Edge analytics - or analyzing data closer to where it's collected - is a relatively new idea in data analytics and, at least so far, we've most often heard it referred to in conjunction with IoT. After all, in a world with sensors everywhere and an increasing amount of data flowing in, edge analytics offers a way to derive value from data in a way that's faster, simpler and, in many cases, more practical. But while edge analytics has provided the technology to leverage IoT, its promise actually extends beyond IoT to the edge of a more traditional data ecosystem. Here we'll take a look at the advantages of processing data on the edge over storing it and applying more traditional analytics, and why many organizations are beginning to seek the ability to choose between those two options to fit their needs.

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Some Data Isn't Worth Saving

In the early days of big data, organizations were all about collecting data. The collective wisdom at the time was that collecting data was a good thing, even if it couldn't be fully analyzed. The problem is that as data collection improved, data volumes started exploding. According to a report released by research organization SINTEF in 2013, 90% of all the world's data had been generated over the previous two years. According to IDC, 1.7 megabytes of new information will be created every second for every person on the planet by 2020. That'll amount to about 44 zettabytes of data.

As the data piled up, the question became obvious: What are we actually going to do with all of this information? Unfortunately, sometimes the answer amounts to very little. A study released by Pricewaterhouse Coopers and Iron Mountain in 2015 found that 43% of companies surveyed were obtaining "little tangible benefit" from the data they collected. A further 23% were found to derive "no benefit whatsoever." What organizations are increasingly learning is that while data collection has major benefits, not all data is useful, and not all data is worth keeping, particularly when it flows from the myriad of sensors we call "IoT."

"A lot of the data that comes from IoT may not necessarily be data that we need to keep at an atomic level," Shawn Rogers, Director of Global Marketing and Channels for Dell Statistica, said.

"I think we're all enjoying the ability to keep more data, analyze more data, and get richer and deeper insights from all of these vast volumes of information. That said, just because you can, doesn't mean you should."

Because edge analytics allows organizations to analyze data closer to where it's actually occurring, it allows for making decisions before data is sent off to be stored. As a result, it can reduce the need to store and consolidate as much data. As data generation and collection continues to expand, that's definitely a good thing.

Agility Counts

There's another benefit to analyzing data closer to its source: agility. In some cases, data is much more useful in real time. This is especially true of the data that flows from IoT sensors. Factory sensors, medical devices, trading and fraud detection applications, and system monitoring, among many other examples, all provide data that may need to be addressed in a faster, more responsive way. This so called "stream processing" is important in applications where data needs to be processed quickly and/or continuously. As the pace of business increases, this capability is becoming more of a necessity in many industries.

"As a consumer of analytics, I want the ability to make strategic decisions about what data to invest in for the long run and what data to derive value from immediately, about what data is worth storing and what data isn't worth storing," Rogers said.

"I think moving analytics to data instead of always moving data to analytics is an important takeaway, and I think it's a demand most customers are going to have as analytics becomes more dispersed."

Storing Data Is Expensive

In the early days of big data storage, a lot of organizations collected a lot of data with the idea that it might be useful someday. The problem is that data collection and storage has a cost, one that often isn't mitigated by the value derived from that data.

"What we've seen in the last decade were people standing up Hadoop clusters, putting data into them and thinking it might be useful someday ... then quickly finding out that even with the advantage of some Hadoop technologies, collecting data still costs a lot of money," Rogers said.

Edge analytics provides a way to not only allow organizations to respond to data more quickly, but to create a better process around their data collection and analytics. Edge analytics also allows organizations to choose what data to keep for longer term, deeper analysis. This can make data easier - and less expensive - to manage.

Data Is Becoming More Distributed

The days of keeping data in a single place are probably over. That creates a need to deploy, manage and optimize analytics around different platforms, as well across the different areas where data is occurring, such as IoT sensors.

"If you're going to distribute your data around different platforms like Hadoop cloud or analytic appliances and so on, then you really need this flexibility to move the analytics to the data. Edge analytics isn't just for the edge of IoT, it takes analytics to the edge of a more traditional data ecosystem," Rogers said.

Less Data (and Complexity) Can Be More

Until quite recently, the conversation around big data collection, storage and analysis was about collecting data from source systems and driving it into a data warehouse. But not only is a data warehouse increasingly less able to keep up with the stresses of analytics, these systems pose issues around complexity and security because they involve transporting data across wide networks in order to analyze it.

"There is a lot of complexity in all of the work that goes into moving data from point A to point B for us to use. Edge analytics allows us to make decisions as to whether we want to move the data to a place for analytics or if we'd like to put the analytics where the data is," Rogers said.

In other words, edge analytics provides more options in terms of how data is used, and helps preserve the resources that are best suited to deeper data analysis.

"Edge analytics certainly affects the world of data management and how we move data from one place to another. The other thing that it does is provide the opportunity for customers to choose which platform would work best and give them the answers as the speed of their business."