Data is the lifeblood of any business. Consequently, the availability of that data is critical. The Internet of Things (IOT) produces a tremendous amount of data that a business needs to understand in order to make decisions. Additionally, this data must be secured.
All of this leads to the requirement to capture and then process data from source locations. This is referred to as “Data at the Edge”.
Ideally, if the data is captured and analyzed at the edge, then only the result sets would need to be captured and stored. This is a much lower volume than all of the data that is being generated at the edge. This process is critical for such things as self-driving cars, oxygen supplies for mines, for ensuring the quality and safety of manufacturing processes, etc.
Benefits of capturing data at the edge: low latency, decreases the workload on networks and bandwidth, and higher security due to data transfers occurring over company networks rather than public ones. The higher security also protects any sensitive data that is being collected.
By consolidating workloads at the edge, companies are able to more effectively manage and store the data and this reduces costs.
The challenge for businesses is managing the data at the edge. This is due to the fact that there are many sources of data, and this volume of data needs to be analyzed at the edge in order to find the key information the business needs within the massive volume of data. Here are a few ways to analyze and manage data at the edge:
With the correct precautions, Data at the Edge can be a powerful tool for most organizations.