RESEARCH & RESOURCES

TDWI Checklist Report | Applying Analytics with Big Data for Customer Intelligence: Seven Steps to Success

July 15, 2014

Customers do not conform to one data type, one channel, or one-size- fits-all styles of marketing. Customers are empowered. Unhappy customers will go to a competitor—and as social influencers, they will take potential customers with them. Organizations cannot afford to be complacent about how well they know their customers. Competitors who are adept at quickly turning data and information into knowledge will have a leg up in the race to attract and retain their customers.

Leading organizations are tapping both traditional and newer “big data” resources to run advanced analytics programs to discover trends, patterns, and other insights. These can fuel new products and services or guide more efficient and effective operations. Although the term big data is often defined inconsistently, it generally refers to new sources of non-transactional, semi-structured, or unstructured data that includes Web clickstreams and logs, machine data, location data, and text. These sources have the potential to deliver what transaction data alone cannot: contextual insights drawn from behavior across channels. These sources help organizations understand and anticipate what behavior leads to a purchase, what types of engagement will keep customers loyal, and how customers influence each other in social networks.

Big data is not exclusive to large organizations. Even small to midsize organizations are confronted with more data volume, variety, and velocity than they can handle with traditional systems. In response, many enterprises today are expanding their customer data architectures to include cloud data services, Hadoop, pre-configured analytic appliances, and data virtualization.

Advanced analytics is essential for deriving maximum value from customer data. Advanced analytics methods and technologies include predictive analytics, statistics, data mining, machine learning, and natural language processing. They enable users to get beyond standard business intelligence (BI) or online analytical processing (OLAP) querying and reporting to explore data for patterns, trends, and correlations. Big data analytics is about applying advanced methods and technologies to derive insights from very large and diverse data sets that often include varied data types and streaming data.

This TDWI Checklist Report discusses steps for realizing value from big data and analytics for better customer intelligence.


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