Augmented Analytics – Is This the Future of Business Intelligence  

Introduction

Every single day, we are generating massive volumes of digital records. To handle this much information, we need more powerful and robust analytics and AI systems to store and make sense of it.   

The term augmented analytics is coined by Gartner. They say that it is the future of data analytics that harnesses disruptive technologies to automate insight discovery, data preparation, and intelligence sharing.    

Augmented analytics has the potential to merge traditional data analytics with technologies such as artificial intelligence (AI), machine learning (ML), and NLP.   

The next wave of BI tools and analytics will be different. They will change the user experience across the BI process with augmented analytics. Here’s how:   

• Data discovery, analysis, ingestion, predictions, and interactions between platforms will be streamlined    
• There will be easy share-ability and dissemination of results across integrated functions   
• Automate and democratize the whole data analytics/ BI process   
• More action-oriented experiences and cost reduction    

Augmented Analytics in Action    

Gartner says that augmented analytics marks another level of disruption in the analytics landscape.   

Data science, AI, and augmented analytics make analytics accessible for the organization. This, in turn, enables them to ask relevant questions and auto-generate insights in an easy manner.    

Augmented analytics systems recommend necessary metrics for your business which can then be analyzed. On the data preparation side, augmented analytics has the power to intelligently drive key insights automatically.    

For instance, assume a data point that indicates that revenue is down by 20%. You can dive deeper to uncover the true meaning behind it and why it is important.   

Augmented analytics will help you put perspective on the reasons behind the decrease. It may be either because your marketing isn’t effective, or is it because it is an industry-wide trend?   

It takes into consideration everything from analyzing the geographical spread, comparing relevant benchmarks, and giving a commentary around it.    

On the contrary, in today’s world, if you just knowing declining revenue you will lose time, money, and energy as it will not be valuable to your organization.   

Instead, you should focus on drawing out the reason for the decline. These are the only actionable insights. With this analytic method, you cannot only help deliver insights automatically but also flag certain threshold breaches.   

What are the Benefits of Augmented Analytics?   

Today, drawing out crucial and relevant insights from data is a huge challenge for businesses. Hence, it is so important for all businesses to invest in this new analytics method.   

It can make the search easier, speeds up a time to value, data literacy more accessible, and visualization faster across the organization.   

It is the best solution for large enterprises that are looking to reduce their analytics load on their teams, or from an e-commerce company detecting out-of-stock events to the order/relevance of news based on user behaviors, all in all, the use cases for analytics are broad.   

What are the Key Capabilities of Augmented Analytics? 

1. Data Preparation:   

Augmented Analytics solves the problem by reducing the process that data analysts need to automate repetitively every time they receive new data sets to work with.    

Also, it helps decrease the time required to clean data in the ETL process and allows for more time to find patterns and relationships, create visualizations, auto-generated code, and propose recommendations in the data.   

Lastly, it automates the process of data preparation, visualization, and analysis.   

2. Contextually-Aware Insights:    

Augmented Analytics takes into account behaviors and intents to create contextual insights. It presents new ways of looking at data and identifies patterns and insights based on questions that companies might have completely missed otherwise. It, thereby, enhance human intellect and transforming the use of analytics.    

It also highlights the relevant hidden insights that are extremely powerful capabilities. For instance, users can manage the selection state (context) at the step of the exploratory process.   

Besides, it also understands data values associated with or without the context. It results in relevant suggestions powerful context-aware.   

3. Enabling a Citizen Data Scientist:    

Augmented analytics can democratize data analytics and automated insight. It can do this by generation them through the use of ML and AI to convert insights into actionable steps. This can benefit companies by reducing their dependence on data scientists and making analytics accessible.   

Gartner says “augmented analytics is the future of data analytics because it moves us closer than ever to that vision of “democratized analytics” because it will be cheaper, easier, and better.”   

What is the Future of Augmented Analytics?

Augmented analytics is capable of communicating, analyzing, and visualizing data. Besides, it can also propose actions.    

Soon, this will have an inherently social component that will link analysis once insights are identified. It will then connect team members to those findings within the company.    

Going forward, we will see augmented analytics systems become more powerful and productive tools.

Author Bio:

Shreeya Chourasia is an experienced B2B marketing/tech content writer, who is diligently committed for growing your online presence. Her writing doesn’t merely direct the audience to take action, rather it explains how to take action for promising outcomes.