15.4 Augmented Analytics and AutoML

Augmented analytics and AutoML are revolutionizing business intelligence. These technologies automate data insights, streamline analytics workflows, and make machine learning accessible to non-experts. They're changing how organizations extract value from data and make decisions.

For BI professionals, this shift means focusing more on strategic decision-making and less on manual analysis. It requires new skills in machine learning concepts and using advanced tools. The impact is significant, opening doors to upskilling and expanding roles in data science.

Augmented Analytics and AutoML

Role of augmented analytics

• Employs machine learning and natural language processing to automate data insights and recommendations streamlines the analytics process
• Enables users to interact with data using natural language queries and narration enhances accessibility and usability
• Automates various stages of the analytics workflow including data preparation, cleansing, modeling, pattern recognition, insight generation, and visualization accelerates time-to-insight
• Assists users in uncovering hidden patterns, trends, and anomalies within data reveals valuable insights (customer behavior, market trends)
• Provides intelligent recommendations and actionable insights based on data analysis guides data-driven decision-making
• Reduces the need for manual data exploration and interpretation saves time and resources

Concept of AutoML

• AutoML (Automated Machine Learning) automates the end-to-end machine learning workflow streamlines the process
• Aims to make machine learning more accessible and efficient for non-experts democratizes access to advanced analytics
• Automates key steps in the machine learning pipeline:
  1. Data preprocessing and feature engineering prepares data for modeling
  2. Algorithm selection and hyperparameter tuning identifies the best-performing models
  3. Model training, evaluation, and deployment operationalizes the models
• Utilizes meta-learning and optimization techniques to find the best-performing models improves model accuracy and performance
• Enables faster experimentation and iteration of machine learning models accelerates model development
• Integrates with BI platforms to enable automated predictive modeling and forecasting enhances BI capabilities (sales forecasting, customer churn prediction)

Benefits of augmented analytics

• Accelerates the time-to-insight by automating data analysis and insight generation enables faster decision-making
• Improves the accuracy of insights and predictions by leveraging machine learning algorithms minimizes human bias and subjectivity
• Democratizes access to advanced analytics capabilities empowers non-technical users (business analysts, domain experts)
• Enables more agile and responsive decision-making allows organizations to adapt quickly to changing conditions (market trends, customer preferences)
• Facilitates real-time monitoring and alerting of key performance indicators enables proactive management and optimization

Impact on BI professionals

• Shifts the focus from manual data analysis to strategic decision-making allows more time for high-value tasks (data storytelling, business consulting)
• Requires development of new skills and competencies:
  • Understanding of machine learning concepts and algorithms
  • Ability to interpret and communicate the results of automated analyses
  • Proficiency in using augmented analytics and AutoML tools and platforms (Tableau, PowerBI)
• Necessitates collaboration between BI professionals and data scientists combines domain expertise with technical skills
• Presents opportunities for upskilling and role expansion:
  • Transition into data science and machine learning roles
  • Develop expertise in specific domains or industries leveraging augmented analytics and AutoML (healthcare, finance)