{"id":648105,"date":"2013-03-21T15:21:33","date_gmt":"2013-03-21T19:21:33","guid":{"rendered":"http:\/\/gigaom.com\/?p=623011"},"modified":"2013-03-21T15:21:33","modified_gmt":"2013-03-21T19:21:33","slug":"why-guavus-analyzes-lots-of-telecommunications-data-before-storing-it-all","status":"publish","type":"post","link":"https:\/\/mereja.media\/index\/648105","title":{"rendered":"Why Guavus analyzes lots of telecommunications data before storing it all"},"content":{"rendered":"

It\u2019s not unusual to think that if data scientists want to analyze data, the first step is to collect it and spend a lot of time looking at it \u2014 asking questions, refining data sets and then getting some possible answers. But at Guavus<\/a>, the emphasis is on analyzing petabytes of data as soon as it comes in to deliver real-time results, Anukool Lakhina, the company\u2019s founder and CEO, told attendees at GigaOM\u2019s Structure:Data conference<\/a> on Thursday. <\/p>\n

A decade ago, when Lakhina worked at Sprint Labs, Sprint employed deep-packet-inspection probes to collect information about how subscribers were using the telecommunications company\u2019s services. It was a good idea \u2014 \u201cif we knew how they were interacting, we\u2019d be invisible, we\u2019d know everything about our business,\u201d Lakhina said. But the data couldn\u2019t really be harnessed quickly. FedEx trucks drove around and picked up quickly-filled storage arrays sitting next to the probes around the Sprint network. Engineers jokingly referred to the process as the \u201cpackage-switch network,\u201d rather than a packet-switch network, Lakhina said. Once the data was collected, researchers reviewed roughly day-old data and matched it with other data. They reported their findings and were roundly turned away, because the data was, well, dated.<\/p>\n

Guavus<\/a>, founded in 2006, automates the FedEx model, so telcos can derive insights from data immediately. Guavus offers its customers customizable dashboards with the self-service simplicity of a consumer application.<\/p>\n

The change in thinking from store first to compute first has led to a lot of clear return on investment, at least as Guavus has applied it. One service provider using Guavus discovered<\/a> that some cab drivers were supposed to be using the network for credit card transactions but were actually carrying live video streams. The use violated the end users\u2019 contract terms and resulted in renegotiations. Another Guavus customer used the product to respond during customer-care calls and explain why end users were getting charged extra for large data use. Data from Guavus can also let customers pass down intelligent information to end users through self-service portals.<\/p>\n

These are $100 million problems, Lakhina said. \u201cAnd you don\u2019t need to do a lot of hunting around to discover these big use cases,\u201d he said.<\/p>\n

Check out the rest of our Structure:Data 2013 live coverage here, and a video embed of the session follows below:<\/p>\n