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Strategic Analysis - U.S. Data Mining and Predictive Analytics Markets for Life
Sciences R&D
Description:
Competitive Prices to Facilitate Customer Acquisition and Retention
Large vendors offering long-term volume-priced solutions have a competitive edge over smaller participants that are struggling to strengthen customer relationships with pharmaceutical and biotech organizations. The need to retain important clients drives these large vendors to provide combined service and product solution packages at market floor rates and tackle extreme price fluctuations. Smaller industry entrants must also adopt flexible pricing strategies to sustain themselves in the new market for life sciences IT, especially for emerging niche markets like data and text mining.
This research analyzes the data mining and predictive analytics markets for life sciences R&D and provides key market drivers and challenges. The study focuses on trends shaping the market and discusses possible ways of gaining market share. Emerging technologies, end-user forecasts, and competitive analysis give participants a clearer perspective of future opportunities.
Large Vendors Can Cross-leverage Technology to Maximize Profits
Expertise in a wide array of technologies as opposed to specialization in a particular vertical is expected to enable large vendors to successfully satisfy the unmet demands of a growing market. This requires effective business development strategies or beneficial partnerships with best-of-class life sciences IT vendors. Certain software application providers have successfully used their core competency in the life sciences vertical to garner steady customer support and maintain market share, says the analyst. Others find their niche in relatively vertical-neutral segments such as hardware, storage, data mining and computing. Changing Business Models Dictate Future of IT Market
The era of blockbuster drugs is going through a revival stage with drug manufacturers struggling to
strengthen their long-term pipelines. The 'multibuster' approach to drug research is gaining popularity with the adoption of specialized or 'personalized drugs for many' strategy that eliminates bottlenecks
encountered in the `single drug for all' approach.
Contents: 1 U.S. DATA MINING AND PREDICTIVE ANALYTICS MARKETS Overview
1. Report Title and Introduction 2. Disclaimer
3. Analyst Certification
4. Research Scope and Methodology 5. Product Segmentation
6. Key Definitions Market Framework 1. The Big Picture 2. Market Framework
3. Emerging Market Consolidation 4. Data Mining Market Drivers 5. Data Mining Market Challenges 6. The Modern Value Chain
7. Hot Areas in Life Sciences Research 8. Hot Technologies in 2004
9. Data Mining: Customer Priorities
Technology Discussion
1. Changing Technology Priorities 2. Common Data Mining Models 3. ROI by Workflow Efficiency
4. Aggregation and Dissipation of Data 5. Breakdown of Architecture
6. Markets for Data Mining Architecture 7. The Technology Challenge
8. Deployment Models: Data Mining and Predictive Analytics 9. Web Market: Data Mining and Predictive Analytics 10. Effect of Linux
Sales Discussion
1. Revenue Breakdown by Buyer Group 2. Industry Sales Metrics
Competitive Landscape
1. Commercial Market and Opportunity Forecast: Data Mining and Predictive Analytics 2. Our Opinions on Growth
3. Emerging Text Mining Market
4. Competitive Quadrants by Technology
5. Competitive Market Shares: Data Mining and Predictive Analytics 6. Average Pricing for Data Mining Products
7. Average License Versus Service Fees
8. IBM Versus Oracle: Battle Between the Business Models Frost & Sullivan Awards
1. Methodology of Awards 2. Technology Innovation Award 3. Vertical Market Penetration Award 4. Product Innovation Award
Frost & Sullivan's Growth Consulting Capabilities 1. Our Capabilities
2. Our Analysis 3. Consulting Clients
4. Growth Consulting Process
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