Business intelligence techniques help understand trends and identify patterns from big data

In the digital world, modern businesses generate big data on daily basis. The recent advancement in technology has opened the door for companies to effectively store and process big data to unleash data-driven decisions and insights. Unfortunately, there is a void between data storage and usage. Many companies, starting from small to big, collect huge data but only use very little of it to make business decisions. In order to mitigate this data gap, business intelligence is being deployed. With the rise in the need for real-time data processing, business intelligence techniques have exploded, making data and analytics accessible for more than just analysts. While business intelligence technology helps decision-makers to analyze data and make informed decisions, top business intelligence techniques drive the initiatives. They help analysts understand trends and aid them to identify patterns in the mountains of big data that businesses build up. The need for more disruption in decision-making and the growing demand for business intelligence has opened the door for a surplus amount of business intelligence techniques. In this Article, Analytics Insight has listed top business intelligence techniques that help companies to get the maximum out of big data.

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Top Business Intelligence Techniques

OLAP

Online Analytical Processing (OLAP) is an important business intelligence technique, that is used to solve analytical problems with different dimensions. A major benefit of using OLAP is that its multi-dimensional nature provides leniency for users to look at data issues from different views. By doing so, they can even identify hidden problems in the process. OLAP is mainly used to complete tasks like budgeting, CRM data analysis, and financial forecasting.

Data Visualization

Data is often stored in form of numbers that are put together as a matrix. But interpreting the matrix to make business decisions is a critical task. A commoner, or even an analyst, can find the progress of data when it is stored as a set. To untangle the knot, data visualization is used. Data visualizations help professionals look at data from more than one dimension and help them make informed decisions. Therefore, visualization of data in charts is an easy and convenient way to understand the stance.

Data Mining

Data mining is the process of analyzing large quantities of data to discover meaningful patterns and rules by automatic or semi-automatic means. In a corporate data warehouse, the amount of data stored is very huge. Finding the actual data that could drive business decisions is quite critical. Therefore, analysts use data mining techniques to unravel the hidden patterns and relationships in data. Knowledge discovery in databases is the whole process of using the database along with any required selection, processing, sub-sampling, choosing the proper way for data transformation.

Reporting

Reporting in business intelligence represents the whole process of designing, scheduling, generating the performance, sales, reconciliation, and saving the content. It helps companies to effectively gather and present information to stand by the management, planning, and decision-making process. Business leaders get to view the reports at daily, weekly, or monthly intervals as per their needs.

Analytics

Analytics in Business Intelligence defines the study of data to extract effective decisions and figure out the trends. Analytics is famous among business companies as it lets analysts and business leaders deeply understand the data they have and drive value from it. Many business perspectives, from marketing to call centers to use analytics in different forms. For example, call centers leverage speech analytics to monitor customer sentiments and improve the way answers are presented.

Multi-Cloud

Following the outbreak of the pandemic and the lockdown that came to effect, companies across the globe started moving their routine working into cloud modes. The rise of cloud technology has greatly impacted many businesses. However, even after the restrictions are lifted, companies still prefer to work over the cloud because of its lenient accessibility and easy-to-use attributes. Moving a step forward, even Research & Development initiatives are being moved to the cloud, thanks to its cost-saving and easy-to-use nature.

ETL

Extraction-Transaction-Loading (ETL) is a unique business intelligence technique that takes care of the overall data processing routine. It extracts data from storage, transforms it into the processor, and loads it into the business intelligence system. They are mainly used as a transaction tool that transforms data from various sources to data warehouses. ETL also moderates the data to address the need of the company. It improves the quality level by loading it into the end targets such as databases or data warehouses.

Statistical Analysis

Statistical analysis uses mathematical techniques to create the significance and reliability of observed relations. It also grasps the change of behavior in people that are visible in data with its distribution analysis and confidence intervals. Post data mining, analysts carry out statistical analysis to devise and get effective answers.

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When Greta Thunberg boarded a transatlantic zero-emissions yacht she garnered the attention of citizens of the world on the fact that aviation is a polluter of the environment that we continuously ignore. The giant industry is responsible for producing 915 million tonnes of carbon dioxide emissions along with other dangerous gases that cause environmental changes like cirrus clouds. These emissions constitute two percent of the world’s greenhouse emissions.

From the electrification of jets to biofuel many ideas have been suggested to make flying more eco friendly. The problem with these ideas is that they are just appetizers without main course. Most of these projects are either prototypes or in the pipeline getting developed forever. 

Now, for the first time, to address major challenges, the idea of AI in aviation was presented at the Singapore Air Show. The Singapore Aerospace Association A*STAR, hosted the Singapore Aerospace Technology leadership forum (STALF). The conference looked at two areas of emerging technologies: Artificial intelligence and digitization of AI operations. 

Air Alaska to the rescue

Air Alaska became the first aviation giant to successfully implement the technology that would revolutionize the aviation industry. With a fleet of 320 aircraft, the company produces enough carbon emissions to take note. With artificial intelligence, the company aimed to solve managerial problems.  The Artificial intelligence vendor, Airspace Intelligence visited the network centers of Alaska Airlines and observed all problems that caused the planes to consume more fuel and used them to build a development process for the airlines. Numerous trials were conducted on the planes that changed the business management of the airlines.

The company developed a 4D real-time map for the airlines. It displayed all relevant information on an easy-to-understand screen. The screen displays every information like FAA data feeds, turbulence routes, weather reports. It also allows the ATC to fly eight hours forward into time and access the data and suggest the best routes to follow for minimum fuel consumption. The programme also has built-in monitoring and predictive abilities. It continuously monitors schedules and active flights across the United States and autonomously looks over operational safety, the efficiency of flights running, and ATC compliance. It then makes recommendations that the operator is free to accept or reject. This allows the decision-makers to quickly access the air space, make decisions, and implement them. 

The benefits of the implementation of artificial intelligence were enormous. The airline saved 480,000 gallons of fuel in six months. It is an enormous quantity of fuel for a business whose major expenditure is fuel. It also reduced 4600 tons of carbon dioxide. It is a significant dent in carbon emissions considering that only one airline leveraged the technology.

In 2020 when the world was reeling from the Covid-19 pandemic, Air France slashed its greenhouse emissions by half by leveraging AI in flight operations. Instead of counterbalancing the carbon output, the airline introduced a programme that reduces emissions by optimizing data coming from the plane’s black boxes. Using artificial intelligence, the system evaluates the whole volume of data coming from the plane’s communication systems, flight plans, and data recorders. Combining this data with real-time data about the flight itself. The algorithm can generate a deep analysis of the efficiency of fuel utilization on any flight. Utilizing this data to make recommendations saved in fuel consumption and reduced emissions.

The Skybreathe programme designed by Open Airlines has other customers like Norwegian, Go Air, Malaysian Airlines, and more. The company helped these airlines save more than $150 million in fuel costs and in reducing carbon dioxide emissions by 590,000 tonnes.

Predictive maintenance and air traffic control will be revolutionized by Artificial intelligence and Machine learning. Optimizing the systems will make airline transport more efficient and environmentally friendly. It can accelerate the digital transformation in terms of optimizing trajectories, creating greener routes, and increasing prediction accuracy. 

It is a pioneering change in the aviation industry. Fuel consumption is affected by all kinds of changes from weather, speed to the altitude at which a plane is flying. By presenting the data to pilots to improve the efficiency of these flights by suggesting actionable changes. The recommendations can vary from changing flight routes due to turbulence, reducing acceleration altitude, or controlling when the engine is cut-off during touchdown. Also, it can advise on the modernization of fleets to improve operational efficiency. 

Optimizing these operations with artificial intelligence and machine learning will make air travel safer, efficient, and more environmentally friendly. It will be a milestone in the sector achieving the goals of the Paris Agreement that it has been committed to.

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