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March 29, 2021
Telecoms.com periodically invites expert third parties to share their views on the industry’s most pressing issues. In this piece Rohit Maheshwari, Head of Strategy and Products at Subex, looks at how Augmented Analytics provides the ability to democratise data analytics across the entire data value chain.
Gaining considerable traction, augmented analytics is one of the latest data and analytics trends, and with good reason. Coined by Gartner in 2017, augmented analytics leverages artificial intelligence (AI) and machine learning (ML) techniques, and it’s causing a wave of disruption within the market.
As organisations increasingly realise the importance of Big Data and its role in the process of decision making, the sheer volume of data that is now available is making effective interpretation a challenge. According to Forrester, less than 0.5% of all data is analysed and used, while only 12% of enterprise data is even leveraged when making business decisions.
The ability to effectively use the data being collected is set to become even more challenging. According to IDC, data generated by connected internet of things (IoT) devices will grow from 13.6 zettabytes (ZB) in 2019 to 79.4 ZB by 2025. This explosion of data will stimulate increased demand for and adoption of augmented analytics. With the ability to transform how analytics content is developed, consumed, and shared: it eases bottlenecks, increases productivity and efficiency, improves accuracy, and delivers faster insights.
The current analytics approach
Across the data value chain, many processes remain largely manual and prone to bias. This includes managing and preparing the data for analysis, building AI and ML models, interpreting the results, and creating actionable insights. This manual effort often results in business users left to find their own patterns, and data scientists to build and manage their own models.
The outcomes of the current analytics approach are that business users and data scientists need to explore their own hypotheses, key findings are overlooked, and conclusions are often inaccurate. The results of this approach is corroborated by Forrester Research that found that only 29% of organisations are successful at connecting analytics to actions.
The benefits of augmented analytics: A workflow comparison
Taking into consideration data management, data science, and data visualisation; when augmented analytics is adopted, processes are streamlined for increased productivity, improved accuracy, and faster insights.
Analytics Workflow – Current State
45% of time is spent handling manual tasks such as data cleaning, profiling, cataloging, etc.
34% of time is spent on manual feature engineering model selection, and model training and deployment
21% of time is spent using interactive techniques such as filtering, pivoting, linking, grouping, and user-defined calculation
Issue: Less time spent on more productive tasks such as gaining insights from the data
Issue: Less time spent on model validation, testing delivery, and operationalisation
Issue: Relies on the individual user to interpret the data
Augmented Analytics Workflow
Data preparation and discovery time is reduced by 50 – 80%
Automates data science tasks, reducing time spent by 40%
From query to insights, cycle time is reduced by 50%
Issue: Manual data preparation, data quality, and cataloging
Issue: Manual feature engineering and model building
Issue: Manual exploration of data using interactive visualisation
Solution: Uses AI to automate data preparation
Solution: Uses AI/ML techniques (AutoML – an AI-based solution, automates the process of applying ML to real-world problems) to automate data science tasks such as auto generation of features, and model management is augmented
Solution: Uses natural language processing (NLP) for auto visualisation of relevant patterns, and automates data insights
Benefits: Increased productivity and efficiency
Benefits: Improved accuracy of the model and removes bias
Benefit: Faster insights from the data
Augmented analytics democratises AI across the data value chain, automating the data preparation process and key aspects of data science, while NLP enables users to obtain faster insights. AutoML leverages techniques and relevant insights using NLP and conversational analytics. It includes:
Augmented data preparation: Uses AI/ML automation to accelerate manual data preparation tasks such as data profiling and quality, enrichment, metadata development, data cataloging, and various aspects of data management like data integration and database administration.
Augmented data science: Uses AI/ML techniques to automate key aspects of data science such as feature engineering and model selection, as well as model operationalisation, model explanation, and model tuning.
Augmented analytics: Is part of business intelligence (BI) platforms and embeds AI/ML techniques to automatically find and visualise the data, and narrate the relevant findings through conversational interfaces, including natural language query (NLQ) technologies that are supported by natural language generation (NLG).
The promise of augmented analytics
Augmented analytics provides numerous benefits, beginning with its ability to democratise data analytics across the entire data value chain. This is especially important for less business-savvy users, such as citizen data scientists that don’t have specialised training or skills in data science or analysis. Additional benefits include automating data preparation, reducing time to insights, eliminating human analytical bias, mitigating the risk of missing important insights, and providing actionable insights to the executive team.
Benefits don’t end at the theoretical level, measurable benefits include:
48% improvement in analytics effectiveness
35% YoY increase in total customers
31% YoY improvement in employee retention
23% YoY increase in operation profitability
On average, companies that invest in augmented analytics can experience:
Faster insights since huge data will run 10X to 100X quicker
A 5X increase in the number of citizen data scientists
A 51% increase in decision-making confidence
A 50% increase in analytics efficiency
As the amount of data continues to rise, organisations will increasingly see the need for an augmented analytics platform to connect disparate and live data sources, find relationships within the data, create visualisations, and enable personnel to quickly and effortlessly share their findings. Augmented analytics is poised to change how users experience analytics and BI by providing insights currently unimaginable.
Rohit Maheshwari is the Head of Strategy and Products at Subex. He is responsible for delivering business growth using innovation and product strategy. He leverages his expertise in artificial intelligence (AI), analytics and digital services to contribute to Subex’s solutions and enables its clients to build new offerings, drive business growth and deliver great customer experience.
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