Reducing manual effort to the minimum using advanced cloud analytics

The shift does not come from the complexity of the project but from the impact it has on business operations. Introducing seemingly small changes can bring significant results. 

An example has been set by our client, a major airline in the Middle East. The company identified specific processes they wanted to simplify with the latest Microsoft technologies.

Using Artificial Intelligence in place of manual effort saved their employees hours on analyzing data, allowing them to focus on providing efficient services to their customers. 

The organization decided to use the potential of AI to streamline its operations in two areas. One involved financial activities, and the other knowledge management.

Supporting airline engineers

In the beginning, we worked with the airline’s engineering department. The workers had to spend a lot of time finding relevant information related to aircraft maintenance and management. The goals of the first engagement included: 

• Speeding up document processing – data entry and extraction needed to be faster.
• Reducing the number of errors – repetitive and monotonous tasks had to be eliminated, so employees could focus on rectifying issues.
• Standardization of processes – all documents were to undergo the same automated processing to minimize manual effort and discrepancies it might cause.

Speeding up operations with intelligent search 

The airline gets lengthy documents, amounting to hundreds of pages, detailing the technical specifications of their aircraft. These publications are often updated and need to be cross-referenced to identify changes. If an engineer gets a report of a defect to fix, finding a solution manually can be difficult. 

To add to the problem, a lot of information was amassed over time, not just in print but also in written form. A standard search would make finding relevant solutions to a problem virtually impossible.

Moreover, because all the information was held in multiple sources and formats, engineers were unable to identify if there were any links or patterns between them. We wanted to minimize this tedious effort, so they could focus on solving potential issues. Azure Cloud provided a solution.

Within 3 months, we implemented a Knowledge Mining mechanism based on Azure Cognitive Search. It identifies and explores relevant content on a large scale and is fully customizable.

Azure Cognitive Search is available in multiple languages and can work with several data types, like Office documents, PDFs, images, or JSON files. 

The service is complemented by the Content Extractor. It uses text mining and machine learning algorithms to locate specified and relevant elements among the entire content. 

We also proposed a tailor-made web app interface that employees can operate with ease. Documents are retrieved in order of relevance to the user, speeding up the process. They can also be categorized and filtered by specific fix types, making finding the right answer simpler. Engineers can now find the necessary information in minutes instead of hours. 

Seeing the potential of advanced analytics in practice, the company decided to use it in another area of its business. This time, it was to help their finance team. 

Enhancing finance operations with advanced analytics

This time, the company was looking to boost its fraud detection capabilities. The key goals of the project were to:

• Capture errors & anomalies in outgoing payments – so any potential issues could be resolved sooner.
• Prevent the organization from transferring unjustified or unauthorized sums of money – to minimize the risk of erroneous transactions.

Previously, employees reviewed all transactions manually for potential mistakes, such as duplicates or overpayments. This approach was time-consuming and subject to human error. To ensure no anomalous transactions are missed, the company decided to use analytics services based on the Microsoft Azure cloud to automate the procedure.  

We worked alongside the client’s team on creating a solution that would maintain the accuracy of their financial operations. The data was gathered from on-premises sources and then moved to the Azure cloud for analysis and storage. 

Results are displayed on interactive Power BI dashboards, immediately indicating if there are any causes for concern. Additionally, the Machine Learning model is able to detect problems that might have been missed in manual detection. Based on continuous feedback, the model will also improve its capabilities over time.  

Automating this task allowed the company to save around 600 hours of manual work per year. Instead of gathering and checking data, employees can instead focus on issues brought to light by automated checks.

Continuing a data-driven strategy 

The organization continues on its cloud journey by streamlining various aspects of its operations. Introducing advanced analytics based on Azure technologies allowed them to save time and optimize their costs. Improvements to their processes were made with little to no disruption to everyday operations.  

The organization proactively adopts the newest technology to minimize unnecessary effort and allow its people to focus on core business. A leader in its space, the company also leads by example, using technological advances to improve its operations.

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