As businesses are moving towards using more and more data for decision-making, data-driven insights
have become the most valuable asset for any company. Today, businesses are feeling the need to process data and access analytics in real-time. In the past, businesses collected data from various IoT devices and sensors, centralized it in a data warehouse or data lake
, and then analyzed it to get insights.
What if businesses could bypass the data centralization or integration stage entirely and go straight to the analysis stage? This technique is known as edge analytics. This method allows businesses to accomplish autodidact machine learning, improve data security, and reduce data transfer costs.
With edge analytics and edge computing, businesses can not only generate more sales but also boost efficiency, enhance productivity, and save costs.
Let’s dive deeper into edge analytics, how it complements cloud computing, and why businesses are increasingly opting for it.
How can Edge Analytics Complement Cloud Computing?
Real-time decision-making is still challenging in IoT systems
due to factors like bandwidth, latency, power consumption, cost, and various other considerations. This problem, however, can be addressed by using of artificial intelligence in edge analytics, which also makes cloud computing better.
Cloud computing and edge computing are very different approaches and purely depend on the software implemented. These two technologies don’t discredit each other, but rather complement each other.
Reduces utilization of data bandwidth or transfer
Ends the need for continuous connectivity to the cloud
Boosts the real-time performance with faster processing
Enhances data security
Common Pitfalls to Dodge with Edge Analytics and Edge Computing
According to Statista, the number of Internet of Things (IoT) devices will reach 30.9 billion units by 2025. Moreover, the global IoT market is expected to grow to $1.6 trillion by 2025.
The cost of transferring and storing all of that data, combined with the lack of a clear advantage, has led many to question whether the IoT is worth the hype. That is why the industry is shifting its focus to edge analytics or computing to fully leverage the data collected from IoT devices. Let’s take a look at some of the challenges that can be addressed with the help of edge analytics:
Many industrial IoT solutions require complete uptime.
Consumer IoT apps need to process localized events in real-time.
A power outage might result in a security breach.
Difficulties in adhering to data regulations.
Why You Should Employ Edge Analytics?
“To remain competitive in the post-cloud era, innovative companies are adopting edge computing due to its endless breakthrough capabilities that are not available at the core.”
- David Williams, managing principal at AHEAD.
Edge analytics solutions assist businesses wherever data insights are needed at the edge. It can be used in various industries for numerous things, such as retail customer behavior analysis
, remote monitoring and maintenance, detecting fraud at ATMs and other financial sites, and monitoring manufacturing and logistical equipment. Here are some reasons you should choose edge analytics and edge computing for your business.
The prime objective of adopting an edge analytics system is to filter out unnecessary information prior to analysis, and only relevant data is sent via higher-order methods. This saves a lot of time when it comes to processing and uploading data, which makes the complex analytical process done on the cloud a lot more valuable and effective.
The use of edge analytics in IoT cuts the cost of data storage and administration. It also saves operating expenses, bandwidth requirements, and resources spent on data processing. All of these things add up to substantial financial savings.
Edge analytics assists in the preservation of privacy when sensitive or confidential data is gathered by a device, such as GPS data or video streams. This sensitive data is pre-processed on-site rather than being transferred to the cloud for processing. This additional step ensures that only data that complies with privacy laws leaves the device for further analysis.
Reduces Data Analysis Delay
Edge analytics tools enables faster, autonomous decision-making since insights are identified at the data source, preventing latency. It is more effective to analyze data on the defective device itself and shut down the faulty equipment immediately instead of waiting for the data from the equipment to be transferred to a central data analytics environment
and waiting for the result.
Solves Connectivity Issues
By making sure that applications are not disrupted by restricted or interrupted network access, edge analytics in IoT helps to safeguard against possible connectivity disruptions in IoT. It is particularly beneficial in rural areas or for minimizing connection costs when utilizing costly technologies such as cellular networks.
Industries Leveraging Edge Analytics
Edge analytics is an exciting field, with businesses in the Internet of Things (IoT)
sector growing their expenditures every year. Leading vendors are actively investing in this rapidly growing market. Edge analytics provides measurable business advantages in certain industries such as retail, manufacturing, energy, and logistics by decreasing decision latency, scaling out analytics resources, resolving bandwidth issues, and perhaps reducing expenditures. The potential at the edge leads to a very exciting future of smart computing as sensors get more affordable, applications need more real-time analytics, and developing optimized, cost-effective edge algorithms becomes simpler.
What distinguishes edge analytics from regular analytics?
Except for the location of the analysis, edge analytics offers remarkably similar capabilities to regular analytics systems. One significant difference is that edge analytics apps can run on edge devices that can have memory, processing power, or communication.
What are edge devices, and what are some examples?
An edge device serves as an access point to the core networks of businesses or service providers. Some examples include routers, switching devices, integrated access devices (IADs), multiplexers, and other metropolitan area network (MAN) and wide area network (WAN) access devices.
What exactly are edge machines?
Edge ML is a technology that allows smart devices to analyze data locally through local servers or at the device level. This is done with the help of machine and deep learning algorithms, decreasing dependency on cloud networks.