How to drive business value with edge analytics

As data exponentially outpaces centralized storage and management, learn how edge analytics can help organizations meet this challenge

As the world becomes more connected, the choice of smart devices that can collect and analyze data is greater than ever. Today’s cars generate large amounts of data from sensors and computers built into their designs. Retail stores collect data on everything from inventory and shipments to customer purchases. The wind turbines that produce our renewable electricity are equipped with hundreds of sensors and generate millions of data points per minute.

The volume of information required for these everyday events is growing faster than the available bandwidth of the networks designed to store and organize it. It is no longer possible to transfer all generated data to a central location where it can be organized and analyzed using conventional means.

Enter edge analytics: a type of decentralized data analytics that analyzes data at its source — at the “edge” of the information network.

In traditional systems, data is transferred from where it is collected to a central repository where it can be analyzed. But even today’s most powerful networks do not have the capacity to carry all of the data generated in most use cases, so decisions must be made about what to omit.

Because raw data can be analyzed at its source, Edge Analytics avoids the need to transmit data back to a central system, while still bringing all insights together for centralized decision making. This dramatically accelerates the speed at which analyzes can be performed without sacrificing the quality of the results.

>See also: How edge computing will benefit from 5G technology

Why you should use edge analytics

Given the potential value of analytics in improving decision-making processes and business outcomes, companies cannot afford not to evaluate their options. Edge analytics offers a variety of benefits in terms of analyzing more of your data faster and potentially more cheaply.

Edge analytics when using an in-memory database further accelerates these benefits by allowing organizations to analyze raw data as it is received. This gives organizations real-time results so changes and adjustments can be made quickly.

Edge analytics can also help address one of the most common issues faced by today’s digitally savvy businesses: cloud costs. Storing data in the cloud costs money, as does transferring data between the cloud and local storage or between cloud service providers. These costs typically increase very quickly as cloud usage scales—so by doing more analytics at the edge, organizations can reduce cloud storage spend and transmission costs.

Security is another benefit of edge analytics, especially when organizations deal with sensitive data like personally identifiable information (PII). Having all of a company’s raw data in one place can be inherently risky. By using edge analytics, organizations can keep sensitive data where it is and only transfer pre-aggregated data to the central data warehouse, meaning sensitive information does not need to be hosted and protected.

industries on the fringes

So: where do these advantages apply? Where can we see companies benefit from edge analytics?

One of the most important is the renewable energy sector, which has rapidly adopted edge analytics. For example, hundreds of sensors are built into wind turbines to ensure that every part of the turbine is functioning properly and can adapt to external conditions. When data analysis can be performed on each individual turbine, preventive maintenance is possible, where problems are proactively identified and isolated for each individual turbine, minimizing the impact on the entire group in the event of an accident.

Another area where edge analytics will gain traction is in supply chain management due to its complexity. Supply chains often have hundreds of individual moving parts, including sourcing and tracking raw materials that need to be transported to multiple locations for manufacturing, managing warehousing facilities, and monitoring thousands of IoT devices like RFID chips that keep track of the shipped item.

In this situation, supply chain managers could have a central location that organizes the movement of products around the world, and an “edge” could be the storage facilities with RFID-tagged goods. Analyzing data directly at these storage facilities in near real-time with edge analytics is useful for orchestrating the rest of the chain. Supply chain managers also need to consider weather, for example, which is where more advanced analytics like AI and ML modeling could come into play, which can also crunch at the edge.

>See also: The digital transformation of supply chains requires online and offline integration

Ready, Set, Go

Implementing edge analytics doesn’t have to come at the expense of traditional centralized databases—they’re best used together. Raw data can be analyzed at the edge before being aggregated and sent to a central database or data warehouse for on-demand storage and advanced analysis.

Setting up Edge Analytics in your organization starts with the right in-memory database. To analyze large amounts of data at the edge in real time, you need a powerful analytics solution that can integrate edge infrastructure and core data warehouse infrastructure. With the right database, you can build a grid consisting of a centralized system and the edge, with the platform acting as a conduit, helping to get data where it needs to go.

As the world of IoT continues to expand and companies start adding more AI and ML integrations to the edge to automatically optimize real-time data analysis for better and faster results, edge analysis will only become more important to a tide to avoid data being processed and analyzed centrally. Suffice it to say that the key to better data analysis is living on the edge.

Jens Graupmann is Senior Vice President of Product and Innovation at Exasol.

Related:

Four Methodologies in IoT Software Development – Vincent Delaroche, CEO and Founder of CAST, identifies four key methodologies in IoT software development.

What PSTN Shutdown Means for the IoT – Examine how the upcoming PSTN shutdown could impact future IoT operations.

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