Introduction To Augmented Analytics

Data driven decisions are necessary to stay ahead in today’s VUCA landscape. Although data is readily available today, research shows that organizations are are still a long way behind leveraging data to the fullest. 

A latest study by Forbes Insights and Treasure Data revealed that only 13% of the companies surveyed leveraged their customer data to its full potential. 

One of the reasons for this is that most enterprises, especially the SMEs, spend more time on data preparation and data visualization than on data analysis because of the sheer volume of data that they receive every day. This is where Augmented Analytics enters into the picture. 

What is Augmented Analytics?

In the past decade, we saw how visualization-based data discovery tools disrupted the traditional analytics industry. Today we are witnessing yet another disruption in the analytics space with Augmented analytics.

Augmented analytics leverages artificial intelligence (AI), machine learning, text mining, natural language processing and generation, and automated data processing to identify actionable patterns and remove bias from data. With Augmented analytics, the amount of machine learning that is used in human and machine partnership is increased, so as to improve human productivity.

As per the latest industry reports, the global augmented analytics market size is set to grow at a CAGR of 28.4%, from $4,094 million in 2017 to $29,856 million by 2025. The emergence of this field is in response to the scale and complexity of data that does not yield easily to human handling.  

Augmented Analytics – How Does It Work

Hailed by Gartner as the “third major wave for data and analytics capabilities,” Augmented analytics employs automation for designing algorithms to discover any schema of relevance and identify metadata. As most of the operational functions, data preparation and pattern identification is automated, it will enable users to connect with data in a non- specialized and less laborious way. 

One of the major ways in which Augmented analytics differ from the conventional business intelligence (BI) and other analytics  tools is that it is not reliant on predefined data models. Augmented analytics can automatically correlate relevant data through investigation of metrics at each level by detecting root causes and drivers. With this process of developing intelligent visualizations, real-time insights can be generated. 

Augmented analytics tools perform as virtual data scientists, executing data to insight to action activities such as data preparation, data interpretation and using data insights to build models, distribute and operationalize it. With this, a lot of time and resources are saved, that otherwise would be spent on generating business insights from the available data. 

Augmented Analytics In Practice

The US government and several organizations in the US have already jumped into the bandwagon of augmented analytics. By partnering with Stories.bi, an augmented analytics player, the US government plans to get insights from public data on the U.S. opioid crisis. The U.S. Health Insurance Company has been using Einstein Discovery, Salesforce’s AIm powered analytics tool for tracking cost metrics based on the patient’s illness. Another U.S. company that has been employing this technology is Workday, an on‑demand human capital management and financial management software vendor that has been using it for generating actionable insights from HR data. 

Tech giants such as Microsoft and Google have developed products around augmented analytics. For instance, Chevron Corp, a US-based multinational energy corporation was one of the early-adopters of Google’s augmented AutoML technology, that is designed to enable users with limited ML knowledge to build and train analytical models. Chevron’s imaging and seismic processing team used the alpha version of an AutoML Vision image analysis tool for analyzing internal documents as part of the process to evaluate new opportunities for oil drilling. 

In a similar way, Microsoft has incorporated augmented analytics functionality to its cloud-based Azure Machine Learning platform. This helped the software to detect algorithms that will run applications in an efficient way and carry out optimization of the working of analytic models for users. Other than these biggies, organizations such as ThoughtSpot, H2O.ai, and DataRobot have also dipped their toes in augmented analytics.  

Impact Of Augmented Analytics On Job Roles

In an organizational context, augmented analytics is going to leave an impact on certain job roles more than the others. Let’s have a brief look at some of the roles: 

Marketing professionals

Non-technical areas like marketing,  is going to be radically transformed with augmented analytics. It is a usual practice for brand managers, chief marketing officers, and other marketers to depend on an analyst for data processing and analysis. This dependency on third-party is time consuming, cost-heavy and inefficient. 

Augmented analytics enables the marketing professional to be self-reliant with machine automated analytics

“CPG Analytics: The Definitive Guide”, explains this by saying that, “From opportunity analysis to churn analytics to attribution studies and even customer journey mapping, new advancements in business intelligence make it so that you can make quick and effective decisions.”

Data Scientists

Augmented analytics frees up data scientists to solve more complex problems by automating the other repetitive and laborious tasks that would otherwise consume a lot of time.With automation, basic queries and repetitive reports can be done with machine aid. 

Sales professionals

Sales professionals would benefit greatly from direct data access and receiving detailed and automated analysis of their sales-pitches, win-losses, and performance metrics tracking. Having a responsive analytics solution would enable them to be more agile.

As they do not have to wait around for weekly or monthly reporting, it would help them to improve immediately. With an augmented analytics platform, they will be able to get quick competitor performance comparisons and brand analysis. 

Conclusion

As you can see Augmented Analytics is surely going to disrupt the field of data Science. You can appreciate this technology better if you get your basics right by an artificial intelligence certification training.

About Mohanaraj Jagadesan

Mohanaraj Jagadesan

Mohanraj, SpringPeople’s technical consultant & expert, is a well-recognized name in the training industry. An innovator at heart, he has several notable projects to his credit, specifically in the area of automation & scripting.


Posts by Mohanaraj Jagadesan

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