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Embracing Data & Analytics in the IoT with Our Partners

- April 14, 2015 - 4 Comments

Massive amounts of data are being created in new places. A Boeing 787 creates half a terabyte of data per flight. An offshore oil well can create up to 10 terabytes in just 24 hours. These are examples of the Internet of Things (IoT). Within the IoT, a huge volume of these non-traditional devices (i.e. things) are being connected by the network.

Imagine if these ‘things’ could talk.

What could they tell us about safety, operational efficiency, and interactions with people using this technology? Well….these things can talk. In fact, they are talking all the time through the large volumes of data they produce. In order to utilize this data to empower business decision-making, we need to understand it. That is where analytics come in. Simply put, analytics is using software to look for patterns in large volumes of data. Patterns help you understand some aspect of your business, so you can make better decisions to achieve the desired outcome.

Traditionally, to perform analytics, it required all data to be moved to a central location to be analyzed. In the IoT, with the amount of data being created in multiple places, moving data to a central location for analysis is difficult, expensive and time-consuming. Together with our partners, we are creating solutions that involve doing edge analytics, without the need to move device data to a central place for analysis. The result? Organizations can improve their ability to make decisions quickly by doing the analyzing where the data is created.

MikeBlog_Panasonic

To hear more, watch the video of my discussion with Rance.

At Cisco Live Milan, I had the opportunity to sit down with Rance Poehler, President of Panasonic North America, to discuss how his company is using IoT technologies across various industry verticals, such as retail. Powershelf is an intelligent retail shelf that automates pricing and tracks inventory. With edge analytics, data that flows from a connected device like Powershelf can be analyzed in real-time throughout a retail organization to unveil trends and predictions.

Most retail organizations will collect information about its customers and sales trends through traditional ways – such as customer loyalty cards and sales reports direct from store managers. This information generally lives in an organization’s data center and is what we would refer to as historical data. While it is collected regularly and crucial to a business, it cannot tell us about what is going on inside a retail store in real-time. When we supplement edge analytics with analysis of historical data, it can empower decision making throughout a retail organization – locally, regionally and even globally – as it relates to supply chain, sales forecasting and customer experience.

For example, a local store manager can make stock ordering decisions based on the real-time availability of current stock and sales patterns. At a regional or global level, with access to stock and sales patterns in all stores combined with historical sales trends, an organization can make decisions to boost production of an item that is continuously out of stock or adjust the pricing of an item in an effort to increase sales. This is just one example of the many possibilities of Data & Analytics in the IoT.

Tell us where your data growing. How is it impacting your business? What is your plan for addressing it or analyzing it to inform business decisions? Please comment below to share.

 

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Learn More from My Colleagues

Check out the blogs of Mala Anand, Rohit Shrivastava, Bob Eve and Nicola Villa to learn more.

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4 Comments

  1. NIce share, I want to get more information about cisco. I'm sure It's professional.

    • Thanks Sutriadi. For more information about Cisco's Data & Analytics solutions, I recommend you check out cisco.com/go/dataanalytics.

  2. I like the idea of power shlef.Local business will have great help in understanding buyer behaviour.

  3. I like the idea of power shlef.Loacl business will have great help in understanding buyer behaviour.

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