From using analytics to predict how our storage arrays will perform in the field, to engineering product configurations to best meet customers’ future needs, EMC is just beginning to tap into the gold mine of intelligence waiting to be extracted from our new data lake.
In fact, we are currently working on dozens of business use cases that are projected to drive millions in revenue opportunities. And we are just scratching the surface. There’s a lot more data available, more to be harvested, and more analytics to be built out as data scientists and business users hit their stride in exploring a new era of data-driven innovation at EMC.
As I noted in my earlier blog ( The Analytics Journey Leading to the Business Data Lake), EMC IT embarked on creating a data lake to transition from traditional business intelligence to advance analytics more than two years ago. A key focus of this effort was to address the fact that data scientists and business users seeking to leverage our growing amount of data were stifled by the need for such projects to go through IT, which was a costly and slow process that discouraged innovation.
We now have the foundation and tools in place to use data and analytics to create sustainable, long-term competitive differentiation. To get here, we worked closely with EMC affiliate Pivotal Software, Inc. to mature together and leverage the multi-tenancy capabilities of their Big Data Suite.
Viktor Mayer-Schonberger and Kenneth Cukier, authors of Big Data: A Revolution That Will Transform How We Live, Work and Think, wrote, “If big data teaches us anything, it is that just acting better, making improvements – without deeper understanding – is often good enough.”
EMC IT not only recognizes the hidden value of Big Data, but also strives to generate better outcomes. So, we at EMC IT can act better and faster to improve our customers’ experience.
In his November 2013article, Dan Inbar from EMC’s IT organization eloquently presented what IT has been doing to improve the operations of our Exchange email environment. PAITO (Predictive Analytics for IT Operations) is our Big Data analytics solution for outage prediction that allows our IT operations team to collect, analyze, store, and leverage key indicators to predict and prevent interruption in mission-critical operations. The journey that started more than a year ago as a pilot has evolved into a full-fledged IT data lake and analytics platform for various IT managed areas, including applications, servers, devices, licenses, network, storage, security and workloads. (more…)
One of the challenges hardware (and software) manufacturers are facing is estimating the future level of support required in maintaining their products. Underestimating the support requirements would lead to major loses on the support contract while overestimating hurts the competitive edge of the product.
Future level of support includes: replacements, repairs, remote and on-site support. To that end, manufacturers develop reliability models for everything fromhard/flash drives to cars and aircraft. These models take into account different configuration parameters of the final product and its internal components.
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In 2007, Google conducted a large-scale analysis for a subset of its drive population. It utilized an environment containing a large number of disk drives, collected different types of data from these drives to a Big Data store (Google’s Bigtable) and conducted an analysis of the different Key Performance Indicators (KPIs) and their correlation with drive mortality:
Manufacturer, Models and Vintage
Self-Monitoring, Analysis and Reporting Technology (M.A.R.T)
Contrary to expectations, Google’s researchers found that these KPIs are more useful for predicting trends for a large population than for predicting a single drive failure.
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