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Predictive and Prescriptive Analytics Market (2013-2018) : By Industry
(Finance & Credit, Banking & Investment, Retail, Healthcare &
Pharmaceutical, Insurance & Others); By Delivery (On-Site & Cloud Based)
Description: Business analytics depends on analysis of sufficient volumes of high quality data. The amount of data available nowadays is huge and running into thousands of peta-bytes. Harnessing this data to make better informed decisions is what business analytics is. With improvements in technology and also implementation of statistical and quantitative techniques, querying and analysis of this huge amount of data is almost done in real-time today. Business analytics takes advantage of statistical and quantitative data for explanatory, predictive and prescriptive modeling. It focuses on solution-oriented systems which create value by converting information into knowledge. With the growing importance of Big data(large volumes of data generated, both in structured and unstructured form), Business Analytics is a fast growing market which includes complex applications such as enterprise information and performance management, data warehousing, data mining, Business Intelligence (BI), business risk compliance and governance among others.
All such applications under business analytics can be broadly classified into three stages -Descriptive or Diagnostic Analytics, Predictive Analytics and Prescriptive Analytics.
Descriptive or Diagnostic Analytics is the traditional form of analytics which looks at past performance and understands that performance, by analyzing historical data to look for the reasons behind past successes or failures. This stage deals with questions such as what happened in the past and why it happened.
The predictive analytics market is estimated to be at $ 1.9 Bn in 2013, and expected to grow at a CAGR of 15% for the next five years, to reach $ 4.4 Bn by 2018.
Predictive Analytics is the second stage of business analytics which answers the question what will happen in the future. In this stage, data from historical performances is combined with rules, algorithms, and external data to determine the probable future outcome of an event or the likelihood of a situation occurring. Predictive analytics involves a variety of tools from statistics, data modeling, machine learning, and data mining such as mathematical regression modeling, statistical neural nets, machine learning, genetic algorithms, text mining, decision trees, clustering, and data exploration techniques to analyze and get insights from current and historical data and make predictions about future, or unknown events. It also helps enterprises to uncover the hidden trends and patterns from big and complex data sets. It evaluates the structured as well as unstructured data such as e-mails, audio and video chat interactions, and social media interactions. Predictive analytics basically gives us valuable insights as to how we can improve the current working business model thereby making it more cost-efficient and profitable.
Prescriptive Analytics is the final stage in Business Analytics which follows Predictive Analytics. Prescriptive analytics is the area of business analytics, which is dedicated to finding the best course of action for a given situation. These tools suggest actions to benefit from the predictions which were made by using predictive analytic tools by showing the implications of each decision options. To put it simply, this stage not only shows the way to make future decisions but also tries to explain the consequences of each of the suggested decision pathways. So basically, prescriptive analytics not only focuses on anticipating what and when things will happen, but also why they might happen. Further, prescriptive analytics suggests decision options on how to take advantage of a future opportunity or avoid a future risk and shows the implications of each decision option. Prescriptive analytics can continually take in new data to re-predict and re-prescribe, thereby automatically improving prediction accuracy and prescribing better decision options. Prescriptive analytics also takes hybrid data, a combination of structured (numbers, categories) and unstructured data (videos, images, sounds, texts), and business rules to predict what lies ahead and to prescribe how to take advantage of this predicted future without compromising other priorities.
Contents: 1. Global Predictive and Prescriptive Analytics Market - Market Overview 2. Executive Summary
3.1. Market Share Analysis 3.2. Comparative Analysis 3.2.1. Product Benchmarking 3.2.2. End user profiling 3.2.3. Top 5 Financials Analysis
4. Global Predictive and Prescriptive Analytics Market - Market Forces 4.1. Market Drivers
4.1.1. Favorable Risk, Regulatory and Performance Management Initiatives
4.1.2. Emerging Sectors - Online Gaming, Sports, Manufacturing, Social Media & Automotive
4.1.3. Increasing Operational Efficiency, Smart Decision Management, and Product Differentiation has a Significant Impact on the usage of Predictive Analytics.
4.1.4. Cloud-Based Predictive Analytics Demand to Increase 4.2. Market Constraints
4.2.1. Conversion of Data from Non Compatible Legacy Storing Systems
4.2.2. Complexity of Tools and their Associated Cost Factors Hindering the growth in this sector for Medium to Large Scale Companies
4.2.3. Scalability Problems in Sectors Like Healthcare 4.3. Market Challenges
4.3.1. Managing Data Warehouses 4.3.2. Quality, Quantity the Data Available 4.3.3. Integrity of the Data
4.4. Attractiveness of the Industry 4.4.1. Power of Suppliers
4.4.2. Power of Customers 4.4.3. Threat of New entrants 4.4.4. Threat of Substitution 4.4.5. Degree of Competition
5. Global Predictive and Prescriptive Analytics Market - Strategic Analysis 5.1. Opportunities Analysis
5.2. Market Life Cycle Analysis
6. Global Predictive and Prescriptive Analytics Market -By Industry 6.1. Finance & Credit
6.2. Banking & Investment 6.3. Retail
6.4. Healthcare & Pharmaceutical 6.5. Insurance
6.6. Telecom
6.7. Energy (Oil & Gas Exploration) 6.8. Mining
6.9. Public Sector 6.10. Others
7. Global Predictive and Prescriptive Analytics Market -By Business Verticals 7.1. Marketing and Sales
7.2. Manufacturing
7.3. Supply-Chain Management 7.4. Finance
7.5. Human Resource 7.6. Operations
8. Global Predictive and Prescriptive Analytics Market -By Software-Systems 8.1. Customer Relationship Management
8.2. Data-Mining
8.3. Decision Support Systems
8.4. Performance Management Systems 8.5. Fraud Detection Systems
8.6. Risk Assessment and Management Systems
9. Global Predictive and Prescriptive Analytics Market -By Operations Management 9.1. Inventory Planning
9.2. Distribution Management 9.3. Others
10. Global Predictive and Prescriptive Analytics Market -By Delivery 10.1. On-Site
10.2. Cloud Based
11.1. Collection Analytics 11.2. Marketing Analytics 11.3. Supply-Chain Analytics 11.4. Behavioral Analytics 11.5. Talent Analytics
12. Global Predictive and Prescriptive Analytics Market- Emerging Avenues Or Markets 12.1. Overview
12.2. Manufacturing 12.3. Gaming 12.4. Logistics
12.5. Travel and Hospitality 12.6. Sports
12.7. Social Media 12.8. Entertainment 12.9. Industrial Chemistry
13. Global Predictive and Prescriptive Analytics Market -Geographic Analysis 13.1. Introduction
13.2. North and South America 13.3. Europe
13.4. Asia Pacific 13.5. Rest of the World 14. Market Entropy
14.1. New Product Launches
14.2. M&As, Collaborations, JVs and Partnerships
15. Company Profiles (Overview, Financials, SWOT Analysis - Top 5 Companies, Developments, Product Portfolios) 15.1. Accenture 15.2. Alteryx 15.3. Angoss 15.4. Ayata 15.5. FICO 15.6. IBM 15.7. Information Builders 15.8. Inkiru 15.9. KXEN 15.10. Microsoft 15.11. Megaputer 15.12. Oracle 15.13. Pegasystems 15.14. Pitney Bowes 15.15. QlikTech
15.16. Rapid -I / Rapidminer, Inc. 15.17. Revolution Analytics 15.18. SAS Institute Inc. 15.19. SAP AG 15.20. StatSoft 15.21. Stone Analytics 15.22. Splunk Anlytics 15.23. Tableau 15.24. Teradata Corp 15.25. TIBCO 15.26. Versium Inc. 15.27. Zemantis 16. Appendix 16.1. Abbreviations 16.2. Sources 16.3. Research Methodology 16.4. Bibliography
16.5. Compilation of Expert Insights 17. Disclaimer
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