Augmented Analytics: Overview, Benefits, and Supporting Statistics

Augmented analytics is an advanced form of data analysis that incorporates AI, Machine Learning (ML), and Natural Language Processing (NLP) to enhance and automate data preparation, analysis, and visualization. This approach allows non-technical users to easily analyze complex datasets, democratizing access to data and insights that were previously limited to skilled analysts.

Key Benefits of Augmented Analytics

1. Improved Decision-Making

Augmented analytics enables organizations to make data-driven decisions much more efficiently. By providing faster and more accurate insights, it greatly improves strategic and operational planning.

2. Better Data Literacy

Augmented analytics tools empower non-technical users to perform data interpretations and queries with NLP. This makes obtaining and understanding insights a lot easier. By enabling users to perform complex analytics with simple commands, these tools improve data literacy within organizations, leading to better-designed solutions.

3. Mitigation of Analytical Biases

Augmented analytics helps reduce interpretation bias by automating parts of the data analysis process. While human analysts may introduce subjective judgments, automated systems apply consistent methods and can be audited using large datasets to detect and correct potential biases.

4. Enhanced Productivity

Data preparation tasks, such as data cleaning, are now automated, speeding up analytics processes.

This automation frees up analysts’ time, allowing them to focus on interpreting results rather than manual data collection. 

5. Real-Time Insights

Augmented analytics provides businesses with immediate, comprehensive analysis. This enables companies to react quickly to any shifts in the market or operational issues.

6. Higher Accuracy

Augmented analytics uses advanced algorithms to ensure data is clean and well-structured, which reduces analysis errors.

7. Cost-Effectiveness

Augmented analytics empowers employees to conduct complex analyses. This reduces operational costs by eliminating the need for data scientists to perform mundane tasks. 

Supporting Statistics

Market Growth Statistics

According to IMARC, the augmented analytics market is projected to grow from $15.5 billion in 2024 to $80.4 billion by 2033, a compound annual growth rate (CAGR) of 19.87% from 2025 to 2033.

According to Markets and Markets, it grew from USD 4.8 billion in 2018 to USD 18.4 billion by 2023, demonstrating a 30.6% CAGR. 

Performance Metrics from Implementations

Payal Paranjape, a Product Marketing Specialist at Subex, a telecom AI company, highlights that companies adopting augmented analytics reported a 51% increase in decision-making efficiency and a 50% increase in overall efficiency.

Automation and Efficiency

Augmented analytics approach improves the efficiency of strategic data utilization by almost 48%. Gartner had predicted that by this year, 95% of data-driven decisions would at least be partially automated. These changes will significantly increase productivity across all organizations.

Accuracy and Error Reduction

With augmented analytics, businesses are able to attain and analyze data in real time.

Errors caused by analytical biases are reduced because machine learning algorithms are employed to sift through large data volumes to find patterns that would often go undetected.

With the growing use of augmented analytics, organizations can dramatically improve their decision-making processes. This method makes advanced analytic techniques available to less skilled users, simplifying the access and analysis of information, regardless of data complexity.