Augmented analytics: Is this the future of data analytics? - The EE

Augmented analytics: Is this the future of data analytics?

Rohit Maheshwari of Subex

Globally, organisations are increasingly realising the importance of Big Data, and its role in making business decisions. However, the sheer volume of data available to organisations, combined with the manual processes that span the data value chain, is making accurate interpretation of the data a considerable challenge.

According to Forrester, less than 0.5% of all data is analysed and used. More concerning is that only a mere 12% of enterprise data is actually utilised when making business decisions, says Rohit Maheshwari, head of strategy and products, Subex

An emerging data and analytics trend augmented analytics is gaining considerable traction, and it couldn’t have happened at a better time. IDC predicts that 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 augmented analytics, as it goes beyond the world of data and analytics by leveraging Artificial Intelligence (AI) and Machine Learning (ML) techniques to transform how analytics content is developed, consumed, and shared.

The adoption of augmented analytics eases bottlenecks, increases productivity and efficiency, improves accuracy, and delivers faster insights.

Augmented analytics creates business opportunities

Using the current analytics approach, business users are left to find their own patterns, and data scientists to build and manage their own models. This manual effort results in users examining their own hypotheses, missing key findings and ultimately coming to incorrect conclusions, which adversely affects business decisions, actions, and outcomes. The results of this approach are echoed by Forrester’s research that found that only 29% of organisations are successful at connecting analytics to actions.

Similar to the traditional analytics workflow, the augmented analytics workflow consists of data management, data science, and data visualisation. The difference lies in the solutions and benefits provided by the techniques and technologies Augmented Analytics leverages.

Data management

Issue – Manual data preparation, data quality, and cataloging

Solution – Automate data preparation using AI automation

Benefits – Increases productivity and efficiency

Data science

Issue – Manual feature engineering and model building

Solution – Automate data science tasks such as auto-generation of features using model selection (AutoML), and augment model management AI/ML techniques

Benefits – Improves the accuracy of the model and removes user bias

Data visualisation

Issue – Manual exploration of data using interactive visualisation

Solution – Automate visualisation of relevant patterns, as well as data insights through natural language processing (NLP) and conversational analytics

Benefits – Faster insights derived from the data

Delving deeper into the techniques and technologies, business opportunities can include the following:

  • Augmented data preparation : usesAI/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, including feature engineering and AutoML, as well as model operationalisation, model explanation, model tuning, and management. It widens the accessibility of data science and AI/ML to citizen data scientists in an organisation, and allows highly skilled data scientists to focus on creative tasks.
  • Augmented analytics, as part of Business Intelligence (BI) platforms, embeds AI/ML techniques to automatically explore, visualise the data, and narrate the relevant findings via conversational interfaces and natural language processing (NLP) supported by natural language query (NLQ) and natural language generation (NLG) technologies.

The benefits of augmented analytics are automating data preparation, reducing time to insights, eliminating human analytical bias, and mitigating the risk of missing important insights. It also includes the ability to democratise data analytics for less business-savvy users, such as citizen data scientists that don’t have specialised training or skills in data science or analysis. It also enables the adoption of actionable insights for the executive team across organisational business units.

However, the benefits don’t end there. Quantifiable benefits are plentiful and include:

  • 48% improvement in analytics effectiveness
  • 23% greater YoY increase in operational profitability
  • 31% greater YoY improvement in employee retention
  • 35% greater YoY increase in the total number of customers

Is augmented analytics the future of data analytics? The answer is a resounding yes. As the amount of data continues to rise, companies both large and small will need the capabilities it provides for quick access to accurate data for actionable insights. It will change how users experience analytics and business intelligence, and deliver a level of insights currently unimaginable.

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

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