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Posts from the ‘Big Data’ Category

6M Developers Are Creating Big Data And Advanced Analytics Apps Today

  • analytics-development2M developers are working on IoT applications, increasing 34% since the last year.
  • Over 50% of the developers working on IoT applications are writing software that utilizes sensors in some capacity.
  • 4M enterprise developers play decision-making roles when it comes to selecting organizational IT development resources. Another 5.2 million hold decision-making authority for selecting IT deployment resources.
  • 4M developers (26% of all developers globally) are using the cloud as a development environment today
  • The APAC region leads the world with approximately 7.4M developers today, followed by EMEA with 7.2M, North America with 4.4M and Latin American with 1.9M.

These and many other fascinating insights are from the Evans Data Corporation Global Developer Population and Demographic Study 2016 (PDF, client access) published earlier this week. The methodology Evans Data has created to produce this report is the most comprehensive developed for aggregating, analyzing and predicting developer populations globally. The study combines Evans Data’s proprietary global developer population modeling with the current results of their semi-annual global developer survey.

Key takeaways from the study include the following:

  • 6M developers (29% of all developers globally) are involved in a Big Data and Advanced Analytics project today. An additional 25% of developers, or 5.3M, are going to begin Big Data and Advanced Analytics projects within the next six 13% or 2.6M of all developers globally are going to start Big Data and Advanced Analytics projects within the next 7 to 12 months.  The following graphic provides an overview of the involvement of 21M developers in Big Data and Advanced Analytics projects today. Please click on the image to expand for easier viewing.

involvement in big data analytics

  • 4M developers (26% of all developers globally) are using the cloud as a development environment today. Developers creating new apps in the cloud had increased 375% since Evans began measuring developer participation in mobile development in 2009 when just slightly more than 1.2M developers were using the cloud as their development platform. 4.5M developers (21% of all global developers) plan on beginning app development on cloud platforms in the next six months, and 3.9M (18% of all global developers) plan on starting development on the cloud in 7 – 12 months. Please click on the image to expand for easier viewing.

plans for cloud development

  • 8M developers in APAC (24% of all developers in the region) are currently developing on cloud platforms. 29% of APAC developers are planning to start cloud-based development in six months, and 20% in 7 – 12 months. The following graphic compares the number of developers currently using the cloud as a development environment today and the number who plan to in the future. Please click on the image to expand for easier viewing.

plans for cloud development by region

  • 34% of all Commercial Independent Software Vendors (ISVs) globally today (1.8M developers) are using the cloud as a development environment. An additional 1.4M are planning to begin cloud development in the next six months.  28% of developers globally creating apps in the cloud are from custom system integrators (SI) and value-added resellers (VARs).  23% or approximately 1.2M are from enterprises.  The following graphic compares the percent of developers by developer segment who are currently creating new apps in cloud environments. Please click on the image to expand for easier viewing.

Plans for cloud development by developer segment

  • 30% of developers (6.2M developers globally) are currently developing software for connected devices or the Internet of Things today, with an additional 26% planning to begin projects in 6 months. Evans Data found that this increased 34% over the last year. Also, 2.1M developers plan to begin development in this area within the next 7 to 12 months. The following graphic compares the number of developers globally by stage of development for creating software for connected devices or the Internet of Things. Please click on the image to expand for easier viewing.

Plans for Internet of Things Development

  • 41% of global developers creating connected device and IoT software today are from 27% are from North America, 24% are from EMEA and 7% from Latin America.  There are 6,072,048 developers currently working on connected device and IoT software today globally.  The following graphic provides an overview of the distribution of developers creating connected device and IoT software by region today. Please click on the image to expand for easier viewing.

Development for Connected Devices By Region

  • 34% of developers actively creating software for connected devices or the Internet of Things work for custom System Integrators (SI) and VARs today. ISVs are the next largest segment of developers working on IoT projects (30%) followed by enterprises (21%). The following graphic provides an overview of the global base of developers creating software for connected devices and IoT. Evans Data found there are 6.1M developers currently creating apps and solutions in this area alone. Please click on the image to expand for easier viewing.

Development for connected devices by developer segment 2

Roundup Of Analytics, Big Data & BI Forecasts And Market Estimates, 2016

  • World map technologyBig Data & business analytics software worldwide revenues will grow from nearly $122B in 2015 to more than $187B in 2019, an increase of more than 50% over the five-year forecast period.
  • The market for prescriptive analytics software is estimated to grow from approximately $415M in 2014 to $1.1B in 2019, attaining a 22% CAGR.
  • By 2020, predictive and prescriptive analytics will attract 40% of enterprises’ net new investment in business intelligence and analytics.

Making enterprises more customer-centric, sharpening focus on key initiatives that lead to entering new markets and creating new business models, and improving operational performance are three dominant factors driving analytics, Big Data, and business intelligence (BI) investments today. Unleashing the insights hidden in unstructured data is providing enterprises with the potential to compete and improve in areas they had limited visibility into before. Examples of these areas include the complexity of B2B selling and service relationships,  healthcare services, and maintenance, repair, and overhaul (MRO) of complex machinery.

Presented below are a roundup of recent analytics and big data forecasts and market estimates:

  • The global big data market will grow from $18.3B in 2014 to $92.2B by 2026, representing a compound annual growth rate of 14.4 percent. Wikibon predicts significant growth in all four sub-segments of big data software through 2026. Data management (14% CAGR), core technologies such as Hadoop, Spark and streaming analytics (24% CAGR), databases (18% CAGR) and big data applications, analytics and tools (23% CAGR) are the four fastest growing sub-segments according to Wikibon. Source: Wikibon forecasts Big Data market to hit $92.2B by 2026.

Wikibon big data forecast 2016

  • In 2015, the Global Analytics and Business Intelligence applications market grew 4% to approach nearly $11.6B in license, maintenance and subscription revenues with SAP maintaining market leadership. SAP led the marketing with 10% market share and $1.2B in Analytics and Business Intelligence (BI) product revenues, riding on a 23% jump in license, maintenance, and subscription revenues. SAS Institute was No. 2 achieving 9% share; IBM was the third at 8%, and Oracle and Microsoft were fourth and fifth place with 7% and 5%, respectively. Source: Apps Run The World: Top 10 Analytics and BI Software Vendors and Market Forecast 2015-2020.

analytics market shares

IDC FutureScape

  • The Total Data market is expected to nearly double in size, growing from $69.6B in revenue in 2015 to $132.3B in 2020. The specific market segments included in 451 Research’s analysis are operational databases, analytic databases, reporting and analytics, data management, performance management, event/stream processing, distributed data grid/cache, Hadoop, and search-based data platforms and analytics. Source: Total Data market expected to reach $132bn by 2020; 451 Research, June 14, 2016.

Worldwide total revenue by segment

overall adoption of big data

  • Improving customer relationships (55%) and making the business more data-focused (53%) are the top two business goals or objectives driving investments in data-driven initiatives today. 78% of enterprises agree that collection and analysis of Big Data have the potential to change fundamentally the way they do business over the next 1 to 3 years. Source: IDG Enterprise 2016 Data & Analytics Research, July 5, 2016.

Data Helps Customer Focused Organizations

  • Venture capital (VC) investment in Big Data accelerated quickly at the beginning of the year with DataDog ($94M), BloomReach ($56M), Qubole ($30M), PlaceIQ ($25M) and others receiving funding. Big Data startups received $6.64B in venture capital investment in 2015, 11% of total tech VC.  M&A activity has remained moderate (FirstMark noted 35 acquisitions since their latest landscape was published last year). Source: Matt Turck’s blog post, Is Big Data Still a Thing? (The 2016 Big Data Landscape).

big data landscape

  • IDC forecasts global spending on cognitive systems will reach nearly $31.3 billion in 2019 with a five-year compound annual growth rate (CAGR) of 55%. More than 40% of all cognitive systems spending throughout the forecast will go to software, which includes both cognitive applications (i.e., text and rich media analytics, tagging, searching, machine learning, categorization, clustering, hypothesis generation, question answering, visualization, filtering, alerting, and navigation). Also included in the forecasts are cognitive software platforms, which enable the development of intelligent, advisory, and cognitively enabled solutions.  Source:  Worldwide Spending on Cognitive Systems Forecast to Soar to More Than $31 Billion in 2019, According to a New IDC Spending Guide.
  • Big Data Analytics & Hadoop Market accounted for $8.48B in 2015 and is expected to reach $99.31B by 2022 growing at a CAGR of 42.1% from 2015 to 2022. The rise of big data analytics and rapid growth in consumer data capture and taxonomy techniques are a few of the many factors fueling market growth. Source: Stratistics Market Research Consulting (PDF, opt-in, payment reqd).

Additional sources of market information: 

Analytics Trends 2016 The Next Evolution, Deloitte.

Big data analytics, Ericsson White Paper Uen 288 23-3211 Rev B | October 2015

Big Data and the Intelligence Economy in Canada Big Data: Big Opportunities to Create Business Value, EMC.

The Forrester Wave™: Big Data Hadoop Distributions, Q1 2016

The Forrester Wave™: Big Data Hadoop Cloud Solutions, Q2 2016

The Forrester Wave™: Big Data Text Analytics Platforms, Q2 2016

The Forrester Wave™: Big Data Streaming Analytics, Q1 2016

The Forrester Wave™: Customer Analytics Solutions, Q1 2016

From Big Data to Better Decisions: The ultimate guide to business intelligence today (Domo)

Gartner Hype Cycle for Business Intelligence and Analytics, 2015

IBM: Extracting business value from the 4 V’s of big data

IDC Worldwide Big Data Technology and Services 2012 – 2015 Forecast

Opportunities in Telecom Sector: Arising from Big Data. Deloitte, November 2015

Who will win as Finance doubles down on analytics?

10 Ways Machine Learning Is Revolutionizing Manufacturing

machine learningBottom line: Every manufacturer has the potential to integrate machine learning into their operations and become more competitive by gaining predictive insights into production.

Machine learning’s core technologies align well with the complex problems manufacturers face daily. From striving to keep supply chains operating efficiently to producing customized, built- to-order products on time, machine learning algorithms have the potential to bring greater predictive accuracy to every phase of production. Many of the algorithms being developed are iterative, designed to learn continually and seek optimized outcomes. These algorithms iterate in milliseconds, enabling manufacturers to seek optimized outcomes in minutes versus months.

The ten ways machine learning is revolutionizing manufacturing include the following:

  • Increasing production capacity up to 20% while lowering material consumption rates by 4%. Smart manufacturing systems designed to capitalize on predictive data analytics and machine learning have the potential to improve yield rates at the machine, production cell, and plant levels. The following graphic from General Electric and cited in a National Institute of Standards (NIST) provides a summary of benefits that are being gained using predictive analytics and machine learning in manufacturing today.

typical production improvemensSource: Focus Group: Big Data Analytics for Smart Manufacturing Systems

  • Providing more relevant data so finance, operations, and supply chain teams can better manage factory and demand-side constraints. In many manufacturing companies, IT systems aren’t integrated, which makes it difficult for cross-functional teams to accomplish shared goals. Machine learning has the potential to bring an entirely new level of insight and intelligence into these teams, making their goals of optimizing production workflows, inventory, Work In Process (WIP), and value chain decisions possible.

factory and demand analytics

Source:  GE Global Research Stifel 2015 Industrials Conference

  • Improving preventative maintenance and Maintenance, Repair and Overhaul (MRO) performance with greater predictive accuracy to the component and part-level. Integrating machine learning databases, apps, and algorithms into cloud platforms are becoming pervasive, as evidenced by announcements from Amazon, Google, and Microsoft. The following graphic illustrates how machine learning is integrated into the Azure platform. Microsoft is enabling Krones to attain their Industrie 4.0 objectives by automating aspects of their manufacturing operations on Microsoft Azure.

Azure IOT Services

Source: Enabling Manufacturing Transformation in a Connected World John Shewchuk Technical Fellow DX, Microsoft

  • Enabling condition monitoring processes that provide manufacturers with the scale to manage Overall Equipment Effectiveness (OEE) at the plant level increasing OEE performance from 65% to 85%. An automotive OEM partnered with Tata Consultancy Services to improve their production processes that had seen Overall Equipment Effectiveness (OEE) of the press line reach a low of 65 percent, with the breakdown time ranging from 17-20 percent.  By integrating sensor data on 15 operating parameters (such as oil pressure, oil temperature, oil viscosity, oil leakage, and air pressure) collected from the equipment every 15 seconds for 12 months. The components of the solution are shown

OEE Graphic

Source: Using Big Data for Machine Learning Analytics in Manufacturing

  • Machine learning is revolutionizing relationship intelligence and Salesforce is quickly emerging as the leader. The series of acquisitions Salesforce is making positions them to be the global leader in machine learning and artificial intelligence (AI). The following table from the Cowen and Company research note, Salesforce: Initiating At Outperform; Growth Engine Is Well Greased published June 23, 2016, summarizes Salesforce’s series of machine learning and AI acquisitions, followed by an analysis of new product releases and estimated revenue contributions. Salesforce’s recent acquisition of e-commerce provider Demandware for $2.8B is analyzed by Alex Konrad is his recent post,     Salesforce Will Acquire Demandware For $2.8 Billion In Move Into Digital Commerce. Cowen & Company predicts Commerce Cloud will contribute $325M in revenue by FY18, with Demandware sales being a significant contributor.

Salesforce AI Acquisitions

Salesforce revenue sources

  • Revolutionizing product and service quality with machine learning algorithms that determine which factors most and least impact quality company-wide. Manufacturers often are challenged with making product and service quality to the workflow level a core part of their companies. Often quality is isolated. Machine learning is revolutionizing product and service quality by determining which internal processes, workflows, and factors contribute most and least to quality objectives being met. Using machine learning manufacturers will be able to attain much greater manufacturing intelligence by predicting how their quality and sourcing decisions contribute to greater Six Sigma performance within the Define, Measure, Analyze, Improve, and Control (DMAIC) framework.
  • Increasing production yields by the optimizing of team, machine, supplier and customer requirements are already happening with machine learning. Machine learning is making a difference on the shop floor daily in aerospace & defense, discrete, industrial and high-tech manufacturers today. Manufacturers are turning to more complex, customized products to use more of their production capacity, and machine learning help to optimize the best possible selection of machines, trained staffs, and suppliers.
  • The vision of Manufacturing-as-a-Service will become a reality thanks to machine learning enabling subscription models for production services. Manufacturers whose production processes are designed to support rapid, highly customized production runs are well positioning to launch new businesses that provide a subscription rate for services and scale globally. Consumer Packaged Goods (CPG), electronics providers and retailers whose manufacturing costs have skyrocketed will have the potential to subscribe to a manufacturing service and invest more in branding, marketing, and selling.
  • Machine learning is ideally suited for optimizing supply chains and creating greater economies of scale.  For many complex manufacturers, over 70% of their products are sourced from suppliers that are making trade-offs of which buyer they will fulfill orders for first. Using machine learning, buyers and suppliers could collaborate more effectively and reduce stock-outs, improve forecast accuracy and met or beat more customer delivery dates.
  • Knowing the right price to charge a given customer at the right time to get the most margin and closed sale will be commonplace with machine learning.   Machine learning is extending what enterprise-level price optimization apps provide today.  One of the most significant differences is going to be just how optimizing pricing along with suggested strategies to close deals accelerate sales cycles.

Additional reading:

Cisco Blog: Deus Ex Machina: Machine Learning Acts to Create New Business Outcomes

Enabling Manufacturing Transformation in a Connected World John Shewchuk Technical Fellow DX, Microsoft 

Focus Group: Big Data Analytics for Smart Manufacturing Systems

GE Predix: The Industrial Internet Platform

IDC Manufacturing Insights reprint courtesy of Cisco: Designing and Implementing the Factory of the Future at Mahindra Vehicle Manufacturers

Machine Learning: What It Is And Why It Matters

McKinsey & Company, An Executive’s Guide to Machine Learning

MIT Sloan Management Review, Sales Gets a Machine-Learning Makeover

Stanford University CS 229 Machine Learning Course Materials
The Economist Feature On Machine Learning

UC Berkeley CS 194-10, Fall 2011: Introduction to Machine Learning
Lecture slides, notes

University of Washington CSE 446 – Machine Learning – Winter 2014

Sources:

Lee, J. H., & Ha, S. H. (2009). Recognizing yield patterns through hybrid applications of machine learning techniques. Information Sciences, 179(6), 844-850.

Mackenzie, A. (2015). The production of prediction: What does machine learning want?. European Journal of Cultural Studies, 18(4-5), 429-445.

Pham, D. T., & Afify, A. A. (2005, July). Applications of machine learning in manufacturing. In Intelligent Production Machines and Systems, 1st I* PROMS Virtual International Conference (pp. 225-230).

Priore, P., de la Fuente, D., Puente, J., & Parreño, J. (2006). A comparison of machine-learning algorithms for dynamic scheduling of flexible manufacturing systems. Engineering Applications of Artificial Intelligence, 19(3), 247-255.

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

 

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, 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).

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.

Where Big Data Jobs Will Be In 2015

Big Data Drives Rapid ChangesDemand for Computer Systems Analysts with big data expertise increased 89.9% in the last twelve months and 85.40% for Computer and Information Research Scientists.

Demand for Python programming expertise increased 96.9% in big-data related positions in the last twelve months.

These and other key insights are from a recent analysis completed of big data hiring trends using WANTED Analytics, the leading provider of data analytics on the workplace.  For purposes of this analysis, the term “big data” is comprised of the four skill sets of data analysis, data acquisition, data mining and data structures. The WANTED Analytics taxonomy references these skill sets when queries are made on the term “big data”.

The company currently maintains a database of more than one billion unique job listings and is collecting hiring trend data from more than 150 countries. WANTED Analytics has never been a client, they provided complimentary access based on my requesting a trial account. Many Forbes readers are interested in staying current on big data hiring trends, which led me to complete this analysis.

Key Take-aways include the following:

  • Demand for big data expertise across a range of occupations saw significant growth over the last twelve months. There was a 123.60% jump in demand for Information Technology Project Managers with big data expertise, and an 89.8% increase for Computer Systems Analysts. The following table provides an overview of the distribution of open positions by occupation and the percentage growth in job demand over time.

job growth matrix

  • The five leading industries with the most job openings requiring big data expertise include Professional, Scientific and Technical Services (27.14%), Information Technologies (18.89%), Manufacturing (12.35%), Retail Trade (9.62%) and Sustainability, Waste Management & Remediation Services (8.20%). The following graphic shows the distribution of open positions between September 1, 2014 to today, December 29, 2014:

top 20 industries hiring

  • The Hiring Scale is 76 for jobs that require big data skills with 12 candidates per job opening as of December 29, 2014.  The higher the Hiring Scale score, the more difficult it is for employers to find the right applicants for open positions. Nationally an average job posting for an IT professional with cloud computing expertise is open just 47 days.

big data hiring scale

  • The median salary for professionals with big data expertise is $103,000 a year. Sample jobs in this category include Big Data Solution Architect, Linux Systems and Big Data Engineer, Big Data Platform Engineer, Lead Software Engineer, Big Data (Java, Hadoop, SQL) and others.  The distribution of median salaries across all industries shown below:

big data market salary

  • San Jose – Sunnyvale – Santa Clara, CA, San Francisco – Oakland – Fremont, CA, and Washington – Arlington – Alexandria, DC are the top three U.S. employment markets for big data related jobs as of today.  Mapping the distribution of job volume, salary range, candidate supply, posting period and hiring scale by Metropolitan Statistical Area (MSA) or states and counties is supported by WANTED Analytics and shown in the following graphic. A summary of the top twenty employment markets is also shown following the map:

US Hiring Map

US Top Markets

  • Cisco (NASDAQ:CSCO), IBM (NYSE: IBM) and Oracle (NYSE:ORCL) have the most open big data-related positions today. Cisco, its supplier, partner and support ecosystem companies have 3,613 related big data positions available.  The following table shows the top ten big data employers today, the distribution of jobs, and the number of new jobs added over the last year.

top ten employers

  • Python programming (96.90%), Linux expertise (76.60%) and Structured Query Language (SQL) (76%) are the three most in-demand skills in positions that mention big data as a requirement.  The following table provides an overview of the top 10 most in-demand skills:

skills

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

 

BCG’s Value Creators Report Shows How Software Is Driving New Business Models

boston-300x211Boston Consulting Group (BCG) recently released their fifth annual technology, media and telecommunications (TMT) value report. The 2013 TMT Value Creators Report: The Great Software Transformation, How to Win as Technology Changes the World (free, opt-in required, 41 pgs).

The five trends that serve as the foundation of this report include the increasing pervasiveness of software, affordable small devices, ubiquitous broadband connectivity, big-data analytics and cloud computing.  BCG’s analysis illustrates how the majority of TMT companies that deliver the most value to shareholders are concentrating on the explosive growth of new markets, the rise of software-enabled digital metasystems, and for many, both.

The study is based on an analysis of 191 companies, 76 in the technology industry, 62 from media and 53 from telecom.  To review the methodology of this study please see page 28 of the report.

Here are the key takeaways from this years’ BCG TMT Value Creators Report:

  • BCG is predicting 1B smartphones will be sold in 2013, the first year their sales will have exceeded those of features phones.  By 2018, there will be more than 5B “post-PC” products (tablets & smartphones) in circulation. There are nearly as many mobile connections in the world as people (6.8B) according to the United Nation’s International Telecommunication Union (ITU).

bcg figure 1

  • 27 terabytes of data is generated every second through the creation of video, images social networks, transactional and enterprise-based systems and networks.  90% of the data that is stored today didn’t exist two years ago, and the annual data growth rate in future years is projected to be 40% to 60% over current levels according to BCG’s analysis.

bcg figure 2

  • The ascent of communications speeds is surpassing Moore’s Law as a structural driver of growth.  BCG completed the following analysis graphing the progression of microprocessor transition count (Moore’s Law) relative to Internet speed (bps) citing Butter’s Law of Photonics which states that the amount of data coming out of an optical fiber is doubling every nine months. BCG states that these dynamics are democratizing information technology and will lead to the cloud computing industry (software and services) reaching nearly $250B in 2017.
    bcg figure 3
  • BCG predicts that India will see a fivefold increase in digitally-influenced spending, ascending from $30B in 2012 to $150B in 2016, among the fastest of all nations globally according to their study. India will also see the value of online purchases increase from $8B in 2012 t5o $50B in 2016.

bcg figure 4

  • 3D printing is forecast to become a $3.1B market by 2016, and will have an economic impact of $550B in 2025, fueling rapid price reductions in 3D printers through 2017.  BCG sees 3D printing, connected travel, genomics and smart grid technologies are central to their digital metasystem.   The following graphic illustrates the key trends in each of these areas along with research findings from BCG and other sources.

bcg figure 5

  • Only 7% of customers are comfortable with their information being used outside of the purpose for which it was originally gathered.

bcg figure 6

  • BCG reports that mobile infrastructure investments in Europe have fallen 67% from 2004 to 2014.  Less than 1% of mobile connections in Europe were 4B as of the end of 2012, compared to 11% in the U.S. and 28% in South Korea.   European operators have also been challenged to monetize mobile data as well, as the following figures illustrate.

bcg figure 7

bcg figure 8

  • Big Data is attracting $19B in funding across five key areas according to BCG’s analysis.  These include consumer data and marketing, enterprise data, analytical tools, vertical markets and data platforms.  A graphical analysis of these investments is shown below.

bcg figure 9

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