Four Steps to a Big Data Strategy

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Before a Big Data project is launched, a strategic readiness test should be performed to assess the adoption of the new paradigm. Business analysts will need to be retrained or repurposed. The goal of shifting to a Big Data platform may include changing from reactive analysis (did that campaign work?) to proactive (what should be our next campaign offer?). Now we can proactively influence non-buyers to follow behavior patterns of loyal customers, or re-stimulate active customers when their behavior pattern begins to look like a lost customer.

Is Big Data a business initiative or an IT initiative? For decades, businesses have been trying to make sense of disorganized transaction data sprinkled throughout the enterprise, relying heavily on human resources to analyze data via data warehouses. Steps traditionally include collecting, cleaning, conforming, consolidating and organizing the data for business analysts to perform ad-hoc queries to answer key questions such as “How are my sales, by region?” or “How is my inventory, by product?” Of course, to query data, it must be structured in a data warehouse and browsed via business intelligence tools, correct? Well, not anymore.

Big Data has changed the rules. Before embarking on a new Big Data project, a policy for handling this data must be in place. Joe Caserta, founder and CEO at Caserta Concepts, a consulting and technology services firm that specializes in data warehousing, business intelligence and Big Data analytics, and co-author of the industry best seller ‘The Data Warehouse ETL Toolkit,’ shares four steps to a Big Data strategy for success.


Related Topics : Vulnerabilities and Patches, Resellers, Broadcom, Broadband Services, Supercomputing

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