Edge Data Management: What, Why and Best Practices




Wissen Team


July 2, 2024

In this era of Big Data, the companies that master the emerging discipline of data science and management are the ones reaping significant rewards. But what most don’t understand is that not all data can be centrally managed and analyzed at an offshore data center of the cloud.

Some situations call for decision-making in real-time, which requires data to be stored, managed, and analyzed where it is generated. In today’s age of intelligent sensors, it all boils down to managing data at the edge.

What Is Edge Data?

Edge data is data that is generated by digital devices such as sensors at the source – before it is transmitted to a data center or a cloud for storage or processing purposes. Unlike modern cloud platforms that collect data from devices and store it in a centralized location for further analysis, edge platforms look to analyze the data as it is generated by the device. This allows decision-makers to make time-sensitive decisions on-site, using real-time, freshly generated data.

So, what significance does edge data hold?

  • Edge data helps generate incremental value for applications, allowing for real-time analytics and rapid decision-making.
  • Because data is analyzed at the source, organizations can save a lot of time and costs in sending the data to the cloud for analysis and acting on the insights generated.
  • Edge data offers several benefits for applications and use cases where data can’t be sent off-site, for security or compliance reasons.
  • It also paves the way for better bandwidth availability while allowing businesses to overcome latency constraints that delay critical business decision-making.

In the realm of real-time monitoring, edge data offers significant benefits in creating operational efficiencies. Let’s take the example of an autonomous vehicle. While on the road, the vehicle must make decisions in real-time. If a pedestrian is crossing the road, brakes must be applied immediately. Such instantaneous decision-making is possible only when data from the vehicle is processed at the edge. There is not enough time to send the data to the cloud for processing and making a real-time decision on braking.

Why Does Edge Data Need to Be Managed?

Analyzing data where it is generated sounds good in theory. But how does one store and manage all that edge data that is generated? With the number of edge use cases ballooning, the volume and the complexity of edge data are only growing. This is where edge data management comes into the picture. It enables organizations to:

  • Manage and retrieve data at the edge for efficient analysis and processing while reducing latency.
  • Assess the capabilities of edge-based systems/devices and understand usage patterns of data at the edge.
  • Evaluate data security and governance requirements and ensure compliance with necessary data privacy guidelines.
  • Maintain the performance and reliability of data being generated and enable high throughput.
  • Expand capabilities as needed by adding more processing power while keeping current systems as-is.
  • Process only extremely critical data at the source while sending not-so-critical data to the cloud for analysis.

What Are Some Best Practices for Edge Data Management?

Successful edge data management brings the realm of data analytics closer to data sources such as IoT devices or local edge servers. If you want to minimize the proximity of analysis to the data source to drive faster insights and improved response times, here are some best practices to keep in mind:

Build a Comprehensive Plan

A successful edge strategy starts with a comprehensive plan for managing edge data and the underlying infrastructure. While a massive amount of data is generated by intelligent devices, not all of it needs to be analyzed immediately. Having a plan in place can help in prioritizing data that needs instant processing and ensuring the rest is sent to the cloud for later processing.

Ensure Adequate Network Strength

The need to reduce latency and improve resiliency has been a critical factor in the growth of edge computing. As more and more IT infrastructure is moved to the edge, it is essential to ensure wired and wireless networks meet the required speed, capacity, bandwidth, throughput, resiliency, and latency requirements.

Backup Is Key

Storing data at the edge can be risky and vulnerable to downtime, which can be disastrous for a smart energy grid or a patient health monitoring system. To boost operational resiliency, it is critical to constantly back up edge data to another site. You could also consider investing in software-defined WANs to centralize edge data yet make it easily accessible from anywhere.

Enable Effective Data Partitioning

If you want to overcome the architectural challenge of storing so much edge data, you must enable effective data partitioning to store and maintain data being constantly generated. The data partitioning technique you use should be determined by usage patterns at the edge, data governance, and security, as well as your physical storage requirements.

Maintain Security and Governance

Given the sensitive nature of data being gathered (and processed) at the edge, security and governance must be at the forefront of edge data management. Since bad or corrupted data can lead to irreparable damage, investing in edge-based security technology for encryption and authentication is important. It also pays to have robust data policies in place to put guardrails around how edge data is stored, managed, and used.

Ensure Continuous Remote Monitoring

Effectively managing data at the edge also requires you to carry out continuous remote monitoring. This will help monitor edge data health and flag issues while also allowing technicians to implement necessary upgrades and security patches. Using a combination of remote management tools, analytic capabilities, and databases, you can ensure a high-performance and cost-effective means to maintain smooth functionality and make data easily accessible to users.

Summing Up

As the business world gets increasingly connected, the use of IoT sensors and smart systems is at an all-time high. But just implementing an ecosystem of intelligent devices isn’t enough. You also need to be able to manage all the data being generated by these devices – preferably on the edge!

With 75% of enterprise-generated data expected to be created and processed outside a traditional data center or cloud, now is the time to hop onto the edge data management trend and maximize the value of data with speed and sharpness.

Working with a trusted partner can help you gain a competitive edge on your edge journey. Contact us today to reliably store and process edge data with as little latency as possible.