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Jun 20, 2013

Data in motion vs. data at rest

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Data in Motion vs. Data at RestGaining insights from big data is no small task. Having the right technology in place to collect, manage and analyze data for predictive purposes or real-time insight is critical. Different types of data may require different computing platforms to provide meaningful insights. Understanding the difference between data in motion vs. data at rest can help determine the type of technology and processing capabilities required to glean insights from the data.

Data at rest
This refers to data that has been collected from various sources and is then analyzed after the event occurs. The point where the data is analyzed and the point where action is taken on it occur at two separate times. For example, a retailer analyzes a previous month’s sales data and uses it to make strategic decisions about the present month’s business activities. The action takes place after the data-creating event has occurred. This data is meaningful to the retailer, and allows them to create marketing campaigns and send customized coupons based on customer purchasing behavior and other variables. While the data provides value, the business impact is dependent on the customer coming back in the store to take advantage of the offers.

Data in motion
The collection process for data in motion is similar to that of data at rest; however, the difference lies in the analytics. In this case, the analytics occur in real-time as the event happens. An example here would be a theme park that uses wristbands to collect data about their guests. These wristbands would constantly record data about the guest’s activities, and the park could use this information to personalize the guest visit with special surprises or suggested activities based on their behavior. This allows the business to customize the guest experience during the visit. Organizations have a tremendous opportunity to improve business results in these scenarios.

Infrastructure for data processing
You might be wondering what type of IT Infrastructure would be needed to support data processing for both of these types. The answer depends on which method you choose, and your business objectives for the data.

For data at rest, a batch processing method would be most likely. In this case, you could spin up a bare-metal server during the time you need to analyze the data and shut it back down when you are done. With no need for “always on” infrastructure, this approach provides access to high-performance processing capabilities as needed.

For data in motion, you’d want to utilize a real-time processing method. In this case, latency becomes a key consideration because a lag in processing could result in a missed opportunity to improve business results. By eliminating the resource constraints of multi-tenancy, bare-metal cloud offers reduced latency and high performance levels, making it a good choice for processing large volumes of high-velocity data in real time.

Both types of data have their advantages, and can provide meaningful insights for your business. Determining the right processing method and infrastructure depends on the requirements for your specific use case and data strategy.

Learn more about the benefits of bare-metal cloud for different types of big data workloads.

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May 16, 2013

Retail’s big data evolution

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The amount of data collected at every part of the supply chain has steadily increased, enabling retailers to make extremely informed decisions about what to sell, when to sell it and who to sell it to. The science of collecting these vast amounts of data and trying to draw meaningful conclusions falls under the term “big data.” This vast amount of information quickly becomes complex, unruly and difficult to store; and it’s nearly impossible to extract meaningful insight using traditional databases and computational techniques. The ability to compare Apple iPads to orange t-shirts in a meaningful way is a recent development, and this kind of knowledge is more than power – it’s profit.

Fifty years ago, if you walked into a local retailer to purchase a record player and a copy of Please Please Me, the debut album of a then-unknown band from England, you would have simply walked up to the register with your items, paid the cashier, received a receipt and been on your way (to having your musical world turned upside down, but that’s outside the scope of this blog post). Once a month, every store employee would stay late to manually look through every item on the shelves to take inventory.

Twenty years ago, if you walked into a local retailer to purchase a cassette player and a copy of The Cranberries’ debut album Everybody Else Is Doing It, So Why Can’t We?, you would have walked up to the register, paid the cashier, the now-computerized register would have automatically removed the items from the store’s inventory system, you’d be furnished with an itemized receipt, and you’d be on your way in record time. At the end of each week, the store’s computer would provide the manager with a report showing current inventory levels along with statistics on what was sold during the week. The manager would use that basic data to make informed decisions about what to order and when to order it.

Ten years ago, if you walked into a local retailer to purchase a CD player and a copy of The Strokes’ Room on Fire, you would walk up to the register, the cashier would scan your items and a prompt would pop up on their screen suggesting that perhaps you might want to preorder Radiohead’s upcoming album, Hail To The Thief. You’d ponder for a moment and remember that you used to enjoy Radiohead, so you add it to your order, pay the cashier, get your itemized receipt and preorder voucher, and leave the store, not only excited about your current purchase, but rife with anticipation for the new Radiohead album. At the end of the week, the store’s computer system would automatically place an order to replenish stock on items that were sold, and your preorder would be secured along with those of 50 other patrons – 30 of whom, like you, had entered the store unaware that Radiohead had a new album coming out.

Today, thanks to big data and predictive analytics, retailers like Walmart know what their consumers are going to buy before they even enter the store. When it rains on a Sunday in San Diego California, Walmart knows that on the following Monday they’re going to sell three times as many iPods than normal. They can even identify specific genres of music that will see a temporary boost in sales. Does your business have the ability to translate this big data knowledge into profit?

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