Getting more from your data with predictive analytics
Organizations realize that to compete in an increasingly global and regulated business environment they must find new ways to use and analyze the vast amount of data they have been collecting for so long but not exploited to its fullest effect. Predictive analytics can help improve data analytics over conventional business intelligence (BI) analysis by bringing a new level of intelligence and foresight to the process of turning data into information for competitive advantage.
Research shows that high-performing companies are those that effectively use predictive analytics. It is no coincidence that these companies have experienced significantly higher profit margins and revenue growth and acquire and retain more customers compared with their industry peers. This technology is already proving itself in many industries by helping organizations understand customer behavior, identify unexpected opportunities and threats, and anticipate operational business problems, all before they happen.
Predictive analytics, however, is an exciting but complex technology. It applies a variety of complex algorithms, statistical models, and mathematics to large volumes of data to discover hidden patterns or relationships within that data. Likewise, the data preparation and data modeling aspects of predictive analytics require highly skilled and experienced resources; these are the business analysts and statisticians who need to have a deep understanding of the business and know how to prepare the data, use statistical tools, and interpret the results. These skills are also in short supply, and this makes them expensive.
However, there are possible solutions to these issues. Advances in computing power and processing models such as MapReduce and in-memory and in-database analytics are increasingly being leveraged for computational-intensive tasks such as predictive analytics to provide performance and scale to complex data analysis. Similarly, the cloud and commercial open-source languages such as “R” are helping lower the cost and complexity of predictive analytics.
Predictive analytics brings huge potential to organizations, providing them with greater intelligence and foresight. However, successful implementation relies on organizations realizing the heavy human element to predictive analytics that requires a commitment and desire to invest in skills and use information in new, different, and interesting ways.
Research shows that high-performing companies are those that effectively use predictive analytics. It is no coincidence that these companies have experienced significantly higher profit margins and revenue growth and acquire and retain more customers compared with their industry peers. This technology is already proving itself in many industries by helping organizations understand customer behavior, identify unexpected opportunities and threats, and anticipate operational business problems, all before they happen.
Predictive analytics, however, is an exciting but complex technology. It applies a variety of complex algorithms, statistical models, and mathematics to large volumes of data to discover hidden patterns or relationships within that data. Likewise, the data preparation and data modeling aspects of predictive analytics require highly skilled and experienced resources; these are the business analysts and statisticians who need to have a deep understanding of the business and know how to prepare the data, use statistical tools, and interpret the results. These skills are also in short supply, and this makes them expensive.
However, there are possible solutions to these issues. Advances in computing power and processing models such as MapReduce and in-memory and in-database analytics are increasingly being leveraged for computational-intensive tasks such as predictive analytics to provide performance and scale to complex data analysis. Similarly, the cloud and commercial open-source languages such as “R” are helping lower the cost and complexity of predictive analytics.
Predictive analytics brings huge potential to organizations, providing them with greater intelligence and foresight. However, successful implementation relies on organizations realizing the heavy human element to predictive analytics that requires a commitment and desire to invest in skills and use information in new, different, and interesting ways.
SUMMARY
Impact
Ovum view
Key messages
THE BUSINESS VALUE OF PREDICTIVE ANALYTICS
Predictive analytics brings a higher degree of insight to your business
Predictive analytics extends beyond sales and marketing
A mix of business and technology trends is driving adoption of predictive analytics
Predictive analytics differs from traditional BI
WHAT IS PREDICTIVE ANALYTICS?
Predictive analytics combines a mix of disciplines
Application
Techniques
Algorithms
Vendors invest significant amounts in algorithm development
Supervised and unsupervised learning
Supervised learning
Unsupervised learning
In reality, a hybrid approach is used within predictive analytics applications
Choosing the right technique is vital to success
Predictive analytics is continuous and iterative
Data selection
Data transformation
Data exploration
Modeling
Deployment
Model management
ENABLING TECHNOLOGIES
Supporting the full process lifecycle
Data integration and quality
Data management
Predictive analytics tools
High-performance architectures and processing models are improving scalability and performance
Massively parallel processing
MapReduce and Hadoop
In-database analytics
Columnar databases
In-memory
Cloud computing
RECOMMENDATIONS
Recommendations for enterprises
Predictive analytics requires commitment from the top
Predictive analytics is not an answer to all a company’s ills
Data preparation is the dirty secret of predictive analytics
Consider alternative data warehousing architectures and processing models
Consider how you will source predictive analytics modeling skills
Keep end users in mind
Recommendations for vendors
Improve usability for business users
Package and verticalize predictive analytics offerings
APPENDIX
Further reading
Methodology
Impact
Ovum view
Key messages
THE BUSINESS VALUE OF PREDICTIVE ANALYTICS
Predictive analytics brings a higher degree of insight to your business
Predictive analytics extends beyond sales and marketing
A mix of business and technology trends is driving adoption of predictive analytics
Predictive analytics differs from traditional BI
WHAT IS PREDICTIVE ANALYTICS?
Predictive analytics combines a mix of disciplines
Application
Techniques
Algorithms
Vendors invest significant amounts in algorithm development
Supervised and unsupervised learning
Supervised learning
Unsupervised learning
In reality, a hybrid approach is used within predictive analytics applications
Choosing the right technique is vital to success
Predictive analytics is continuous and iterative
Data selection
Data transformation
Data exploration
Modeling
Deployment
Model management
ENABLING TECHNOLOGIES
Supporting the full process lifecycle
Data integration and quality
Data management
Predictive analytics tools
High-performance architectures and processing models are improving scalability and performance
Massively parallel processing
MapReduce and Hadoop
In-database analytics
Columnar databases
In-memory
Cloud computing
RECOMMENDATIONS
Recommendations for enterprises
Predictive analytics requires commitment from the top
Predictive analytics is not an answer to all a company’s ills
Data preparation is the dirty secret of predictive analytics
Consider alternative data warehousing architectures and processing models
Consider how you will source predictive analytics modeling skills
Keep end users in mind
Recommendations for vendors
Improve usability for business users
Package and verticalize predictive analytics offerings
APPENDIX
Further reading
Methodology
LIST OF TABLES
Table 1: Predictive analytics usage scenarios by function
Table 2: The scope of conventional BI and predictive analytics tools
Table 1: Predictive analytics usage scenarios by function
Table 2: The scope of conventional BI and predictive analytics tools
