15.4 Augmented Analytics and AutoML
3 min read•july 18, 2024
and are revolutionizing business intelligence. These technologies automate data insights, streamline analytics workflows, and make 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
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- Employs machine learning and 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 , , , , , and accelerates time-to-insight
- Assists users in uncovering hidden patterns, trends, and anomalies within data reveals valuable insights (customer behavior, market trends)
- Provides and based on data analysis guides
- Reduces the need for manual and interpretation saves time and resources
Concept of AutoML
- AutoML () 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:
- and prepares data for modeling
- and identifies the best-performing models
- , evaluation, and deployment operationalizes the models
- Utilizes and 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 and enhances BI capabilities (, )
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 and subjectivity
- Democratizes access to advanced analytics capabilities empowers non-technical users (business analysts, )
- Enables more agile and responsive decision-making allows organizations to adapt quickly to changing conditions (market trends, customer preferences)
- Facilitates and alerting of enables and optimization
Impact on BI professionals
- Shifts the focus from manual data analysis to strategic decision-making allows more time for high-value tasks (, )
- 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 (, 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)
Key Terms to Review (41)
Actionable insights: Actionable insights are data-driven findings that can be directly translated into strategies or actions to drive business decisions and improvements. These insights go beyond just analysis; they provide specific recommendations based on data patterns, trends, and customer behavior, enabling organizations to take informed steps toward achieving their goals.
Algorithm selection: Algorithm selection is the process of choosing the most appropriate algorithm for a specific data analysis task or problem based on various criteria such as the nature of the data, desired outcomes, and computational resources. This choice can significantly impact the performance of models and the accuracy of predictions, especially in fields that leverage advanced techniques like augmented analytics and AutoML.
Augmented analytics: Augmented analytics is the use of machine learning and artificial intelligence to enhance data preparation, insight generation, and sharing of data analytics. This approach automates various aspects of data analysis, making it easier for users to discover insights without needing deep technical skills. By enabling faster and more accurate decision-making, augmented analytics transforms how organizations utilize data, creating a landscape where business intelligence tools become more intuitive and powerful.
Automated machine learning: Automated machine learning, often referred to as AutoML, is the process of automating the end-to-end process of applying machine learning to real-world problems. This includes data preprocessing, model selection, hyperparameter tuning, and model evaluation. By automating these tasks, it allows users with limited knowledge in machine learning to build models effectively, enhancing productivity and enabling more individuals to leverage data insights without needing deep technical expertise.
Automated predictive modeling: Automated predictive modeling refers to the process of using algorithms and machine learning techniques to automatically create models that predict future outcomes based on historical data. This approach streamlines the model-building process, reduces the need for extensive manual intervention, and allows businesses to quickly gain insights and make data-driven decisions.
AutoML: AutoML, or Automated Machine Learning, refers to the process of automating the end-to-end process of applying machine learning to real-world problems. It simplifies the complex tasks of model selection, feature engineering, and hyperparameter tuning, making machine learning more accessible for non-experts while also optimizing performance for experienced data scientists. This automation enhances augmented analytics by allowing users to derive insights from data without needing extensive programming skills or in-depth knowledge of machine learning algorithms.
Business Analyst: A business analyst is a professional who evaluates and improves business processes, systems, and strategies by analyzing data and requirements to drive business success. They play a crucial role in bridging the gap between stakeholders, including IT and business teams, ensuring that solutions align with organizational goals. Their expertise is vital in various areas such as operational data management, project implementation, and leveraging advanced analytics tools for decision-making.
Business consulting: Business consulting is a professional service that helps organizations improve their performance through expert advice and strategic planning. Consultants analyze business problems, provide tailored solutions, and guide companies in implementing changes to achieve their objectives. This practice often involves leveraging advanced technologies like augmented analytics and AutoML to enhance decision-making processes.
Cleansing: Cleansing refers to the process of identifying and correcting inaccuracies, inconsistencies, and errors in data to ensure its quality and reliability. This crucial step enhances the overall effectiveness of data analysis by allowing algorithms in augmented analytics and AutoML to operate on accurate datasets, ultimately leading to more reliable insights and decision-making.
Customer churn prediction: Customer churn prediction is the process of using data analysis and predictive modeling techniques to identify customers who are likely to stop using a company's products or services. By analyzing various factors, such as customer behavior, demographics, and engagement levels, businesses can anticipate churn and implement strategies to retain these customers, thereby improving overall customer loyalty and profitability.
Data exploration: Data exploration is the process of analyzing and visualizing data sets to summarize their main characteristics, often using statistical graphics and other exploratory techniques. This step is crucial as it helps in understanding the data, uncovering patterns, identifying anomalies, and generating insights that can drive decision-making. By employing various tools and methods, data exploration supports more advanced analytics processes, including augmented analytics and AutoML.
Data preparation: Data preparation is the process of cleaning, transforming, and organizing raw data into a suitable format for analysis. This step is crucial because it ensures that the data used for insights and decision-making is accurate, consistent, and relevant. A thorough data preparation process enables effective data mining and enhances the capabilities of advanced analytics techniques, including machine learning and augmented analytics.
Data preprocessing: Data preprocessing is the process of transforming raw data into a clean and usable format, essential for effective analysis and modeling. This includes various techniques such as cleaning, normalization, transformation, and integration of data to ensure its quality and relevance. Proper data preprocessing is crucial as it directly impacts the performance of analytical methods, particularly in fields like text mining and augmented analytics where large and complex datasets are common.
Data Storytelling: Data storytelling is the practice of using data to convey a narrative or message that is both compelling and informative. This approach combines the analytical aspects of data with visual elements and narrative techniques to enhance understanding and engagement. By integrating various components such as architecture, visualization principles, chart types, interactivity, and advanced analytics, data storytelling transforms raw data into meaningful insights that can drive decision-making.
Data storytelling: Data storytelling is the practice of using narrative techniques to communicate insights derived from data analysis in a compelling way. This approach combines data visualization, context, and narrative to help the audience understand complex information and make informed decisions based on that data. It emphasizes the importance of framing data within a relatable story to engage stakeholders and drive action.
Data Visualization: Data visualization is the graphical representation of information and data, enabling individuals to see trends, patterns, and outliers in large datasets. By transforming complex data into visual formats like charts, graphs, and maps, it helps communicate insights effectively, making it easier to interpret and analyze information across various business intelligence functions.
Data-driven decision-making: Data-driven decision-making is the process of making choices based on data analysis and interpretation rather than intuition or personal experience. This approach allows organizations to utilize quantitative evidence to guide their strategies, identify trends, and improve outcomes. With advancements in technology, the significance of data has grown, leading to its integration in various decision-making processes across industries.
Domain Experts: Domain experts are individuals with specialized knowledge and experience in a specific field or area, enabling them to provide valuable insights and guidance on complex problems. Their expertise is crucial in various contexts, especially when integrating advanced technologies such as augmented analytics and AutoML, as they bridge the gap between technical capabilities and real-world applications.
Feature engineering: Feature engineering is the process of using domain knowledge to create new input features or transform existing ones to improve the performance of machine learning models. This practice is essential because the quality and relevance of features can significantly impact the accuracy of predictive models. By transforming raw data into meaningful variables, feature engineering helps models learn patterns more effectively, making it a critical step in predictive modeling, artificial intelligence, and augmented analytics.
Forecasting: Forecasting is the process of predicting future outcomes based on historical data, trends, and statistical models. It plays a vital role in decision-making, enabling organizations to prepare for potential future scenarios by analyzing past behaviors and patterns. In today’s data-driven world, leveraging advanced techniques and tools enhances the accuracy of forecasts, allowing businesses to optimize resources and strategies.
Human bias: Human bias refers to the systematic and often unconscious prejudices or preferences that influence an individual's judgments and decision-making processes. This concept is crucial in data analysis, especially when using technologies like augmented analytics and AutoML, as it can significantly affect the outcomes of data-driven insights and predictions.
Hyperparameter tuning: Hyperparameter tuning is the process of optimizing the settings or configurations of a machine learning model that are not learned from the data but set before training. These hyperparameters significantly impact the model's performance and can dictate how well the model generalizes to new data. By systematically adjusting these settings, practitioners can enhance model accuracy and efficiency, making hyperparameter tuning a critical step in predictive analytics and automated machine learning processes.
Insight generation: Insight generation is the process of deriving meaningful information and understanding from data analysis, enabling organizations to make informed decisions. This process often involves identifying patterns, trends, and correlations within the data, transforming raw information into actionable insights that can guide strategic planning and operational improvements.
Intelligent recommendations: Intelligent recommendations are data-driven suggestions made by systems that utilize algorithms and machine learning to analyze user behavior and preferences. These recommendations enhance decision-making by providing personalized insights and suggestions, enabling users to find relevant content or make informed choices quickly.
Key Performance Indicators: Key Performance Indicators (KPIs) are measurable values that demonstrate how effectively an organization is achieving its key business objectives. KPIs help businesses track performance over time, assess success at reaching targets, and identify areas for improvement, making them crucial for informed decision-making and strategy development.
Machine Learning: Machine learning is a subset of artificial intelligence that focuses on the development of algorithms that enable computers to learn from and make predictions or decisions based on data. By utilizing patterns in data, machine learning can help organizations extract valuable insights, automate processes, and improve decision-making in various contexts, including big data analysis, cloud environments, and augmented analytics.
Meta-learning: Meta-learning, often referred to as 'learning to learn,' is the process where algorithms learn from previous experiences to improve their performance on new tasks. This involves understanding the best strategies or models for different types of problems and adapting to new situations more effectively. In the context of augmented analytics and AutoML, meta-learning plays a crucial role in optimizing model selection, feature engineering, and hyperparameter tuning by leveraging knowledge gained from past learning experiences.
Model deployment: Model deployment refers to the process of making a machine learning model available for use in a production environment, allowing it to make predictions or decisions based on new input data. This process is crucial as it transforms a theoretical model into a practical tool that can generate insights and value in real-world applications. Successful deployment involves integrating the model with existing systems, monitoring its performance, and ensuring it can adapt to changing data conditions.
Model evaluation: Model evaluation is the process of assessing the performance and effectiveness of a predictive model using specific metrics and methodologies. This step is crucial as it helps determine how well a model generalizes to new, unseen data and can significantly influence decision-making in analytics. By applying various evaluation techniques, analysts can compare different models, identify strengths and weaknesses, and ultimately choose the best model for deployment.
Model training: Model training is the process of teaching a machine learning algorithm to make predictions or decisions based on data. It involves feeding input data into the model and adjusting its parameters to minimize errors in its predictions, often using techniques like gradient descent. This process is crucial for building accurate predictive models, especially in the context of augmented analytics and AutoML, as it allows for automated learning from large datasets and enables users to gain insights without requiring deep expertise in data science.
Modeling: Modeling is the process of creating a mathematical or computational representation of a real-world scenario or dataset, allowing for analysis, predictions, and decision-making. This technique is essential for extracting valuable insights from data, enabling organizations to identify patterns, test hypotheses, and make informed choices based on the results. In many cases, modeling involves building statistical models or machine learning algorithms that can learn from data and improve their performance over time.
Natural Language Processing: Natural Language Processing (NLP) is a field of artificial intelligence that focuses on the interaction between computers and humans through natural language. It involves the ability of machines to understand, interpret, and generate human language in a way that is both meaningful and contextually relevant. This capability is crucial for tasks like text mining, machine learning applications, conversational analytics, and augmented analytics, which all aim to derive insights from unstructured data and improve user experiences through automation and smarter analytics.
Natural language processing: Natural language processing (NLP) is a field of artificial intelligence that focuses on the interaction between computers and humans through natural language. It enables machines to understand, interpret, and respond to human language in a valuable way, making it crucial for tasks like text analysis, sentiment analysis, and conversational interfaces.
Optimization techniques: Optimization techniques are methods used to make a system, design, or process as effective or functional as possible within a given set of constraints. In the context of augmented analytics and AutoML, these techniques focus on enhancing the performance of models by finding the best parameters and configurations that yield the most accurate predictions or insights from data.
Pattern recognition: Pattern recognition is the cognitive process of identifying and classifying data patterns or trends within large datasets. It involves analyzing information to uncover relationships, anomalies, and predictions that can guide decision-making. This process is essential in various analytical tasks, such as optimizing data organization and enhancing predictive models.
Power BI: Power BI is a business analytics tool developed by Microsoft that enables users to visualize and share insights from their data. It helps in transforming raw data into interactive dashboards and reports, making data analysis accessible to a wider audience. With features like augmented analytics and AutoML, Power BI empowers users to leverage advanced analytics and machine learning without needing extensive programming knowledge.
Proactive Management: Proactive management refers to a forward-thinking approach in organizational leadership that emphasizes anticipating future challenges and opportunities rather than reacting to them after they occur. This method focuses on data-driven decision-making and strategic planning, allowing organizations to optimize performance and minimize risks by leveraging insights from analytics.
Real-time monitoring: Real-time monitoring refers to the continuous observation and analysis of data as it is generated, allowing organizations to make timely and informed decisions. This capability is crucial for identifying trends, detecting anomalies, and responding promptly to changes in data, which enhances operational efficiency and strategic planning. In the realm of analytics and machine learning, real-time monitoring plays a vital role in augmenting insights and automating processes.
Sales Forecasting: Sales forecasting is the process of estimating future sales revenue based on historical data, market trends, and various other factors. This practice helps businesses make informed decisions regarding inventory, staffing, budgeting, and strategic planning, ultimately supporting overall business growth and efficiency.
Tableau: Tableau is a powerful data visualization tool that enables users to create interactive and shareable dashboards, providing insights through visual representation of data. It plays a vital role in the historical evolution of business intelligence, allowing users to transform raw data into comprehensible graphics and charts, thereby enhancing decision-making processes.
Visualization: Visualization is the graphical representation of data and information, making complex data easier to understand and interpret. By transforming raw data into visual formats like charts, graphs, and maps, it allows users to identify patterns, trends, and insights quickly, facilitating better decision-making. This approach is crucial in augmented analytics and AutoML as it helps users grasp advanced analytical processes and outcomes without needing deep technical expertise.