The data epoch
The present age of big data has welcomed new startups, new customers, and new players in the digital market. The voluminous amount of data has exceeded the processing limit of the erstwhile digital system. Needless to mention, the data footprint has seen an enormous amount of expansion with increasing activity on social media networks and other digital platforms. Ranging from the collection of data to its processing, the demand for automation has now considerably picked up. As such, it is impossible for the traditional tools of business intelligence to handle and process the increasing volumes of big data. The present age of business intelligence is pairing up with a new technology called augmented analytics.
The lens of augmented analytics can understand the alphabets of business
There are four major cornerstones of augmented analytics that can considerably shape the future of business intelligence. These are Big Data Analytics, Artificial Intelligence, and related systems, machine learning techniques, and natural language processing. The greatest feature of Big Data Analytics that can cater to augmented analytics is its ability to handle both structured and unstructured data sets. This is important because it helps to capture, store, manage, and visualize information that is vital for business intelligence. Next in line is machine learning techniques which businesses are getting accustomed to with growing automation. Machine learning techniques are simplifying and automating the processing and analysis of voluminous quantities of data without human intervention. By advanced algorithms, machine learning tools are adapting and improving themselves so that the newest streams of data can be processed with a lot of ease.
Artificial intelligence is another technology that has considerably improved augmented analytics. Artificial intelligence is enabling us to create an automated system that can solve problems that traditional systems cannot. With the aid of speech recognition capabilities and natural language processing abilities, AI systems are changing the face of augmented analytics.
Advanced, enhanced, and improved business analytics
It is important to understand the use cases of augmented analytics and how it can improvise our business processes. Business analytics is slowly moving towards an advanced level when we talk of the telecommunication system. Credit again goes to augmented analytics for enhancing the scope of predictive analytics when it comes to the manufacturing sector. Augmented analytics has also improved the domain of customer analytics when it comes to the marketing sector. In retail and e-commerce, augmented analytics has redefined the process of data visualization.
Shaping the future
There are a lot of benefits that are emerging because of augmented analytics. First and foremost is the level of automation that augmented analytics brings to the domain of business intelligence. There is no doubt in the fact that the tedious process of data cleansing, validation, and analysis is a very herculean task. Such tasks are prone to numerous errors. These errors can not only be minimized but can be eliminated if the operational processes are fully integrated with augmented analytics. The efficiency and accuracy of such processes can also be enhanced with the help of augmented analytics. Similarly, it isn’t difficult to manage multiple processes effectively even with the growing volumes of data. Augmented analytics not only speeds up the operating processes but also delivers output within the stipulated time. As per a report by Statista, augmented analytics reduces the time of data processing by more than 60% if data is available in a structured format.
In addition to this, augmented analytics can reduce time and improve the objectivity of various techniques that we use in data processing. Augmented analytics is devoid of human bias and delivers results and output which are free from controversy. This feature of augmented analytics has found application in various human resource departments and data management tasks. Augmented analytics works as a fully automated process. It enables scalability and accommodates many data sets, thereby processing them with high accuracy. As per a report by Gartner, more than 30% of organizations will have automated their data management tasks by the end of 2022.
The road ahead
While augmented analytics is a relatively new technology for smaller startups, bigger players have started experimenting with it in the last few years. Not only is augmented analytics the panacea to handle the large streams of data but it is also the remedy to process and visualize massive volumes of information.
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