Augmented analytics: the combination of AI and analytics is the latest innovation in data analytics. For organizations, data analysis has evolved from hiring “unicorn” data scientists – to having smart applications that provide actionable insights for decision-making in just a few clicks, thanks to AI.

Augmenting by definition means making something greater in strength or value. Augmented analytics, also known as AI-driven analytics, helps in identifying hidden patterns in large data sets and uncovers trends and actionable insights. It leverages technologies such as Analytics, Machine Learning, and Natural Language Generation to automate data management processes and assist with the hard parts of analytics.

According to Gartner, by the end of 2024, 75% of enterprises will operationalize AI, driving a 5x increase in streaming data and analytics infrastructures. The capabilities of AI are poised to augment analytics activities and enable companies to internalize data-driven decision-making while enabling everyone in the organization to easily deal with data. This means AI helps in democratizing data across the enterprise and saves data analysts, data scientists, engineers, and other data professionals from spending time on repetitive manual processes.


The latest advances in Artificial Intelligence play a significant role in making business processes more efficient and powerful with the help of automation. Analytics, too, is becoming more accessible and automated because of AI. Here are a few ways in which AI is contributing to analytics:

  • With the help of machine learning algorithms, AI systems can automatically analyze data and uncover hidden trends, patterns, and insights that can be used by employees to make better-informed decisions.

  • AI automates report generation and makes data easy-to-understand by using Natural Language Generation.

  • Using Natural Language Query (NLQ), AI enables everyone in the organization to intuitively find answers and extract insights from data, thereby improving data literacy and freeing time for data scientists.

  • AI helps in streamlining BI by automating data analytics and delivering insights and value faster.


While traditional BI used rule-based programs to deliver static analytics reports from data, augmented analytics leverages AI techniques such as Machine Learning and Natural Language Generation to automate data analysis and visualization.

  • Machine Learning learns from data and identifies trends, patterns, and relationships between data points. It can use past instances and experiences to adapt to changes and improvise on the data.

  • Natural Language Generation uses language to convert the findings from machine learning data into easy-to-decipher insights. Machine Learning derives all the insights, and NLG converts those insights into a human-readable format.

Augmented analytics can also take in queries from users and generate answers in the form of visuals and text. This entire process is of generating insights from data is automated and makes it easy for non-technical users to easily interpret data and identify insights.


Business Intelligence can help in making improved business decisions and driving better ROI by gathering and processing data. A good BI tool collects important data from internal and external sources and provides actionable insights out of it. Augmented analytics simply improves business intelligence and helps enterprises in the following ways:

1. Accelerates data p