Four Steps to a Big Data Strategy

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The paradigm shift to Big Data introduces a new role in the corporate organization: the data scientist. This role requires deep understanding of advanced mathematics, system engineering, data engineering and domain (business) expertise. In practice, it’s common to utilize a data science team, where statisticians, technologists and business subject matter experts collectively solve problems and provide solutions.

Every Big Data strategy must include continuous monitoring and maintenance of the technical solution. As data volume and analytic requirements increase, the configuration of the solution must evolve and grow. The distributed system will need to have nodes added, data redistributed/balanced, replication adjusted, and the configuration for all of the above continuously fine-tuned for optimal performance.

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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