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TDWI Upside - Where Data Means Business

How Edge Analytics Can Deliver the Competitive Edge Your Business Needs

Despite the rapid growth of edge analytics, you may be unsure how to use it to your enterprise’s advantage or how to get the best results from the technology. Here’s what you need to know.

In today's fast-paced world, businesses need to make decisions quickly, sometimes even in real time. However, traditional data analytics models are often too slow and complex to enable that, which can prevent businesses from keeping up with the pace of change. Enter edge analytics.

For Further Reading:

Using Edge Technology to Achieve Near Real-Time Insights

Ubiquitous Smart Devices and the Coming Age of Edge Computing

Don’t Start with Data: 3 Tips for Implementing AI Analytics

Edge analytics is an approach to data analytics that processes data and extracts insights at the edge -- that is, where data is collected. It empowers organizations, which traditionally had to send data to the core or the cloud for analysis, to make decisions much faster and more efficiently.

Traditional Data Analytics Can’t Keep Up

Data has grown at a rapid rate, and data volumes are projected to nearly double by 2025. Traditional data analytics models struggle to keep up with all the data that’s being generated.

Traditional data analytics is also no match for today’s data velocity. As the speed at which data is created continues to grow, there will be an even greater need for real-time processing. The interpretation and application of real-time analytics can vary based on the specific industry and its requirements. Real-time analytics is a broad concept that is adapted to suit the needs of different industries and sectors.

Furthermore, artificial intelligence and machine learning are outstripping the capabilities of traditional data analytics, which use statistical models that are unable to capture the nuances of real-world data. As a result, traditional data analytics often cannot handle complex, real-world scenarios.

Benefits of Edge Analytics

By addressing these traditional data analytics challenges, edge analytics is becoming more prominent. It’s a natural progression -- taking data and business where they need to go now.

One key advantage of edge analytics is that most data is being generated at the edge already. Nearly a third (29%) of 400 senior decision makers recently surveyed said that at least 50% of their data volumes now reside on the edge, according to a December 2022 report from Insight and IDG Foundry; over a third (36%) of the group cited integrating insights from edge devices into data analysis as a top priority, compared to 27% the previous year.

Businesses can move faster with edge analytics because of its reduced latency. This is possible because edge analytics processes data closer to where it was generated, so organizations get data insights quicker. Reduced latency is particularly critical for applications that require real-time response such as battlefield scenarios, fraud detection, and supply chain management.

Because edge analytics reduces the data load on the network, it also saves energy, reduces carbon emissions, and helps organizations meet their sustainability goals to protect the planet.

Edge analytics also improves security and privacy by limiting periods when data is in motion. When data is processed locally, you don’t have to haul it across the network, which decreases the chance it will be intercepted or stolen.

Scalability is another benefit of edge analytics. When businesses can distribute data processing across multiple edge devices, they can increase overall processing capacity of the system -- and handle more data and more users without overloading the network.

Of course, the effectiveness of edge processing can vary significantly based on the hardware and processing power available. The choice of hardware, including the age of the equipment and chipset used, plays a crucial role in determining how efficiently data is processed at the edge.

How to Get Started

Begin your edge analytics work by defining your goals and objectives. Identify the right data sources, which will include a mix of structured and unstructured data, to feed your edge analytics.

Find a partner with operational technology experience as well as IT technology expertise to help you navigate this initial work all the way through to implementation. Also develop the right talent within your own organization to support your edge analytics initiatives. That entails ensuring that you have data scientists, engineers, and security experts in addition to IT professionals involved in your edge analytics efforts.

For Further Reading:

Using Edge Technology to Achieve Near Real-Time Insights

Ubiquitous Smart Devices and the Coming Age of Edge Computing

Don’t Start with Data: 3 Tips for Implementing AI Analytics

Understand that although edge analytics’ ability to process data closer to the source means that bad actors have less opportunity to steal or tamper with data, edge analytics is not bulletproof. You will still need to secure your data whether in motion or at rest. Work with a trusted partner to implement the most appropriate security measures, and secure the flow among edge, core, and cloud.

Be aware that every edge analytics solution is unique, requiring specialized hardware and software. Temperature and vibration tolerance of gear on a military Humvee will be different than that required for a manufacturing or entertainment environment. Work with edge analytics experts to understand exactly what you need to get the optimal solution.

Don’t be afraid of change, but do start small and scale up your edge analytics efforts as needed. This approach will allow you to learn along the way and help you to avoid costly mistakes.

Use Cases for Edge Analytics

The opportunities to leverage edge analytics to enhance customer experiences, improve decision-making, and increase innovation are endless. Here is a taste of what’s possible.

A company that sells coffee might want to understand the availability of a certain piece of equipment -- for example, a roaster with a rotating bin. The company might decide that the roaster qualifies as “available” when it’s running above 50 RPMs. The company could then take the aggregated time the RPM is above 50 and divide that time by the total elapsed production time. With the right data integration and analytics, that company could quickly take in the raw data, inspect it and clean it up if needed, and perform data analytics. With this information, the manufacturer could then determine whether and when it needed to perform maintenance on that equipment.

Analyzing data at the edge would improve productivity because decisions can happen faster. The company can use a similar process to collect other types of information about its equipment, including energy usage and anomaly detection. Plus, organizations with hundreds of pieces of equipment can create digital twins to simulate and test how machines behave in different scenarios.

In healthcare, edge analytics allows medical images such as X-rays to be processed locally. Now patients and their doctors don’t have to wait for large image files to go to and from a central location for processing. Getting information faster can lead to better patient outcomes.

There are also many ways that edge analytics can enable smart cities to improve safety and other citizen experiences. That can include such simple but important things as counting people and redirecting crowds for ease of movement or alerting city maintenance crews that a trash receptacle at a certain intersection is full and planning a route for the garbage truck to drive to locate and empty those trash cans before they become a hazard.

Edge analytics is moving safety and availability forward in the transportation sector. A major European city has attached sensors to everything on its trains from the brakes to the doors. It sends the sensor data into an edge analytics platform, enabling the city to predict when something on the train is going to break. It can then take the train out of service for maintenance during off hours rather than having to shut it down during peak ride hours.

A Final Word

Edge analytics is a growing market and a key enabler of the Fourth Industrial Revolution (4IR). It empowers organizations by reducing costs, improving efficiency, enhancing safety, ensuring compliance, increasing uptime, and providing the agility and ability to get and act on insights in real time. If you want to stay ahead of the competition and improve operations, it’s time to adopt edge analytics.

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