Vendor Comparison in Predictive Analytics 2016: MnM DIVE Matrix


◆タイトル:Vendor Comparison in Predictive Analytics 2016: MnM DIVE Matrix
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Predictive analytics is the practice of extracting meaningful information from present as well as historical data sets. The practice determines the patterns of data and predicts the future outcomes and trends. Basically, predictive models and analysis are used to estimate future probabilities with an acceptable level of consistency. Predictive analytics implements mathematical, regression modeling, statistical, neural nets, machine learning, genetic algorithms, text mining, decision trees, clustering, and data exploration techniques to extract insights from historical and present data. It also helps organizations uncover the hidden trends and patterns from big and complex data sets. It evaluates the structured as well unstructured data such as e-mails, chat interaction, and social media interaction. Predictive analytics estimates which type of involvement will be required, where, and when.
The key benefit of predictive analytics is that it enables organizations to identify untapped opportunities and expose hidden risks buried inside vast amounts of data. By making predictive insights available to everyone, organizations are empowered to make the right choices at the right time and shape their future in a positive way. The major growth drivers of the predictive analytics market include increase in the amount of data generated across industry verticals, increased focus on market and competitive intelligence, and higher and cheaper computing power. The predictive analytics market faces challenges such as lack of transformation from legacy architecture and lack of appropriate analytical skills. Factors such as complex analytical workflow and diversity of data models based on business needs are expected to limit the market growth.

The report on vendor comparison in predictive analytics based on MarketsandMarkets DIVE methodology reviews major players that offer predictive analytics solutions and outlines the findings and analysis on the basis of two broad categories: Product Offerings and Business Strategies. Each category carries various criteria, based on which the vendors are evaluated.

Based on the extensive secondary and primary research, key information about the vendor’s product offering and business strategies was gathered. After the completion of data gathering and verification process, the scores and weightage for shortlisted vendors against each parameter was finalized. After evaluating all the vendors, a comparison scorecard was prepared and each vendor was placed in the MnM DIVE matrix based on their product offering and business strategy scores.

This report is instrumental in helping the stakeholders, such as predictive analytics vendors, system integrators, consultants, value-added resellers, and technology partners, to make business decisions on predictive analytics solution deployments.

The report covers the comprehensive study of key predictive analytics vendors, including Alpine Data Labs, Alteryx, Inc., Angoss Software, Birst, Inc., Fair Isaac Corporation (FICO), IBM Corporation, RapidMiner, SAS Institute, Tableau Software, and TIBCO Software.


1 Introduction
1.1 About the Document
1.2 Market Definition
1.3 Stakeholders

2 Premium Insights

3 MnM Dive Overview
3.1 Dive Evaluation Overview
3.2 Vendor Inclusion Criteria
3.3 Vendors Evaluated

4 MnM Voice – Predictive Analytics
4.1 Predictive Analytics: A Prevailing Tool to Analyze Big Data and Predict the Future of Business Outcomes
4.2 Predictive Analytics: Best Practices

5 Predictive Analytics: Vendor Comparision
5.1 MnM Dive Scorecard
5.2 MnM View

6 Vendor Profiles
6.1 IBM Corporation
6.2 SAS Institute
6.3 Angoss Software Corporation
6.4 Tableau Software
6.5 Rapidminer
6.6 Tibco Software, Inc.
6.7 Alpine Data Labs
6.8 Fair Isaac Corporation (FICO)
6.9 Alteryx
6.10 Birst

7 Appendix
7.1 MnM Dive Vendor Comparision Methodology
7.2 Quadrant Description
7.3 List of Abbreviations
7.4 Related Research
7.5 Endnotes/References



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