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In the current business scenario, if organizations have to truly succeed and achieve great results, the data they have has to be converted into actionable insights that can be used immediately and effortlessly for decision-making. This task is accomplished by augmented analytics, which blends visual analytics and business intelligence to easily interact with data.

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According to Gartner, “Augmented analytics is the use of enabling technologies such as machine learning and AI to assist with data preparation, insight generation, and insight explanation to augment how people explore and analyze data in analytics and BI platforms”.

The need for augmented analytics

With massive amounts of data getting generated every day, it is manually cumbersome to analyze and process it before extracting relevant insights. The time-consuming process starts with gaining an understanding of data and requirements and building an algorithm before evaluating it. Furthermore, the complexity of data is increasing too, making it difficult to depend on traditional Business Intelligence (BI) platforms.

With augmented analytics, the dependency on manual processes and data scientists has significantly reduced, as AI and ML algorithms are leveraged for insight generation

It is here, augmented analytics comes in to play a key role, by automating the entire process of data analysis and relevant insights generation by identifying patterns and showing trends and visualization. Insights are automated with the use of Machine Learning and Natural Language Processing, thereby ensuring data is more accessible and delivers high value for all users. Data analysts can gain valuable business insights with augmented analytics.

Evolution of augmented analytics

Traditional BI, which was earlier leveraged by IT teams, required very highly skilled analysts and experts. They had to extract data from databases, clean it to build models, and mine it to deliver data-rich reports and static dashboards, which was a very time-intensive process. The quality of data suffered too as there were no predictive insights, driving experts to make presumptions that were not based on solid data. This traditional model was upgraded to self-service business intelligence solutions. 

This could be leveraged by both business users and data analysts as it was accessible to everyone, on-demand. However, analysts had to manually analyze all permutations and combinations of different data points to extract vital insights. But this model had last mile challenge as operating the visualization interface was difficult for business users. Additionally, with the growing complexity of data, manual analysis of every combination to generate insights started to become unattainable.

Augmented analytics, the next-generation BI, addresses all the above challenges. With automation, data analysis is simplified, making it accessible to all users as it uses Natural Language Generation (NLG) to deliver insights and visualization.

Transformation of BI to augmented analytics

With augmented analytics, the dependency on manual processes and data scientists has significantly reduced, as AI and ML algorithms are leveraged for insight generation. Hence, errors and inconsistencies are eliminated to a large extent. With BI, the data preparation stage involves the expertise of data scientists and developers. By leveraging advanced analytics for modern BI, large amounts of data could be analyzed. But purification of data requires experts and is a long-drawn process.

On the other hand, businesses can effectively use augmented analytics for data preparation. Machine Learning can speed up the data preparation process and enhance the productivity of data scientists.

With BI, business users may not be able to comprehend the hidden data trends that impact businesses, especially with the growing complexity of data.

Augmented analytics can fix this issue by spotting outliers and making the respective correlation with the AI and ML algorithms, without missing any important insights. Furthermore, the latter also uses NLG to alert augmented analytics platform users about any significant observation in the data.

Benefits of augmented analytics to enterprises

Data exploration which is a time-consuming process otherwise is speeded up with augmented analytics, where false and irrelevant findings are detected. During analysis, the granular level of data is visible in seconds, enabling accurate insights with deeper analysis. This also equips business users to make smart decisions for better business performance.

Data exploration which is a time-consuming process otherwise is speeded up with augmented analytics, where false and irrelevant findings are detected

The chance of missing critical insights is reduced and insights are delivered in real-time so business users can leverage them for opportunities that are time-sensitive. Decisions and the activities that are followed are optimized.

With the democratization of data, anybody across the organization can gain access to insights in real-time and build custom visualizations for better interpretation. This will also free up the time of data scientists and analysts to perform more important tasks.

A few use cases captured

Augmented analytics platform has been used across industry verticals to solve several burning industry challenges. It helps pharmaceutical companies to build better go-to-market strategies as they are able to manage data faster by significantly reducing the time for analysis. Evolving customer requirements in retail can be easily capitalized and operations can be improved considerably by leveraging the platform. 

With the financial services industry facing fierce competition, it has constant pressure to innovate and match the needs of demanding customers by delivering customized products. Augmented analytics can help in processing and analyzing data that can help in acquiring, retaining, and serving customers far better than before. Improving patient care outcomes will provide immense patient satisfaction and add to the hospital’s bottom line. 

With augmented analytics, healthcare providers can analyze vital metrics, like the bed occupancy rate and the duration of patients’ stays. The insights gained will enable better resource allocation and deliver operational excellence for the entire hospital.

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The platform can also help manufacturing companies analyze the production capabilities of the assembly lines, workers’ schedules, availability of warehouses, and more. The insights gained can guide manufacturers to make business decisions and change product manufacturing based on the current market demand thereby ensuring better revenues.

Augmented analytics is certainly the coming wave of disruption in the BI and data analytics domain. It empowers organizations by transforming the way they are using data to make smart business decisions.

Guest contributor Anjna Bhati is the Director Data Analytics and AI at BluePi Consulting, and IT organisation that that works through data, cloud, and digital transformation. Any opinions expressed in this article are strictly that of the author.


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