Guest Blog: Harnessing Data Insights for Profit and Problem-solving

By Ramana Venkata, DataGravity advisory board member

Smart enterprise leaders now recognize that the data their companies create and store is among the most valuable assets they have. It has been less widely understood that the enterprise data can be used to run both offensive and defensive plays. Examples of the former include increasing revenue per customer or identifying and acquiring the best customers. Reducing expenses by smoothing operational cost spikes or lowering risk by foreseeing and curbing potential problems are illustrations of the latter. The former gets all the media attention, but it is only half the story. Enterprises can derive extraordinary benefits by better storing, analyzing and managing their operational and internal data as well.

To illustrate this point, we can look at a couple of enterprises that have gotten a lot of media attention recently: the National Security Agency (NSA) and Target.

The NSA phone tapping controversy stirred up a hornet’s nest over the idea of government agencies listening in on telephone calls, including those of American citizens. However, the value to the NSA in that program actually stemmed more from the metadata. Apart from their content, the call logs themselves created patterns that agency analysts could parse for valuable intelligence. Who did the subjects call? How long did they talk? When did the calls occur? How often? Where did they originate and whom did they connect? The answers to these questions are extremely valuable, because they illuminate relationships that inform our intelligence actions, reactive or proactive.

Many commercial enterprises also employ data analytics to increase revenue. They extract patterns from their transactional data, e-commerce clickstreams, social media data streams etc. to better understand and predict customer behavior, convert “browsers” to “buyers”, reward and retain best customers, improve marketing campaign effectiveness etc. In many cases, the data scientists and analysts have to correlate related data attributes from different structured, semi-structured and unstructured data streams into a unified data model, and analyze that model to extract actionable trends and patterns. This process, once prohibitive and time-consuming, has now become much cheaper and fast enough to be actionable – thanks to new advances in software and systems collectively referred to as “Big Data” technologies.

In comparison, a few enterprises also devote attention to understanding their data streams from operational and internal sources. For example, they collect, store and analyze data from their service delivery and customer satisfaction groups, IT and infrastructure operations, employee-generated content etc. This helps them better understand, control and reduce their customer-dissatisfaction and -loss reasons, operational bottlenecks and cost sinks, information security risks, ediscovery and legal risks. Mining the content generated by their employees helps them foster better communications, identify and incentivize internal expertise, manage knowledge flow etc.

Target Corporation serves as an example of an enterprise that excelled on one side of this equation, and seemingly fell short on the other. On the revenue side of the house, Target data analysts have set the retail standard for finding patterns in customer behavior to inform marketing efforts. However, the recent data breach that affected the company – and potentially millions of its customers – suggests that Target only got half the data analytics story right. It analyzed for revenue, but not as effectively for identifying information security risks and problem-solving. I don’t mean to pick on Target here, for we are all aware of only an incomplete picture. But, if true, this behavior is replicated in many companies that have mastered transactional data analysis to optimize marketing efforts and create revenue, but who continue to struggle with data analysis on the operational side of the business, where better insights into data patterns could also reduce risks and curb problems before they occur or overwhelm.

Affordable new technologies now make it possible for every organization, both large and small, to extract information from structured, semi-structured and unstructured data to inform offensive and defensive strategies. Enterprises should look at the intelligence and patterns in the entirety of their data as a source of insights for both revenue and problem-solving. As they do so, they’ll need even cheaper and better technology to help them create, store, analyze and manage the data that represents the lifeblood of their organizations.

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Ramana Venkata

Ramana Venkata

is a member of DataGravity's advisory board. He is Co-founder and CEO of Affimity, Inc. He was founder, President and CEO of Stratify, which was acquired by Iron Mountain in 2007. He served as President and Chief Operating Officer at Iron Mountain following the acquisition.