analysts can then use tools to design analytics and reporting workflows that align and stay in step with line-of-business needs over time.

alteryx-gallery1-300x1691Once an application or workflow is complete, business analysts can publish and distribute their analytics applications enterprise-wide. The Alteryx Analytics Gallery (shown to the right) gives customers the opportunity to share their analytics applications with each other.  The gallery is helping business analysts learn from each other, serving as a catalyst for broader analytic consumption.

This is the same model ServiceNow (NYSE:NOW) has been so successful with in the area of IT Service Management.  I attended Knowledge13 earlier this year and found their customer base to be one of the most enthusiastic I’ve ever met.  What ServiceNow has done IT Service Management, Alteryx is on its way to accomplishing in analytics.

Why All This Matters For Customers

Getting analytics applications and tools in the hands of business analysts significantly improves the customer experience and reduce errors at the same time. At Kaiser Permanente, business analysts focus on cost saving projects that improve customer service.

Kaiser has a continual stream of customer interactions across multiple channels going on daily.  Supported by legacy IT systems, Microsoft Excel spreadsheets and manual processes to keep the entire system working, the healthcare provider was seeing patient satisfaction levels drop as they didn’t have a clear view of their customers.  The legacy and manual systems also made coordinating customer service teams very difficult and replicating analytics tools very difficult.


Kaiser Permanente was able to aggregate and cleanse the myriad of data sources they rely on and gain greater insights into their customer’s needs. Creating analytics and reporting workflows that business analysts and lean leaders in their Service Organization use to stay on top of customer needs has led to a five-fold increase in customer service performance according to Greg Hall, Senior Service Optimization Leader.

Best- And Worst-Performing Cloud Computing Stocks In The First Half Of 2013

Cloud computing stocks continue to show wide variation in performance throughout the first half of this year.

Ten of the twenty companies in the Cloud Computing Stock Index delivered returns to shareholders with NetSuite leading with a 37.30% share gain, delivering $13,730 on $10,000 invested on January 2, 2013.

To more fully define the stock performance of these companies, I’ve added Earnings Per Share (EPS), Price/Earnings Ratio, Year-To-Date (YTD) Total Gains or Loss, Annualized Gain or Loss, and Total Dollar Value of $10,000 invested on January 2, 2013.  You can download the latest version of the Cloud Computing Stock Index here.  The filter applied to these companies is that 50% or more of their revenues are generated from cloud-based applications, infrastructure and services.  Additional details of the index are provided at the end of this post.


Best Performing



(1/2/13 – 7/5/13)Total Gain or Loss

Annualized Gain or Loss

Total Dollar Value of $10K invested in this stock on Jan. 2, 2013 as of July 5th:

NetSuite Inc





Keynote Systems, Inc.





CA, Inc.





Workday Inc





Cisco Systems, Inc.





Symantec Corporation




$11,884.00, Inc.






Worst Performing



(1/2/13 – 7/5/13)Total Gain or Loss

Annualized Gain or Loss

Total Dollar Value of $10K invested in this stock on Jan. 2, 2013 as of July 5th:

Rackspace Hosting, Inc.





Fusion-IO, Inc.





F5 Networks, Inc.





VMware, Inc.





Riverbed Technology…





Red Hat, Inc.





Key Take-Aways:

  • NetSuite leads the index with a 37.3% gain in their stock price, and $10K invested in their stock on January 2nd of this year would be worth $13,730 as of July 5th.  Cloud-based Enterprise Resource Planning (ERP) systems acceptance is accelerating, evidenced by the success NetSuite is having with their two-tier ERP strategy and recent announcement they are moving into manufacturing.  Their recent alliance with Oracle also shows upside potential.   A cloud-based ERP provider leading the index is good news for Acumatica and Plex Systems especially, the leader in cloud-based ERP systems for manufacturing and one of the most enthusiastic customer bases in enterprise software.  Both of these companies are privately held or they would have been included in the index.
  • The 20 companies that comprise the Cloud Computing Stock Index attained a 29.6% return from July 10, 2012 to July 5, 2013.  The Dow Jones Industrial Average (DJIA) gained 18.83%;  Microsoft, 14.02%; Oracle, 7.17%; and SAP, 27.51%.  The following chart compares the performance of each. Please click on the index to expand it for easier viewing.

  • Widespread adoption of Amazon Web Services, success using the Kindle series of tablets as customer acquisition tools for digital content, market leadership of the online retail landscape, and successful pilots of the AmazonFresh online grocery business in Los Angeles and Seattle are all fueling Amazon’s stock performance this year.

Specifics on the Cloud Computing Stock Index

I used The Cloud Times 100 as the basis of the index, and included the 20 following companies, all of which are publically traded.  The latest edition of the Cloud Computing Stock Index is shown here.  Please click on the index to expand it for easier viewing.

 Note: I do not hold equity positions or work for any of the companies mentioned in this blog post or included in the Cloud Computing Stock Index.  

What’s Hot In CRM 2013: Strong Interest In Mobile For Streamlining Sales And Service

Whats Hot in CRM 2013 imageGartner published the report What’s Hot in CRM Applications in 2013, by Ed Thompson on June 20, 2013.  The report covers areas of interest by clients in the four areas of marketing, sales, customer service and e-commerce.

The report states that “the 2013 What’s Hot list was compiled after examining Gartner inquiry volumes by topic. It was then supplemented by asking all Gartner CRM analysts to offer their opinions on what has been generating the most interest during all the client inquiries they have taken since the end of 2012 and in the beginning of 2013.”

Big data, cloud, social, mobile and the Internet of Things are the five catalysts that are driving inquiries in the hottest areas of interest.  Gartner’s Ed Thompson, author of the report, states that “this is where our clients’ interests lie, although not their current CRM spending.”  Technologies highlighted in red are the hottest in terms of interest, shown in the following table Highest CRM Application Priorities for 2013.

What This Says About the Future of CRM

Mobility is just one part of delivering an excellent customer experience.

  • It is surprising that Gartner clients aren’t looking to create a more unified strategy to customer experience across all channels at all times. As the report states, “The refreshing of an aging agent desktop with a new, more intelligent and unifying user interface has shot to the top of the heat charts once more.” The findings of this Gartner analysis make the highly promoted claims of usability by many CRM vendors look overly hyped.  I think usability is the fastest path to greater system adoption of any CRM system, and that has to include mobile.  It is surprising that a related technology in this area didn’t rise farther in the rankings.
  • Second, mobile sales on smartphones and tablets dominate, followed immediately by Social – Internal Collaboration and Social – Integration with Social Data. What is fascinating about this group of four top items in Sales is the indication that the behavior of how sales teams work individually and together is changing fast. Collaboration is a strong catalyst for Return on Investment (ROI) from social technologies and the sequence of these priorities in Sales underscores that.
  • Third, the vision of the mobile-enabled support representative able to be autonomous yet fully supported to solve customer problems is rapidly approaching.  Of all patterns emerging from this data, this is one shows the greatest profit potential.  Service Lifecycle Management (SLM) and the many forms of service management all have very significant profitability associated with them for manufacturers.  The quicker this area of mobility moves, the faster SLM and Maintenance, Repair and Overhaul (MRO) strategies will grow – giving manufacturers and service providers the ability to mine their installed bases for more profits.
  • Fourth, predictive analytics and big data are reordering how marketing strategies are designed, implemented and managed.  Given the increasing complexity of marketing automation systems and the strategies they support, predictive analytics and big data are starting to dominate the conversations I’ve personally had with Chief Marketing Officers (CMOs) and many demand generation professionals.  I expect the predictive analytics aspects of marketing, combined with big data, to accelerate quickly over the next year.
  • Fifth, the rapid adoption of mobile-based platforms including the Apple iPad in the Configure, Price, Quote (CPQ) continues throughout the professional services, discrete and process manufacturing companies I often visit.  One manufacturer I often work with on their CPQ strategies has the ability today to present a completed 3D model of the proposed product, embed it in a quote and e-mail it to the prospect all from an iPad.  The future of CPQ is going to be dominated by mobility and enterprise support for key order management, pricing and product configuration options.
 Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

Get every new post delivered to your Inbox.

Join 409 other followers

%d bloggers like this: