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Posts from the ‘Hype Cycle’ Category

Jun 21

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2021 Roundup Of AI And Machine Learning Market Forecasts Show Strong Growth

2021 Roundup Of AI And Machine Learning Market Forecasts Show Strong Growth

Demand for TensorFlow expertise is one of the leading indicators of machine learning and AI adoption globally. Kaggle’s State of Data Science and Machine Learning 2020 Survey found that TensorFlow is the second most used machine learning framework today, with 50.5% of respondents currently using it.

TensorFlow expertise continues to be one of the most marketable machine learning and AI skills in 2021, making it a reliable leading indicator of technology adoption. In 2020, there were on average 4,134 LinkedIn open positions that required TensorFlow expertise soaring to 8,414 open LinkedIn positions this year in the U.S. alone. Globally, demand for TensorFlow expertise has doubled from 12,172 open positions in 2020 to 26,958 available jobs on LinkedIn today.  

Demand for machine learning expertise, as reflected in LinkedIn open positions, also shows strong growth. Increasing from 44,864 available jobs in 2020 to 78,372 in 2021 in the U.S. alone, organizations continue to staff up to support new initiatives quickly. Globally, LinkedIn’s open positions requiring machine-learning expertise grew from 98,371 in 2020 to 191,749 in 2021.

Market forecasts and projections also reflect strong growth for AI and machine learning spending globally for the long term. The following are key takeaways from the machine learning market forecasts from the last year include the following:

  • Forrester says the AI market will be defined and grow within four software segments, with AI maker platforms growing the fastest, reaching $13 billion by 2025, helping drive the market to $37 billion by 2025. Forrester is defining the four AI software segments as follows: AI maker platforms for general-purpose AI algorithms and data sets; AI facilitator platforms for specific AI functions like computer vision; AI-centric applications and middleware tools built around AI for specialized tasks like medical diagnosis; and AI-infused applications and middleware tools that differentiate through advanced use of AI in an existing app or tool category.  New AI-centric apps built on AI functions such as medical diagnosis and risk detection solutions will be the second-largest market, valued at nearly $10 billion by 2025. Source: Sizing The AI Software Market: Not As Big As Investors Expect But Still $37 Billion By 2025, December 10, 2020.
2021 Roundup Of AI And Machine Learning Market Forecasts Show Strong Growth
  • IDC predicts worldwide revenues for the artificial intelligence (AI) market, including software, hardware, and services, will grow from $327.5 billion in 2021 to $554.3 billion in 2024, attaining a five-year compound annual growth rate (CAGR) of 17.5%. IDC further predicts that the AI Software Platforms market will be the strongest, with a five-year CAGR of 32.7%. The slowest will be AI System Infrastructure Software, with a five-year CAGR of 13.7% while accounting for roughly 36% of AI software revenues. IDC found that among the three technology categories, software represented 88% of the total AI market revenues in 2020. It’s the slowest growing category with a five-year CAGR of 17.3%. AI Applications took the largest share of revenue within the AI software category at 50% in 2020. Source: IDC Forecasts Improved Growth for Global AI Market in 2021, February 23, 2021
2021 Roundup Of AI And Machine Learning Market Forecasts Show Strong Growth
  • AI projects continued to accelerate in 2020 in the healthcare, bioscience, manufacturing, financial services, and supply chain sectors despite economic & social uncertainty. Two dominant themes emerge from the combination of 30 diverse AI technologies in this year’s Hype Cycle. The first theme is the democratization or broader adoption of AI across organizations. The greater the democratization of AI, the greater the importance of developers and DevOps to create enterprise-grade applications. The second theme is the industrialization of AI platforms. Reusability, scalability, safety, and responsible use of AI and AI governance are the catalysts contributing to the second theme.  The Gartner Hype Cycle for Artificial Intelligence, 2020, is shown below: Source: Software Strategies Blog, What’s New In Gartner’s Hype Cycle For AI, 2020, October 20, 2020.
2021 Roundup Of AI And Machine Learning Market Forecasts Show Strong Growth
  • Capgemini finds that Life Sciences, Retail, Consumer Products, and Automotive industries lead in the percentage of successfully deployed AI use cases today. Life Sciences leads all interviewed industries to AI maturity, with 27% of companies saying they have deployed use cases in production and at scale. Retail is also above the industry average of 13% of companies that have deployed AI in production at scale, with 21% of companies in the industry has adopted AI successfully.  17% of companies in the Consumer Products and Automotive industries now have AI in production, running at scale. Source: Capgemini, Making AI Work For You, (The AI-powered enterprise: Unlocking the potential of AI at scale) 2021
2021 Roundup Of AI And Machine Learning Market Forecasts Show Strong Growth
  • Between 2018 and 2020, there’s been a 76% increase in sales professionals using AI-based apps and tools. Salesforce’s latest State of Sales survey found that 57% of high-performance sales organizations use AI today. High-performing sales organizations are 2.8x more likely to use AI than their peers. High-performing sales organizations rely on AI to gain new insights into customer needs, improve forecast accuracy, gain more significant visibility of rep activity, improve competitive analysis, and more. Source: Salesforce Research, 4th Edition, State of Sales, June 2020
2021 Roundup Of AI And Machine Learning Market Forecasts Show Strong Growth
  • While 24% of companies are currently using AI for recruitment, that number is expected to grow, with 56% reporting they plan to adopt AI next year. In addition, Sage’s recent survey of 500 senior HR and people leaders finds adoption of AI as an enabling technology for talent management increasing. AI is proving effective for evaluating job candidates for potential, improving virtual recruiting events, and reducing biased language in job descriptions. It’s also proving effective in helping to improve career planning and mobility. Josh Bersin, a noted HR industry analyst, educator, and technologist, recently published an interesting report on this area titled The Rise of the Talent Intelligence Platform. Leaders in the field of Talent Intelligence Platforms include Eightfold.ai. Grounded in Equal Opportunity Algorithms, the Eightfold® Talent Intelligence Platform uses deep-learning AI to help each person understand their career potential, and each enterprise understands the potential of their workforce.Sources: VentureBeat, 8 ways AI is transforming talent management in 2021, March 25, 2021, and Eightfold.ai.
2021 Roundup Of AI And Machine Learning Market Forecasts Show Strong Growth
  • 84% of marketers are using AI-based apps and platforms today, up from 28% in 2018. Salesforce Research’s latest State of Marketing survey finds that high-performing marketers use an average of seven different applications or use cases. The familiarity high-performing marketers have with AI is a primary factor in 52% of them predicting they will increase their use of AI-based apps in the future. Source: Salesforce Research, 6th Edition, State of Marketing, June 2020
2021 Roundup Of AI And Machine Learning Market Forecasts Show Strong Growth
  • Marketing and Sales lead revenue increases due to AI adoption, yet lag behind other departments on cost savings.  40% of the organizations McKinsey interviewed see between a 6 and 10% increase in revenue from adopting AI in their marketing and sales departments. Adopting Ai to reduce costs delivers the best manufacturing and supply chain management results based on the McKinsey survey results. Revenue increases and cost reductions based on AI adoption are shown in the graphic below. Source: McKinsey & Company, The state of AI in 2020, November 17, 2020
2021 Roundup Of AI And Machine Learning Market Forecasts Show Strong Growth
  • AI sees the most significant adoption by marketers working in $500M to $1B companies, with conversational AI for customer service as the most dominant. Businesses with between $500M to $1B lead all other revenue categories in the number and depth of AI adoption cases. Just over 52% of small businesses with sales of $25M or less use AI for predictive analytics for customer insights. It’s interesting to note that small companies are the leaders in AI spending, at 38.1%, to improve marketing ROI by optimizing marketing content and timing. Source: The CMO Survey: Highlights and Insights Report, February 2019. Duke University, Deloitte, and American Marketing Association. (71 pp., PDF, free, no opt-in).
2021 Roundup Of AI And Machine Learning Market Forecasts Show Strong Growth
  • Three out of four companies are fast-tracking automation initiatives, including AI.  Bain & Company found that executives would like to use AI to reduce costs and acquire new customers, but they’re uncertain about the ROI and cannot find the talent or solutions they need. Bain research conducted in 2019 found that 90% of tech executives view AI and machine learning as priorities that they should be incorporating into their product lines and businesses. But nearly as many (87%) also said they were not satisfied with their Company’s current approach to AI. Source: Bain & Company, Will the Pandemic Accelerate Adoption of Artificial Intelligence? May 26, 2020
2021 Roundup Of AI And Machine Learning Market Forecasts Show Strong Growth
  • Gartner’s Magic Quadrant for Data Science and Machine Learning Platforms predicts a continued glut of exciting innovations and visionary roadmaps from competing vendors. Competitors in the Data Science and Machine Learning (DSML) market focus on innovation and rapid product innovation over pure execution. Gartner said key areas of differentiation include UI, augmented DSML (AutoML), MLOps, performance and scalability, hybrid and multicloud support, XAI, and cutting-edge use cases and techniques (such as deep learning, large-scale IoT, and reinforcement learning). Please see my recent article on VentureBeat, Gartner’s 2021 Magic Quadrant cites ‘glut of innovation’ in data science and ML, March 14, 2021.
2021 Roundup Of AI And Machine Learning Market Forecasts Show Strong Growth
  • 76% of enterprises are prioritizing AI & machine Learning In 2021 IT Budgets. Algorithmia’s survey finds that six in ten (64%) organizations say AI and ML initiatives’ priorities have increased relative to other IT priorities in the last twelve months. Algorithmia’s survey from last summer found that enterprises began doubling down on AI & ML spending last year. The pandemic created a new sense of urgency regarding getting AI and ML projects completed, a key point made by CIOs across the financial services and tech sectors last year during interviews for comparable research studies. Source: Algorithmia’s Third Annual Survey, 2021 Enterprise Trends in Machine Learning.
2021 Roundup Of AI And Machine Learning Market Forecasts Show Strong Growth
  • Technavio predicts the Artificial Intelligence platforms market will grow to $17.29 billion by 2025, attaining a compound annual growth rate (CAGR) of nearly 35%. The research firm cites the increased levels of AI R&D investments globally combined with accelerating adoption for pilot and proof of concept testing across industries. Technavio predicts Alibaba Group Holding Ltd., Alphabet Inc., and Amazon.com Inc. will emerge as top artificial intelligence platforms vendors by 2025. Source:  Artificial Intelligence Platforms Market to grow by $ 17.29 Billion at 35% CAGR during 2021-2025. June 21, 2021
2021 Roundup Of AI And Machine Learning Market Forecasts Show Strong Growth
  • Tractica predicts the AI software market will reach $126 billion in worldwide revenue by 2025.  The research firm predicts AI will grow fastest in consumer (Internet services), automotive, financial services, telecommunications, and retail industries. As a result, annual global AI software revenue is forecast to grow from $10.1 billion in 2018 to $126.0 billion by 2025. Source: T&D World, AI Software Market to Reach $126.0 Billion in Annual Worldwide Revenue by 2025.

Sources of Market Data on Machine Learning:

Aug 3

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Gartner Releases Their Hype Cycle for Cloud Computing, 2012

Enterprises are beginning to change their buying behaviors based on the deployment speed, economics and customization that cloud-based technologies provide.  Gartner cautions however that enterprises are far from abandoning their on-premise models and applications entirely for the cloud.

Based on an analysis of the Gartner Hype Cycle for Cloud Computing, 2012, the best results are being attained by enterprises that focus on a very specific strategy and look to cloud-based technologies to accelerate their performance.  Leading with a strategic framework of goals and objectives increases the probability of cloud-based platform success. Those enterprises that look to cloud platforms only for cost reduction miss out on their full potential.

The Hype Cycle for Cloud Computing, 2012 is shown below:

Cloudwashing and Inflated Enterprise Expectations

While the hype surrounding cloud computing may have peaked, cloudwashing continues to cause confusion and inflated expectations with enterprise buyers.  This just slows down sales cycles, when more straightforward selling could lead to more pilots, sales and a potentially larger market. Cloud vendors who have the expertise gained from delivering cloud platforms on time, under budget, with customer references showing results are starting to overtake those that using cloudwashing as part of their selling strategies.

Additional take-aways from the Gartner Hype Cycle for Cloud Computing include the following:

  • Cloud Email is expected to have a 10% adoption rate in enterprises by 2014, down from the 20% Gartner had forecasted in previous Hype Cycles.  This represents modest growth as the adoption rate of this category had been between 5 and 6% in 2011.
  • Big Data will deliver transformational benefits to enterprises within 2 to 5 years, and by 2015 will enable enterprises adopting this technology to outperform competitors by 20% in every available financial metric.  Gartner defines Big Data as including large volumes processed in streams, in addition to batch.  Integral to Big Data is an extensible services framework that can deploy processing to the data or bring data to the process workflow itself. Gartner also includes more than one asset type of data in their definition, including structured and unstructured content.  The Priority Matrix for Cloud Computing, 2012 is shown below:

  • Master Data Management (MDM) Solutions in the Cloud and Hybrid IT are included in this hype cycle for the first time in 2012.  Gartner reports that MDM Solutions in the Cloud is getting additional interest from Enterprise buyers as part of a continual upward trend of interest in MDM overall.  Dominant vendors in this emerging area include Cognizant, Data Scout, IBM, Informatica, Oracle and Orchestra Networks, are among those with MDM-in-the-cloud solutions.
  • PaaS continues to be one of the most misunderstood aspects of cloud platforms.  The widening gap between enterprise expectations and experiences is most prevalent in this market.  Gartner claims this is attributable to the relatively narrow middleware functions delivered and the consolidation fo vendors and service providers in this market.
  • By 2014 the Personal Cloud will have replaced the personal computer as the center of user’s digital lives.
  • Private Cloud Computing is among the highest interest areas across all cloud computing according to Gartner, with 75% of respondents in Gartner polls saying they plan to pursue a strategy in this area by 2014.  Pilot and production deployments are in process across many different enterprises today, with one of the major goals being the evaluation of virtualization-driven value and benefits.
  • SaaS is rapidly gaining adoption in enterprises, leading Gartner to forecast more than 50% of enterprises will have some form of SaaS-based application strategy by 2015.  Factors driving this adoption are the high priority enterprises are putting on customer relationships, gaining greater insights through analytics, overcoming IT- and capital budget-based limitations, and aligning IT more efficiently to strategic goals.
  • More than 50% of all virtualization workloads are based on the x86 architecture. This is expected to increase to 75% by 2015.  Gartner reports this is a disruptive innovation which is changing the relationship between IT and enterprise where service levels and usage can be tracked.

Bottom line: Gartner’s latest Hype Cycle for Cloud Computing  shows that when cloud-based platforms are aligned with well-defined strategic initiatives and line-of-business objectives, they deliver valuable contributions to an enterprise.  It also shows how Infrastructure as a Service (IaaS) and Platform as a Service (PaaS) are the catalysts of long-term market growth.  The following slide from the presentation  High-Tech Tuesday Webinar: Gartner Worldwide IT Spending Forecast, 2Q12 Update: Cloud Is the Silver Lining (free for download) also makes this point.

Jun 26

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The Marketing of Cloud Multitenancy: How Early Adopters Are Killing The Hype

It’s impressive how quickly the teams evaluating CRM cloud-based applications are learning how to deflate the hype surrounding multitenancy.

One gets the impression that hype-hunting has now become a sport in these teams.  In engineering-centric companies it’s a badge of honor to find out just how multitenant a cloud-based application or platform is.  Multitenancy isn’t the only area they are looking at, but given the massive amount of hype surrounding this issue on the part of vendors, it generates more attention because evaluation teams are skeptical.

Teams evaluating CRM applications aren’t satisfied with an easily customized and used graphical interface or series of workflows, they are getting more interested in the architecture itself .  In some cases they’ve been burned by claims of an application being SaaS-based when in fact the architecture is a glorified series of Citrix-like sessions running in the background or worse.  I have seen a healthy amount of skepticism in the evaluations going on right now and recently completed of SaaS applications and entire cloud platforms.  Gartner’s inquiry calls from corporate accounts must be accelerating as their clients look for guidance on how to sort out the multitenancy hype.

CRM, Multitenancy and the Hype Cycle for Cloud Computing

Gartner’s search analytics show that cloud computing and related terms had 29,998 searches in the last twelve months with cloud computing alone generating 10,062 searches.  SaaS and related terms had a search volume of 19,000.  These terms are among the most popular across all Gartner search terms for the last twelve months.  In comparison, CRM had over 42,000 searches in the same period.

It’s in this area of CRM applications where multitenancy has gone into hype overdrive. Looking for differentiators, some CRM vendors are claiming not just multitenancy – but their specific brand of it.  This confuses their prospects, which immediately energizes evaluation teams to do a more thorough job than they have ever done before.  By claiming their own type of multitenancy, CRM vendors are ironically not just slowing down their own sales cycles, they are making the entire industry slow down.  No wonder Gartner places multitenancy along the Peak of Inflated Expectations in the latest Hype Cycle for Cloud Computing which is shown below.

Making Sense of Elasticity and Multitenancy

It’s paradoxical that enterprise software vendors, especially those selling SaaS-based CRM applications,  are attempting to turn multitenancy into a differentiator.  What is needed is a greater focus on usability, flexibility in aligning workflows to specific needs, and better enterprise integration technologies.  Sell the value not the product features.

Given the confusion differentiating on multitenancy is creating and the calls Gartner is getting on this issue, they published Gartner Reference Model for Elasticity and Multitenancy.  It includes what Gartner believes a cloud services provider must implement in terms of a multitenant service in addition to what SaaS-based applications need to provide.  Here are their checklists for each area:

Multitenancy Service Requirements for Cloud Services Providers

  • Isolation of tenant data
  • Isolation of the tenant workspace (memory)
  • Isolation of tenant execution characteristics (performance and availability)
  • Tenant-aware security, monitoring, management, reporting and self-service administration
  • Isolation of tenant customizations and extensions to business logic
  • Continuous, tenant-aware version control
  • Tenant-aware error tracking and recovery
  • Tracking and recording of resources use per tenant
  • The ability to allocate resources to tenants dynamically, as needed and based on policy Horizontal scalability to support real-time addition/removal of tenant resources, tenants or users without interruptions to the running environment

Multitenancy in Cloud Application Services (Software as a Service) Applications

  • Be available 24/7, because of the potential global user base
  • Adopt new versions without disrupting the continuous operations of tenants, and preserve user customizations
  • Scale up or down on demand
  • Allow individual rollback and restore for each tenant
  • Not allow a “noisy neighbor” tenant to affect the performance of other tenants, or increase their costs
  • Be accessible from various locations, devices and software architectures to meet potentially global demand
  • Offer tenant-aware self-service

Gartner also released their Reference Architecture for Multitenancy, which is shown below.  One of the key assumptions of this model is that multitenancy is a mode of operation where multiple, independent and secured instances of applications run in a shared environment.  The model includes the seven different models of multitenancy Gartner has seen in their research.  These seven models, listed across the top of the model beginning with Shared Nothing and progressing to Custom Multitenancy are across the top of the model.

The majority of enterprises I’ve worked with are looking to the Shared Hardware approach in an attempt to create backward compatibility to their legacy applications via Virtual Machines. Another area of interest is the Shared Container approach which relies on a separate logical or physical instance of a DBMS, and often isolates its own business logic.  This is ideal for distributed order management systems and SaaS-based ERP systems for example.  Yet the legacy application support in this type of multitenancy can get expensive fast.

Shared Everything Multitenancy is ideal for quickly on-ramping and off-ramping applications, tenants and individual system users and is what nearly all enterprise vendors claim to do.  In reality only a handful do this well.  This approach to multitenancy is based on the Shared Container approach including support for shared DBMS sessions.  Salesforce.com’s Force.com platform, VMWare WaveMaker and Zoho Creator are all examples of companies who have successfully delivered Shared Everything multitenancy.

With so much to gain by positioning an application or solution suite in the 6th and 7th models, vendors are rushing to define their own versions of Shared Everything and Custom Multitenancy.  The land grab is on in this area of the multitenancy market right now.  IBM, Microsoft and Oracle are all expected to endorse and eventually have many of their cloud-based applications in the Shared Everything model.  Each of these companies and many others will have a multi-model based approach to selling multitenancy as well.

Gartner Reference Model for Elasticity and Multitenancy

Source:  Gartner Reference Model for Elasticity and Multitenancy

Bottom line: Enterprise software vendors can accelerate evaluation cycles and sell more by differentiating on the user experience and value delivered instead of trying to create fear, uncertainty and doubt (FUD) by creating their own definition of multitenancy.

Aug 13

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SaaS-based Analytics and Business Intelligence Market Update, August 2011

Challenging, uncertain economic times accelerate sales cycles and lead to more closed deals for business intelligence software providers.  Companies get an urgency to reduce costs and risks, relying on the insights gained from these applications.

There’s an interesting dichotomy starting to emerge in how experts and analysts define just how these markets will mature however.  Both agree that economic uncertainty are growth catalysts yet they diverge on adoption rates, roadblocks, and which analytics and BI technology will dominate in the years ahead.

This week I read Balancing Custom And Packaged Apps In Your Application Portfolio Strategy by George Lawrie, Mike Gilpin and Adam Knoll from Forrester and the latest Hype Cycle of Business Intelligence, 2011 by a collection of Gartner authors led by Andreas Bitterer.  I’ve summarized the key points of each below.

Forrester Sees SaaS Applications Overtaking Custom Application Development

Forrester sees SaaS-based applications starting to replace in-house custom application development, gathering momentum through 2013.  Gartner, with their Hype Cycle for Business Intelligence, 2011 just released this week, shows BI platforms having greater near-term benefit than SaaS-based analytics and BI.  Custom application development projects are going to face continued pressure to keep up with business requirements that SaaS applications are proving able to handle more effectively and economically than ever before.

In-house development makes more sense for specific analytics and reporting requirements,  yet will continually be eroded by SaaS-based applications that can meet most requirements at a lower cost.  Forrester has in the past said SaaS-based adoption of analytics applications in general and predictive applications specifically would be very slow due to data integration challenges.  This study points to a potential shift in their mindset, as the data shows SaaS-based analytics beginning to replace custom in-house developed applications.

Here are the key take-aways from the report:

  • Analytics processes are supported 79% of the time with custom application development.  Procure-to-pay (33%) and record-to-report (33%) are the second-most supported.  Multiple responses were allowed in the survey.
  • When asked which process areas they are automating with SaaS, analytics (33%), record-to-report (18%), order-to-cash  (15%), and purchase-to-pay (12%) were the most common responses.  There was a small sample size on the Forrester report and the most startling insight was how quickly respondent companies plan to migrate from custom application development to SaaS-based analytics and BI.
  • Nearly 50% of the respondents to the Forrester survey have between five and 19 SaaS-based applications today with 18% expecting to have 35 or more by 2013.  In addition 63% of respondents expect to deploy between five and 34 SaaS-based applications by 2013, a significant shift in just two years.
  • 36% of survey respondents say their  SaaS applications run completely standalone.  Another 36% mention they use a combination of on-premises Master Data Management (MDM) and process integration tools.  Ironically only 3% are deploying their applications on cloud-based MDM or process integration-based platforms.

Gartner’s Hype Cycle for Business Intelligence, 2011

Unlike the hype cycle for cloud computing, this hype cycle has fewer technology categories (25), a narrative firmly grounded in business process and strategy, and more practical and pragmatic insights versus just theoretical.  At 50 pages it’s  quick read and while there are many excellent points made, I have summarized the key take-aways pertaining to the highest hype points and SaaS adoption below:

  • Mobile Business Intelligence (BI) is the latest entry to the Hype Cycle for Business Intelligence based on the massive hype around analyzing locational and application data.  The hype surrounding the Apple iPad Series, Google Android and other tablet and smartphone platforms has made this one of the most hyped areas of the last year according to the analysis.
  • Consumerization, Decision Support, analysis of non-traditional data and “Big Data” are the areas of the greatest innovation today.  The hype cycle points to search, mobile, visualization and data discovery being the catalyst of Consumerization.  Predictive analytics, which is on the Slope of Enlightenment on this latest hype cycle, is critical to decision support.  The non-traditional and “Big Data” area of innovation is further supported by content, text analytics, in-memory DBMSs and columnar DBMSs.
  • SaaS-based Business Intelligence is at the apex of the Peak of Inflated Expectations yet will continue to have low adoption rates.  Gartner believes that the  lack of trust in third parties managing confidential data, and the inertia and fear many companies have in moving to a new architecture are slowing adoption.  This is in contrast to the survey Forrester released this week showing analytics being one of the most popular SaaS-based applications planned by 2013 in their base of respondents.
  • Gartner sees SaaS-based Business Intelligence of the most value to midsize and smaller organizations who lack IT staff yet have very specific, targeted information needs.  Website analytics, social media monitoring, dashboards, predictive analytics and Excel as a BI front-end all apply.  Both Forrester and Gartner agree on this point and see this type of custom development going away quickly internally.
  • There is a massive amount of hype surrounding in-memory computing, particularly from SAP at its Sapphire conferences .  Gartner believes that SAP’s vision of in-memory computing exceeds  in-memory analytics to include analytical and transactional processing.  As a result, In-Memory Database Management Systems are at the Peak of Inflated Expectations.


Source: Hype Cycle for Business Intelligence, 2011, Published 12 August 2011 | ID:G00216086 By Andreas Bitterer.  Gartner, Inc.

What Both Agree On

Forrester’s survey shows SaaS eventually replacing custom application development while Gartner’s Hype Cycle for Business Intelligence shows the practical, pragmatic technologies including dashboards, predictive analytics combined with the more complex Business Activity Monitoring (BAM), Business Intelligence Platforms, and Data-Mining Workbenches delivering the most value.  Despite these differences, both agree on the following:

  • The overall market for BI, Analytics and Performance Management continues to grow at between 8 to 12% per year depending on the forecast used.  The following forecast is from the report  Market Trends: Business Intelligence, Worldwide, 2011-2014, 7 June 2011 | ID:G00213483 by Dan Sommer and James Richardson.
Source: Market Trends: Business Intelligence, Worldwide, 2011-2014, 7 June 2011 | ID:G00213483 by Dan Sommer and James Richardson
  • 2011 continues to see large, strategic deals for analytics and BI closing more rapidly than they have in the past.
  • SaaS-based analytics and BI continues to gain a greater share of spending in midsize and smaller companies.  Both also agree that the proliferation of smaller SaaS-based analytics and Bi vendors concentrating on a specific niche have successfully displaced in-house custom development of competitive applications.  Trust in the smaller vendor, their track record, customer references and financial viability are what are winning deals for SaaS-based analytics and BI software providers today.
  • The market transition from build to buy is now in full force as budgets become available again.  This is key assumption of both analyses and means that smaller, more niche-oriented SaaS-based analytics and BI vendors stand a chance to get new reference accounts and grow, despite a challenging economy.