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Reader Forum: Extracting value from big data with Hadoop

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Communications service providers have access to a wealth of actionable information about their customers. This data can be used for many good things: optimizing network operations, improving the customer experience, fueling smarter marketing campaigns, predicting next best actions and driving service innovations.

Sounds great, right? It is. But obviously – no matter how advanced and capable your data storage and processing platform is – you can’t implement all of the above initiatives all at once, unless you’re an adrenaline addict or a glutton for punishment.

For the rest of us, the first step in putting your big data to use is developing an action plan. That plan should address project specifics such as your focus and goals, as well as policy issues such as data ownership, quality verification and regulatory compliance concerns.

Breaking down the silos

CSP enterprise data is still typically stored in data silos, often segmented by services, functions and products. This may have made sense from a business process standpoint, but more value can be extracted when data is viewed holistically through a customer-centric lens.

Breaking down silos entirely may not be feasible due to business divisions, data ownership concerns and regulatory issues. Complicating the issue further, some analytics solutions actually require data to be shuttled off to specialized silos for processing. Enterprise-grade Hadoop enables multiple types of data to be directly processed in place. It’s a pragmatic and effective way to solve the all too familiar problem of segregated data.

Hadoop also fully supports data integration from multiple sources, across different data storage technologies.

Security and governance

Before a company begins to utilize captured data in new ways, it must ensure that all personally identifiable and sensitive information is classified and then managed and protected to help ensure privacy and security. New policies may need to be developed to address these issues. User access controls and roles may need to be redefined and implemented. Employees and executives may need to receive training.

Compliance and governance concerns are sharpened when data is shared with upstream partners to enable e-commerce applications, location-based services and machine-to-machine communications. Customer opt-in/out permissions and best practice ethics around data usage and protection should be reviewed and remediated if necessary.

These requirements should not be seen as a deal-breaker for a big data project. Instead view it as a valuable opportunity to audit and strengthen your governance, risk management and compliance profile. As we all know, GRC is always an ongoing process, not a finite project.

Data quality and certification

CSPs have been capturing data for a long time, and many have now extended their acquisition efforts to include data that lives outside of the enterprise. Before information is made accessible to analytics solutions, it’s important to review the data to determine where it has been, when and how it was collected, and by whom.

Only data that can be certified as correct, compliant and current should be used in big data projects. Information gathered from the ERP, customer retention management and BI systems across the enterprise is usually clean, but data originating outside of the company can be problematic. Choose your sources wisely. Verify, then trust.

Plotting and scheming

The typical big data implementation plan goes like this: choose a desired outcome or use case, an area that provides value to the business. Most CSPs find that projects with a customer-centric emphasis best meet their business priorities.

Customer-focused initiatives can include utilizing big data to provide enhanced customer support, develop new services and optimize network performance. It’s best to choose a project that provides measurable results.

Develop a strategy that is likely to lead to the selected outcome. Test your strategy and implementation with a proof of concept pilot. If the pilot proves successful, roll out the deployments. Capture the metrics, crunch the data, report the results, congratulate yourself and your team, and start planning your next project.

Sameer-pic

Sameer Nori is senior product marketing manager at MapR Technologies. Nori has 10-plus years of experience in the technology industry in marketing, sales and consulting. With an executive MBA from the Fuqua School of Business, Duke University, Nori’s domain of expertise is in business intelligence and analytics.

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