Over the last year or so I have noticed that many of the companies I have been working with have really stepped up their focus on customers both in terms of retaining existing customers and acquiring new ones. Several surveys in the market reflect this. For example, The Customer-activated Enterprise Survey published by the IBM Institute of Business Value in January 2014 shows that over half of the 884 CEOs interviewed indicated that after the C-suite executives themselves, the group of people who has the next greatest strategic influence in their organizations were customers. Also, in the same survey, over 4,000 CxOs indicated that their highest priority focus was improving customer experience management.
Given these priorities, there is now a major requirement to know more about customers to remain competitive. That means capturing more data from new data sources so that new insights can be produced. This includes both customer behavioral data and customer interaction data. A very important new source of customer behavioural data is clickstream, which provides detailed insights into customer online behavior. In addition, understanding customer interactions across all channels over time gives a much more comprehensive understanding of customer activity; it may also help identify the best channels to engage the customer. Also, sentiment data is in demand to help identify customers who are dissatisfied with products or customer service.
These new big data sources bring new challenges.For example, new, more complex varieties of data must be handled. In addition, big data may need to be cleaned and integrated with master data or data warehouse data to provide a business context for analysis. Also a new architecture is needed with new platforms like Hadoop and streaming analytics, in addition to data warehouses, to cater for new analytical workloads. Integration across these new platforms and existing data warehouses is also needed. This includes making sure enterprise information management (EIM) tools manage data flows into and between analytical platforms. They should also exploit Hadoop for scalability and manage information security across this new analytical ecosystem. Support for new analytical tools and techniques are also needed, and integration of operational systems with Hadoop allows new insights to be made available in business processes.
Having looked at the requirements for big data analytics and defined what is needed in a new big data analytical ecosystem you might ask “What has this got to do with IBM System z?” The answer is that IBM has implemented this ecosystem in an IBM System z environment to enable organizations to transform big data into insights that can be used to drive competitive advantage. To find out more about this please join me and IBM in this webinar "Transforming big data into insights with IBM System z: An independent consultant’s perspective" June 24, 2014, 11:00 a.m. ET.