From adapting energy use to maximizing data consolidation, Big Data (BD) analytics has taken the guesswork out of optimizing the modern data center.
More than ever, the modern data center is a living, changing environment, with new technologies coming in, old technologies being cycled out, and evolving energy efficiency strategies to keep it all humming. We have to make sure we have the space and power to install the latest technology, while we still have the old equipment in place.
Up until recently, orchestrating this shifting ecosystem was only partially data-driven and the rest was based on gauging changing needs from past experience. At EMC IT—like most IT organizations—we had long tracked metrics on our data center facilities, including space, power, cooling, humidity, temperature, etc. And we collected storage data—server utilization, virtual machines, growth trends. But we lacked the tools to process this vast amount of data and we were never able to aggregate this information into one data base.
Sometimes, when your organization is leading the way in adopting bleeding-edge technology, you need to be a trail blazer to help to advance the innovation as an early adopter. EMC IT has a history of doing that, most recently with its groundbreaking deployment of SAP HANA in the Business Intelligence landscape.
EMC IT has hit a new milestone in its use of SAP HANA as part of its Enterprise Resource Planning (ERP) transformation, a good example of how such efforts can pay off for us and our customers. After three years of using an interim, standalone HANA appliance for its real-time reporting needs, Big Data Services has successfully incorporated the real-time environment into our main Business Warehouse (BW) environment on HANA.
The migration and consolidation of data over the past six months has resulted in a more agile, efficient and cost-effective data environment where both real-time and batch operations are performed from a central location. The move not only saved EMC an estimated one-time $1.5 million, the transition also allows EMC IT to reuse the capacity freed up by eliminating duplicate data sets to expand our burgeoning BW.
More than ever, businesses see their futures tied to their ability to harness the explosive growth in data. You may even be familiar with the Business Data Lake concept—a central repository of vast information which can be used across an enterprise to drive all business intelligence, advanced analytics and even, eventually, intelligent applications.
We, at EMC IT, are in the process of creating a Business Data Lake, and I will be sharing insights about our efforts in this blog. To start, let’s trace the vision that’s leading EMC IT and other businesses to the shores of this new data landmark.
Second in a series on Enterprise Vendor Management
Selecting IT sourcing partners and negotiating effective contracts are key to the success of your organization’s IT sourcing strategy. Failure to dedicate enough time to these items will almost certainly lead to issues down the road, either in project failure or not achieving intended goals. In this the second blog on Enterprise Vendor Management, we will review some foundational steps to get you started in the right direction. (Read the first part on Framing a Better Sourcing Strategy)
Selecting a partner
Before deciding who you want to purchase services from or partner with, you must clearly define your goals and requirements including timeframe and budget. If possible, do this without a specific partner in mind to avoid tailoring your requirements. Depending on the scope of your project, you may want to issue a formal request for proposal (RFP).
When it comes to running your IT operation like a business to deliver IT as a Service (ITaaS) and competing with outside providers, Service Portfolio Management (SPM) is where the rubber meets the road.
SPM is the process by which your IT organization makes sure your service catalog is providing the right mix of services that will meet customers’ needs and deliver business value while at the same time enabling you to be a financially viable service provider. Or, put in plain business terms, SPM is how you make sure you are selling the right product mix to meet your customers’ demands (and needs) at the right price to keep you in business–to keep IT relevant. It is basic supply and demand.
That said, achieving SPM as you transform your traditional IT operation to ITaaS has its challenges. EMC IT has been in the process of transforming to an ITaaS model for several years now. And just as our transformation journey has been a learning process, so has our journey to effective SPM.
IT Proven allows you to leverage EMC IT’s first-hand knowledge and best practices to accelerate your own IT transformation journeys, transforming operations and delivering IT as a Service through the power of cloud computing. IT Proven highlights how EMC IT transformed into an agile, innovative, and competitive service provider.
Big Data is changing the way IT organizations operate and deliver solutions to the business. It is a new, contemporary approach for IT to help business users harness and interpret information to drive more efficiency, productivity, performance and value for the business. As EMC IT embraces Third Platform, we are breaking new ground with Big Data analytics to better position the organization to deliver a more competitive solutions.
EMC CIO Vic Bhagat (@VicBhagat) addressed this topic and more in a recent interview with the Pivotal Blog, tackling the questions, challenges and opportunities facing both EMC IT and global CIOs. Where can IT organizations begin? How can they drive new behaviors? How should they address internal clients?
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.
The first step we ask as data scientists when we approach a new project is what’s the customer’s available data? While some of the time the answer will be a table or file with lots of nice numbers just waiting to be ingested by a machine learning classifier, most of the time a big chunk of the information will be stored in free text columns or documents.
As a customer-facing organization we store information describing EMC’s interaction with clients: some of it structured such as time to close, problem codes, etc.— but also free text fields such as problem summary or comments from the customer satisfaction survey. These free text fields can be used for accurately routing service requests to the right support team to improve resolution times and customer satisfaction, identify burning issues in the customer satisfaction survey or identify emerging problems.
Similarly, Sales would like to use a potential customer’s web site in order to categorize that company’s needs and identify products sold to similar companies.
This is a great time to be a data scientist –a bit like rock stars with all the fans always trying to catch some private time with us. While there’s is no clear definition of what a data scientist is (see related blog or view diagram of DS skillset) our take on this role is quite simple:
Work with stakeholders to elevate high impact business related questions
Find the means to answer these questions
This blog aggregates our collective experiences as members of EMC’s Corporate IT Data-Science-as-a-Service (DSaaS) team. Our team has been active since 2012, providing Data Science (DS) services to different business units as part of EMC IT’s transformation to an agile and innovative IT-as-a-Service model.
Although we aimed for a technical blog, we thought that the first post should provide a broader context to the DSaaS offering and it will, therefore, be dedicated to the process of innovating and driving data science projects in the corporate environment.
The changing IT landscape requires not only the provisioning of the latest tools, technologies and assets, but the embracing and leveraging the technology – especially Big Data analytics – to achieve true transformation. Through greater inclusion of Big Data analytics into our IT portfolio, EMC IT has made tremendous strides optimizing how it runs IT. However, the greater value rests within our ability to package Big Data analytics and provide the business with a consumable, self-service model.
For instance, with Business Analytics as a Service, we are empowering our constituents to plan, map and create solutions that best support their specific needs. Leveraging Greenplum technology, the analytics generated allow users to co-locate terabytes of corporate information from various data warehouses within a single platform. The solution is agile, reduces time to market and fosters a strong relationship between IT and the business.
In a recent Q&A with DataQuest magazine, EMC Chief Information Officer Vic Bhagat addresses the role of Big Data analytics in driving business value, how emerging technologies affect his role as CIO and much more.
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