by Angela Guess
Prat Moghe recently wrote in Information Management, “Conversations about analytics and data processing have shifted notably in my career. Even 15 years ago, enterprise data warehouse discussions focused on processing speed and performance. Today, the bottlenecks have moved. Performance is still important, yes, but no longer the biggest impediment to success. Now the challenges are more about data access, movement, security and governance as enterprises struggle to get the right data to the right people in the right form — and in time to make a measurable difference. With the proliferation of data and analytics everywhere, this is no small feat. That’s why now, instead of ‘speeds and feeds,’ I recommend that organizations focus on Time to Analytics (TTA). This is an emerging metric that’s more helpful to track in today’s agile business environment. Time to Analytics measures the time between when an enterprise gets data to when the right stakeholder has access to that data for analysis – both initially and ongoing.”
Moghe goes on, “Many enterprises today have TTAs for analytic workloads that are often measured in quarters or years. That’s not remotely fast enough in today’s competitive environment. And unfortunately, TTA is getting worse as big data introduces new technical complexities. How do you take big data from many sources, move it to the right place and get it to the right people, in a way that works with their preferred tools? Long TTAs make everyone unhappy. Business and product teams are frustrated; they can’t get the data they need, or they get it too late to make an impact. Lack of resources stymies IT, as do the scarcity of big data skills and traditional waterfall development cycles. New data technologies (Cloud, Hadoop, Spark, etc.) exacerbate the issues by introducing new technical and integration complexities.”
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