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?

We define augmented analytics as a process that uses automation and artificial intelligence to make analytics more accessible and insightful for all users. By reducing the requirement for technical skills, 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.

How is AI used in augmented Analytics?

Augmented analytics leverages AI to enhance data processing, enabling businesses to gain deeper insights with less manual effort. AI-driven algorithms sift through vast datasets to identify trends, anomalies, and correlations that might go unnoticed by human analysts. By automating the data discovery process, AI allows users to focus on strategic decision-making rather than data mining.

Furthermore, AI augmented analytics enhances data visualization by providing interactive and intuitive graphical representations of complex datasets, facilitating a clearer understanding of the underlying patterns and insights. This integration of AI into analytics tools not only streamlines the analytical workflow but also increases the precision of forecasts and predictions.

AI is the driving force behind the evolution of augmented analytics, transforming raw data into a strategic asset for organizations seeking to maintain a competitive edge in the data-driven digital era.

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

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

Reduced Bias

Machine learning algorithms can 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

Automating repetitive tasks gives 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.

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What are Common Augmented Analytics Examples and Use Cases?

Augmented analytics is revolutionizing the way businesses interact with data. By embedding artificial intelligence (AI) and machine learning into analytics workflows, augmented analytics tools enhance human intelligence, making complex data analysis more accessible to a broader range of users.

Data Preparation

Preparing data for analysis can be time-consuming and error-prone when done manually. However, AI assistants have changed the game: they swiftly analyze vast amounts of data, detect underlying patterns, categorize information accordingly, and organize it with minimal human intervention. This seamless process significantly reduces the time needed to clean and prepare data, thereby enabling analysts to focus on deriving insights and making informed decisions more quickly.

Self-Service Analytics

One of the most transformative aspects of augmented analytics is its ability to empower users at all levels of technical expertise. Through intuitive natural language query interfaces and user-friendly “Try it” recommendations, individuals can dive into data exploration without needing extensive coding knowledge or experience.

Augmented analytics assistants not only generate compelling visualizations and executive summaries but also proactively suggest potential next steps in the analytical process, further streamlining the workflow.

Automated Insights

The deployment of autonomous modeling techniques like clustering and forecasting — as well as other sophisticated predictive algorithms — is another key feature of augmented analytics. These powerful methods run independently, scouring datasets to uncover patterns and correlations that may not be immediately apparent to human analysts. The automated detection of such insights augments the human ability to interpret complex data, offering opportunities for deeper understanding and smarter decision-making within organizations.

Storytelling With Data

Communicating the findings from data analysis in a clear, narrative format is crucial to driving action within an organization. Augmented analytics excels in this area by facilitating narrative reporting, aiding in the construction of presentation materials, and simplifying the explanation of queries. By presenting analytical findings within an easy-to-grasp business context, stakeholders can readily comprehend the implications of data and participate meaningfully in data-driven discussions.

The Future of Augmented Analytics

In the ever-evolving realm of technology, Artificial Intelligence (AI) and machine learning are at the forefront, driving innovations and redefining potential across numerous industries. With these technologies continuing to advance at a rapid pace, the prospects for augmented analytics appear increasingly promising.

What to Expect in the Future?

A greater level of sophistication in augmented analytics capabilities. These improvements are likely to be centered around enhanced cognitive functionalities, including natural language processing and generation, which will allow users to interact with data in more intuitive ways. As these systems grow more advanced, they’ll be able to provide deeper insights, forecasts, and recommendations with exceptional accuracy.

What are the Implications of This Sophistication?

Augmented analytics can democratize the use of complex data by simplifying the process of data analysis and interpretation. It provides individuals without extensive training in data science the ability to understand and leverage insights through user-friendly interfaces.

This means that decision-makers across various levels of an organization can engage directly with data-driven insights, fostering a more informed and agile business environment.

Changing Use Cases

Its integration will stretch beyond traditional business sectors and into fields not typically associated with heavy data analysis. Healthcare, education, manufacturing, retail, and numerous other industries stand to benefit from the deployment of these sophisticated tools. They could dramatically improve outcomes by providing actionable intelligence derived from vast data.

In essence, the trajectory of augmented analytics points towards a future where data-driven decision-making is ubiquitous. By simplifying complex analytical tasks, enabling insightful discoveries, and promoting accessibility, augmented analytics is poised to become an indispensable facet of modern business practices.

It represents a significant step closer to realizing the vision of truly informed decision-making environments, where every strategic move is supported by robust, data-backed evidence.

Augmented Analytics and Qrvey

At Qrvey, we recognize that having a high-performing embedded analytics solution begins with data management. Our multi-tenant analytics 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 augmented data profiling – which is to understand structures – as well as connectors to AWS AI/ML services for predictive and generative AI 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 data analytics from setup through continuous querying and exploration.

To learn more about how AI impacts 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.  

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