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Posts from the ‘Business Intelligence’ Category

Jun 16

What Matters Most In Business Intelligence, 2019

  • Improving revenues using BI is now the most popular objective enterprises are pursuing in 2019.
  • Reporting, dashboards, data integration, advanced visualization, and end-user self-service are the most strategic BI initiatives underway in enterprises today.
  • Operations, Executive Management, Finance, and Sales are primarily driving Business Intelligence (BI) adoption throughout enterprises today.
  • Tech companies’ Operations & Sales teams are the most effective at driving BI adoption across industries surveyed, with Advertising driving BI adoption across Marketing.

These and many other fascinating insights are from Dresner Advisory Associates’ 10th edition of its popular Wisdom of Crowds® Business Intelligence Market Study. The study is noteworthy in that it provides insights into how enterprises are expanding their adoption of Business Intelligence (BI) from centralized strategies to tactical ones that seek to improve daily operations. The Dresner research teams’ broad assessment of the BI market makes this report unique, including their use visualizations that provide a strategic view of market trends. The study is based on interviews with respondents from the firms’ research community of over 5,000 organizations as well as vendors’ customers and qualified crowdsourced respondents recruited over social media. Please see pages 13 – 16 for the methodology.

Key insights from the study include the following:

  • Operations, Executive Management, Finance, and Sales are primarily driving Business Intelligence (BI) adoption throughout their enterprises today. More than half of the enterprises surveyed see these four departments as the primary initiators or drivers of BI initiatives. Over the last seven years, Operations departments have most increased their influence over BI adoption, more than any other department included in the current and previous survey. Marketing and Strategic Planning are also the most likely to be sponsoring BI pilots and looking for new ways to introduce BI applications and platforms into use daily.

  • Tech companies’ Operations & Sales teams are the most effective at driving BI adoption across industries surveyed, with Advertising driving BI adoption across Marketing. Retail/Wholesale and Tech companies’ sales leadership is primarily driving BI adoption in their respective industries. It’s not surprising to see the leading influencer among Healthcare respondents is resource-intensive HR. The study found that Executive Management is most likely to drive business intelligence in consulting practices most often.

  • Reporting, dashboards, data integration, advanced visualization, and end-user self-service are the most strategic BI initiatives underway in enterprises today. Second-tier initiatives include data discovery, data warehousing, data discovery, data mining/advanced algorithms, and data storytelling. Comparing the last four years of survey data, Dresner’s research team found reporting retains all-time high scores as the top priority, and data storytelling, governance, and data catalog hold momentum. Please click on the graphic to expand for easier reading.

  • BI software providers most commonly rely on executive-level personas to design their applications and add new features. Dresner’s research team found all vertical industries except Business Services target business executives first in their product design and messaging. Given the customer-centric nature of advertising and consulting services business models, it is understandable why the primary focus BI vendors rely on in selling to them are customer personas. The following graphic compares targeted users for BI by industry.

  • Improving revenues using BI is now the most popular objective in 2019, despite BI initially being positioned as a solution for compliance and risk management. Executive Management, Marketing/Sales, and Operations are driving the focus on improving revenues this year. Nearly 50% of enterprises now expect BI to deliver better decision making, making the areas of reporting, and dashboards must-have features. Interestingly, enterprises aren’t looking to BI as much for improving operational efficiencies and cost reductions or competitive advantages. Over the last 12 to 18 months, more tech manufacturing companies have initiated new business models that require their operations teams to support a shift from products to services revenues. An example of this shift is the introduction of smart, connected products that provide real-time data that serves as the foundation for future services strategies. Please click on the graphic to expand for easier reading.

  • In aggregate, BI is achieving its highest levels of adoption in R&D, Executive Management, and Operations departments today. The growing complexity of products and business models in tech companies, increasing reliance on analytics and BI in retail/wholesale to streamline supply chains and improve buying experiences are contributing factors to the increasing levels of BI adoption in these three departments. The following graphic compares BI’s level of adoption by function today.

  • Enterprises with the largest BI budgets this year are investing more heavily into dashboards, reporting, and data integration. Conversely, those with smaller budgets are placing a higher priority on open source-based big data projects, end-user data preparation, collaborative support for group-based decision-making, and enterprise planning. The following graphic provides insights into technologies and initiatives strategic to BI at an enterprise level by budget plans.

  • Marketing/Sales and Operations are using the greatest variety of BI tools today. The survey shows how conversant Operations professionals are with the BI tools in use throughout their departments. Every one of them knows how many and most likely which types of BI tools are deployed in their departments. Across all industries, Research & Development (R&D), Business Intelligence Competency Center (BICC), and IT respondents are most likely to report they have multiple tools in use.

Aug 20

Global State Of Enterprise Analytics, 2018

  • 71% of enterprises globally predict their investments in data and analytics will accelerate in the next three years and beyond.
  • 57% of enterprises globally have a Chief Data Officer, a leadership role that is pivotal in helping to democratize data and analytics across any organization.
  • 52% of enterprises are leveraging advanced and predictive analytics today to provide greater insights and contextual intelligence into operations.
  • 41% of all enterprises are considering a move to cloud-based analytics in the next year.
  • Cloud Computing (24%), Big Data (20%), and AI/Machine Learning (18%) are the three technologies predicted to have the greatest impact on analytics over the next five years.
  • Just 16% of enterprises have enabled at least 75% of their employees to have access to company data and analytics.

These and many other fascinating insights are from MicroStrategy’s latest research study, 2018 Global State of Enterprise Analytics Report.  You can download a copy here (PDF, 44 pp., opt-in). The study is based on surveys completed in April 2018 with 500 globally-based enterprise analytics and business intelligence professionals on the state of their organizations’ analytics initiatives across 20 industries. Participants represented organizations with 250 to 20,000 employees worldwide from five nations including Brazil, Germany, Japan, the United Kingdom and the United States. For additional details on the methodology, please see the study here. The study’s results underscore how enterprises need to have a unified data strategy that reflects their growth strategies and new business models’ information needs.

Key takeaways from the study include the following:

  • Driving greater process and cost efficiencies (60%), strategy and change (57%) and monitoring and improving financial performance (52%) are the top three ways enterprises globally are using data and analytics today. The study found that enterprises are also relying on data and analytics to gain greater insights into how current products and services are used (51%), managing risk (50%) and attain customer growth and retention (49%). Across the five nations surveyed, Japan leads the world in the use of data and analytics to drive process and cost efficiencies (65%). UK-based enterprises lead all nations in their use of data and analytics to analyze how current products and services are being used.  The report provides graphical comparisons of the five nations’ results.

  • Cloud Computing, Big Data, and AI/Machine Learning are the three technologies predicted to have the greatest global impact on analytics over the next five years. Japanese enterprises predict cloud computing will have the greatest impact on the future of analytics (28%) across the five nations’ enterprises interviewed. AI/Machine Learning is predicted to have the greatest impact on analytics in the U.K. (26%) globally as is Big Data in Germany (29%). Please see the study for country-specific prioritization of technologies.

  • 52% of enterprises are leveraging advanced and predictive analytics today to provide greater insights and contextual intelligence into operations. Additional leverage areas include distribution of analytics via e-mail and collaboration tools (49%), analytics embedded in other apps including Salesforce (44%) and mobile productivity apps (39%). Japanese enterprises lead the world in their adoption of advanced and predictive analytics (60%). German enterprises lead the world in the adoption of analytics for collaboration via e-mail and more real-time data and knowledge-sharing methods (50%).

  • 59% of enterprises are using Big Data Analytics, leading all categories of intelligence applications. Enterprise reporting (47%), data discovery (47%), mobile productivity apps (44%) and embedded apps (42%) are the top five intelligence applications in use globally by enterprises today. Big Data’s dominance in the survey results can be attributed to the top five industries in the sampling frame is among the most prolific in data generation and use. Manufacturing (15%) is the most data-prolific industry on the planet. Additional industries that generate massive amounts of data dominate the survey’s demographics including software technology-based businesses (14%), banking (13%), retail (11%), and financial services/business services (6%).

  • 27% of global enterprises prioritize security over any other factor when evaluating a new analytics vendor. The three core attributes of a scalable, comprehensive platform, ease of use, and a vendor’s products having an excellent reputation are all essential. Enterprises based in four of the five nations also prioritize security as the most critical success factor they evaluate potential analytics vendors to do business with. Enterprise scalability is most important in the U.S., with 26% of enterprises interviewed saying this is the most important priority in evaluating a new analytics vendor.

  • Data privacy and security concerns (49%) is the most formidable barrier enterprises face in gaining more effective use of their data and analytics. Enterprises from four of the five nations say data privacy and security are the most significant barrier they face in getting more value from analytics. In Japan, the greatest barrier is access limited to data across the organization (40%).

  • Globally 41% of all enterprises are considering a move to the cloud in the next year. 64% of U.S.-based enterprises are considering moving to a cloud-based analytics platform or solution in the next year. The U.S. leads enterprises from all five nations in planned cloud-based analytics cloud adoption as the graphic below illustrates.

Jul 4

1 Comment

Where Business Intelligence Is Delivering Value In 2018

  • Executive Management, Operations, and Sales are the three primary roles driving Business Intelligence (BI) adoption in 2018.
  • Dashboards, reporting, end-user self-service, advanced visualization, and data warehousing are the top five most important technologies and initiatives strategic to BI in 2018.
  • Small organizations with up to 100 employees have the highest rate of BI penetration or adoption in 2018.
  • Organizations successful with analytics and BI apps define success in business results, while unsuccessful organizations concentrate on adoption rate first.
  • 50% of vendors offer perpetual on-premises licensing in 2018, a notable decline over 2017. The number of vendors offering subscription licensing continues to grow for both on-premises and public cloud models.
  • Fewer than 15% of respondent organizations have a Chief Data Officer, and only about 10% have a Chief Analytics Officer today.

These and many other fascinating insights are from Dresner Advisory Service’s  2018 Wisdom of Crowds® Business Intelligence Market Study. In its ninth annual edition, the study provides a broad assessment of the business intelligence (BI) market and a comprehensive look at key user trends, attitudes, and intentions.  The latest edition of the study adds Information Technology (IT) analytics, sales planning, and GDPR, bringing the total to 36 topics under study.

“The Wisdom of Crowds BI Market Study is the cornerstone of our annual research agenda, providing the most in-depth and data-rich portrait of the state of the BI market,” said Howard Dresner, founder and chief research officer at Dresner Advisory Services. “Drawn from the first-person perspective of users throughout all industries, geographies, and organization sizes, who are involved in varying aspects of BI projects, our report provides a unique look at the drivers of and success with BI.” Survey respondents include IT (28%), followed by Executive Management (22%), and Finance (19%). Sales/Marketing (8%) and the Business Intelligence Competency Center (BICC) (7%). Please see page 15 of the study for specifics on the methodology.

Key takeaways from the study include the following:

  • Executive Management, Operations, and Sales are the three primary roles driving Business Intelligence (BI) adoption in 2018. Executive management teams are taking more of an active ownership role in BI initiatives in 2018, as this group replaced Operations as the leading department driving BI adoption this year. The study found that the greatest percentage change in functional areas driving BI adoption includes Human Resources (7.3%), Marketing (5.9%), BICC (5.1%) and Sales (5%).

  • Making better decisions, improving operational efficiencies, growing revenues and increased competitive advantage are the top four BI objectives organizations have today. Additional goals include enhancing customer service and attaining greater degrees of compliance and risk management. The graph below rank orders the importance of BI objectives in 2018 compared to the percent change in BI objectives between 2017 and 2018. Enhanced customer service is the fastest growing objective enterprises adopt BI to accomplish, followed by growth in revenue (5.4%).

  • Dashboards, reporting, end-user self-service, advanced visualization, and data warehousing are the top five most important technologies and initiatives strategic to BI in 2018. The study found that second-tier initiatives including data discovery, data mining/advanced algorithms, data storytelling, integration with operational processes, and enterprise and sales planning are also critical or very important to enterprises participating in the survey. Technology areas being hyped heavily today including the Internet of Things, cognitive BI, and in-memory analysis are relatively low in the rankings as of today, yet are growing. Edge computing increased 32% as a priority between 2017 and 2018 for example. The results indicate the core aspect of excelling at using BI to drive better business decisions and more revenue still dominate the priorities of most businesses today.
  • Sales & Marketing, Business Intelligence Competency Center (BICC) and   Executive Management have the highest level of interest in dashboards and advanced visualization. Finance has the greatest interest in enterprise planning and budgeting. Operations including manufacturing, supply chain management, and services) leads interest in data mining, data storytelling, integration with operational processes, mobile device support, data catalog and several other technologies and initiatives. It’s understandable that BICC leaders most advocate end-user self-service and attach high importance to many other categories as they are internal service bureaus to all departments in an enterprise. It’s been my experience that BICCs are always looking for ways to scale BI adoption and enable every department to gain greater value from analytics and BI apps. BICCs in the best run companies are knowledge hubs that encourage and educate all departments on how to excel with analytics and BI.

  • Insurance companies most prioritize dashboards, reporting, end-user self-service, data warehousing, data discovery and data mining. Business Services lead the adoption of advanced visualization, data storytelling, and embedded BI. Manufacturing most prioritizes sales planning and enterprise planning but trails in other high-ranking priorities. Technology prioritizes Software-as-a-Service (SaaS) given its scale and speed advantages. The retail & wholesale industry is going through an analytics and customer experience revolution today. Retailers and wholesalers lead all others in data catalog adoption and mobile device support.

  • Insurance, Technology and Business Services vertical industries have the highest rate of BI adoption today. The Insurance industry leads all others in BI adoption, followed by the Technology industry with 40% of organizations having 41% or greater adoption or penetration. Industries whose BI adoption is above average include Business Services and Retail & Wholesale. The following graphic illustrates penetration or adoption of Business Intelligence solutions today by industry.

  • Dashboards, reporting, advanced visualization, and data warehousing are the highest priority investment areas for companies whose budgets increased from 2017 to 2018. Additional high priority areas of investment include advanced visualization and data warehousing. The study found that less well-funded organizations are most likely to lead all others by investing in open source software to reduce costs.

  • Small organizations with up to 100 employees have the highest rate of BI penetration or adoption in 2018. Factors contributing to the high adoption rate for BI in small businesses include business models that need advanced analytics to function and scale, employees with the latest analytics and BI skills being hired to also scale high growth businesses and fewer barriers to adoption compared to larger enterprises. BI adoption tends to be more pervasive in small businesses as a greater percentage of employees are using analytics and BI apps daily.

  • Executive Management is most familiar with the type and number of BI tools in use across the organization. The majority of executive management respondents say their teams are using between one or two BI tools today. Business Intelligence Competency Centers (BICC) consistently report a higher number of BI tools in use than other functional areas given their heavy involvement in all phases of analytics and BI project execution. IT, Sales & Marketing and Finance are likely to have more BI tools in use than Operations.

  • Enterprises rate BI application usability and product quality & reliability at an all-time high in 2018. Other areas of major improvements on the part of vendors include improving ease of implementation, online training, forums and documentation, and completeness of functionality. Dresner’s research team found between 2017 and 2018 integration of components within product dropped, in addition to scalability. The study concludes the drop in integration expertise is due to an increasing number of software company acquisitions aggregating dissimilar products together from different platforms.

Jun 20

How Artificial Intelligence Is Revolutionizing Enterprise Software In 2017

future-of-artificial-intelligence-and-big-data

  • 81% of IT leaders are currently investing in or planning to invest in Artificial Intelligence (AI).
  • Cowen predicts AI will drive user productivity to materially higher levels, with Microsoft at the forefront.
  • Digital Marketing/Marketing Automation, Salesforce Automation (CRM) and Data Analytics are the top three areas ripe for AI/ML adoption.
  • According to angel.co, there are 2,200+ Artificial Intelligence start-ups, and well over 50% have emerged in just the last two years.
  • Cowen sees Salesforce ($CRM), Adobe ($ADBE) and ServiceNow ($NOW) as well-positioned to deliver and monetize new AI-based application services.

These and many other fascinating insights are from the Cowen and Company Multi-Sector Equity Research study, Artificial Intelligence: Entering A Golden Age For Data Science (142 pp., PDF, client access reqd). The study is based on interviews with 146 leading AI researchers, entrepreneurs and VC executives globally who are involved in the field of artificial intelligence and related technologies. Please see the Appendix of the study for a thorough overview of the methodology. This study isn’t representative of global AI, data engineering and machine learning (ML) adoption trends. It does, however, provide a glimpse into the current and future direction of AI, data engineering, and machine learning.  Cowen finds the market is still nascent, with CIOs eager to invest in new AI-related initiatives. Time-to-market, customer messaging, product positioning and the value proposition of AI solutions will be critical factors for winning over new project investments.

Key takeaways from the study include the following:

  • Digital Marketing/Marketing Automation, Salesforce Automation (CRM) and Data Analytics are the top three areas ripe for AI/ML adoption. Customer self-service, Enterprise Resource Planning (ERP), Human Resource Management (HRM) and E-Commerce are additional areas that have upside potential for AI/ML adoption. The following graphic provides an overview of the areas in software that Cowen found the greater potential for AI/ML investment.

Artificial Intelligence: Entering A Golden Age For Data Science

  • 81% of IT leaders are currently investing in or planning to invest in Artificial Intelligence (AI). Based on the study, CIOs have a new mandate to integrate AI into IT technology stacks. The study found that 43% are evaluating and doing a Proof of Concept (POC) and 38% are already live and planning to invest more.  The following graphic provides an overview of company readiness for machine learning and AI projects.

How Artificial Intelligence Is Revolutionizing Enterprise Software In 2017

  • Market forecasts vary, but all consistently predict explosive growth. IDC predicts that the Cognitive Systems and AI market (including hardware & services) will grow from $8B in 2016 to $47B in 2020, attaining a Compound Annual Growth Rate (CAGR) of 55%. This forecast includes $18B in software applications, $5B in software platforms, and $24B in services and hardware. IBM claims that Cognitive Computing is a $2T market, including $200B in healthcare/life sciences alone. Tractica forecasts direct and indirect applications of AI software to grow from $1.4B in 2016 to $59.8B by 2025, a 52% CAGR.

Artificial Intelligence: Entering A Golden Age For Data Science

  • According to CBInsights, the number of financing transactions to AI start-ups increased 10x over the last six years, from 67 in 2011 to 698 in 2016. Accenture states that the total number of AI start-ups has increased 20-fold since 2011. The top verticals include FinTech, Healthcare, Transportation and Retail/e-Commerce. The following graphic provides an overview of the AI annual funding history from 2011 to 2016.

Artificial Intelligence: Entering A Golden Age For Data Science

  • Algorithmic trading, image recognition/tagging, and patient data processing are predicted to the b top AI uses cases by 2025. Tractica forecasts predictive maintenance and content distribution on social media will be the fourth and fifth highest revenue producing AI uses cases over the next eight years. The following graphic compares the top 10 uses cases by projected global revenue.

ai-use-cases

  • Machine Learning is predicted to generate the most revenue and is attracting the most venture capital investment in all areas of AI. Venture Scanner found that ML raised $3.5B to date (from 400+ companies), far ahead of the next category, Natural Language Processing, which has seen just over $1Bn raised to date (from 200+ companies). Venture Scanner believes that Machine Learning Applications and Machine Learning Platforms are two relatively early stage markets that stand to have some of the greatest market disruptions.

Artificial Intelligence: Entering A Golden Age For Data Science

  • Cowen predicts that an Intelligent App Stack will gain rapid adoption in enterprises as IT departments shift from system-of-record to system-of-intelligence apps, platforms, and priorities. The future of enterprise software is being defined by increasingly intelligent applications today, and this will accelerate in the future. Cowen predicts it will be commonplace for enterprise apps to have machine learning algorithms that can provide predictive insights across a broad base of scenarios encompassing a company’s entire value chain. The potential exists for enterprise apps to change selling and buying behavior, tailoring specific responses based on real-time data to optimize discounting, pricing, proposal and quoting decisions.

Artificial Intelligence: Entering A Golden Age For Data Science

  • According to angel.co, there are 2,200+ Artificial Intelligence start-ups, and well over 50% have emerged in just the last two years. Machine Learning-based Applications and Deep Learning Neural Networks are experiencing the largest and widest amount of investment attention in the enterprise.
  • Accenture leverages machine learning in 40% of active Analytics engagements, and nearly 80% of proposed Analytics opportunities today. Cowen found that Accenture’s view is that they are in the early stages of AI technology adoption with their enterprise clients.  Accenture sees the AI market growing exponentially, reaching $400B in spending by 2020. Their customers have moved on from piloting and testing AI to reinventing their business strategies and models.

Mar 19

Business Intelligence And Analytics In The Cloud, 2017

  • 78% are planning to increase the use of cloud for BI and data management in the next twelve months.
  • 46% of organizations prefer public cloud platforms for cloud BI, analytics and data management deployments.
  • Cloud BI adoption increased in respondent companies from 29% to 43% from 2013 to 2016.
  • Almost half of organizations using cloud BI (46%) use a public cloud for BI and data management compared to less than a third (30%) for hybrid cloud and 24% for private cloud.

These and many other insights are from the BARC Research and Eckerson Group Study, BI and Data Management in the Cloud: Issues and Trends published January 2017 (39 pp., PDF, no opt-in). Business Application Research Center (BARC) is a research and consulting firm that concentrates on enterprise software including business intelligence (BI), analytics and data management. Eckerson Group is a research and consulting firm focused on serving the needs of business intelligence (BI) and analytic leaders in Fortune 2000 organizations worldwide. The study is based on interviews completed in September and October 2016. 370 respondents participated in the survey globally. Given the size of the sample, the results aren’t representative of the global BI and analytics user base. The study’s results provide an interesting glimpse into analytics and BI adoption today, however. For a description of the methodology, please see page 31 of the study.

Key insights from the study include the following:

  • Public cloud is the most preferred deployment platform for cloud BI and analytics, and the larger the organization toe more likely they are using private clouds. 46% of organizations selected public cloud platforms as their preferred infrastructure for supporting their BI, analytics, and data management initiatives in 2016. 30% are relying on a hybrid cloud platform and 24%, private clouds. With public cloud platforms becoming more commonplace in BI and analytics deployments, the need for greater PaaS- and IaaS-level orchestration becomes a priority. The larger the organization, the more likely they are using private clouds (33%). Companies with between 250 to 2,500 employees are the least likely to be using private clouds (16%).

grouped-bi-cloud-platform-graphic

  • Dashboard-based reporting (76%), ad-hoc analysis and exploration (57%) and dashboard authoring (55%) are the top three Cloud BI use cases. Respondents are most interested in adding advanced and predictive analytics (53%), operational planning and forecasting (44%), strategic planning and simulation (44%) in the next year. The following graphic compares primary use cases and planned investments in the next twelve months. SelectHub has created a useful Business Intelligence Tools Comparison here that provides insights into this area.

cloud-bi-use-cases

  • Power users dominate the use of cloud BI and analytics solutions, driving more complex use cases that include ad-hoc analysis (57%) and advanced report and dashboard creation (55%). Casual users are 20% of all cloud BI and analytics, with their most common use being for reporting and dashboards (76%). Customers and suppliers are an emerging group of cloud BI and analytics users as more respondent companies create self-service web-based apps to streamline external reporting.

cloud-bi-power-users

  • Data integration between cloud applications/databases (51%) and providing data warehouses and data marts (50%) are the two most common data management strategies in use to support BI and analytics solutions today. Respondent organizations are using the cloud to integration cloud applications with each other and with on-premises applications (46%).  The study also found that as more organizations move to the cloud, there’s a corresponding need to support hybrid cloud architectures. Cloud-based data warehouses are primarily being built to support net new applications versus existing apps on-premise. Data integration is essential for the ongoing operations of cloud-based and on-premise ERP systems. A useful comparison of ERP systems can be found here.

cloud-data-integration

  • Data integration between on-premises and cloud applications dominates use cases across all company sizes, with 48% of enterprises leading in adoption. Enterprises are also prioritizing providing data warehouses and data marts (48%), the pre-processing of data (38%) and data integration between cloud applications and databases (38%). The smaller a company is the more critical data integration becomes. 63% of small companies with less than 250 employees are prioritizing data integration between cloud applications and databases (63%).

use-cases-of-cloud-management-by-company-size

  • Tools for data exploration (visual discovery) adopted grew the fastest in the last three years, increasing from 20% adoption in 2013 to 49% in 2016. BI tools increased slightly from 55% to 62% and BI servers dropped from 56% to 51%. Approximately one in five respondent organizations (22%) added analytical applications in 2016.

bi-tools-growth

  • The main reasons for adopting cloud BI and analytics differ by size of the company, with cost (57%) being the most important for mid-sized businesses between 250 to 2.5K employees. Consistent with previous studies, small companies’ main reason for adopting cloud BI and analytics include flexibility (46%), reduced maintenance of hardware and software (43%), and cost (38%). Enterprises with more than 2.5K employees are adopting cloud BI and analytics for greater scalability (48%), cost (40%) and reduced maintenance of hardware and software (38%). The following graphic compares the most important reason for adopting cloud BI, analytics and data management by the size of the company.

most-important-reason-for-adopting-cloud-bi-and-data-management

Sep 11

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. All organizations face the daunting task of integrating systems together to enable greater process visibility. enosiX is taking a leadership role in this area, offering real-time integration between SAP and Salesforce systems, giving enterprises the opportunity to be more responsive to suppliers, resellers, partners and most importantly, customers.

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

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?

Jul 2

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.

Oct 23

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

 

Jun 5

1 Comment

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 RapidMiner:  Magic 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

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