Augmented Analytics is one of the latest advancements for business intelligence tools benefits the business. It is a new concept that is said to modernize an organization’s analytical capabilities.
In this article, we delve more into the benefits and the future of Augmented Analytics. It is important that we know the basic concepts and technologies that are its key drivers.
Understanding Augmented Analytics and its Benefits to the Business
What is Augmented Analytics?
According to a report published by Gartner in 2017, “an approach that automates insights using machine learning and natural-language generation mark the next wave of disruption in the data and analytics market.” The article claimed it to be the “future of data analytics.”
Augmented Analytics is a combination of technological components like Automated Insights, Machine Learning, and Natural-Language Generation used to automate and generate actionable insights that benefits the business. It is a process that automatically collects data from raw sources to process and analyze it into information.
Let us understand the components that make Augmented Analytics:
MachineLearning
Machine Learning is a concept that leverages Artificial Intelligence algorithms to assess data. Augmented Analytics uses Machine Learning to develop, prepare and transform data.
Augmented Analysis benefits from ML’s abilities to determine patterns, recognize trends and identify relationships between data points. ML helps Augmented Analytics understand the different aspects that benefit a business and the concepts that need to be avoided.
Natural-Language Generation
ML works with languages like R and Python to communicate with devices to process data. Natural-Language Generation is what translates the processed data into languages that help humans comprehend.
NLG acts as a mediator between the machines and users. The process of presenting data into actionable information is “data analytics”.
Automating Insights
Once the data is processed and translated, it still needs to be analyzed. Automating Insights in Augmented Analytics automates the process of data analysis.
With the help of concepts like ML and NLG, Automating Insights requires no human intervention. It is established that machines evaluate and analyze data much faster.
Hence, while Automating Insights work towards processing, users can utilize that data towards strategies and planning operations.
Accelerates Data Preparation
More than 80% of production time is spent on manual data preparation. Augmented Analytics uses AI, ML, and NLG to automate the process and present outputs within a fraction of the time required.
Here, algorithms replace manual processes. It automatically detects repetition and streamlines data.
The entire process becomes automated and self-reliant. As a result, a process that would take days or weeks can now just be handled within a few hours.
Reducing Data Latency
Data Latency refers to the time it takes for a business to retrieve data from relevant sources. Machine Learning in Augmented Analytics reduces the chance of data repetition.
Algorithms help identify schemas and join the data from different data points. Hence reducing data latency and manual interventions.
Decrease in Analytical Bias
Analytical Bias arises when results are evaluated from a limited perspective. It creates an issue when there are systematic exclusions made from a narrow point of view.
Machine Learning enables Augmented Analytics to perform repetitive tasks. Although, it does not eliminate data points in the process based on assumptions.
Hence, Augmented Analytics reduces the potential of analytical or sampling bias by thoroughly analyzing the data.
Deliver Faster Insights
Augmented Analytics reduces the time taken to provide insights. It automatically looks for correlations, segments, and patterns applied while analyzing data.
It evaluates the data with multiple permutations and combinations of ML algorithms. This machine-led ability provides relevant results statistically faster.
Democratizing Analytics
Democratization refers to creating accessibility of action to everyone. Augmented Analytics increases the accessibility of using AI and ML to perform data analysis.
It creates more opportunities for data scientists and experts to divert their energies on high-value tasks like decision-making and strategizing.
Scalability and Accuracy
Companies that have executed Augmented Analytics have reaped multiple benefits. Augmented Analytics uses ML to train and develop tools that benefit the business.
The tools under augmented analytics tend to run on a cloud, which makes them more scalable. Also, these tools evaluate and calculate available data to pro