Business growth was once about delivering quality goods and building a strong and trusted brand that inspired customer loyalty. As consumers, we had a relatively limited number of options with which to meet our needs.

Today, we are all presented with a myriad of brand new enticing options for shopping, banking, seeking healthcare, hiring advisors and supplying our businesses. You name it, and there are hundreds of brands with clever new technology-enabled services vying for our attention and hard-earned dollars. It’s no wonder that we are easily distracted by the latest bright shiny new service or app.

IoT Data Growth
The IoT Gold Rush Is On
Companies are looking for ways to use data from every aspect of our lives to introduce new offers and differentiate their products and services. It’s clear that the Internet of Things (IoT), with its extended, real-time connectivity to billions of sensors and devices, offers tremendous opportunities to shape the most personalized and engaging experiences imaginable.

Early success stories have triggered a virtual gold rush, with companies racing to access and leverage IoT data for smart products and services that will make our lives better and make their brands bigger. Tremendous value will come from mining the rich veins of IoT data, enabling better and faster business decisions that will transform the way they do business.

Unfortunately, today’s data analysis solutions make most IoT data inaccessible, failing to deliver high performance analytics at the network edge where data is captured. There are countless devices, sensors and systems that house data captured locally. The challenge will be to harness these data sources and mine insights from them when and where they can be impactful and monetized.

3 Ways IoT Data Is Different
The IoT landscape is large and highly fragmented, made up of billions of devices and connected people generating trillions of GBs of a wide variety of data—most of it unstructured. But volume isn’t the only reason IoT data analysis presents such a daunting challenge to enterprise organizations. IoT data types are also very different, presenting seemingly insurmountable obstacles that conventional data analytics tools are not architected to address.

For data-driven business executives, the IoT has introduced an onslaught of new data challenges. We’ll take a look at three ways that IoT data differs from the clean, structured data that enterprises are comfortable dealing with in the first installment in a series on edge computing and the Internet of Things.

1. Variety: IoT Creates an Explosion of Data Types and Formats
Traditionally, IoT data has been associated with monitoring and collecting data from devices. However, organizations are looking to do more than simply monitor data. They see the significant value in being able to get a clearer view of markets and buyers to boost profits, enhance customer service and improve employee productivity.

IoT devices such as mobile phones, sensors, RFID tags, video cameras or mobile phones produce a wide variety of data, from structured to unstructured, small to large file sizes, text to images. Today’s analytics platforms were not designed to analyze these different types and formats of data together to produce relevant insights in real-time, especially at the network edge where the data is gathered and used. To fit the strict parameters of conventional analytics tools, these different types of data must be transformed prior to analysis. Yet the ETL and data preparation process slows down the data pipeline, creating bottlenecks and delaying the insights needed to act in time.

2. Velocity: IoT Data Comes at You Fast and Furious
Unlike structured data, typically stored by enterprises in a centralized data warehouse, IoT data is generated by a sprawl of different devices from any number of locations, coming from stationary or mobile devices. Finding value in IoT data can be like panning for gold in the ocean. Connected devices stream massive amounts of data very quickly, and extracting insights from IoT feeds usually requires some type of correlation between that streaming data and historical data stored in a centralized data warehouse or lake.

The challenge with dynamic, poorly structured data is that legacy relational database systems have a difficult time keeping up with it. And a variety of NoSQL approaches have similar problems for semi-structured and structured data, especially at the network edge. The transformation or indexing that is needed to even begin to analyze the data can often take hours, days or even weeks. This is a serious problem for contemporary analytics products. Aside from the obvious network and transport inefficiencies, the correlations often required to gain critical insights from streaming and historical data often come too little too late—or not at all.

3. Location: Data Sources Are Widely Distributed
Before we ever dreamed of calling it the Internet of Things or the Internet of Everything, businesses were deploying remote sensors in the wild—from retail outlets to remote oil rigs to planes, trains and automobiles. Billions of devices, sensors and networks connected to the Internet are creating and receiving data around the clock. ABI Research estimated that the volume of data captured by IoT-connected devices exceeded 200 exabytes in 2014, and will grow to 1.6 zettabytes by 2020. ABI also estimates that more than 90 percent of IoT-generated data is stored or processed locally, rendering it inaccessible for real-time analytics. This translates to tremendous amounts of data that must be transferred to another location for storage and analysis. As the number of devices expands and the volume of data increases, the costs of data transport and data storage can quickly become prohibitive.

There are many complexities involved in gaining the type of insights that businesses increasingly require from their growing IoT data. Organizations need to prioritize re-evaluating the transportation and execution of analytics on IoT data from a centralized environment. Moving data to a single, centralized location is not feasible for many types of data and could delay insights. In order to leverage real-time analytics, organizations should investigate solutions that allow them to capture and analyze IoT data at the edge of their network.

For a deeper look at IoT, edge computing and how to make the most of your data, check out our on-demand webinar, Harnessing IoT Data at the Edge. Also, be sure to stay tuned to learn how to unlock answers hidden in IoT data in the next installment in our edge computing blog series.

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