Accelerating time to insight for mid-market firms using in-memory analytics
Although mid-sized firms have many things in common with large enterprises, they differ radically in terms of the level of budget and resources they have at their disposal. Not surprisingly this is a major factor governing the choice and selection of any business intelligence (BI) reporting, analysis, or planning solution. In-memory analytics provides mid-sized companies with a faster, more flexible, and arguably lower-cost way of accessing and processing information. All features are especially important to mid-size companies who are turning to BI and analytics to enable them to become more agile and respond quicker to changing market conditions.
Summary
Impact
Ovum view
Key messages
In-memory provides a faster way to access information
Evolution of in-memory analytics
Hardware advances are making in-memory more viable
Users have high expectations about information access and response
Faster speed of response
Improving self service through analytic flexibility
Fast tracking the analytic process
Supporting specialized business analytic requirements
Utilizing in-memory for what-if analysis
Reducing the IT burden
In-memory supports ‘load and analyze’ paradigm
Removing the need to build a conventional BI environment
In-memory architectural approaches vary
Different approaches
Recommendations
Recommendations for enterprises
Ensuring a complementary approach to existing BI deployments
In-memory analytics provides a great starting point
Guaranteeing data quality within in-memory environments
Check vendors 64-bit support
Recommendations for suppliers
Ramp up integration with other parts of the BI stack
Improve scalability
Appendix
Further reading
Impact
Ovum view
Key messages
In-memory provides a faster way to access information
Evolution of in-memory analytics
Hardware advances are making in-memory more viable
Users have high expectations about information access and response
Faster speed of response
Improving self service through analytic flexibility
Fast tracking the analytic process
Supporting specialized business analytic requirements
Utilizing in-memory for what-if analysis
Reducing the IT burden
In-memory supports ‘load and analyze’ paradigm
Removing the need to build a conventional BI environment
In-memory architectural approaches vary
Different approaches
Recommendations
Recommendations for enterprises
Ensuring a complementary approach to existing BI deployments
In-memory analytics provides a great starting point
Guaranteeing data quality within in-memory environments
Check vendors 64-bit support
Recommendations for suppliers
Ramp up integration with other parts of the BI stack
Improve scalability
Appendix
Further reading
LIST OF TABLES
Table 1: The different approaches to in-memory
Table 1: The different approaches to in-memory
