- 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.
- 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.
- 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.
- 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%.
- 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.
- 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.
- 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.
- 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.
- 73% of midmarket companies say the complexity of their stored data requires big data analytics apps and tools to better gain insights from.
- 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..
- 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%).
- 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.
- 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.
- 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
- 42.6% of all big data apps developed for manufacturing are being created by enterprises today.
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.
- 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.
- 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.
- 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).
- 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.
- 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.
- 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.
- 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.
- 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.
- 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%).
Bottom 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:
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Salesforce (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.
- 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, KNIME.com, 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).
- 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 RapidMiner: Magic Quadrant for Advanced Analytics Platforms Published: 19 February 2015 Analyst(s): Gareth Herschel, Alexander Linden, Lisa Kart (reprint; free, no opt-in).
- 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).
- 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).
- 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.
- 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.
- 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.
- 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.
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.
McKinsey & 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:
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.
- 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:
- Attitudes on How Big Data will Affect Manufacturing Performance. Posted by Greg Goodwin on Thu, Mar 06, 2014. LNS Research. http://blog.lnsresearch.com/blog/bid/194972/Attitudes-on-How-Big-Data-will-Affect-Manufacturing-Performance-DATA From Value to Vision: Reimagining the Possible with Data Analytics, David Kiron, Renee Boucher Ferguson and Pamela Kirk Prentice, MIT Sloan Management Review, March 2013. http://sloanreview.mit.edu/reports/analytics-innovation/introduction/
- Merck Optimizes Manufacturing With Big Data Analytics, Information Week, Doug Henschen. April 2, 2014. http://www.informationweek.com/strategic-cio/executive-insights-and-innovation/merck-optimizes-manufacturing-with-big-data-analytics/d/d-id/1127901
- Takeaways from the MIT/Accenture Big Data in Manufacturing Conference. Posted by Greg Goodwin on Wed, Nov 27, 2013. http://blog.lnsresearch.com/blog/bid/190482/Takeaways-from-the-MIT-Accenture-Big-Data-in-Manufacturing-Conference
- The Internet of Things and the future of manufacturing. McKinsey and Company. Executives at Robert Bosch and McKinsey experts discuss the technology-driven changes that promise to trigger a new industrial revolution. June 2013 | by Markus Löffler and Andreas Tschiesner. http://www.mckinsey.com/insights/business_technology/the_internet_of_things_and_the_future_of_manufacturing
- The Rise of Industrial Big Data: Leveraging large time-series data sets to drive innovation, competitiveness and growth — capitalizing on the big data opportunity, GE Intelligent Platforms White Paper, April 2012. http://www.ge-ip.com/library/detail/13170
- When Big Data Meets Manufacturing. Stephen Chick, Serguei Netessine, INSEAD Professors of Technology and Operations Management and Arnd Huchzermeier, Chaired Professor of Production Management at WHU-Otto Beisheim School of Management | April 16, 2014. http://knowledge.insead.edu/operations-management/when-big-data-meets-manufacturing-3297
87% of enterprises believe Big Data analytics will redefine the competitive landscape of their industries within the next three years. 89% believe that companies that do not adopt a Big Data analytics strategy in the next year risk losing market share and momentum.
These and other key findings are from a Accenture and General Electric study published this month on how the combination of Big Data analytics and the Internet of Things (IoT) are redefining the competitive landscape of entire industries. Accenture and GE define the Industrial Internet as the use of sensor, software, machine-to-machine learning and other technologies to gather and analyze data from physical objects or other large data streams, and then use those analyses to manage operations and in some cases to offer new, valued-added services.
Big Data Analytics Now Seen As Essential For Competitive Growth
The Industrial Internet is projected to be worth $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:
- Big Data analytics has quickly become the highest priority for aviation (61%), wind (45%) and manufacturing (42%) companies. The following graphic provides insights into the relative level of importance of Big Data analytics relative to other priorities in the enterprises interviewed in the study:
- 74% of enterprises say that their main competitors are already using Big Data analytics to successfully differentiate their competitive strengths with clients, the media, and investors. 93% of enterprises are seeing new competitors in their market using Big Data analytics as a key differentiation strategy. The single greatest risk enterprises see from not implementing a Big Data strategy is that competitors will gain market share at their expense. Please see the following graphic for a comparison of the risks of not implementing Big Data strategy.
- 65% of enterprises are focused on monitoring assets to identify operating issues for more proactive maintenance. 58% report having capabilities such as connecting equipment to collect operating data and analyzing the data to produce insights. The following graphic provides an overview of Big Data monitoring survey results:
- Increasing profitability (60%), gaining a competitive advantage (57%) and improving environmental safety and emissions compliance (55%) are the three highest industry priorities according to the survey. The following table provides an analysis of the top business priorities by industry for the next three years with the shaded areas indicating the highest-ranked priorities by industry:
- The top three challenges enterprises face in implementing Big Data initiatives include the following: system barriers between departments prevent collection and correlation of data for maximum impact (36%); security concerns are impacting enterprises’ ability to implement a wide-scale Big Data initiative (35%); and consolidation of disparate data and being able to use the resulting data store (29%), third. The following graphic provides an overview of the top three challenges organizations face in implementing Big Data initiatives:
Gartner presented their top 10 strategic technology trends for 2015 at their annual Gartner Symposium/ITxpo 2014 held in Orlando earlier this month. Computing Everywhere, the Internet of Things (IoT) and 3D Printing are projected to be the three most important strategic technology trends in 2015.
3D Printing Will Continue To Revolutionize Prototyping And Manufacturing
3D printing is forecast to reach a tipping point in the next three years due to streamlined prototyping and short-run manufacturing. Improving time-to-market, ensuring greater accuracy of highly customized products, and reducing production costs over the long-term are three of the many benefits companies are adopting 3D printing for today. Be sure to read Larry Dignan’s excellent post covering the conference and top ten strategic technology trends, 3D printing turns strategic in 2015, says Gartner.
Taking Analytics To The Next Level in 2015
Advanced, persuasive and invisible analytics, context-rich systems, and smart machines also are included in the top 10 strategic technology trends for 2015. Given how quickly analytics is maturing as a technology category, it’s understandable why Gartner ranked this area as the 4th most strategic. In 2015, analytics will move beyond providing dashboards with metrics and Key Performance Indicators (KPIs) to a more intuitive series of applications that give business analysts the flexibility to define models and test them in real-time. Alteryx and Tableau are interesting companies to watch in this area and Tableau Public is worth checking out and learning due to its advanced visualization features (free, opt-in).
Cloud Computing Becomes Part Of The New IT Reality
The last four technology trends Gartner mentions include cloud/client computing, software-defined applications and infrastructure, Web-scale IT and risk-based security and self-protection.
The following graphic provides an overview of the top 10 strategic technology trends for 2015.