Edge computing is the current trend used in BI apps in the business landscape, but what is edge computing? Edge computing is the processing of information right next to the source of data, either next to the gateway or close to the sensor. But in a corporate field, edge computing is a mesh network of several small footprint data centers of approximately 100 square feet. Just like other computing terms, this term has been loosely used in other tech terms like content delivery networks (CDN), the blockchain, mesh computing, peer to peer computing, and grid computing.
When we talk about edge computing in relation to any technical term even when talking aboit ad-hoc reporting, it has to describe the fast data analysis and related processes that are done over a short distance between the data processing area and where the output (e.g. ad-hoc reporting) is done.
When you bring about insights of business intelligence into actionable insights, it is an important consideration. Although embedded BI and edge computing are a great combination, we need to consider a lot before merging the two.
How edge computing stacks
Edge computing is still at its discovery stage, just a few apps are using edge computing according to an analysis that was done by TECHnalysis. Below is the list of the top apps for edge computing:
- Operating analytics
- Process monitoring
- Employee monitoring
- Remote asset monitoring
- Workplace safety compliance
- Predictive maintenance
- Physical asset tracking on-site
The major reasons as to why migrating cloud apps to edge computing is done is to reduce cost, improve security, improve local control, reduce latency and reduce the traffic in the network.
When you look at edge computing through BI apps, opportunities, and efficiencies are enhanced. It becomes beneficial to migrate apps from the cloud or add analytics in the existing IoT apps to help you become super fast. For instance, instead of data streaming and data analysis from a factory floor, you can use the endless amount of information that is repeatedly generated by the sensor.
4 tips for BI and edge computing
It’s easy to jump into the ship of edge computing, but you need a strategy to do just that. Here are a few tips you need to consider when planning your BI strategy and also the edge computing strategy.
Re-evaluate your IoT plan for additional opportunities for data mining
For instance, a manufacturer or a grocer might decide to use its supply chain data like trucking sensors and refrigeration to validate or establish where the raw materials are coming from. When this information is added to a blockchain that is sustainable, it can be used to attract consumers who are environment-friendly in the marketing strategy.
In a retail store, a retailer can use edge computing and computer vision to scan consumers and make a 3D presentation on how the clothing can fit a customer. This could ideally improve the sales of the retail store and eliminate the need for using dressing rooms and the much-needed privacy and security concerns. The data can be transferred to the cloud where it is blended by consumer data to contribute to the larger strategy of the company.
You need to look at more opportunities that you could use from your BI apps, what else can the data generated by the apps be used for?
Decide the BI apps that need to be at the edge
Migration of a BI app can be done, or you can also decide to create a custom app or have embedded analytics; it is dependent on the objectives of the company. OpenDev conference by OpenStack is ideal for learning more about developing BI apps for edge computing. Alternatively, you can go for apps that are offered by vendors of edge computing or the embedded analytics that you can get from embedded BI app vendors.
Choose the technology you want to try
You can go ahead and request for demos from vendors so that you can have a good understanding of the desired tech, the BI apps available, and you can also get guidance on the BI apps you can develop. For instance, Microsoft Azure IoT Edge, and Lambda@Edge from AWS and others offer a blend of BI apps and embedded analytics for edge computing in IoT.
You can look at blockchain offers, P2P, CDN, IBM Corp., Dell Inc., and HP Enterprise. Try out the options you pick before evaluating the ones you want to use. Take into account the types of IoT that are in your company and the ones your company is planning to adopt before talking to vendors.
Plan your evolution
Your organization has to grow in a mature way with BI and edge. This might bring about a collaboration of BI apps at some point from different vendors. Additionally, look for ways that you can decouple cloud tech so that edge can also be used. You will be able to drive down costs and increase efficiencies through smarter BI apps that are being used across different ecosystems instead of using systems from one vendor.