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Posts from the ‘Analytics’ Category

Salesforce On The State Of Analytics, 2015

  • analytics predictions 2015Between 2015 and 2020, the number of data sources analyzed by enterprises will jump 83%.
  • 9 out of 10 enterprise leaders believe analytics is absolutely essential or very important to their overall business strategies and operational outcomes.
  • 54% of marketers say marketing analytics is absolutely critical or very important to creating a cohesive customer journey.
  • High performing enterprises are 5.4x more likely than underperformers to primarily use analytics tools to gain strategic insights from Big Data.

These and many other interesting insights are from the 2015 State of Analytics study from Salesforce Research. Salesforce conducted the study in mid-2015, generating 2,091 responses from business leaders from enterprises (not limited to Salesforce customers). Geographies included in the study include the U.S., Canada, Brazil, U.K., France, Germany, Japan, and Australia.  While Salesforce is a leading provider of analytics, the report strives to deliver useful insights beyond just endorsing their product direction.

10 insights and predictions on the state of analytics include the following:

  • Between 2015 and 2020, the number of data sources analyzed will jump 83%. Salesforce Research found that the number of data sources actively analyzed by businesses has grown just 20% in the last five years. This is projected to accelerate rapidly, attaining a compound annual growth rate of 120% in the 10-year forecast period. High performing enterprises will be relying on a projected 50 different data sources by 2020, leading all performance categories tracked in the study.

data explosion

  • Relying on manual processes to get all the data in one view (53%) is one of the greatest challenges enterprises face today. Additional factors driving enterprises to integrate more data sources into their analytics applications include finding that too much data is left unanalyzed (53%), spending too much time updating spreadsheets (52%), and analysis is performance by business analysts, not end users of the data (50%).  All of these factors and those shown in the graphic below form the catalyst that is driving greater legacy, 3rd party and broader enterprise data integration into analytics applications.

lack of automation

  • 9 out of 10 enterprise leaders believe analytics is absolutely essential or very important to their overall business strategies and operational outcomes. In addition, 84% of high performers are projecting that the importance of analytics will increase substantially or somewhat in the next two years. 65% of all business leaders surveyed are predicting that the importance of analytics will increase substantially or somewhat in the next two years.

analytics is critical to driving business strategy

  • High performing enterprises are 4.6x more likely than underperformers to agree that data is driving their business decisions. In addition, 60% of high performing enterprises’ leaders agree with the statement that their organizations have moved beyond numbers keeping score to data driving business decisions. Salesforce Research also found that 43% of high performers rely on empirical data, developing hypotheses and then experimenting and observing the outcomes before making a decision.

data drives decisions

  • Driving operational efficiencies and facilitating growth (both 37%) are the two areas enterprises are initially focused on with analytics today.  Once analytics apps are delivering insights and are part of daily workflows, enterprises expand their use into optimizing operational processes (35%), identifying new revenue streams (33%) and predicting customer behavior (32%). The following graphic provides a comparison of the top ten use cases.

analytics every corner

  • High performance enterprises consistently analyze more than 17 different kinds of data across their analytics apps.  In contrast, underperforming organizations only analyze 10 different data sources, and moderate performers, 15. The following graphic provides an overview of the top ten most-used sources of data.

companies track a wide variety of data

  • High performers are 3.5x more likely than underperformers to extensively use mobile reporting tools to analyze data wherever they are. 55% of high performing enterprises are more likely to be extensively using mobile reporting tools to analyze data.  The following graphic compares mobile analytics adoption across high, medium and low performing enterprises.

top teams tap mobile analytics

  • Speed of deployment (68%), ease of use for business users (65%) and self-service and data discovery tools (61%) are the three top three priorities leaders place on selecting new analytics apps.  Mobile capabilities to explore and share data (56%) and cloud deployment (54%) are the fourth and fifth factors leaders mentioned.  The following graphic compares the decision factors that go into selecting an analytics app.

decision factor analytics app

  • Industries who have the greater analytics adoption today (over 50% of users active on apps and tools) include high tech (36%) and financial services (32%). Automotive (30%) and media & communications (30%) also have attained significant adoption.


  • High performing enterprises are 5.4x more likely than underperformers to primarily use analytics tools to gain strategic insights from Big Data. Leaders in high performance enterprises see the value of Big Data (76%) to a much greater extent than their lower performing counterparts (14%).   High performing enterprises are 3.1x more likely than underperformers to be confident in ability to manage data from internal systems, customers, and third parties.

Key Take-Aways From The 2015 Pacific Crest SaaS Survey

  • Cloud Computing M&A40% of SaaS companies are using Amazon Web Services (AWS) to deliver their apps today.
  • Median subscription gross margins for SaaS companies in 2015 are 78%.
  • Overall, SaaS companies are projecting median revenue growth of 46% in 2015.
  • Channel sales and inside sales strategies delivered the highest revenue growth rates in 2014.
  • Companies in the $5M – $7.5M range achieved 70% revenue growth in 2014, surpassing the median 36% growth rate last year.

These and many other insights are from the 2015 Pacific Crest SaaS Survey published by David Skok of Matrix Partners in collaboration with Pacific Crest Securities. You can download a free copy of Part I of the study here (PDF, opt-in, 72 pp). 305 SaaS companies were interviewed, 31% from international locations and 69% from North America.  David Skok and Pacific Crest Securities will publish Part 2 of the results in the near future. SaaS Metrics 2.0 – Detailed Definitions provides a useful reference for many of the SaaS metrics mentioned in the study.

This year’s survey attracted an eclectic base of respondents, with median revenues of $4M a year, with 133 companies reporting less than $5M, and 57 over $25M. Annual Contract Value (ACV) across all respondents is $21K, with 17% of respondents reporting ACVs over $100K.  Please see pages 3 & 4 of the study for a description of the methodology. Key take-aways from the study include the following:

  • SaaS GAAP revenue growth is accelerating in 2014 and is projected to increase further in 2015 from 44% to 46%. Median revenue growth in 2014 for all survey respondents was 44%, with the aggregate projected growth for 2015 reaching 46%. When SaaS companies with less than $2.5M in revenues are excluded, median GAAP growth was 35% in 2014 and is expected to reach that same level in 2015.

grow SaaS Revenue


  • SaaS companies with mixed customer strategies are growing at 57% a year.  Excluding respondent companies with less than $2.5M in revenues, a mixed customer strategy dominates all others. Concentrating on enterprises and small & medium businesses (SMBs) both drove 33% revenue growth of respondent companies this year.

median growth rate as a function of customer


  • 40% of SaaS companies are using Amazon Web Services (AWS) to deliver their apps today. AWS is projected to increase to 44% three years from now, with Microsoft Azure increasing from 3% today to 6% in 3 years.

SaaS Delivered


  • 41% of all SaaS companies surveyed rely primarily on field sales.  Factoring out the companies with less than $2.5M in revenue, field sales accounts for 32%.

primary mode of distribution


  • Field sales dominates as the most effective sales strategy when median deal sizes are $50K or more. In contrast, inside sales dominates $5K to $15K deal sizes, and the Internet dominates deal sizes less than $1K.  The following graphic provides insights into the primary mode of sales by median initial contract size.

mode by initial contract size


  • 16% of new Average Contract Value (ACV) sales is from upsells, with the largest companies being the most effective at this selling strategy. One of the strongest catalysts of a SaaS companies’ growth is the ability to upsell customers to a higher ACV, generating significantly greater gross margin in the process. SaaS companies with revenues between $40M to $75M increase their ACV by 32% using upsells. Larger SaaS companies with over $75M in sales generate 28% additional ACV with upsell strategies.

ACV Value


  • The highest growth SaaS companies are relying on upsells to fuel higher ACV.  There is a significant difference between the highest and lowest growth SaaS companies when it comes to upsell expertise and execution.  The following graphic provides an overview by 2014 GAAP revenue category of percent of ACV attributable to upsells.

fast upsell


  • 60% are driving revenues with “Try Before You Buy” strategies, with 30% generating the majority of their revenues using this approach.  On contrast, only 30% of companies generate revenues and ACV from freemium.


The Hottest Cloud-based Marketing Startups of 2015

  • What's Hot in CRM, 2012 Apttus, Booker, Lattice Engines, Segment and Tubular Labs are the five hottest cloud-based marketing startups of 2015.
  • 13 of the hottest 34 cloud-based marketing startups are from the Bay Area, followed by Los Angeles with 3, and Bangalore and New York, both with 2.
  • 14 are in Pre Series A, 7 in A-Stage, 5 in B-Stage and 3 in C-Stage funding rounds.

These and other insights are from a quick analysis completed today using Mattermark Pro, in response to reader requests for more research on marketing startups.

Mattermark uses a combination of artificial intelligence and data quality analysis to provide insights into over 1 million private companies, over 470,000 with employee data, and over 100,000 funding events. In the interest of full disclosure I’m not today and have never done any consulting work of any kind with Mattermark.

Finding The Hottest Cloud-based Marketing Startups

To find the hottest cloud-based marketing startups, an initial query requesting startups competing in the cloud computing and marketing industries was completed. Next, advanced query tools in Mattermark Pro were used to filter out all startups that had exited as indicated by their stage status in Mattermark’s data. This filtered out startups who had been acquired, completed an IPO or had exited through other means. The table below is the result of an analysis completed today with Mattermark data.  You can download the table here in Microsoft Excel format.

hottest cloud-based marketing startups

The Mattermark Growth Score shown in the table below and downloadable Excel file is a measure of how quickly a company is gaining traction at a given point in time. It incorporates the Mindshare Score (web traffic, social traction) as well as business growth metrics (e.g. employee count over time, funding). The underlying assumption is that companies who see growth across these signals are shipping product and talking to customers, and are more likely to continue to grow as a result. 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.

Businesses Adopting Big Data, Cloud & Mobility Grow 53% Faster Than Peers

  • London sykline duskOrchestrating big data, cloud and mobility strategies leads to 53% greater growth than peers not adopting these technologies.
  • 73% of midmarket companies say the complexity of their stored data requires big data analytics apps and tools to better gain insights from.
  • 41% of midmarket companies are using big data to better target marketing efforts.
  •  54% of midmarket companies’ security budgets are invested in security plans versus reacting to threats.

These and many other insights are from Dell’s second annual Global Technology Adoption Index (GTAI 2015) released last week in collaboration with TNS Research. The Global Technology Adoption Index surveyed IT and business decision makers of mid-market organizations across 11 countries, interviewing 2,900 IT and business decision makers representing businesses with 100 to 4,999 employees.

The purpose of the index is to understand how business users perceive, plan for and utilize four key technologies: cloud, mobility, security and big data. Dell released the first wave of its results this week and will be publishing several additional chapters throughout 2016. You can download Chapter 1 of the study here (PDF, no opt-in, 18 pp.).

Key take-aways from the study include the following:

  • Orchestrating big data, cloud and mobility strategies leads to 53% greater growth than peers not adopting these technologies. Midmarket organizations adopting big data alone have the potential to grow 50% more than comparable organizations. Effective use of Bring Your Own Device (BYOD) mobility strategies has the potential to increase growth by 53% over laggards or late adopters..

orchestrating tech for greater growth

  • 73% of North American organizations believe the volume and complexity of their data requires big data analytics apps and tools.  This is up from 54% in 2014, indicating midmarket organizations are concentrating on how to get more value from the massive data stores many have accumulated.  This same group of organizations believe they are getting more value out of big data this year (69%) compared to last year (64%).  Top outcomes of using big data include better targeting of marketing efforts (41%), optimization of ad spending (37%), and optimization of social media marketing (37%).

top outcomes

  • 54% of an organization’s security budget is invested in security plans versus reacting to threats. Dell & TNS Research discovered that midmarket organizations both in North America and Western Europe are relying on security to enable new devices or drive competitive advantage.  In North America, taking a more strategic approach to security has increased from 25% in 2014 to 35% today.  In Western Europe, the percentage of companies taking a more strategic view of security has increased from 26% in 2014 to 30% this year.

security strategic

  • IT infrastructure costs to support big data initiatives (29%) and costs related to securing the data (28%) are the two greatest barriers to big data adoption. For cloud adoption, costs and security are the two biggest barriers in midmarket organizations as is shown in the graphic below.

security costs

  • Cloud use by midmarket companies in France increased 12% in the last twelve months, leading all nations in the survey.  Of the 11 countries surveyed, France had the greatest increase in cloud adoption within midmarket companies.  French businesses increased their adoption of cloud applications and platforms from 70% in 2014 to 82% in 2015.

Sources: Dell Study Reveals Companies Investing in Cloud, Mobility, Security and Big Data Are Growing More Than 50 Percent Faster Than Laggards. October 13, 2015


2015 Big Data Market Update

big data market udpate

  • 42.6% of all big data apps developed for manufacturing are being created by enterprises today.
  • 38.2% of all big data and advanced analytics apps in use today are in customer-facing departments including marketing, sales, and customer service.
  • 33.2% of all big data and advanced analytics developers are concentrating on the software & computing industry.
  • 19.2% of big data app developers say quality of data is the biggest problem they consistently face when building new apps.

These and other insights are from the recently published report Big Data and Advanced Analytics Survey 2015, Volume I by Evans Data Corporation. The survey is based on 444 in-depth interviews with developers who are currently working with analytics and databases and are both currently working on and planning big data and advanced analytics projects. The survey’s results provide a strategic view of the attitudes, adoption patterns and intentions of developers in relation to big data and analytics. You can more on the methodology of the report here.

Key take-aways from the report include the following:

  • Software & computing (18%), financial (11.6%), manufacturing (10.9%) and retail (9.8%) industries have the highest percentage of programmers creating big data and analytics applications today.  Additional industries where big data app development is active and growing include entertainment (7.7%), telecommunications (7.5%), utilities & energy (6.6%) and healthcare (4.6%). The following graphic provides an overview of the industries addressed.

industries addressed

  • Capturing more information than traditional database practices (22.60%), capturing and analyzing unstructured data (21.10%) and the potential for visualizing or analyzing data differently (20.70%) are the three top use cases driving app development today.  Evans Data found that capturing more information than traditional database practices allow increased 6% since last year, making it the top use case in 2015. The following graphic provides the distribution of responses by use cases from the developers surveyed.

top three use cases

  • Total size of the data being processed (40.8%), complex, unstructured nature of the data (38.1%) and the need for real-time data analysis (17.7%) are the top three factors driving big data adoption over traditional database solutions.  Evans Data found that the size and complexity of structured and unstructured data is the catalyst that gets enterprises moving on the journey to big data adoption. The ability to gain greater insights into their data with descriptive, predictive and contextually-driven analytics is the fuel that keeps big data adoption moving forward in all companies.

reasons to move to big data

  • 33.2% of all big data and advanced analytics developers are concentrating on the software & computing industry. Of these developers, 36.7% are working in organizations of 101 to 1,000 employees, 32.9% are in enterprises of 1,000+ employees, and 30.1% are in organizations of 100 employees or less. 42.6% of all big data software development in manufacturing begins in enterprises (1K+ employees).

Industries being targeted by big data by company size


  • Enterprises competing in the software & computing industry (17.5%), manufacturing (15.8%) and financial industry (14%) are investing the heaviest in big data and analytics app development. Overall, 32% of big data and analytics projects are custom-designed and produced by system integrators and value-added resellers (SI, VAR). 70% of big data and advanced analytics apps for manufacturing are created by enterprise and system integrator/value-added reseller (SI/VAR) development teams.  The following graphic provides an overview of industries targeted by big data, segmented by developer segment.

industries being targeted by big data by developer segment


  • Sales and customer data (9.6%), IT-based data analysis (9.4%), informatics (8.7%) and financial transactions (8.4%) are the most common big data sets app developers are working with today.  In addition marketing, system management, production and shop floor data, and web & social media-generated data are also included.  Evans Data found that informatics data sets grew the fastest in the last six months, and scientific computing is now competing with transaction processing systems as a dominant data set developers rely on to create new apps.

kinds of information that feed your company's data stores

  • Marketing departments have quickly become the most common users of big data and advanced analytics apps (14.4%) followed by IT (13.3%) and Research & Development (13%). Evans Data asked developers which departments in their organizations are putting big data and advanced analytics apps to use, regardless of where they were created.  38.2% of all big data use in organizations today are in customer-facing departments including marketing, sales, and customer service.

departments using analytics and big data

  • Availability of relevant tools (10.9%), storage costs (10.2%) and siloed business, IT, and analytics/data science teams (10.0%) are the top three barriers developers face in building new apps. It’s interesting to note that compliance and having to transition from legacy systems did not score higher in the survey, as these two areas are inordinately more complex in more regulated, older industries.  For big data and advanced analytics to accelerate across manufacturing and financial industries, compliance and legacy systems integration barriers will need to first be addressed.

three barriers

  • Quality of data (19.2%), relevance of data being acquired (13.5%), volume of data being processed (12.6%) and ability to adequately visualize big data (11.7%) are the four biggest problem areas faced by big data developers today.  Additional problem areas include the volume of data in storage (10.5%), ability to gain insight from big data (10.1%) and the high rate of data acquisition (7.6%).  The remainder of problem areas are shown in the graphic below.   

biggest problem

  • Providing real-time correlation and anomaly detection of diverse security data (29.9%) and high-speed querying of security intelligence data (28.1%) are the two most critical areas vendors can assist developers with today. Big data and analytics app developers are looking to vendors to also provide more effective security algorithms for various use case scenarios (17.6%), flexible big data analytics across structured and unstructured data (14.2%) and more useful graphical front-end tools for visualizing and exploring big data (5.1%).

vendor provide


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:


10 Ways Big Data Is Revolutionizing Supply Chain Management

supply chain managementBottom line: Big data is providing supplier networks with greater data accuracy, clarity, and insights, leading to more contextual intelligence shared across supply chains.

Forward-thinking manufacturers are orchestrating 80% or more of their supplier network activity outside their four walls, using big data and cloud-based technologies to get beyond the constraints of legacy Enterprise Resource Planning (ERP) and Supply Chain Management (SCM) systems. For manufacturers whose business models are based on rapid product lifecycles and speed, legacy ERP systems are a bottleneck.  Designed for delivering order, shipment and transactional data, these systems aren’t capable of scaling to meet the challenges supply chains face today.

Choosing to compete on accuracy, speed and quality forces supplier networks to get to a level of contextual intelligence not possible with legacy ERP and SCM systems. While many companies today haven’t yet adopted big data into their supply chain operations, these ten factors taken together will be the catalyst that get many moving on their journey.

The ten ways big data is revolutionizing supply chain management include:

Figure 1 SCM Data Volume Velocity Variety

  • Enabling more complex supplier networks that focus on knowledge sharing and collaboration as the value-add over just completing transactions.  Big data is revolutionizing how supplier networks form, grow, proliferate into new markets and mature over time. Transactions aren’t the only goal, creating knowledge-sharing networks is, based on the insights gained from big data analytics. The following graphic from Business Ecosystems Come Of Age (Deloitte University Press) (free, no opt-in) illustrates the progression of supply chains from networks or webs, where knowledge sharing becomes a priority.

figure 1 big data scm

  • Big data and advanced analytics are being integrated into optimization tools, demand forecasting, integrated business planning and supplier collaboration & risk analytics at a quickening pace. These are the top four supply chain capabilities that Delotte found are currently in use form their recent study, Supply Chain Talent of the Future Findings from the 3rd Annual Supply Chain Survey (free, no opt-in). Control tower analytics and visualization are also on the roadmaps of supply chain teams currently running big data pilots.

Figure 2 use of supply chain capabilities

  • 64% of supply chain executives consider big data analytics a disruptive and important technology, setting the foundation for long-term change management in their organizations.  SCM World’s latest Chief Supply Chain Officer Report provides a prioritization of the most disruptive technologies for supply chains as defined by the organizations’ members.  The following graphic from the report provides insights into how senior supply chain executives are prioritizing big data analytics over other technologies.

disruptive tech

  • Using geoanalytics based on big data to merge and optimize delivery networks.  The Boston Consulting Group provides insights into how big data is being put to use in supply chain management in the article Making Big Data Work: Supply Chain Management (free, opt-in). One of the examples provided is how the merger of two delivery networks was orchestrated and optimized using geoanalytics. The following graphic is from the article. Combining geoanalytics and big data sets could drastically reduce cable TV tech wait times and driving up service accuracy, fixing one of the most well-known service challenges of companies in that business.

Figure 4 geoanalytics

figure 6 big data


figure 7 big data

  • Greater contextual intelligence of how supply chain tactics, strategies and operations are influencing financial objectives.  Supply chain visibility often refers to being able to see multiple supplier layers deep into a supply network.  It’s been my experience that being able to track financial outcomes of supply chain decisions back to financial objectives is attainable, and with big data app integration to financial systems, very effective in industries with rapid inventory turns. Source: Turn Big Data Into Big Visibility.

figure 8 traceability

  • Traceability and recalls are by nature data-intensive, making big data’s contribution potentially significant. Big data has the potential to provide improved traceability performance and reduce the thousands of hours lost just trying to access, integrate and manage product databases that provide data on where products are in the field needing to be recalled or retrofitted.
  • Increasing supplier quality from supplier audit to inbound inspection and final assembly with big data. IBM has developed a quality early-warning system that detects and then defines a prioritization framework that isolates quality problem faster than more traditional methods, including Statistical Process Control (SPC). The early-warning system is deployed upstream of suppliers and extends out to products in the field.

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



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