Magic Quadrant for Advanced Analytics Platforms

Discussion in 'Giải pháp ERP, CRM, EPM and BI' started by bsdinsight, Dec 5, 2014.

  1. bsdinsight

    bsdinsight Well-Known Member

    Predictive analytics and other categories of advanced analytics are becoming a major factor in the analytics market. We evaluate the leading providers of advanced analytics platforms that are used to build solutions from scratch.

    Market Definition/Description
    Gartner defines advanced analytics as, "the analysis of all kinds of data using sophisticated quantitative methods (for example, statistics, descriptive and predictive data mining, simulation and optimization) to produce insights that traditional approaches to business intelligence (BI) — such as query and reporting — are unlikely to discover."

    Organizations adopt advanced analytics in a number of different ways. The use of advanced analytics platforms is one approach and constitutes the market evaluated in this document. The two most common alternative approaches are:
    • To work with advanced analytics service providers (such as Accenture, Mu Sigma, or Opera Solutions), whose employees use either commercial or proprietary analytics tools to deliver insights to the organization.
    • To deploy, either on-premises or through SaaS, packaged analytics applications that target specific business domains (such as insurance fraud detection or retail merchandise planning).
    Each of these alternative approaches represents a series of discrete markets and is described in other Gartner research (for example, for a decision framework regarding these alternatives, see "An Eight-Question Decision Framework for Buying, Building and Outsourcing Data Science Solutions"), but not included in this evaluation. An advanced analytics platform provides a full suite of tools for a knowledgeable user to perform a variety of analyses on different types of data. In today's market much of this analysis is predictive in nature, although elements of descriptive analysis are not uncommon (see Note 1). While these capabilities remain important, in the future other techniques such as optimization and simulation are likely to increase in importance.

    Magic Quadrant

    Figure 1. Magic Quadrant for Advanced Analytics Platforms

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    (Source)
     
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  3. bsdinsight

    bsdinsight Well-Known Member

    Vendor Strengths and Cautions
    Actuate
    Actuate (www.actuate.com) is a traditional BI platform provider based in San Mateo, California, U.S. and Barcelona, Spain. Actuate acquired Quiterian in October 2012, and rebranded it as BIRT Analytics, to extend its advanced analytic capabilities. BIRT Analytics focuses on customer analytics across a number of industries, including financial services, telecommunications, retail/e-commerce and healthcare.

    Strengths
    • While established in BI, and with a strong focus toward data visualization, Actuate is in the early stages of providing a mature advanced analytics platform — though its acquisition of Quiterian indicates its commitment to this market.
    • Quiterian's region of origin is Spain — for Spanish clients this product may have several strong points such as localization and local experts. (BIRT Analytics also has customers in North America, the U.K., France, Russia and Latin America.)
    • Customer references cited high levels of satisfaction with the product's capabilities (see Note 2) for data access, visualization and exploration/discovery, data filtering and manipulation, advanced descriptive analytics, and user experience.
    • Customer references cited no significant problems with the upgrade experience.
    Cautions
    • Feedback from customer references indicates strained vendor relationships. An unusually high proportion of product problems were reported as "not yet resolved," little credit is given to Actuate for factoring customer input into the product road map, and opportunities to network with other customers were poor. Overall, willingness to recommend Actuate for advanced analytics was very low.
    • Customer references cited low levels of satisfaction with the product's capabilities for predictive analytics, analytical business use cases, delivery, integration and deployment, platform and project management, and performance and scalability.
    • The product has a fairly weak UI, as it doesn't allow visual composition (see Note 3) of the workflow.
     
  4. bsdinsight

    bsdinsight Well-Known Member

    Alpine Data Labs
    Alpine Data Labs (www.alpinenow.com) is based in San Francisco, California, U.S., and offers an analytics platform with a focus on analyzing big datasets by running analytic workflows natively within existing Hadoop or other parallel platforms. Alpine also offers a strongly collaborative approach to predictive analytics to assist with model development and reuse. The company has grown rapidly in a variety of industries.

    Strengths
    • Alpine's unique selling point is its scalability due to the tight integration with Hadoop.
    • It also features a browser-based, state-of-the-art visual composition UI, which caters to the novice data scientist and business analyst.
    • Alpine was one of the strongest vendors in incorporating customer input into the product road map and, overall, had very high levels of customer satisfaction.
    • Alpine customer references cited data access, user experience, and performance and scalability as particular strengths for the product, as well as a high degree of product reliability.
    Cautions
    • Alpine clearly still lacks breadth and depth of functionality when compared with the Leaders.
    • Due to its small size, Alpine struggles to establish significant visibility for itself. As larger vendors increase their focus on this market and develop their "big data" stories more fully, this will pose an increasing challenge to Alpine's ability to succeed.
    • Customer references cited visualization and exploration/discovery, and platform and project management as areas of (relative) product weakness.
     
  5. bsdinsight

    bsdinsight Well-Known Member

    Alteryx
    Alteryx (www.alteryx.com), based in Irvine, California, U.S., provides a data-blending and advanced analytics platform that allows analysts to blend internal, third-party and cloud data, and then analyze it using spatial and predictive tools. This is done in a single workflow, with no programming required. Alteryx is particularly strong in the retail and communications industries.

    Strengths
    • Alteryx has solid offerings geared toward customer analytics and location intelligence and provides a modern UI with drag-and-drop functionality for the R language, and scalable performance through its partnership with Revolution Analytics.
    • Alteryx had one of the highest levels of overall customer satisfaction and also received positive feedback for its user conferences.
    • Alteryx was most frequently selected by customers based on ease of use and speed of model development.
    • Alteryx's customer references cite high levels of satisfaction with the data access and the data filtering and manipulation components of the product.
    Cautions
    • Alteryx's solution is targeted at line-of-business analysts, rather than traditional data scientists whose needs Alteryx may not be able to support — although it's integration with Revolution Analytics may offer something of a remedy for this situation.
    • Alteryx's references cited one of the highest frequencies of problems with product reliability and the upgrade process.
    • Customer references cited visualization and exploration/discovery, and platform and project management as two areas of (relative) product weakness.
     
  6. bsdinsight

    bsdinsight Well-Known Member

    Angoss
    Angoss (www.angoss.com), based in Toronto, Ontario, Canada, has a long history of advanced analytics in its KnowledgeSEEKER decision tree product. Angoss focuses on three main markets — risk analytics, marketing analytics and CRM analytics — with its largest customer base in financial services.

    Strengths
    • KnowledgeSTUDIO is a fairly mature, easy-to-use offering with a reasonable breadth and depth of analytic functions.
    • Angoss received positive feedback from clients for incorporating input into the product road map, and for the alignment of pricing with the value the clients derive from the product (pricing is on a named-user workstation, client/server or enterprisewide basis; hosted cloud solutions are priced on an annual subscription basis).
    • Customer references cite high levels of satisfaction with the visualization and exploration/discovery, predictive analytics and user experience components of the product.
    • For analysts who like working with decision trees (or who require them), Angoss is a good fit — with the ability to transform other types of predictive models into a tree format and assign actions to create a strategy.
    Cautions
    • Angoss has been a long-term competitor in this market, but — despite the popularity of its KnowledgeSEEKER product — has yet to establish a dominant presence in the market. Its recent transaction, to become a private company and improve its access to capital, might help improve its credibility as a stand-alone provider and its appeal as an acquisition candidate.
    • The company's product range caters to most needs of data scientists; however, graph analysis, time-series analysis, support vector machines, instance-based approaches, and more advanced functions are not yet supported.
    • Customer references cited data filtering and manipulation, simulation, and platform and project management as areas of (relative) product weakness.
     
  7. bsdinsight

    bsdinsight Well-Known Member

    FICO
    FICO (www.fico.com), based in San Jose, California, U.S., was a pioneer in credit scoring, but has broadened across a range of other domains with a focus on decision management and operationalizing analytics. FICO has a focus on banking, insurance, retail and healthcare, but also has a growing presence in other industries.

    Strengths
    • FICO's experience and reputation in credit scoring bring it considerable credibility in this market. The emphasis on ensuring value from the analytic work — through its emphasis on decision making — also positions it well for the future.
    • FICO was frequently selected based on the user's ability to build models with exceptional accuracy, to model efficiently against wide datasets (with lots of variables) and for its support for collaboration between business users and the analytics team.
    • FICO had good levels of customer satisfaction and high levels of product reliability.
    • Customer references cite high levels of satisfaction with the optimization and performance and scalability components of the product.
    Cautions
    • FICO's functional breadth and depth for most analytic categories (besides optimization) is limited compared with offerings from the Leaders.
    • Reference clients indicated that the company's pricing model does not reflect the way FICO's platform delivers value to their organizations. (The current model is licensed, on-premises software priced on a per-seat basis; FICO is also adding cloud-based subscription and usage-based options.)
    • The product has limited visualization and exploration/discovery capabilities and was rated low by customers in this area, along with data filtering and manipulation.
     
  8. bsdinsight

    bsdinsight Well-Known Member

    IBM
    IBM (www.ibm.com), based in Chicago, Illinois, U.S., acquired SPSS in 2009 and has evolved its portfolio so that predictive analytics are accessible to multiple user types and skill levels. Best known for its Statistics and Modeler (data mining workbench) products and solutions, IBM SPSS resolves a wide range of challenges related to the analytics of customers, operations, threat and risk.

    Strengths
    • IBM has devoted considerable corporate emphasis to this product space. For example, the breadth and often depth of IBM's analytic offerings (not just SPSS but also Watson and ILOG) and the successful positioning of them under the corporate Smarter Planet branding.
    • IBM was frequently selected based on: speed of model development/ability to build large numbers of models, ease of use and product quality.
    • Customer references cite high levels of satisfaction with the data access, data filtering and manipulation, advanced descriptive analytics, predictive analytics, user experience, and the performance and scalability components of the product.
    • IBM received high marks for innovation: incorporating R&D such as entity analytics (see Note 4) into SPSS Modeler; and, the recently announced IBM Watson Analytics incorporating IBM's Rapidly Adaptive Virtualization Engine (RAVE) visualization capabilities merged with SPSS algorithms; along with in-memory investments such as IBM Blu Acceleration and integration with continued acquisitions such as Vivisimo.
    Cautions
    • IBM is working to integrate and align the variety of different analytic capabilities it has developed and acquired. The complexity of this process occasionally results in a lack of understanding, both within IBM and among some of its channel partners, about the product road map.
    • IBM's product stack and the individual offerings can make it difficult to use in settings where the required functionality stretches across many discrete product offerings. IBM is evaluating the effectiveness of new bundling options such as predictive maintenance and quality.
    • References did not feel that IBM listens to their input regarding product direction, and were consistently negative about the pricing structure (based on users and cores): saying, in particular, that it was neither predictable nor controllable. (The new, simplified license model may address these concerns.)
    • Customer references cited simulation as an area of (relative) product weakness.
     
  9. bsdinsight

    bsdinsight Well-Known Member

    InfoCentricity
    InfoCentricity (www.infocentricity.com), based in Novato, California, U.S., is a private company best known as a specialized provider of predictive analytics for credit risk decisions in the banking industry (it has also demonstrated success in getting its flagship Xeno product to the retail, education and marketing sectors).

    Strengths
    • InfoCentricity had one of the highest levels of customer satisfaction of any of the vendors surveyed, as well as high scores for product reliability and the upgrade process.
    • InfoCentricity was frequently selected based on ease of use, product quality, and the quality of its internal experts.
    • InfoCentricity's strategy was built from the beginning on the alignment of product and supporting services — with an emphasis on knowledge transfer as a key element of the services value proposition.
    • Xeno customer references cite high levels of satisfaction with the visualization and exploration/discovery, predictive analytics, analytical business use cases, user experience, and performance and scalability components of the product.
    Cautions
    • Xeno is scorecard-centric and does lack in breadth of functionality (although it provides integrated decision trees, clustering and supporting of variable generation and reporting capabilities), so clients should check if Xeno will cater to their future as well as their current needs.
    • InfoCentricity suffers from a lack of awareness outside of the credit risk market and will need to move beyond the perception of being FICO's competitor (many of its executives are ex-FICO) if it is to establish a broader market relevance.
    • Customer references cited optimization and simulation as areas of (relative) product weakness.
     
  10. bsdinsight

    bsdinsight Well-Known Member

    Knime
    Knime (www.knime.com), based in Zurich, Switzerland, offers a free, open-source, desktop-based advanced analytics platform. It also offers a commercial, server-based on-site or customer cloud solution providing additional enterprise functionality. Knime has a presence across a range of industries, but with particular experience in life science, government, education and communications.

    Strengths
    • The Knime platform supports an extensive breadth and depth of functionality.
    • Knime had the joint-highest levels of overall customer satisfaction as well as some of the best scores for customer engagement (conferences and online community).
    • Knime was frequently selected based on support for open-source capabilities, ease of use, and license cost (even from customers who subsequently paid license fees). The bundling and pricing of the software is very customer friendly and should also cater not only to the top-tier firms, but also to the midmarket.
    • Customer references cite high levels of satisfaction with the data access, data filtering and manipulation, predictive analytics, further advanced analytics, and analytical business use case components of the product.
    Cautions
    • Despite the large number of installed customers, Knime does not have high visibility in the market beyond the data mining community.
    • Knime's company size can affect support for staffing-intensive vendor selection and customer support requests.
    • Because of its free desktop solution, it often lacks credibility in the more comprehensive commercial environment.
    • Although Knime has a broad range of functionality, simulation is an area of (relative) product weakness.
     
  11. bsdinsight

    bsdinsight Well-Known Member

    Megaputer
    Megaputer (www.megaputer.com), based in Bloomington, Indiana, U.S., is a privately held software firm and, despite its Russian heritage and a Moscow-based development center, most of its clients are based in North America. Its flagship product is PolyAnalyst, which caters to the broad needs of advanced analytics and has fairly wide industry traction.

    Strengths
    • PolyAnalyst has good functional coverage and a particularly strong focus on integrating text into the predictive analytics environment.
    • Megaputer had high levels of overall customer satisfaction.
    • Megaputer was frequently selected based on its ability to support a wide variety of data types (particularly textual data, which it has bundled into its extended PolyAnalyst for Text product offering), ease of use, and the expertise of its internal experts.
    • Customer references cite high levels of satisfaction with the data access, data filtering and manipulation, advanced descriptive analytics, and analytical business use cases components of the product.
    Cautions
    • PolyAnalyst is not currently positioned to cater to the requirements of very advanced data scientists — it lacks extensibility options and will not be able to scope with the most challenging big data demands (its user interface will require significant modernization).
    • Megaputer's references were not satisfied with the degree of customer community facilitated by the company through its online forums or customer conferences.
    • Customer references cited visualization and exploration/discovery as areas of (relative) product weakness.
     
  12. bsdinsight

    bsdinsight Well-Known Member

    Microsoft
    Microsoft (www.microsoft.com) is based in Seattle, Washington, U.S., and its predictive analytics capability is embedded within SQL Server. The capabilities can be accessed either directly through SQL Server or through an Excel plug-in that acts as a front end to SQL Server.

    Strengths
    • The availability of predictive analytics through the widely adopted SQL Server platform gives Microsoft great reach into organizations that can serve as a springboard for future development.
    • Microsoft has a sophisticated development team that recognizes the importance of this market. Coupled with its corporate strength, this should ensure sufficient resources are available for Microsoft to execute its ambitious plans for advanced analytics.
    • Microsoft was frequently selected based on product quality, the availability of skills, low implementation cost and effort, and alignment with existing data infrastructure investments.
    • Customer references cite high levels of satisfaction with the data access, data filtering and manipulation, delivery, integration and deployment, platform and project management, and performance and scalability components of the product.
    Cautions
    • SQL Server 2012 Analysis Services lacks in breadth and depth, and also usability, for the 13 advanced analytics capabilities when compared with the Leaders.
    • Microsoft received the lowest scores of any vendor on its willingness to incorporate customer feedback into future versions of the product, although this may change as Microsoft ramps up for a significant overhaul of the product in late 2014.
    • Customer references cited visualization and exploration/discovery, advanced descriptive analytics, predictive analytics and further advanced analytics as areas of (relative) product weakness.
     
  13. bsdinsight

    bsdinsight Well-Known Member

    Oracle
    Oracle (www.oracle.com) is based in Redwood Shores, California, U.S. Its Advanced Analytics Option (OAA), an optional component of the Oracle Database Enterprise Edition, has been implemented in multiple different geographies and industries and facilitates a range of deployment options — from on-premises and hosted to cloud-based and embedded in applications.

    Strengths
    • Oracle has the corporate strength to deliver both development and sales and marketing resources to this product line.
    • OAA is tightly integrated with Oracle Database 12c and this often brings lots of scalability and simplicity; that is, no need to create extra copies inside a separate analytical data store.
    • Oracle was most often selected based on its support for open-source capabilities (R programming language) and alignment with existing data infrastructure investments.
    • Customer references cite high levels of satisfaction with the data access, predictive analytics, further advanced analytics (such as text analysis), delivery, integration and deployment, and performance and scalability components of the product.
    Cautions
    • OAA is database-centric (that is, all data to be analyzed has to be in the database). There are pros and cons to this approach. A database-centric approach eliminates the need to move data out of the data warehouse and into a separate analytic engine. However, advanced analytics can be very workload-intensive, so organizations need to be careful how they manage the modeling and scoring processes to balance system performance with the availability of the data for analysis purposes.
    • Customer references cited visualization and exploration/discovery, advanced descriptive analytics, optimization, simulation, platform and project management as areas of (relative) product weakness.
    • Oracle references were negative regarding the pricing structure (based on a per-processor fee); suggesting that even though it is not expensive, it does not align well with the way its customers receive value and, in particular, saying that pricing was neither predictable nor controllable.
     
  14. bsdinsight

    bsdinsight Well-Known Member

    RapidMiner
    RapidMiner (www.rapidminer.com), formerly known as Rapid-I, is based in Cambridge, Massachusetts, U.S. RapidMiner is an open-source, client/server-based solution also available as a commercial solution with the ability to work on larger datasets and to connect to more data sources. The platform derives its extensibility via source-code availability and integration of other open-source solutions (for example, R and Weka).

    Strengths
    • The RapidMiner platform supports an extensive breadth and depth of functionality, and with that it comes quite close to the market Leaders.
    • RapidMiner's references reported good levels of overall satisfaction, a strong user community and consistent incorporation of product requests into future releases.
    • RapidMiner was most frequently selected based on ease of use, license cost, and speed of model development/ability to build large numbers of models. A number of templates guide users on the most common set of predictive use cases.
    • Customer references cite high levels of satisfaction with the data access, data filtering and manipulation, predictive analytics and further advanced analytics components of the product.
    Cautions
    • Despite the large number of installed customers, RapidMiner does not have high visibility in the market outside the data mining community.
    • RapidMiner struggles to motivate clients — already using the free-to-download version of the product — to upgrade to the commercial version (which includes the ability to work on larger datasets, Web-based reporting, model management, collaboration features and additional deployment alternatives).
    • Customer references cited analytical business use cases, and platform and project management as areas of (relative) product weakness.
     
  15. bsdinsight

    bsdinsight Well-Known Member

    Revolution Analytics
    Revolution Analytics (www.revolutionanalytics.com) is based in Mountain View, California, U.S., and provides an enterprise-grade, multiplatform execution framework and an ecosystem of partnerships to the increasingly popular open-source R language.

    Strengths
    • Revolution Analytics was early to recognize the rise in popularity of R along with the limitations of R for enterprises, and was first to address these market needs (for example, multiplatform scalability and support) by developing a commercial software product that enhances and extends open-source R.
    • Revolution Analytics has high market visibility and sales momentum and tends to be the default choice for organizations without an existing provider seeking an R-based solution.
    • Revolution Analytics was most frequently selected based on support for open-source capabilities and the relatively low license cost.
    • Customer references cite high levels of satisfaction with the data access, advanced descriptive analytics, predictive analytics and simulation components of the product.
    Cautions
    • Revolution Analytics is (like R) demanding on the coding skills that are required to make the best use of the platform; however, partnerships (such as with Alteryx) increase the usability of its platform.
    • References did not feel that pricing (that is, priced by the number of computing cores on servers, grids and appliances, and by the number of nodes on Hadoop) was predictable or controllable. The company introduced a new pricing structure in 4Q13 to address value consistency.
    • Overall customer satisfaction for Revolution Analytics was not strong, although (with the exception of pricing and some issues with product reliability) there were no areas of stand-out poor performance. In late 2013, the company added 24/7 support, additional training offerings and new deployment services to help customers succeed.
    • Customer references cited visualization and exploration/discovery, platform and project management and user experience as areas of (relative) product weakness.
     
  16. bsdinsight

    bsdinsight Well-Known Member

    SAP
    In October 2013, SAP (www.sap.com), which is based in Walldorf, Germany, closed the acquisition of KXEN. The InfiniteInsight product is a sound addition to the SAP stack, which includes SAP Predictive Analysis (PA) and SAP Hana Predictive Analysis Library (PAL).

    Strengths

    • SAP InfiniteInsight is known for its ability to create many derived variables and quickly home in on the most salient subset of those. This is useful for business analysts, but also enables increased productivity for some data scientists.
    • SAP InfiniteInsight was frequently selected based on speed of model development/ability to build large numbers of models, ease of use, and ability to model efficiently against wide datasets (those with lots of variables).
    • SAP PA and SAP Hana PAL provide an interface and framework suited to data scientists requiring more scalability and flexibility than is provided with SAP InfiniteInsight.
    Cautions

    • Until the integration of SAP InfiniteInsight and SAP PA is complete, data scientists will have to switch between these two products in order to have access to all the functionality they need to perform their analysis. SAP is expected to address this in 2014 with a unified release.
    • Overall customer satisfaction with SAP was relatively low, and users did not feel the pricing structure aligned with the delivery of value to their organizations (KXEN's pricing structure was changed in December 2013).
    • As expected, KXEN's narrow focus on automating the predictive process led customer references to cite simulation, and visualization and exploration/discovery, and InfiniteInsight's data filtering and manipulation, as areas of (relative) product weakness.
     
  17. bsdinsight

    bsdinsight Well-Known Member

    SAS
    SAS (www.sas.com) is based in Cary, North Carolina, U.S. With more than 40,000 customers and the largest ecosystem of users and partners, SAS has traditionally been the safe choice for organizations seeking an advanced analytics environment. SAS has strength in banking, insurance, business services and government.

    Strengths
    • The SAS product stack is by far the widest in the industry, only rivaled by the open-source programming environment R — with its thousands of libraries.
    • Willingness to listen to customer input, the strength of the user community, and high product scores all helped drive a high level of overall customer satisfaction.
    • SAS was frequently selected based on product quality, availability of skills and the ability to model efficiently against wide datasets.
    • Customer references cite high levels of satisfaction with the entire spectrum of capabilities, in particular with the data access, data filtering and manipulation, advanced descriptive analytics, predictive analytics, and further advanced analytics components.
    Cautions
    • The SAS product stack is highly fragmented and often multiple products exist to do one thing (for example, predictive modeling).
    • Given the complexity of some of the SAS products, this is not a choice for the fainthearted. The forthcoming SAS Visual Statistics aims to remedy the situation by becoming a common interface.
    • SAS emerged from the reference survey with poor evaluations for both product reliability and the upgrade process, with many customers reporting significant problems (many of them still unresolved).
    • Although the pricing structure itself was not a major cause of dissatisfaction, a significant number of survey respondents indicated dissatisfaction with the high cost of SAS compared with other solutions.
     
  18. bsdinsight

    bsdinsight Well-Known Member

    StatSoft
    StatSoft (www.statsoft.com), based in Tulsa, Oklahoma, U.S., is a privately held software firm, and one of the pioneers in the advanced analytics industry, with a long history in the academic and desktop analytics space. Its Statistica offering has traction in all major industries and regions of the world.

    Strengths
    • StatSoft has a very wide range of functionality in all categories and meets the functional requirements of advanced analytics very well.
    • StatSoft had high levels of overall customer satisfaction, and some of the highest evaluations for product reliability and the upgrade experience of any vendor.
    • StatSoft was most frequently selected based on speed of model development/ability to build large numbers of models, license cost, and ability to support a wide variety of data types — including unstructured data.
    • Customer references cite high levels of satisfaction with the advanced descriptive analytics, predictive analytics, further advanced analytics, and performance and scalability components of the product.
    Cautions
    • Although it aspires to reach more business users, the Statistica UI appears old-fashioned — as do some of the visualizations because they follow MS Windows UI standards on the desktop. However, StatSoft does provide UIs with a more contemporary look and feel for domain-specific solutions.
    • References did not feel that StatSoft license costs were predictable and controllable. StatSoft attempts to provide clear visibility of some costs based on a commitment to a 20% rate of maintenance fees. References were also critical of the lack of opportunity to interact with other customers at StatSoft-managed conferences and online forums.
    • Customer references cited platform and project management as an area of (relative) product weakness.
     
  19. bsdinsight

    bsdinsight Well-Known Member

    Vendors Added and Dropped

    We review and adjust our inclusion criteria for Magic Quadrants and MarketScopes as markets change. As a result of these adjustments, the mix of vendors in any Magic Quadrant or MarketScope may change over time. A vendor's appearance in a Magic Quadrant or MarketScope one year and not the next does not necessarily indicate that we have changed our opinion of that vendor. It may be a reflection of a change in the market and, therefore, changed evaluation criteria, or of a change of focus by that vendor.

    Added
    This is the first version of this Magic Quadrant, so all vendors are new.

    Dropped
    This is the first version of this Magic Quadrant, so no vendors have been dropped.

    Inclusion and Exclusion Criteria
    To be included in the Magic Quadrant analysis, vendors must meet all of the following criteria (see Note 5):
    1. Offer advanced analytics functionality as a stand-alone product that can be deployed and used separately from other BI or business applications. This product must be application-neutral (that is, it can support multiple different use cases across the organization), rather than a packaged application for a specific domain or business problem.
    2. Offer at least three different approaches to predictive analytics, and three approaches from advanced descriptive analytics, optimization or simulation.
    3. Generate at least $2 million in total advanced-analytics-related software license revenue annually, or have more than 1,000 active deployments.
    4. At least 15% of revenue must be collected outside the region of origin.
    5. Must be able to achieve a minimum of 15 completed customer survey responses.
     
  20. bsdinsight

    bsdinsight Well-Known Member

    Evaluation Criteria
    Ability to Execute
    Most elements of the ability to execute were rated as of medium importance. The product evaluation scores are considered to be an important aspect of the vendor's ability to deliver, so we rated this with high importance. The sales execution and pricing we evaluated primarily in terms of client opinions regarding the pricing structure (not the absolute cost of the solution), and although significant this was less important in the vendor's overall ability to either remain viable or deliver a robust product. We also rated the operations criteria (mainly evaluations of the product's reliability and the upgrade experience) as of relatively low importance, since (although important) the implications of this can also be reflected in other criteria (for example, product score evaluations) and we did not want to overemphasize its impact by according it a greater weight.

    Table 1. Ability to Execute Evaluation Criteria
    business analytics 2014 1.jpg
     
  21. bsdinsight

    bsdinsight Well-Known Member

    Completeness of Vision
    Market understanding and innovation we rated as of high importance to the vendor's vision. These criteria evaluate both current levels of vision embedded into the product (innovation) and the vendor's ability to sustain a strong vision into the future (market understanding).

    The marketing, sales and offering strategy criteria were included, but with low weighting, to allow vendors we considered to have strong insights in these dimensions to gain recognition for them without, however, allowing these to overwhelm the more important market understanding and innovation criteria that we consider to be the basis of a vendor's vision and its ability to influence the market.

    The vertical and industry strategy is a significant issue in the overall advanced analytics market, but is less significant in the platform segment of the market (which is industry-neutral) than in the various application markets (which are often defined by industry).

    Business model and geographic strategy were not used as evaluation criteria, because there is little significant difference between the vendors' vision under these criteria that is not already captured in other criteria (such as marketing and sales strategy).

    Table 2. Completeness of Vision Evaluation Criteria
    business analytics 2014 2.jpg
     

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