What is Augmented Analytics? A Quick Guide.

Augmented analytics

Understanding the Future of Data-Driven Insights with AI

Augmented analytics aims to provide users with insights from data through augmented intelligence. It leverages techniques like natural language processing (NLP), machine learning (ML), artificial intelligence (AI) and automation to simplify analytics for business users.

What is Augmented Analytics?

Augmented analytics uses automation and artificial intelligence to make analytics more accessible and insightful for all users. By reducing the technical skills needed, more teams can leverage analytics to drive decisions.

Augmented analytics augments human analysts by:

  • Automating routine tasks like data preparation
  • Providing recommendations for visualizations, model parameters, and more
  • Enabling natural language querying of data
  • Generating narrative explanations of analytical findings
  • The goal is to augment human capabilities, not replace them. People still oversee the process and have the final say. But AI streamlines workflows and surfaces more nuanced insights.

What are the Benefits of Augmented Analytics?

Enhanced insights:

Augmented analytics uses advanced algorithms and machine learning to analyze big and complex datasets, giving better insights into business data.

Faster decision-making:

Augmented analytics automates data tasks, speeding up decision-making by reducing manual data processing time.

Democratization of analytics:

Augmented analytics tools are easy to use, so anyone can analyze data without needing help from experts or scientists.

Reduced bias:

Machine learning algorithms in augmented analytics reduce human bias in data analysis, resulting in objective and unbiased insights.

Improved data quality:

Augmented analytics tools often incorporate data cleansing and data quality checks, resulting in improved data accuracy and reliability.

Scalability and agility:

Augmented analytics can handle big data. It can also adjust to changing requirements. This helps organizations scale and respond to evolving business needs more effectively.

Enhanced productivity:

Augmented analytics automates repetitive tasks, giving analysts more time for important activities like interpreting insights, developing strategies, and making informed decisions.

Cost efficiency:

Augmented analytics reduces the reliance on specialized data experts, lowering operational costs associated with data analysis and increasing overall cost efficiency.

How Does it Work?

Augmented analytics solutions work in the background to support users during the entire analytics process.

Data Preparation

AI assistants analyze data, find patterns, and organize it without much human help, making it easier to use.

Self-Service Analytics

Natural language interfaces and “Try it” recommendations let users explore without coding. Assistants generate visualizations, and summaries and suggest the next analytic steps.

Automated Insights

Modeling techniques like clustering, forecasting, and other predictive algorithms run autonomously. Pattern detection yields insights often missed by humans alone.

Storytelling With Data

Narrative reporting, presentation building, and simple query explanations present findings in an easy-to-understand business context.

The Future of Augmented Analytics

As AI and machine learning continue advancing, augmented analytics will only become more sophisticated. It can help more industries use data-driven decision-making by making insights easier to understand and access for everyone.

Augmented Analytics and Qrvey

At Qrvey, we recognize that having a high-performing embedded analytics solution begins with data management. Our data management solution enables augmented analytics in several key ways. By connecting natively to AWS, Qrvey leverages the power of the cloud to automate and optimize the data workflow. This includes automated data profiling to understand structures, as well as connectors to AWS AI/ML services for predictive capabilities.

Qrvey also facilitates augmentation using non-traditional data types. Through AWS image and text analysis, our solution can extract insights from unstructured data sources and enhance reporting capabilities. Overall, Qrvey provides the data foundation necessary to power augmented analytics from setup through continuous querying and exploration.

To learn more about how AI is impacting analytics solutions, listen to our webinar with our CTO David Abramson. He will discuss the role of generative AI in embedded analytics and share his thoughts and predictions.