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Wikitip

Disproving Myths of Big Data

At a recent conference, I heard a dozen speakers interchangeably refer to big data as “e-commerce” or “digital marketing”. Those are components of big data, but the terms should not be used interchangeably. Unstructured big data refers to information in the cloud, including e-commerce, digital marketing, geo-spacial information, RFID, mobile offers, video, mobile wallets and payments, comments, tweets, NFC (Near Field Communication), blogs, “likes”, schematics, photos, infographics, clicks, QR codes, onlinesearches and much more.

Structured big data mostly refers to internal data, including shipments, manufacturing, orders, inventory, CRM, promotions, POS, forecasts, spreadsheets, syndicated data, etc. Characteristics of big data include volume, variety and velocity. Relational Solutions extends that description to include complexity. That’s because, unstructured big data has significant value, but that value grows exponentially when you are able to leverage it against your internal, structured data. There is a lot of complexity involved in that process. Some technology vendors claim there is no longer a need for structured data. This is untrue.

Structured data exists in applications from Oracle, SAP, IBM, Microsoft, etc. These companies all offer solutions for unstructured data, but their structured solutions will not be going away any time soon. These two technologies can be merged together leveraging a sound infrastructure that accommodates growth and change.

What if you could: Use comments to explain why sales are down in certain stores rather than sending out a rep? Localize sentiment and identify what’s impacting sales in certain regions? Proactively “push” offers out to customers when they arrive at the store? Turn negative commentators into advocates? Determine what clicks are leading to sales? Integrate Amazon sales with internal sales? Big data is about much more than just e-commerce and digital marketing.

It’s about leveraging all forms of information to streamline productivity,understand customers better, improve public perception, service retailers better and maximize sales.

Warm Regards,

Bhavish Madurai (www.linkedin.com/in/bhavish/ )

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Virtualization Energizes Cal State University

John Charles is the CIO of California State University, East Bay (CSUEB) and Rich Avila is Director, Server & Network Operations. In late 2007 they were both looking down the barrel of a gun. The total amount of power being used in the data center was 67KVA. The maximum power from the current plant was 75kVA. PG&E had informed them that no more power could be delivered. They would be out of power in less than six months. A new data center was planned, but would not be available for two years.

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A main impediment to storage virtualization is the lack of multiple storage vendor (heterogeneous) support within available virtualization technologies. This inhibits deployment across a data center. The only practical approach is either to implement a single vendor solution across the whole of the data center (practical only for small and some medium size data centers) or to implement virtualization in one or more of the largest storage pools within a data center.

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