Hadoop makes it possible for any organization to analyze huge amounts of data, at low cost and on commodity hardware. This is a major improvement from the past, when data analysis involved expensive servers – and therefore big budgets. Now, predictive analytics has become democratized – it can be performed by anyone with the necessary know-how.
However, simply analyzing transactional behavior will not necessarily give you an insight into customer behavior. This can only be achieved when contextual information is also made available.
Contextual information could mean the demographics of the customer, such as location, age, income, race, gender, education level, shopping habits, marital status, number of children etc. Businesses need this demographic information to identify their consumers and develop optimal marketing strategies such as pricing, product packaging, advertisements etc. Or it could mean a vehicle’s “demographics”, such as its current and previous ownership, and maintenance history.
Contextual information provides the essential context that adds value to the predictive analytics being conducted on the big data. Unfortunately, contextual information is often stored in other systems, such as CRM, billing or logistics systems. How do you get hold of it?
This is where data virtualization proves invaluable, because it gives you access to data of all types, irrespective of format, technology and location – including contextual information. It pulls all this data together to help organizations make better sense of the advanced analytics being performed.
Analytics in the context of the business adds real value. Data virtualization can help you integrate contextual information with your big data to maximize your investment in Hadoop and provide value to your business.
Businesses collect vast amounts of real-time customer data and predictive analytics uses this historical data, combined with customer insight, to predict future events. Predictive analytics enables organizations to use big data (both stored and real-time) to move from a historical view to a forward-looking perspective of the customer.
Insurance companies, for example, use data virtualization to integrate analytics for risk modeling done in Hadoop with customer data management systems. This enables them to create an efficient insurance quote management system.
Denodo has partnered with leading global Hadoop providers, such as Cloudera, to provide a platform that allows Hadoop data stores to be combined with siloed data sources. The practice of extracting information from different data sets allows customers to determine patterns and predict future outcomes and trends.
The technology partnership between Cloudera and Denodo means Cloudera Enterprise and Denodo Platform together can process, integrate and make information available to consuming applications that is meaningful to the business.
Denodo is a member of the Cloudera Accelerator Program, is certified on Cloudera Enterprise (with Impala), and together with Cloudera enables their joint customers to leverage their big data. For more information about the Cloudera and Denodo partnership, and to learn how you can maximize your investment in Hadoop, visit http://www.denodo.com/en/partner/cloudera.