The Development of Augmented Analytics

By Simran Yadav

If data is the gas in a car, then, analytics is the car itself

Currently, there are a few trends and topics in tech without which the talk around technology and innovation is incomplete — analytics, artificial intelligence, blockchain to name a few. Augmented analytics is an extension of analytics that focuses on three main areas — Machine Learning, Natural language generation (NLP) and Insight automation. The basic premise of augmented analytics is the elimination of painstaking tasks in the process of data analysis and, replacing them by automation thus, refocusing human attention on modern analytics, business process, and business value generation.

As per predictions made by Gartner, over 40% of tasks involved in data science will be automated thus, increasing productivity, quickening the process, and initiating broader usage of data and analytics.

Augmented analytics is touted as the subsequent wave of disruption in the field of data and analytics. The field will experience further adoption by data analysts and data scientists as more use-cases and value-generation is seen. Furthermore, Gartner suggests that automated analytics will be a “bridge between mainstream analytics done by business users and the advanced analytics techniques of data science.”

Through the use of statistical models and linguistic techniques to improve data management and performance, the core focus of augmented analytics is providing assistance in painstaking and time-consuming tasks in the stages of data analysis, data sharing, and business intelligence. This technology is not there to replace humans but supports them through enhanced interpretation capabilities. Additionally, it will enable companies to revolutionize the way business intelligence is produced and consumed.


How?

Data analytics software with augmented analytics leverage machine learning and NLP to recognize the structure of the data and interact with it.

The process typically starts similar to the traditional data analysis method: data is gathered, prepared, and then analyzed to extract meaningful insights. But instead of a human being involved in every stage of the process, augmented analytics will be automating the data gathering, preparation, and insights generation aspects. Humans will come into the picture for business decision making and implementation.

Currently, augmented analytics is being used to deliver improved and more accessible solutions to customers in areas like telecom, healthcare, government, retail, and so on.

Goal?

The paramount goal of augmented analytics is to automate the painstaking and time-consuming part of collecting and preparing data which typically takes data analysts or data scientists 80% of the time. The ultimate goal of augmented analytics is to make a shift from data science to automation and artificial intelligence. The incorporation of augmented analytics will alter the analysis process from data collection to business recommendations to decision-makers.

Embedding AI into BI will make analytics work easier. Augmented analytics will undoubtedly help in better decision-making, accurate predictions through analytics leading to better products, prices, finances, and other aspects that drive business value.

Potential Growth:

As per Global Forecast to 2023, the global augmented analytics market size is expected to grow at a rate of about 30.6% from a market size of USD 4.8 billion in 2018 to USD18.4 billion in 2023. The exponential growth in the field will be seen as a way of simplifying the process of analyzing data and providing data analysts/data scientists more time for coming up with actionable insights.

Currently and in the future, some of the business will use augmented analytics for: collecting critical data in a faster manner while maintaining data quality, better understanding data through automated reports while will save money and time, and, lastly, rather than losing majority data in the cleansing process, utilizing the huge amount of big data rather than leaving it unused or underused.

 

Conclusion:

With the increasing development and adoption of Artificial Intelligence and its subsets, the marriage between data science and Artificial Intelligence will help augmented analytics grow exponentially and at twice the rate of non-augmented (traditional) analytics. Through its automation capabilities, it will allow people to propose more questions for a dataset and automatically generate insights from easy, point-and-click methodology thus, enabling individuals and organizations to deliver better services and get more value to their business.

Key benefits: Delivers value faster | Enhanced Business Intelligence (BI) | Increase data literacy | More accuracy and trust.