Introduction

Augmented Analytics is revolutionizing the way businesses extract insights from their data by leveraging Artificial Intelligence (AI) and Machine Learning (ML) to automate data preparation, analysis, and insight generation. By automating repetitive tasks and providing actionable recommendations, augmented analytics empowers business users, analysts, and executives to make smarter decisions without the need for deep technical expertise. This course focuses on the integration of AI, ML, and natural language processing (NLP) into Business Intelligence (BI) processes, enabling participants to harness advanced analytics capabilities and transform their data-driven decision-making.

The Augmented Analytics in Business Intelligence Training Course is designed for professionals who want to gain an in-depth understanding of how augmented analytics can enhance BI platforms, automate insights, and provide more advanced analytics capabilities to organizations. The course will also cover practical use cases, tool integration, and techniques to implement augmented analytics into BI workflows.

Objectives

By the end of this course, participants will be able to:

• Understand the core concepts of augmented analytics, its benefits, and its impact on Business Intelligence (BI).
• Learn how AI and ML techniques are integrated into BI platforms for automated data analysis and insights generation.
• Explore natural language processing (NLP) and how it can be used to generate insights from text data and enhance decision-making.
• Gain practical experience in leveraging AI-powered BI tools like Power BI, Tableau, and Qlik to implement augmented analytics capabilities.
• Understand the key data preparation and data transformation techniques required for augmented analytics in BI.
• Implement automated anomaly detection, predictive analytics, and trend forecasting with AI-driven BI tools.
• Apply data storytelling with augmented insights to enhance data visualization and communication.
• Ensure the ethical use of augmented analytics, considering transparency, bias, and accountability in AI/ML models.

Who Should Attend?

This course is ideal for:

• Business Intelligence Professionals who want to integrate AI, ML, and advanced analytics techniques into their BI workflows.
• Data Analysts and Data Scientists looking to enhance their BI skills by using augmented analytics for predictive analysis and data visualization.
• Business Leaders and Decision-Makers seeking to understand how augmented analytics can help improve data-driven decision-making in their organizations.
• IT and Data Engineers responsible for integrating AI-powered BI tools and data processing pipelines.
• BI Tool Users who want to learn how to use augmented analytics features in tools like Power BI, Tableau, Qlik, and others.
• Consultants working with organizations to implement advanced analytics capabilities in BI environments.

Day 1: Introduction to Augmented Analytics and Its Impact on BI

• What is Augmented Analytics?

  • Overview of augmented analytics and its role in the modern BI ecosystem.
  • Key benefits: automation, advanced insights, self-service analytics, and improved decision-making.
  • How augmented analytics is transforming data analysis in business environments.

• AI and ML Fundamentals for BI

  • Introduction to AI and ML in the context of BI: supervised vs unsupervised learning, predictive analytics, and classification.
  • The role of AI/ML in data cleaning, data transformation, and anomaly detection.
  • Overview of natural language processing (NLP) and its applications in BI for extracting insights from unstructured data.

• Augmented Analytics and Its Key Components

  • Automated data preparation: Data wrangling and cleaning through AI.
  • Advanced data analysis using machine learning models for predictive insights.
  • Insight automation: How BI tools can automatically surface insights based on user queries or trends.

• Hands-on Exercise:

  • Introduction to an AI-powered BI tool (e.g., Power BI, Tableau) with built-in augmented analytics features.
  • Set up and explore key functionalities like auto-detecting trends, recommendations, and automated insights generation.

Day 2: Automated Data Preparation and Transformation

• The Role of AI in Data Preparation

  • How augmented analytics tools automate the process of data cleaning, wrangling, and transformation.
  • Identifying and handling missing, duplicate, or inconsistent data automatically.
  • Using AI-driven tools for data profiling and data quality assurance in BI.

• Data Integration for Augmented Analytics

  • How to integrate multiple data sources (structured and unstructured) into BI systems for augmented analysis.
  • Using cloud platforms, APIs, and ETL tools for seamless data integration.
  • Ensuring data accuracy and integrity across multiple sources through AI-powered automation.

• Feature Engineering and Transformation with AI

  • Automated feature selection and transformation techniques for BI tools.
  • Using machine learning algorithms to enhance the relevance of data for predictive modeling.
  • Transforming raw data into meaningful features for advanced analytics in BI tools.

• Hands-on Exercise:

  • Prepare a dataset using an AI-powered BI tool for automated transformation, cleaning, and feature engineering.
  • Visualize how the tool handles missing values, outliers, and other data issues without manual intervention.

Day 3: Applying Predictive Analytics and Anomaly Detection

• Introduction to Predictive Analytics in BI

  • Overview of predictive analytics and its importance in business decision-making.
  • Understanding how augmented analytics tools use ML models for forecasting and trend analysis.
  • Types of predictive models: regression, classification, and time-series forecasting.

• Anomaly Detection with AI

  • How augmented analytics tools detect anomalies and outliers in large datasets.
  • Using machine learning models to automatically flag unusual patterns and behaviors in business data (e.g., fraud detection, sales anomalies).
  • Best practices for interpreting and acting upon anomalies detected by AI-driven BI tools.

• Building Predictive Models in BI Tools

  • Training and deploying predictive models within BI platforms (e.g., forecasting sales, customer churn prediction).
  • Leveraging AI-driven forecasting and trend analysis to support business decisions.

• Hands-on Exercise:

  • Use an AI-powered BI tool to build a predictive model on a sample dataset (e.g., sales forecast, demand prediction).
  • Implement an anomaly detection algorithm to identify outliers in the data.

Day 4: Data Storytelling with Augmented Insights and NLP

• Data Storytelling with Augmented Analytics

  • How augmented analytics helps create meaningful data stories from complex data sets.
  • The role of data visualization in presenting AI-generated insights to stakeholders.
  • Best practices for translating complex analytics results into compelling narratives.

• Natural Language Processing (NLP) in BI

  • Introduction to NLP and its role in generating insights from text data (e.g., customer feedback, social media, and reports).
  • Using NLP for text analytics, sentiment analysis, and generating business insights from unstructured data.
  • How to integrate NLP with BI platforms to automate text-based insight generation.

• Enhancing BI Dashboards with AI-generated Insights

  • How to automate the inclusion of AI-driven insights into dashboards and reports.
  • Making BI dashboards more interactive with natural language queries and automated insights.

• Hands-on Exercise:

  • Create a data story using augmented insights and visualizations.
  • Apply NLP to analyze customer feedback data and generate actionable insights.

Day 5: Implementing Augmented Analytics in BI and Ethical Considerations

• Implementing Augmented Analytics in BI Workflows

  • How to integrate augmented analytics capabilities into existing BI processes and platforms.
  • Customizing Power BI, Tableau, and Qlik with augmented analytics functionalities.
  • Setting up automated reports, dashboards, and insight generation for business users.

• Ethical Considerations in Augmented Analytics

  • Addressing bias and transparency in AI/ML models used for augmented analytics.
  • Ensuring fairness and accountability in decision-making powered by augmented analytics.
  • The importance of explainability in AI models to ensure stakeholders understand how insights are generated.

• The Future of Augmented Analytics in BI

  • Trends in AI-driven analytics, self-service BI, and the democratization of data.
  • The evolving role of AI and ML in BI and the skills needed for future BI professionals.
  • Preparing your organization for the future of augmented analytics.

• Final Project and Wrap-Up

  • Develop an augmented analytics strategy for a business case, integrating AI/ML, NLP, and predictive analytics into BI processes.
  • Present your strategy to the group and receive feedback.
  • Recap of key concepts and resources for continuing learning in augmented analytics.