How to harness the potential of augmented analytics - The EE

How to harness the potential of augmented analytics

Rohit Maheshwari of Subex

First defined by Gartner, Augmented Analytics uses enabling technologies such as machine learning and Artificial Intelligence (AI) to assist with data preparation, insight generation, and insight explanation.

As Rohit Maheshwari, head of strategy & products at Subex says, it empowers experts as well as non-data scientists by automating many aspects of data science, including model development, management and deployment of AI models.

AI is still in the early stages of meaningful adoption in most businesses. Enterprises are eager to harness the promise of AI and machine learning technology. Big Data and its effective analysis add immense value to the decision-making process across the entire business and consumer value chain.

However, the sheer amount of data is creating its own issues. According to IDC, data generated by the Internet of Things (IoT) will grow from 13.6 zettabytes (ZB) in 2019 to 79.4 ZB by 2025. Businesses need the ability to transform how analytics content is developed, consumed, and shared in order to benefit from more accurate insights.

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 machine learning (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 are corroborated by Forrester Research in their findings that only 29% of organisations are successful at connecting analytics to actions.

Most enterprises struggle to implement AI with business value at its centre. Instead, AI initiatives are driven by data science teams, with little alignment between the priorities of business and IT. Organisations need more autonomy and wider access to AI and ML to transform data into reliable insights with greater speed and efficiency.

Automating the analytics process and augmenting with AI and ML using a built-for-purpose intelligent platform unleashes the true value of data analytics. This approach gives users one-place access to batch and streaming data across multiple data formats and lets them search, enrich, structure, and validate the data collected from across the business. Users can apply rules and run data audits in real-time, profile information, monitor and forecast business KPIs.

Augmented analytics 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. Pre-built analytics use cases in marketing, finance, and technology verticals enable enterprises to deliver ultra-fast results, as well as enabling them to build their own tailor-made, AI-powered analytics applications.

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 revolutionise the way businesses behave.

The author is Rohit Maheshwari, head of strategy & products at Subex.

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