The Forrester Wave : Big Data - Victa Business Intelligence
For: ApplicationDevelopment& DeliveryProfessionalsThe Forrester Wave : Big DataPredictive Analytics Solutions, Q2 2015by Mike Gualtieri and Rowan Curran, April 1, 2015Key TakeawaysEnterprises Have Lots Of Solid Choices For Big Data Predictive AnalyticsSolutionsAmong the 13 big data predictive analytics solution vendors Forrester evaluated, wefound three Leaders, eight Strong Performers, and two Contenders.Modern Tools Bring Predictive Power To More Classes Of UsersOrganizations in every industry are perking up to the value of predictive analytics. Witha growth in demand, predictive analytics vendors are providing tools that lower thebarrier to entry and increase appeal for those with less statistics skills.Predictive Analytics Enable Organizations To Embed Intelligence And InsightPredictive analytics has limited value unless the exposed insights can be deployeddirectly into software applications and business processes. API calls, web services, andpredictive model markup language (PMMLs) are some of the methods that companiesare using to seamlessly integrate predictions into their business.Download The Forrester Wave Model Spreadsheet For Deeper InsightUse the detailed Forrester Wave model to view every piece of data used to scoreparticipating vendors and create a custom vendor shortlist. Access the report online anddownload the Excel tool using the link in the right-hand column under “Tools & Templates.”Alter Forrester’s weightings to tailor the Forrester Wave model to your specifications.Forrester Research, Inc., 60 Acorn Park Drive, Cambridge, MA 02140 USATel: 1 617.613.6000 Fax: 1 617.613.5000 www.forrester.com
For Application Development & Delivery ProfessionalsApril 1, 2015The Forrester Wave : Big Data Predictive AnalyticsSolutions, Q2 2015Predict And Prosper With One Of These 13 Solutionsby Mike Gualtieri and Rowan Curranwith Holger Kisker, Ph.D. and Sophia ChristakisWhy Read This ReportGood news! Predictive analytics is within easy reach for all enterprises if they choose the right big datapredictive analytics solution to meet their needs. In Forrester’s 45-criteria evaluation, we identified 13 bigdata predictive analytics solutions providers — Alpine Data Labs, Alteryx, Angoss Software, Dell, FICO, IBM,KNIME.com, Microsoft, Oracle, Predixion Software, RapidMiner, SAP, and SAS — and researched, analyzed,and scored their current market offerings. This report details our findings about how well each vendor fulfillsour criteria and where they stand in relation to each other to help application development and delivery(AD&D) professionals select the right solution to grace their enterprise with the power to predict.Table Of ContentsNotes & Resources2 Predictive Analytics Is A Business GameChangerForrester established evaluation criteria,conducted comprehensive productevaluations, interviewed vendors, andsurveyed users and customers about theevaluated solutions.5 Market Overview: Big Data PredictiveAnalytics Solutions6 Big Data Predictive Analytics SolutionsEvaluation Overview9 Enterprises Have Lots Of Solid Choices11 Vendor Profiles14 Supplemental MaterialRelated Research DocumentsInstant Insight: The Truth About AdvancedAnalyticsThe Forrester Wave : Big Data StreamingAnalytics Platforms, Q3 2014Predictive Analytics Can Infuse YourApplications With An “Unfair Advantage” 2015, Forrester Research, Inc. All rights reserved. Unauthorized reproduction is strictly prohibited. Information is based on best availableresources. Opinions reflect judgment at the time and are subject to change. Forrester , Technographics , Forrester Wave, RoleView, TechRadar,and Total Economic Impact are trademarks of Forrester Research, Inc. All other trademarks are the property of their respective companies. Topurchase reprints of this document, please email clientsupport@forrester.com. For additional information, go to www.forrester.com.
For Application Development & Delivery Professionals2The Forrester Wave : Big Data Predictive Analytics Solutions, Q2 2015Predictive Analytics Is A Business Game ChangerPredictive analytics has never been more relevant, and easier, than it is now. Big data, gobs ofcompute power, and modern tools are making predictive models more efficient, accurate, andaccessible to enterprises. Why do it? Because enterprises that predict will win, retain, and servecustomers better than those that don’t. That’s the bottom line of every business — serve customersbetter than your competitors. Enterprises must gain predictive powers in three areas:1. Provide direct insights about customers and business processes. Dashboards and reportingare the most common use for predictive analytics within organizations today. Exposinginformation on causative trends and projections into the future, many traditional businessintelligence vendor tools contain simple predictive models. These tools surface valuableinformation to managers and executives, but often lack the link to business decisions, processoptimization, customer experience, or any other action based on the predictive insights.2. Intelligent, adaptable customer interactions and business processes. If organizations don’t usepredictions to change the future, then they’re making their data scientists as helpless as Troy’sCassandra. Today’s top predictive analytics tools can deploy their models or scoring enginesinto the applications where there is a need for insights. Today, organizations are using predictiveto enhance business processes by detecting fraud at the moment of swiping at point-of-service,automatically adjusting digital content based on user context, or initiating proactive customerservice for at-risk revenue sources.3. Reimagine customer engagement and inspire new digital products. The potential utility ofpredictive analytics goes far beyond the mainstream uses most companies focus on today. Modelbuilding and deployment continue to accelerate, enabling application developers to usepredictive analytics quickly and with increasing ubiquity in deployed applications. Compoundedwith the use of app data, developers are able to focus features and bugs that predict the greatestcustomer value and anticipate the impact of new app functionality or aesthetics.Predictive Analytics Is Not Limited To Ph.D.-Level Data ScientistsLarge enterprises need data scientists to do the heaviest predictive analytics lifting. However,application development professionals and businesspeople are also using today’s tools — the morepeople that can do predictive analytics, the better.1 Some examples include: Data scientists forging predictions with flexible, powerful tools. Data scientists have neverbeen more popular, which has placed increasing demands on them to build more models moreaccurately and in far less time. They need tools to make them more productive and to analyzedata sets of unprecedented size. Once the analysis executes, the insights must be operationalizedthrough API calls, PMMLs, or other documentation for creating scoring engines andembedding predictive actions in applications. 2015, Forrester Research, Inc. Reproduction ProhibitedApril 1, 2015
For Application Development & Delivery Professionals3The Forrester Wave : Big Data Predictive Analytics Solutions, Q2 2015 App developers looking to the future to enhance software experiences and businessprocesses. Even though every organization can benefit from predictive analytics, the demandfor data scientists is far outpacing the number available, and inevitably not everyone will be ablehire their own. Recognizing this growing chasm, vendors are providing tools for users who mayonly have a computer science or undergraduate statistics backgrounds. These tools use moderndevelopment interfaces that will be familiar to application developers who have experienceworking in integrated development environments like Visual Studio or Eclipse. When it comestime to deploy the models, APIs, web services, and PMMLs all allow for the smooth integrationof predictive insights into applications. Business analysts exploring and consuming well-baked predictions. Predictive insightsneed to be elucidated from data when neither data scientists nor app developers are available,which would have previously left organizations in a lurch. Today, the demand for predictiveanalytics capabilities is becoming so ubiquitous that options are available for the most naïveusers. Some vendor tools provide “one-click predictive modeling,” generating predictive modelsautomatically by running a series of algorithms against the data and finding the one with thehighest accuracy. There are also an increasing number of exchanges and marketplaces forprebuilt predictive applications, such as the Alteryx Analytics Gallery and the Azure MachineLearning Marketplace.The Predictive Analytics Life Cycle Starts With Great QuestionsPredictive analytics uses algorithms to find patterns in data that might predict similar outcomesin the future. A common example of predictive analytics is to find a model that will predict whichcustomers are likely to churn. For example, telecommunications firms can use customer data suchas calls made, minutes used, number of texts sent, average bill amount, and hundreds of othervariables to find models that will predict which customers are likely to change mobile carriers. Ifa carrier can predict the reasons why customers are likely to churn, it can try to take preemptiveaction to avoid this undesirable outcome.This isn’t a one-time operation; firms must rerun their analysis on new data to make sure the modelsare still effective and to respond to changes in customer desires and competitors. Many firmsanalyze data weekly or even continuously. Game-changing insights start with asking creative, deepquestions. Once the question has coalesced, use these six steps to answer them in a continuouslyimproving predictive discipline (see Figure 1): Identify data from a variety of sources. Potentially valuable data often exists in multiplehard-to-access locations, both internally (data silos in enterprise applications) and externally(social media, government data, and other public or licensed data sources). Advanced datavisualization tools can help to explore the data from various sources to determine what might berelevant for a predictive analytics project. 2015, Forrester Research, Inc. Reproduction ProhibitedApril 1, 2015
For Application Development & Delivery Professionals4The Forrester Wave : Big Data Predictive Analytics Solutions, Q2 2015 Wrangle the data. Data preparation for predictive analytics is a key challenge. Many users ofpredictive analytics spend more than three quarters of their time preparing the data: calculatingaggregate fields, stripping extraneous characters, filling in missing data, or merging multipledata sources. Build a predictive model. Predictive analytics tools like the ones evaluated in this ForresterWave include dozens of different statistical and machine-learning algorithms that data scientistsor knowledgeable AD&D professionals can choose to run the best predictive model. Thebest algorithm(s) to choose depend on the type and completeness of the data and the type ofprediction desired. Analysts run the analysis on a subset of the data called “training data” andset aside “test data” that they will use to evaluate the model. Evaluate the model’s effectiveness and accuracy. Predictive analytics is not about absolutes; itis about probabilities. To evaluate the predictive power of the model, data analysts use the modelto predict the “test data” set. If the predictive model can predict the test data set, it is a candidatefor deployment. Use the model to deliver actionable prescriptions to your business peers. There is little value ina prediction if it doesn’t enable the seizing of a predictive opportunity or avoiding a negative event.Business peers need to learn to trust in the predictions of models and those creating the modelsneed to learn from their partners in the business what the most actionable insights may be. Monitor and improve the effectiveness of the model. Predictive models are only as accurateas the data fed into them, and over time they may degrade or increase their effectiveness. Tomonitor models for ongoing effectiveness and value, newly accumulated data is rerun throughthe algorithms. If and when the model becomes less accurate, AD&D pros will have to adjustthe model (e.g., by adjusting parameters in the algorithms) and/or seek additional data. 2015, Forrester Research, Inc. Reproduction ProhibitedApril 1, 2015
For Application Development & Delivery Professionals5The Forrester Wave : Big Data Predictive Analytics Solutions, Q2 2015Figure 1 The Six Steps Of Predictive AnalyticsIdentify data that is relevantto the business goalUnderstanddataMeasure theeffectiveness ofthe model in thereal worldPrepare dataMonitorIntegrate and enrichthe data into ananalytical data setPredictiveanalyticsUse the model inapplicationsDeployModelRun statistical andmachine-learningalgorithms tofind the modelEvaluateTest the model to makesure it will work115697Source: Forrester Research, Inc. Unauthorized reproduction or distribution prohibited.Market Overview: Big Data Predictive Analytics SolutionsThe vendors evaluated in this Forrester Wave provide general purpose big data predictive analyticssolutions to facilitate the predictive analytics process and ease the burden of this never-ending,continuous cycle of data preparation, model building, deployment, and optimization that can beapplied to most industries and business domains. In addition to the general purpose solutionsevaluated in this Forrester Wave, firms that wish to benefit from big data predictive analyticssolutions can also choose among: Vertical or horizontal solutions. Many vendors provide solutions that focus on specificindustry or horizontal domains, such as customer analytics. For example, Qubit and Certonaspecifically focus on customer-focused programs and initiatives that drive acquisition, retention,cross-sell/upsell, and targeted marketing campaigns. Companies like Apigee make buildingpredictive software simple with APIs. Other examples of vertical solutions include cloud-based 2015, Forrester Research, Inc. Reproduction ProhibitedApril 1, 2015
For Application Development & Delivery Professionals6The Forrester Wave : Big Data Predictive Analytics Solutions, Q2 2015offerings such as BloomReach, which uses predictive analytics to help eCommerce companiessell more online by showing customers more relevant content, and FusionOps, which uses bigdata predictive analytics to help companies improve logistic processes. Open source programming solutions. The open source software (OSS) community is apowerful force driving predictive analytics into the mainstream for programmers. R, the opensource programming language for statistics and predictive analytics, is ubiquitous in universitysettings and every vendor evaluated in this Forrester Wave supports it. Application developersalso have a plethora of API libraries available to prepare data and build predictive models usingJava, Python, and Scala. Apache Mahout and WEKA have Java APIs. Apache Spark MLlibincludes APIs for Java, Python, and Scala.2 Python developers can use NumPy and SciPy toprepare data and build predictive models. BI platforms that include some predictive analytics capabilities. Most BI platforms offervarying degree of integration with R. Information Builders, MicroStrategy, and Tibco Softwareoffer tight R integration by providing GUI for R model development and execution, passingBI parameters to/from R routines, specialized scalable servers to run R, and import/export ofpredictive routines via PMML.3 The open source nature of R-based BI platforms from OpenTextBIRT and Tibco Jaspersoft make them a natural process to integrate with open source R andHDS (Hitachi Data Systems) Pentaho with open source WEKA.4 Offerings from consulting firms. Enterprises that lack expertise in predictive analytics or thatwish to outsource can choose from among many mainstream or boutique consulting firms thatfocus on predictive analytics. Large consulting companies such as Accenture, Deloitte, Infosys,and Vurtusa have big data and/or predictive analytics practices and solutions. Boutique firmsBeyond The Arc, Cognilytics, Fractal Analytics, Opera Solutions, Salford Systems, and ThinkBig, a Teradata company, provide focused expertise in predictive analytics. These firms willoften use general purpose solutions such as those evaluated in this Forrester Wave, but they alsoprovide deep knowledge and expertise in analyzing data and building predictive models.Big Data Predictive Analytics Solutions Evaluation OverviewTo assess the state of the predictive analytics platform market and see how the vendors stack upagainst each other, Forrester evaluated the strengths and weaknesses of general purpose predictiveanalytics solution vendors.Evaluation Criteria: Current Offering, Strategy, And Market PresenceAfter examining past research, user need assessments, and vendor interviews, we developed acomprehensive set of evaluation criteria. We evaluated vendors against 45 criteria, which wegrouped into three high-level buckets: 2015, Forrester Research, Inc. Reproduction ProhibitedApril 1, 2015
For Application Development & Delivery Professionals7The Forrester Wave : Big Data Predictive Analytics Solutions, Q2 2015 Current offering. We evaluated each solution’s architecture, security, data acquisition andwrangling, data set preparation, supported algorithms and methods, evaluation capabilities, toolusability, business applications, and other features to establish the capabilities of the vendor’scurrent offering. All products evaluated must have been publicly available by October 1, 2014. Strategy. We reviewed each vendor’s strategy to assess how their plans will meet current andemerging customer demands. The core criteria for this category are acquisition and pricingoptions of the solution, the vendor’s support for implementation, as well as their road map andgo-to-market strategy. Market presence. The vendor’s financials, global reach, industries served, market awareness,technology, and service partnerships are the core criteria for evaluating the weight of thevendor’s presence in the market.Predictive Analytics Wave Evaluation Assessed The Capabilities Of 13 VendorsForrester included 13 vendors in the assessment: Alpine Data Labs, Alteryx, Angoss, Dell, FICO,IBM, KNIME, Microsoft, Oracle, Predixion Software, RapidMiner, SAP, and SAS. Each of thesevendors has (see Figure 2): Comprehensive core predictive analytics functionality. We included vendors that offer oneor more solutions that were available for customers to use by October 1, 2014 and that provideat least the following core predictive analytics functional components, tools, and features: Theyhave the ability to connect, extract, transform, cleanse, load, and otherwise prepare analyticaldata sets; develop and evaluate predictive models using both statistical and machine learningalgorithms; deploy predictive models; manage the predictive modeling life cycle; and they havetools for data scientists, business analysts, and application developers to manage the predictiveanalytics life cycle. An original, cross-domain predictive analytics solution. The products included in thisevaluation are general purpose predictive analytics solutions that aren’t technologically orfunctionally focused upon particular functional or horizontal applications — such as enterpriseresource planning (ERP); customer analytics; customer relationship management (CRM);business intelligence (BI); data warehousing (DW); extract, transform, load (ETL); or themiddleware stack. The vendor must develop, market, sell, and implement the solution as a selfsufficient, general purpose big data predictive analytics offering that can stand alone, meaningthat it does not need to be embedded in other applications. Sparked client inquiries and/or has technologies that put the vendor on Forrester’s radar.Forrester clients often discuss the vendors and products through inquiries; alternatively, thevendor may, in Forrester’s judgment, warrant inclusion in this evaluation because of technologytrends or market presence. 2015, Forrester Research, Inc. Reproduction ProhibitedApril 1, 2015
For Application Development & Delivery Professionals8The Forrester Wave : Big Data Predictive Analytics Solutions, Q2 2015Figure 2 Evaluated Vendors: Product InformationVendorProduct evaluatedAlpine Data Labs Alpine ChorusProduct versionVersionevaluatedrelease date5.0September 15, 2014AlteryxAlteryx Analytics9.1September 4, 2014AngossKnowledgeStudio9.3September 9, 2014KnowledgeSeeker9.3September 9, 2014KnowledgeReader8.7April 12, 201312.5.192.11July 30, 2014DellFICOStatisticaKitenga Analytic Suite2.5December 9, 2013Toad Data Point3.6September 23, 2014Toad Intelligence Central2.4September 23, 2014Boomi AtomSphere2014.08August 13, 2014FICO Model Builder7.4August 16, 2013FICO Analytic Modeler Scorecard1.0May 27, 2014FICO Analytic Modeler Scorecard Professional5.0June 30, 20141.7.2August 2, 2014FICO Model Central5.0August 1, 2014FICO Blaze Advisor Business Rule Management System7.2August 2, 2013IBM SPSS Modeler16.0December 10, 2013IBM SPSS ModelerGold EditionDecember 10, 2013IBM SPSS Statistics22.0FICO Analytic Modeler Decision Tree ProfessionalIBMIBM SPSS Analytical Server/IBM SPSS Analytic Catalyst 1.0.1August 13, 2013December 10, 2013IBM Social Media Analytics1.3March 18, 2014IBM SPSS Data Collection7.0March 12, 2013IBM SPSS Predictive Analytics Enterprise2.0June 13, 2014IBM Decision Optimization (CPLEX Optimization Studio)12.6March 27, 2014Source: Forrester Research, Inc. Unauthorized reproduction or distribution prohibited. 2015, Forrester Research, Inc. Reproduction ProhibitedApril 1, 2015
For Application Development & Delivery Professionals9The Forrester Wave : Big Data Predictive Analytics Solutions, Q2 2015Figure 2 Evaluated Vendors: Product Information (Cont.)VendorProduct evaluatedProduct versionevaluatedVersionrelease dateKNIMEKNIME Analytics Platform2.10July 9, 2014MicrosoftSQL Server2014April 1, 2014Excel2013January 29, 2013OraclePower BIFebruary 10, 2014Azure Machine LearningJuly 2014Oracle Advanced Analytics12cR1July 2013 Oracle Data Mining12cR1July 2013 Oracle R Enterprise1.4.1September 2014 Oracle Data Miner4.0.3September 20144.0September 2014Oracle Big Data Connectors Oracle R Advanced Analytics for Hadoop2.4.1April 2014PredixionSoftwarePredixion Insight4.0September 15, 2014RapidMinerRapidMiner Studio6.1May 6, 2014SAPSAP Predictive AnalysisSAP InfiniteInsightSAP Hana SPSSASSAS Analytics Suite1.0.21October 20147.0.1July 201408May 201413.2August 5, 2014Source: Forrester Research, Inc. Unauthorized reproduction or distribution prohibited.Enterprises Have Lots Of Solid ChoicesForrester’s evaluation of general purpose big data predictive analytics solutions uncovered a marketwith three Leaders, eight Strong Performers, and two Contenders (see Figure 3): Leaders. IBM and SAS have unmatched breadth and depth in their solutions. Both haveextremely mature products and neither company has rested on its laurels. Both have highscores in every category. SAP is also a leader rising to challenge IBM and SAS with continuedaggressive investment in predictive analytics capabilities. 2015, Forrester Research, Inc. Reproduction ProhibitedApril 1, 2015
For Application Development & Delivery Professionals10The Forrester Wave : Big Data Predictive Analytics Solutions, Q2 2015 Strong Performers. Alpine Data Labs, Alteryx, Angoss, Dell, FICO, KNIME, Oracle, andRapidMiner are Strong Performers. All of these Strong Performers have a sweet spot that makethem excellent choices for enterprises (see vendor profiles below). With better strategy scores,Alteryx, Angoss, FICO, Oracle, and RapidMiner, would have been Leaders. Contenders. Microsoft and Predixion Software are Contenders. Both start from a niche buthave plenty of running room to grow and unique value for enterprises. Microsoft is pure cloudand Predixion Software empowers Excel to predictive analytics capabilities in the cloud.This evaluation of the predictive analytics solutions market is intended to be a starting point only.We encourage clients to view detailed product evaluations and adapt criteria weightings to fit theirindividual needs through the Forrester Wave Excel-based vendor comparison tool.Figure 3 The Forrester Wave : Big Data Predictive Analytics Solutions, Q2 ongSASIBMRapidMinerAngossAlpine Data LabsKNIMEPredixion SoftwareCurrentofferingSAPOracleFICOAlteryxDellGo to Forrester.com todownload the ForresterWave tool for moredetailed productevaluations, featurecomparisons, andcustomizable rankings.MicrosoftMarket presenceFull vendor participationWeakWeakStrategyStrongSource: Forrester Research, Inc. Unauthorized reproduction or distribution prohibited. 2015, Forrester Research, Inc. Reproduction ProhibitedApril 1, 2015
For Application Development & Delivery Professionals11The Forrester Wave : Big Data Predictive Analytics Solutions, Q2 2015Forrester’sWeightingAlpine Data edixion SoftwareRapidMinerSAPSASFigure 3 The Forrester Wave : Big Data Predictive Analytics Solutions Q2 ‘15 (Cont.)CURRENT OFFERINGArchitectureSecurityDataAnalysisModel managementUsability and toolingBusiness TRATEGYAcquisition and pricingAbility to executeImplementation supportSolution road mapGo-to-market growth 003.003.004.112.555.005.005.003.00MARKET PRESENCECompany financialsCustomer .003.985.003.753.005.005.005.005.00All scores are based on a scale of 0 (weak) to 5 (strong).Source: Forrester Research, Inc. Unauthorized reproduction or distribution prohibited.Vendor ProfilesLeaders IBM assembles an impressive set of capabilities, putting predictive at the center. No matterhow an organization wants to get started with predictive analytics, IBM has an option for them.The solution offers one of the most comprehensive set of capabilities to build models, conductanalysis, and deploy predictive applications: both on-premises and in the cloud. With customersderiving insights from data sets with scores of thousands of features, IBM’s predictive analyticshas the power to take on truly big data and emerge with critical insights. 2015, Forrester Research, Inc. Reproduction ProhibitedApril 1, 2015
For Application Development & Delivery Professionals12The Forrester Wave : Big Data Predictive Analytics Solutions, Q2 2015 SAS continues to be an analytics powerhouse. With a strategic focus on analytics since 1973,it is no surprise that SAS offers predictive analytics solutions that offer almost every feature adata scientist or business user could ever want. SAS also keeps up with the evolving needs ofanalytics users. SAS Visual Analytics provides data scientists with an all-in-one visualizationtool and predictive analytics solution. SAS solutions are also integrated with open source R,Python, and Hadoop. SAP’s relentless investment in analytics pays off. SAP provides a comprehensive set ofpredictive analytics tools for both business users and data scientists that use SAP Hana behindthe scenes. SAP offers a visual predictive analytics tool that lets users analyze data on a numberof databases. SAP Hana customers can leverage SAP’s Predictive Analtyics Library (PAL) toanalyze big data. SAP also provides a tool that lets business users create predictive modelswithout any knowledge of statistical or machine learning algorithms.Strong Performers RapidMiner is a rock-solid enterprise solution with cloud capability. With a platformincluding more than 1,500 methods across all stages of the predictive analytics life cycle,RapidMiner has the breadth and flexibility that enterprises need to consume data and serveinsights across the business. RapidMiner helps reduce time-to-insights and guide best practicesfor data analysts, analyzing the behavior of their users to create “wisdom of the crowds”guidance: The platform helps users avoid repeating the mistakes of the past. With a single-clickintegration to run processes on the cloud, RapidMiner offers one of the most tightly integratedcloud capabilities of the assessed vendors. Alteryx empowers businesspeople to quickly get their hands dirty in predictive. Predictiveanalytics is not just for data scientists. Alteryx’s focus is on providing business users withpredictive capabilities by helping then overcome what is often the hardest part — datapreparation. For predictive analytics, Alteryx uses R behind the scenes to provide a rich setof analysis algorithms. Data scientists can collaborate with business users by hiding R scriptsbehind nodes in their visual tool. Alteryx also provides an analytical apps gallery that lets usersshare their data prep and modeling workflows with other users. Oracle makes scalable pred
prebuilt predictive applications, such as the Alteryx Analytics Gallery and the Azure Machine Learning Marketplace. The Predictive analytics Life Cycle starts with Great Questions Predictive analytics uses algorithms to find patterns i
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May 02, 2018 · D. Program Evaluation ͟The organization has provided a description of the framework for how each program will be evaluated. The framework should include all the elements below: ͟The evaluation methods are cost-effective for the organization ͟Quantitative and qualitative data is being collected (at Basics tier, data collection must have begun)
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provided by each vendor. Forrester evaluated Oracle separately because Oracle chose not to participate in this research. Related Research Documents The Forrester Wave : Customer Analytics Solutions, Q4 2012 October 26, 2012 The Forrester Wave : Big Data Predictive Analytics Solutions, Q1 2013 by Mike Gual
Chính Văn.- Còn đức Thế tôn thì tuệ giác cực kỳ trong sạch 8: hiện hành bất nhị 9, đạt đến vô tướng 10, đứng vào chỗ đứng của các đức Thế tôn 11, thể hiện tính bình đẳng của các Ngài, đến chỗ không còn chướng ngại 12, giáo pháp không thể khuynh đảo, tâm thức không bị cản trở, cái được
