Augmented vs. AI Analytics: Enhance Your Decision-Making Strategies

In the rapidly evolving world of data analytics, staying ahead of the curve means embracing innovative approaches to decision-making. Augmented analytics and AI analytics represent two such advancements, each offering unique benefits and capabilities. In this blog, we'll explore what augmented analytics and AI analytics are, how they compare, and their respective use cases. We'll also discuss how a data catalog can support these analytics methods, helping organizations unlock the full potential of their data.

What is Augmented Analytics?

Augmented analytics is a next-generation approach to data analytics that leverages machine learning (ML) and natural language processing (NLP) to enhance data preparation, analysis, and insight generation. By automating complex processes and providing advanced analytical capabilities, augmented analytics empowers users to gain deeper insights faster and more efficiently.

Key features of augmented analytics include:

• Automated data preparation: Simplifies the process of cleaning, transforming, and integrating data.

• Advanced data visualization: Utilizes ML to create sophisticated visualizations that highlight key insights.

• Natural language queries: Allows users to interact with data using conversational language, making analytics more accessible to non-technical users.

• Insight generation: Automatically identifies patterns, correlations, and trends in the data, providing actionable insights.

Augmented analytics transforms how organizations interact with their data by automating complex processes and making advanced analytics accessible to a broader audience. By leveraging machine learning and natural language processing, augmented analytics empowers users to derive valuable insights quickly and efficiently, ultimately enhancing decision-making and operational effectiveness.

What is AI Analytics?

AI analytics refers to the application of artificial intelligence (AI) techniques, such as machine learning and deep learning, to analyze and interpret complex datasets. AI analytics goes beyond traditional analytics by enabling predictive and prescriptive insights, allowing organizations to anticipate future trends and make data-driven decisions with greater confidence.

Key features of AI analytics include:

• Predictive analytics: Uses ML algorithms to forecast future outcomes based on historical data.

• Prescriptive analytics: Provides recommendations for actions based on predictive insights.

• Automated anomaly detection: Identifies unusual patterns and outliers in the data.

• Advanced pattern recognition: Detects complex patterns and relationships that may not be apparent through traditional analysis methods.

AI analytics enables organizations to unlock deeper insights and predictive capabilities through advanced AI techniques. By applying machine learning and deep learning, AI analytics provides a powerful toolset for anticipating future trends and making data-driven decisions with greater confidence, driving strategic advantages across various domains.

Augmented vs. AI Analytics: Comparison

While both augmented and AI analytics leverage advanced technologies to enhance data analysis, they differ in their focus and applications.

Focus and Purpose

• Augmented Analytics: Primarily aims to simplify and automate the data preparation and insight generation process, making analytics more accessible to a broader range of users.

• AI Analytics: Focuses on applying AI techniques to uncover deeper, predictive, and prescriptive insights, often requiring more technical expertise.

Understanding the distinct focuses of augmented and AI analytics helps organizations choose the right approach for their needs. Augmented analytics simplifies and automates data analysis for broader accessibility, while AI analytics delves deeper into predictive and prescriptive insights. By leveraging both approaches, organizations can optimize their data strategies and achieve comprehensive analytics capabilities.

User Accessibility

• Augmented Analytics: Designed to be user-friendly, allowing non-technical users to perform complex analyses using natural language queries and automated visualizations.

• AI Analytics: Typically requires a higher level of technical expertise to develop and interpret advanced models and algorithms.

Both augmented and AI analytics enhance user accessibility in unique ways. Augmented analytics democratizes data analysis, enabling non-technical users to gain insights through natural language queries and automated visualizations. AI analytics, while more technical, provides powerful tools for those with expertise to uncover complex patterns and predictions. Together, they offer a balanced approach to making analytics accessible to all levels of an organization.

Insights and Outcomes

• Augmented Analytics: Provides immediate, actionable insights through automated data preparation and visualization.

• AI Analytics: Delivers predictive and prescriptive insights, helping organizations anticipate future trends and optimize decision-making.

The insights generated by augmented and AI analytics drive significant value for organizations. Augmented analytics delivers immediate, actionable insights through automated processes, while AI analytics provides predictive and prescriptive insights for strategic decision-making. By combining these strengths, organizations can achieve a comprehensive understanding of their data and make informed decisions to propel their business forward.

Augmented Analytics Use Cases

Augmented analytics can be applied across various industries and functions to enhance decision-making and operational efficiency.

Marketing

• Customer segmentation: Automatically identifies customer segments based on behavior and demographics, enabling targeted marketing campaigns.

• Campaign optimization: Analyzes past campaign performance to recommend improvements and predict future outcomes.

In marketing, augmented analytics enables more precise customer segmentation and campaign optimization. By automating data analysis and insight generation, marketers can make data-driven decisions that enhance campaign effectiveness and customer engagement, ultimately driving better marketing outcomes.

Sales

• Sales forecasting: Uses historical sales data to predict future sales trends and identify potential opportunities.

• Lead scoring: Automatically scores leads based on their likelihood to convert, helping sales teams prioritize their efforts.

For sales teams, augmented analytics provides valuable tools for sales forecasting and lead scoring. By leveraging automated insights and predictive models, sales professionals can prioritize their efforts, optimize their strategies, and achieve better sales performance and revenue growth.

Human Resources

• Employee performance: Analyzes performance data to identify high-performing employees and areas for improvement.

• Attrition analysis: Predicts employee turnover and recommends strategies to improve retention.

Augmented analytics in human resources enhances employee performance management and attrition analysis. By providing data-driven insights, HR teams can identify high-performing employees, address areas for improvement, and implement strategies to improve retention, fostering a more productive and engaged workforce.

AI Analytics Use Cases

AI analytics can drive significant value by providing deeper, predictive insights across various domains.

Finance

• Risk management: Uses predictive models to assess and mitigate financial risks.

• Fraud detection: Identifies suspicious transactions and activities through advanced pattern recognition.

AI analytics in finance drives significant value through advanced risk management and fraud detection. By applying predictive models and pattern recognition, financial organizations can proactively address risks and identify fraudulent activities, ensuring greater security and stability in their operations.

Healthcare

• Disease prediction: Predicts the likelihood of disease outbreaks and patient diagnoses based on historical data.

• Personalized treatment: Recommends personalized treatment plans based on patient data and predictive models.

In healthcare, AI analytics enables predictive disease management and personalized treatment plans. By leveraging patient data and predictive models, healthcare providers can improve patient outcomes, optimize treatment strategies, and enhance overall healthcare delivery.

Manufacturing

• Predictive maintenance: Forecasts equipment failures and recommends maintenance schedules to prevent downtime.

• Quality control: Identifies defects and variations in the manufacturing process to ensure product quality.

AI analytics revolutionizes manufacturing through predictive maintenance and quality control. By forecasting equipment failures and identifying process variations, manufacturers can prevent downtime, ensure product quality, and achieve greater operational efficiency, driving success in the competitive manufacturing landscape.

How a Data Catalog Can Support AI Analytics and Augmented Analytics

A data catalog is a crucial foundation for successful AI and augmented analytics implementations. By providing a centralized repository of metadata, a data catalog enhances data discoverability, governance, and collaboration. Organizations can utilize a data intelligence platform, such as Alation, to jumpstart AI initiatives.

Enhanced Data Discoverability

A data catalog makes it easy for users to find and understand the data they need for their analyses. With Alation’s Intelligent Search functionality, a mix of semantic, behavioral, and keyword search enables users to find the exact content they are looking for without the need for specific technical jargon. By cataloging data assets, organizations can ensure that users have access to relevant, high-quality data, driving more accurate and efficient analytics.

Improved Data and AI Governance

With a data catalog, organizations can enforce data governance policies and standards, ensuring data quality, security, and compliance. Alation’s AI Governance allows users to document their AI models, catalog the upstream and downstream lineage of their datasets for models, and actively govern their models within the enterprise. This is particularly important for AI and augmented analytics, which rely on high-quality data to generate accurate insights.

Facilitated AI and Data Collaboration

A data catalog promotes collaboration by providing a shared and easily searchable repository of contextual data assets. Users can easily share and collaborate on data, fostering a more data-driven culture and enabling better decision-making across the organization.

For more information on how a data catalog can support your AI and augmented analytics efforts, book a demo with us today.