Software SuitesPlatforms for Analytics, Data Mining, Data Science, and Machine Learningcommercial freeopen source.ABCDEFGHIJKLMNOPQRSTUVWXYZAdvanced.Miner from Algolytics, provides a wide range of tools for data transformations, Data Mining models, data analysis and reporting.Alteryx, offering Strategic Analytics platform, including a free Project Edition version.Angoss Knowledge Studio, a comprehensive suite of data mining and predictive modeling tools interoperability with SAS and other major statistical tools.Bayesia. Lab, a complete and powerful data mining tool based on Bayesian networks, including data preparation, missing values imputation, data and variables clustering, unsupervised and supervised learning.Bio. Comp i Suite, constraint based optimization, cause and effect analysis, non linear predictive modeling, data access and cleaning, and more.Open source or free business intelligence software can help your business grow with data visualizations and drilldown capabilities, all for no cost.BLIASoft Knowledge Discovery software, for building models from data based mainly on fuzzy logic.Civis, an easy to use, end to end, extendable, data science platform in the cloud, built by data scientists, for teams who want to make great data driven decisions to drive their organizations forward.CMSR Data Miner, built for business data with database focus, incorporating rule engine, neural network, neural clustering SOM, decision tree, hotspot drill down, cross table deviation analysis, cross sell analysis, visualizationcharts, and more.Coheris SPAD, provides powerful exploratory analyses and data mining tools, including PCA, clustering, interactive decision trees, discriminant analyses, neural networks, text mining and more, all via user friendly GUI.IT, an easy to use 3.D data exploration, data mining and visualization software for most web browsers web applications, windows 1.Pad. Data Applied, offers a comprehensive suite of web based data mining techniques, an XML web API, and rich data visualizations.Data Miner Software Kit, collection of data mining tools, offered in combination with a book Predictive Data Mining A Practical Guide, Weiss and Indurkhya.Data. Detective, the powerful yet easy to use data mining platform and the crime analysis software of choice for the Dutch police.Dataiku Data Science Studio, a software platform combining data preparation, machine learning and visualization in a unique workflow, and that can integrate with R, Python, Pig, Hive and SQL.Data. Lab, a complete and powerful data mining tool with a unique data exploration process, with a focus on marketing and interoperability with SAS.Data. Science. com provides an enterprise data science platform that combines the tools, libraries, and languages data scientists love with the infrastructure and workflows their organizations need.DBMiner 2. 0 Enterprise, powerful and affordable tool to mine large databases uses Microsoft SQL Server 7.Download Pentaho Bi Suite Community Choice' title='Download Pentaho Bi Suite Community Choice' />Plato.Delta Miner, integrates new search techniques and business intelligence methodologies into an OLAP front end that embraces the concept of Active Information Management.ESTARD Data Miner, simple to use, designed both for data mining experts and common users.EWA Systems, complete Java based data mining suite, including a full range of high performance rules based, Bayesian, neural, and SVM techniques.Exeura Rialto provides comprehensive support for the entire data mining and analytics life cycle at an affordable price in a single, easy to use tool.Fair Isaac Model Builder, software platform for developing and deploying analytic models, includes data analysis, decision tree and predictive model construction, decision optimization, business rules management, and open platform deployment.Fast. Stats Suite Apteco, marketing analysis products, including data mining, customer profiling and campaign management.Gain. Smarts, uses predictive modeling technology that can analyze past purchase, demographic, and lifestyle data, to predict the likelihood of response and develop an understanding of consumer characteristics.Generation. 5 Gen.Voy, On Demand Consumer Analytics.Gen. IQ Model, uses machine learning for regression task automatically performs variable selection, and new variable construction, and then specifies the model equation to optimize the decile table.Ghost. Miner, complete data mining suite, including k nearest neighbors, neural nets, decision tree, neurofuzzy, SVM, PCA, clustering, and visualization.FHXN96J_vI4/maxresdefault.jpg' alt='Download Pentaho Bi Suite Community Choice' title='Download Pentaho Bi Suite Community Choice' />GMDH Shell, an advanced but easy to use tool for predictive modeling and data mining.Golden Helix Optimus RP, uses Formal Inference based Recursive Modeling recursive partitioning based on dynamic programming to find complex relationships in data and to build highly accurate predictive and segmentation models.IBM Data Science Experience, an interactive, collaborative, cloud based environment.IBM SPSS Modeler, formerly Clementine, a visual and powerful data mining workbench.JMP, offers significant visualization and data mining capabilities along with classical statistical analyses.K. wiz, from think.Analytics massively scalable, embeddable, Java based real time data mining platform.Designed for Customer and OEM solutions.Kaidara Advisor, formerly Acknosoft KATE, Case Based Reasoning CBR and data mining engine.Kensington Discovery Edition, high performance discovery platform for life sciences, with multi source data integration, analysis, visualization, and workflow building.Kepler, extensible, multi paradigm, multi purpose data mining system.Knowledge. Miner, 6.Model export to Excel.Localized for English, Spanish, German.Free to try. Knowledge.Miner y. X for Excel, a knowledge mining tool that works with data stored in Microsoft Excel for building predictive and descriptive models.Mac. OS. Kontagent k.Suite Data. Mine, a Saa.S User Analytics platform offering real time behavioral insights for Social, Mobile and Web, offering SQL like queries on top of Hadoop deployments.KXEN SAP company, providing Automated Predictive Analytics tools for Big Data.LIONsolver 2. 0, Learning and Intelligent Optimizatio.N modeling and optimization with on the job learning for business and engineering by Reactive Search Sr.L. LPA Data Mining tools support fuzzy, Bayesian and expert discovery and modeling of rules.Lumidatum, Data Science platform that enables personalization and predictive analytics inside your apps, products and services.Lityx. IQ, an integrated set of tools for easily doing many analytic tasks from data preparation to reporting to BI to modeling and scoring to linear optimization.Magnify PATTERN, software suite, contains PATTERN Prepare for data preparation PATTERN Model for building predictive models and PATTERN Score for model deployment.Mathematica solution for Data Analysis and Mining, from Wolfram.MCubi. X from Diagnos, a complete and affordable data mining toolbox, including decision tree, neural networks, associations rules, visualization, and more.MERKUR Miner Plus combines OLAP high speed and visualization with Data Mining to create forecasting and classification models.Microsoft SQL Server 2.Microsoft BI platform, and extensible into any application.Machine Learning Framework, provides analysis, prediction, and visualization using fuzzy logic and ML methods implemented in C and integrated into Mathematica.Model 1, Response Modeler, Segmenter and Profiler, Customer Valuator, and Cross Seller modules with a wizard GUI.Molegro Data Modeller, a cross platform application for Data Mining, Data Modelling, and Data Visualization.Neural Designer, an advanced application for discovering complex relationships, recognizing unknown patterns and predicting actual trends from data sets.Nuggets, builds models that uncover hidden facts and relationships, predict for new data, and find key variables Windows.Oracle Data Mining ODM, enables customers to produce actionable predictive information and build integrated business intelligence applications.Palisade Decision.Tools Suite, Complete risk and decision analysis toolkit.Partek, pattern recognition, interactive visualization, and statistical analysis modeling system.Pentaho open source BI suite, including reporting, analysis, dashboards, data integration, and data mining based on Weka.Polyanalyst, comprehensive suite for data mining, now also including text analysis, decision forest, and link analysis.Supports OLE DB for Data Mining, and DCOM technology.Portrait Software from Pitney.Best Data Discovery Tools 2.Reviews, Pricing Demos.Data discovery is one of the fastest growing and rapidly changing segments of the BI market.These tools differ dramatically from the traditional systems of record that enable IT to push reports and dashboards out to the rest of the organization.In many cases, data discovery tools are purchased by organizations that have already deployed traditional BI systems, in order to solve issues with data access, data preparation and data exploration.Data discovery solutions have also been a godsend for small businesses that cant afford complex data warehouses and lack the expertise to build them.The market for data discovery software is complex and highly fragmented.There are a number of different flavors of data discovery, and a variety of use cases in which one flavor works better than another.In this Buyers Guide, well explain how data discovery software differs from traditional BI and describe the categories into which these tools break down.Heres what well cover How Do Data Discovery Tools Differ From Traditional BI Systems Capabilities of Data Discovery Software.Types of Data Discovery Tools. 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How Do Data Discovery Tools Differ From Traditional BI Systems An easy way to understand this difference is to look at the history of BI solutions.Traditional BI systems were an attempt to solve the difficulty of writing SQL queries in order to retrieve data such as sales information, customer information, shipping records etc.Before BI, users had to be highly familiar with SQL to get the data they needed out of such databases.Thus, traditional BI systems mapped a layer of familiar business terms known as a semantic layer onto the relational databases storage schemas, thereby allowing users to retrieve and combine data without knowing SQL at all.Traditional BI Semantic Layer.The semantic layer is a way of expressing a data model, or a schematic representation of the relationships between data in one or multiple datasets.In particular, the semantic layer schematizes the relationships between data residing in different data sourcesdatabases.For instance, the dimension customer in the semantic layer may be defined as grouping together information from both the sales orders database as well as the customer records database.Business. Objectslater acquired by SAPwas the first BI vendor to use the semantic layer model, and remains one of the most popular semantic layer based solutions.The semantic layer model is still suitable for large enterprises that need unified access to data stored in numerous operational databases.The problem with this model is that the semantic layer needs to be standardized across the organization.In other words, various business units must agree on which databases and tables in these databases the dimension customer will pull from.Moreover, once the semantic layer has been standardized, it remains under IT control.As you can see in the above diagram, traditional tools for ad hoc queries pass analysts queries through the semantic layer, which automatically translates them into SQL queries to retrieve data from SQL databases and other data sources that support SQL querying.Thus, traditional querying tools can only work with data sources that have already been integrated into the semantic layer.Data sources outside the semantic layer a spreadsheet sent in an email, a public data source on the web, 5.Tweets about a product recall etc.IT develops new processes.And, of course, IT cant develop a process for every new data source.When the semantic layer is standardized across the organization, the paths that analysts follow to retrieve and combine data get frozen into place.For instance, if the organization defines store as a subcategory of branch, and branch as a subcategory of sales region, while neglecting to slot customer somewhere into this hierarchy, blended analysis of sales and customer data can become overly complex.Business terms mapped to operational data in SAP Business.Objects. Data discovery tools remedy this situation by providing direct access to the operational databases shown in our chart, instead of going through a semantic layer.This allows users to combine spreadsheets and other data sources outside the semantic layer with operational data.Any data preparation work that needs to be done to combine data sources e.ID to customer is done on the fly, instead of forcing IT to standardize terminology across the organization.Additionally, users can develop their own data models during analysis, instead of being bound to the data model encoded in the semantic layer.This allows greater flexibility for sophisticated queries that depend on blending data from multiple sources.Capabilities of Data Discovery Software.Theres a wide range of data discovery platforms, meaning that listing specific features is pointless.Instead, lets take a quick look at the broad capabilities that define these solutions Graphical front end for data manipulation.Allows for data access and manipulation via visualizations of data sources and patterns in data.Instead of writing a query, you can simply click on a wedge of a pie chart to drill down, or choose a heat map visualization for your data.In memory processing.Processes data by storing it in RAM random access memory instead of writing it to disk.This gives them the processing power to blend massive data sets on a users laptop, instead of doing the blends in the database as traditional BI tools do.See our data blending report for more details.Big data connections.Supports direct connections to data sources, instead of confining access to sources within the semantic layer.Support for flat files.SQL databases. Beyond that, the range of data sources a tool can connect to is generally a point of competitive differentiation.Data cleaningpreparation.Offers features for cleaning and preparing data, since analysts cant rely on pre integration of data sources via a semantic layer.These features are for normalizing dimensions, removing trailing spaces, testing the accuracy of joins etc.Note Several of these definitions of data discovery capabilities were adapted from Gartner research reports, specifically What Data Discovery Means for You by Joao Tapadinhas and Dan Sommer available to Gartner clients.Types of Data Discovery Tools.Data discovery has been an emerging market for at least a decade, but instead of solidifying around a core set of concepts and features, the market has continued to evolve.Data discovery functionality has also been added to traditional systems that use semantic layers, though such systems will still be overkill for many small businesses.There are essentially three categories of data discovery solutions currently on the market Search engine like tools for textual searches of data.Visual interaction tools that provide a graphical front end for data manipulationAI based tools that do the bulk of the pattern recognition for you.Visual data interaction tools are analytics tools that directly access data sources instead of going through a semantic layer.They allow users to process massive datasets on their laptops via in memory caching engines and spot patterns using a visual interface.Data visualizations in Tableau.The point of a visual data discovery tool isnt simply to crunch numbers and then output pretty charts and graphs, which can easily be done with Excel and Powerpoint.Instead, these tools are for interactive manipulation of data via visualizations.For example, you can click on a particular city in a heat map to begin analyzing sales just within that citys stores.You can then add another dimension to your mapsay, aggregate payroll expenses per storeto blend sales and payroll data and spot new patterns.As you click on visualization elements and drag and drop dimensions and measures into your visualizations, an engine within the data discovery tool translates your gestures into SQL queries.Changing the visualization automatically refreshes it with newly processed data from your databases.These tools thus allow for highly interactive and sophisticated database querying without forcing users to learn SQL.Moreover, they allow users to access and blend data from multiple data sources that havent been integrated via a semantic layer.
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