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TECHNOLOGY

Exploring the Use Cases of AR and IoT

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Exploring the Use Cases of AR and IoT


AR and IoT technology are creating a new dimension with their ability to utilize digital information from physical devices to improve the effectiveness and functionality of employees.

Business intelligence is gaining complexity every second, making it difficult to interpret data and communicate business-related ideas. Augmented Reality (AR) possesses an enormous potential and helps in communicating an ideal overlay for torturous network architects and complex systems that characterize the Internet of Things (IoT). The combination of AR and IoT helps in providing employees with important information that makes their functioning in industries more productive and effective.

The Relationship Between AR and IoT

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IoT is a bridge that connects digital infrastructure and physical assets, while AR focuses on bringing these digital emotions to life by connecting them with the physical environment. The rising data volumes have prompted companies to invest in < target=”_blank”a href=”https://www.bbntimes.com/technology/exploring-the-combination-of-the-internet-of-things-and-augmented-reality”>IoT and AR systems to generate insights that can be used at the right time and place to execute tasks better. The AR and IoT strategy focuses on cost savings, higher profits and revenue generation to help in the growth of the institution.

The Rapid Growth of IoT and AR Devices

AR in business is rapidly scaling up as companies are extending its use beyond the testing phase to focus profusely on marketing and sales. The number of companies that are experimenting with this technology has rapidly increased in the past few years. As per reports, IoT-connected devices in 2016 were about 6 million. As per forecasts, by 2025, there will be over 75 million IoT-connected devices. The technology will slowly become a norm and companies who have not developed this strategy will find it difficult to catch up with market trends.

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1. Visualizing Data to Better Interact with the Environment

AR and IoT technology allows employees to proficiently visualize, understand and analyze data, enabling them to easily navigate through their space. Empowering employees with data insights when they are solving problems is an effective way to improve their productivity. The data can help companies make better sense of data collected from disaggregated stores and warehouses. The understanding of this data will help companies optimize the amount of space necessary to store material and help in monitoring the inventory.

2. Identifying and Diagnosing Problems

The combination of IoT and AR merges different parameters under a single view. The excellent analytical properties of this application help in identifying and diagnosing the problems in the physical world. For example, companies can create an AR application that uses IoT data to create a visual imprint of a machine’s components. The visual representation allows workers to look at areas that are difficult to spot and diagnose any issues easily. This enables workers to make smarter decisions by combining data to simplify complex problems.

3. Boosting Performance

The use of AR and IoT technology is the fastest way to boost a company’s performance. By making use of this innovative technology, companies can reduce costs by improving labor efficiency, and reducing material wastage and machine downtime. The optimal work model helps in boosting revenue and creating strategic value in the market by improving brand equity and status.

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Conclusion

The combination of AR and IoT is opening new avenues to improve the productivity of companies. The simple but effective solution will play a pivotal role in enhancing data to simplify complex problems.  



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TECHNOLOGY

How Businesses Can Automate Root Cause Analysis (RCA) With Machine Learning

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How Businesses Can Automate Root Cause Analysis (RCA) With Machine Learning


In the event of a severe incident for your business, you need to analyze what exactly changed (the root cause) to understand its impact.

Using machine learning for root cause analysis can help identify the event that caused the change quickly and easily.

Things can sometimes go wrong in your business’s daily operations. It can be a minor issue, such as a system outage lasting for a couple of minutes. Or it can be something severe as a cyberattack.

Generally, such events result from a chain of actions that eventually culminate in the event. Identifying the root cause is the best way to solve the issue. But manual root cause analysis takes time and often doesn’t provide the exact cause of a mishap. Using machine learning for root cause analysis can automate the process, helping identify the underlying cause quickly and with higher accuracy.

Power of Machine Learning for Root Cause Analysis

To understand why an issue occurred, you need to identify the root cause. But root cause analysis can often be complex and provide inaccurate results. Using machine learning for root cause analysis helps solve this issue.

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Log Analysis

Using machine learning for root cause analysis can help zero in on the exact location of the problem. You don’t have to scroll through infinite logs to identify which components were impacted and when. The machine learning program can automatically and quickly find the root cause by analyzing a given log data set. 

Moreover, the machine learning program can even predict future incidents as more and more data is available. The program compares real-time data with historical data to predict future outcomes and warns you of any unwanted incident beforehand. This will help improve your incident response, reduce downtime and improve productivity.

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Benefits of Using Machine Learning for Root Cause Analysis

There are many benefits of using machine learning for root cause analysis. It can help teams take the right action at the right time, minimizing your losses. Some of the benefits are discussed below.

Reduces Costs

The cost of solving the issue is reduced as your teams don’t have to guess and work around blind spots. Machine learning tools locate the exact line of code responsible for a performance issue, and your team can start working on fixing it right away.

Saves Time

The time spent fixing the issue is significantly reduced as it helps solve business pain faster by locating the cause quickly and accurately.

Provides Long-Lasting Solutions

Machine learning tools provide a permanent solution for your problems and foster a productive and proactive approach.

Grows Your Business

Using machine learning for root cause analysis helps improve the efficiency and productivity of your organization, which eventually leads to business growth.

 

No system is perfect. Incidents will happen, no matter what. But what you do afterward is in your control. Root cause analysis should be done as soon as possible. Using machine learning for root cause analysis not only improves your incident response, but over time, it can also help prevent incidents from happening in the first place.



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