Four Steps to a Big Data Strategy

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Now armed with a complete Big Data ecosystem, including recommendations created by data scientists, it’s possible to close the loop – feed the results of the analysis into the engine that creates the customer experience: your website, marketing department, sales force, product development and customer service. Moreover, the Big Data machine can now consume recommendations provided as a result of its analytics correlated to new customer behavior patterns and quantify its effectiveness.

Lastly, remember that with any new initiative, there’s risk when implementing a new Big Data project. A tool, a language or a platform alone does not make a solution.

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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