Big Data Integration Solutions
Problem – Analytics Push the Limits of Traditional Data Management
In the best-selling book Competing on Analytics: The New Science of Winning, authors Thomas H. Davenport and Jeanne G. Harris “found a striking relationship between the use of analytics and business performance…High performers (those who outperformed their industry in terms of profit, shareholder return and revenue growth) were 50 percent more likely to use analytics strategically…and five times as likely as low performers.”
Analytics opportunities are abundant, including:
- Pricing optimization;
- Sales and inventory forecasting;
- Customer churn prevention;
- Marketing campaign optimization;
- Fraud detection;
- Supply chain management; and
- Many more.
However, businesses seeking to increase profitability, streamline operations, improve customer retention, extend product lines and reduce risk through analytics are constrained by traditional data integration approaches that slow analytics adoption. These include three significant challenges:
- Data silo and complexity challenge – Effective predictive analytics applications leverage data from multiple internal and external sources, including relational, semi-structured XML, dimensional MDX, and the new “Big Data” data types such as Hadoop.
- Query performance challenge – Large volumes of data must be analyzed making query performance a critical success factor.
- Agility challenge – Dynamic businesses require new and ever changing analyses. This means new data sources need to be brought on board quickly and existing sources modified to support each new analytic requirement.
Solution – Composite Big Data Integration Solutions Turbocharge Analytics
The Composite Data Virtualization Platform provides an agile, high performance data integration approach that overcomes data complexity and disparate silos to provide analytics with both the Big Data and enterprise data needed to outperform the competition.
Composite integrates your enterprise data with all the major types of Big Data including:
- Massively Parallel Processing based Appliances – Examples include EMC Greenplum, HP Vertica, IBM Netezza, SAP Sybase IQ, and more
- Columnar/tabular NoSQL Data Stores – Examples include Hadoop, Hypertable, and more
- XML Document Data Stores – Examples include CouchDB, MarkLogic, and MongoDB, and more
- Key/value Data Stores – Examples include Cassandra, Memcached, Voldemort, and more
Using the Composite Data Virtualization Platform to turbocharge analytics has numerous benefits including:
- Query Optimization for Timely Business Insight – Composite’s query optimization algorithms and techniques are the fastest in the industry, delivering the timely information your analytics require.
- Data Federation Provides the Complete Picture – Composite’s data federation virtually integrates your data in memory to provide the complete picture without the cost and overhead of physical data consolidation.
- Data Discovery Addresses Data Proliferation – Composite’s unique-in-the-industry data discovery automates entity and relationship identification and accelerates data modeling so your analysts can better understand and leverage your distributed data assets.
- Data Abstraction Simplifies Complex Data – Composite’s powerful data abstraction tools simplify your complex data, transforming it from native structures to common semantics for easier consumption.
- Data Access, Caching and Delivery Improves Data Availability – Composite’s flexible standards-based data access, caching and delivery options support your diverse analytic solutions.
- Data Governance Maximizes Control – Composite’s data governance ensures data security, data quality and 7x24 operations to maximize control.
- Layered Data Virtualization Architecture Enables Rapid Change – Composite’s loosely-coupled data virtualization layer architecture and rapid development tools provide the agility required to keep pace with your ever-changing analytic needs.
