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Is Augmented analytics a Gimmick in BI Tools?

Jun 18, 2021

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With the tremendous amount of data being involved in the analysis for arriving at data-driven decisions in every organization, the arena of data analytics is slowly moving towards using machine learning and AI to leverage their capabilities. From data analysis to data sharing, statistical technology usage significantly impacts data management performance, thus transforming a massive amount of raw data into useable and informative datasets. In this blog, we will be looking into augmented analytics’ role in Business Intelligence.

Is Augmented analytics a Gimmick in BI Tools?
Figure 1: Blocks of Augmented Analytics in BI

How advantageous is it to users?

The usage of augmented analytics is beneficial for larger organizations as it reduces the task load of prepping data and analyzing the data of huge volumes for analytics. A few of the major advantages for clients include faster data preparation, rapid analysis, lesser bias, etc. Data preparation which includes cleaning, creating joins, identifying schemas, and performing transformations on the collected data, consumes a lot of time when using traditional data analytics methods, after which the stage of analyzing the data starts. Using augmented analytics for your data analysis can make the data preparation activities easier by suggesting which field has to be cleansed and which join can be used to create a model that gives quicker insights into the data.

Is Augmented analytics a Gimmick in BI Tools?
Figure 2: Advantages of Augmented Analytics in BI

How it affects the analysis capabilities?

As intuitive insights are made possible with machine learning and artificial intelligence capabilities, the analysis of the data and its findings are less prone to errors and inconsistencies when compared to the human efforts involved in the analysis. When a huge amount of data is involved in analysis, the time taken to analyze increases exponentially. It becomes complex to analyze huge sample sets, leading to missing data points in various instances. Using augmented analytics eliminates these kinds of errors which improves the analysis capabilities of BI tools. Critical tasks that are challenging for analysts to do on their own can be performed by using augmented analytics.

Use cases – what essential findings can be obtained from augmented analytics?

Augmented analytics can be implemented to derive insights in various fields ranging from production to healthcare. Their usage is not just restricted to analyzing past data but also includes different forecasting decisions and prediction capabilities. By letting data scientists, researchers, and business users interact with their huge volumes of data, augmented analytics can fill any gap between the crucial business decisions by providing various AI-powered insights, trends, and anomalies.

For example, let us consider the healthcare industry, where lots of data and analysis based on the gathered data comes into play. Using augmented analytics to gain insights on the most important details like outliers, trends, etc., can complement human thinking and reduce the pressure of analytics tasks. The Natural Language Processing feature, which comes as a part of Augmented analytics in various BI Tools, lets the user interact directly with the data, making it easier and quicker to perform analysis even for consumers with no technical expertise on that BI tool.

Future of Augmented analytics in BI Tools

In the surging data economy, it is essential to gather, access, and analyze the data constantly rather than perform analysis on demand, which helps take business actions effectively. To cater to these growing needs, augmented analytics will prove to be a good collaborator for many businesses that rely majorly on their data and their business decisions driven by the gathered information. The faster decision-making capability and the automated recommendations for arriving at data-driven decisions will make augmented analytics in BI a compelling element.  

Is Augmented analytics a Gimmick in BI Tools?
Figure 3: Timeline of Data analysis

Is it serving the user’s needs as promised?

Implementing the augmented analytics features makes data analysis a more manageable task by cutting down various manual activities. It is slowly emerging in the BI tools to cater to user needs, ranging from data cleansing to arriving at insights for business decisions effectively. BI tools like SAP Analytics Cloud, Tableau, Power BI, etc., have their fair share of available features that are advantageous to the users. Though augmented analytics is not widespread currently, it will be a massive trend soon, making augmented analytics a highlight in all BI tools.

To know more about analyzing your data and implement interesting features for gaining insights, reach out to Visual BI Solutions.  


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