It’s Time to Decentralize with Edge Analytics


The edge analytics market is set to explode as more industries adopt edge technologies to experience key benefits in multiple operational areas.

The rate at which enormous volumes of data are being created and consumed today is outpacing the capabilities of centralized storage and management. In fact, global data creation is expected to reach 180 zettabytes by 2025. While the scale of intelligent devices capable of analyzing data across industries is more significant than ever, even today’s most powerful networks don’t have sufficient bandwidth to transfer all the data generated in most use cases.

The imbalance between the amount of generated data and the available bandwidth is striking and consequently forcing businesses to decide what information to include in their analysis and what to leave out. In traditional systems, data is transferred from where it’s collected to a central repository where it can be analyzed. But with edge analytics – a type of decentralized data analytics – data is analyzed at its source or at the ‘edge’ of the information network.

By allowing raw data to be analyzed at its source, edge analytics avoids the need to transfer data back to a central system while still bringing all the insights together for centralized decision-making. This is particularly beneficial for businesses across industries that require or prefer faster speed to data, higher-quality analytics with lower latency, and the ability to scale (think retail, manufacturing, utilities, automotive, and medical, among others). As other industries catch up and continue investing in digital technologies and customer-driven experiences, we’ll see edge computing play an increasingly important role.

Cost benefits of the edge

Not only does edge analytics accelerate the speed at which analytics can take place without compromising on the quality of the results, but it’s also more economical. A key upside to edge analytics is the ability to analyze more data faster and potentially at lower costs. For businesses using an in-memory database, these benefits are multiplied, enabling data analysis as data processes in real time.

As businesses look to curb their cost to optimize operations, it’s worth examining cloud spending. Overall, cloud migration and storage costs vary. But the expenses can add up quickly as cloud use scales up – so by doing more analytics at the edge, companies can reduce spend on cloud storage and transfer costs.

See also: 5 Strategies for Successful Edge Computing Deployment

Edge analytics and data security

In addition to costs, with ongoing digital transformation initiatives, security remains a grave concern. ​​In 2021, some 1.51 billion breaches of IoT devices occurred, a substantial increase from the previous year’s 639 million breaches. For businesses managing sensitive data – credit card numbers, health-related information, educational records, etc. – edge analytics can play an essential role in safeguarding assets. The problem lies in having all of a company’s raw data in one central location – it’s risky. By employing edge analytics, organizations can keep sensitive data where it is and only transfer pre-aggregated data to the central data warehouse, meaning that it doesn’t have to host and protect this sensitive information.

Edge analytics in action

IoT growth hinges on edge expansion, and some industries have been faster than others to invest in edge technologies, thereby reaping the benefits of more immediate, reliable data to guide business processes.

One industry, in particular, is the renewable energy industry. Wind turbines use sensor data to monitor weather conditions as well as their own operating levels so that they can generate the maximum amount of energy while minimizing the risk of damage and wear and tear. The information generated by each wind turbine locally can be enormous in terms of volume and can be used by companies to establish predictive maintenance, which in turn helps to minimize outages and maintenance costs.

The sensors can generate extremely large amounts of data within just seconds, but network bandwidth remains a persisting challenge today. Suppose data analytics can be done at each individual turbine. In that case, predictive maintenance is possible, preemptively identifying and isolating any problems for each one, mitigating the impact on the entire system.

The edge analytics market is set to explode as more industries adopt edge technologies to experience these key benefits. We’re already witnessing this shift with the automotive industry building smarter, more connected vehicles with sensors and computers built into their designs. Likewise, autonomous robots in the construction and manufacturing industries allow businesses to collect valuable information pertaining to job site scanning and site progress monitoring.

With edge analytics, businesses can see the bigger picture by aggregating the summarized data from these devices. Analyzing data directly on-site in close to real-time with edge analytics is helpful in coordinating the rest of the project and can improve the business’s bottom line.

Tailor edge analytics to meet your business needs

Embracing edge analytics doesn’t have to mean giving up on traditional central databases altogether. Using these in tandem is one method with a proven record of success. Raw data can be analyzed at the edge before being aggregated and sent to a central database or data warehouse for storage and more advanced analytics needs. The first step to ensure the best results from edge analytics is identifying the right in-memory database for your business — as the right infrastructure will help direct data where it needs to go.

Welcome to the future of data analytics; it’s time to live on the edge!

Jens Graupman

About Jens Graupman

Jens Graupman is Vice President of Product Management at Exasol. He has over 15 years of experience in product management, business development, and IT management positions and holds a Ph.D. in Computer Science from Saarland University, Germany. His key topics include data management systems, business intelligence, real-time analytics, IT architecture, and cloud services. Jens began his career at the Max-Planck-Institute for Computer Science as a researcher in the Department for Databases and Information Systems. Before Exasol, he was Head of the product management department and QA department at ParStream and was independently consulting in the areas of business intelligence, SaaS, EAI, and SOA prior.

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