Mirek Cerny

BI Glossary

A comprehensive glossary of Business Intelligence terms and concepts.

It covers a range of essential terms in the field of Business Intelligence, providing clear explanations and practical examples to enhance understanding.

Actionable Insights

What are Actionable Insights

Actionable Insights are not just revelations; they are transformative discoveries extracted from data that guide specific and impactful actions. These insights go beyond understanding trends – they provide a clear roadmap for making informed decisions that directly influence business strategies.

Practical example of Actionable Insights

Imagine you’re a sales director, and leveraging data to increase sales is your priority. Rather than simply noting an increase in overall sales, actionable insights in sales might direct you to focus on a particular product category, adjust pricing strategies, or target a specific customer segment. These insights empower your sales team with precise, data-driven actions, leading to improved conversion rates and revenue growth.

Typical issues of Actionable Insights

  • Ambiguity: Lack of clarity in defining actionable insights, making it challenging for decision-makers to determine specific courses of action.
  • Data complexity: Difficulty in translating complex data into actionable recommendations that align with business goals.
  • Implementation hurdles: Challenges in executing and implementing recommended actions due to resource constraints or organizational resistance.
  • Relevance: Generating insights that may not directly address current business challenges or goals.
  • Monitoring and Evaluation: Difficulties in tracking the effectiveness of implemented actions and iterating based on feedback.

Behavioral Segmentation

What is Behavioral Segmentation

Behavioral Segmentation is like creating different tribes within your customer base based on their actions and interactions. It helps businesses understand and target specific groups with tailored strategies.

Practical example of Behavioral Segmentation

Imagine you’re a marketing strategist, and behavioral segmentation is your guide. It helps you identify distinct customer behaviors, allowing you to create personalized marketing campaigns for each group.

Typical Behavioral Segmentation Issues

  • Inaccurate segmentation: Challenges in accurately categorizing customers or users based on behavior.
  • Data Privacy concerns: Risks of violating user privacy or facing regulatory issues.
  • Limited data sources: Difficulties in accessing diverse and comprehensive behavioral data.
  • Interpretation challenges: Potential misinterpretation of user behavior leading to inaccurate conclusions.
  • Dynamic behavior: Handling the dynamic nature of user behavior and adapting segmentation accordingly.


What is Bias

Bias is like a sneaky distortion in data that can make results unfair. It happens when the way data is collected or analyzed is not neutral, leading to skewed or inaccurate outcomes.

Practical example of Bias

Imagine you’re a researcher. If you only survey people from one neighborhood, your findings may not represent the whole city accurately. Fixing bias means making sure your data is diverse and reflects everyone, making your conclusions more trustworthy.

Business Intelligence (BI)

What is Business Intelligence

Business Intelligence (BI) is like the information playground for businesses. It helps a company understand its data and extract valuable insights. When a company wants to enhance its decision-making, BI becomes a crucial ally.

Practical example of Business Intelligence

Imagine being a CEO, and BI is your advisor. It provides you with an overview of how each department is performing, empowering you to steer the company towards success.

Typical Business Intelligence Issues

  • User adoption: Challenges in getting all users to embrace and effectively use BI tools.
  • Training gaps: Insufficient training leading to underutilization of BI capabilities.
  • Data Quality: Poor data quality affecting the accuracy and reliability of BI insights.
  • Integration challenges: Difficulties in seamlessly integrating BI tools with existing systems.
  • Lack of alignment: BI solutions not aligned with actual business needs and objectives.

Contextual Filtering

What is Contextual Filtering

Contextual Filtering is like having a powerful searchlight for your data. It enables you to focus on specific elements, refining your view and extracting precise information based on specific criteria.

Practical example of Contextual Filtering

Suppose you’re a sales manager. You have a sales overview dashboard with various charts on your screen. When you click on a specific region or product category, contextual filtering recalibrates everything else on the dashboard to show data relevant to your selection. It’s like adjusting the spotlight to illuminate the exact details you need, ensuring your insights are tailored to specific contexts.

Typical Contextual Filtering Issues

  • Criteria definition: Challenges in defining relevant and meaningful contextual filter criteria.
  • Consistency across views: Difficulties in maintaining consistency when applying contextual filters across various data views.
  • Complex filtering criteria: Issues in handling complex filtering criteria that accurately capture the desired context.
  • User Interface design: Challenges in designing user-friendly interfaces for applying contextual filters.
  • Interactive recalibration: Potential difficulties in implementing interactive recalibration of data views based on selected contextual filters.

Customer Churn Risk

What is Customer Churn Risk

Customer Churn Risk is like a crystal ball for customer relationships. It assesses the likelihood that a customer may leave or “churn” based on their behavior, interactions, or historical data.

Practical example of Customer Churn Risk

Imagine you’re a customer success manager, and dealing with Customer Churn is your task. You utilize data to identify patterns such as declining usage, decreased interactions, or historical trends associated with customers who left in the past. By recognizing these signs, you can proactively intervene with personalized strategies, such as special offers or targeted communication, to retain at-risk customers and strengthen their loyalty.

Key Customer Churn Risk Considerations

  • Early warning signs: Identifying early warning signs or leading indicators of potential customer churn.
  • Data accuracy: Ensuring the accuracy and reliability of customer data for precise churn risk assessment.
  • Segmentation effectiveness: Creating effective customer segments for targeted churn risk analysis.
  • Predictive modeling challenges: Addressing complexities in developing predictive models to forecast customer churn accurately.
  • Proactive Retention strategies: Formulating proactive retention strategies based on identified churn risk factors.


What is Dashboard

A Dashboard is like a dynamic control center for your business insights. It’s a visual display that consolidates information from various sources into a single, easy-to-read interface, featuring charts and graphs representing key business metrics and performance indicators.

Practical example of Dashboard

Imagine you’re a business director overseeing multiple departments. Your dashboard becomes your strategic hub, displaying critical data on sales, marketing ROI, and operational efficiency. With a quick glance, you can assess the overall health of the business, identify areas of improvement, and make informed decisions—all from a centralized and visually intuitive platform.

Typical Dashboard Issues

  • Cluttered design: Challenges in avoiding a cluttered or overly complex dashboard layout.
  • Real-time updates: Difficulties in providing real-time data updates for dynamic decision-making.
  • Diverse user needs: Issues in creating dashboards that cater to the diverse needs of different users.
  • Data source integration: Challenges in seamlessly integrating data from various sources into the dashboard.
  • Effectiveness measurement: Difficulties in accurately measuring the overall effectiveness and impact of the dashboard.


What is Database

A Database is like a well-organized digital filing cabinet. It’s a structured collection of data that is stored, managed, and easily accessible. Databases can be relational (using tables) or non-relational (using various data models).

Practical example of Database

Suppose you’re an IT manager, and keeping company information structured is your responsibility. You might manage a customer database that stores information such as names, contact details, and purchase history, providing a centralized and efficient way to retrieve and manage customer data.

Typical Database Issues

  • Inconsistent data formats: Challenges in maintaining consistent data formats across the database.
  • Security concerns: Risks of data breaches and challenges in ensuring robust database security.
  • Data integrity: Difficulties in ensuring the integrity and accuracy of data stored in the database.
  • Performance optimization: Issues in optimizing database performance, especially with large datasets.
  • Complex view dependencies: Challenges in managing complex dependencies between database views.


What is Datamart

A Datamart is like a specialty store within the data mall. It’s a subset of a data warehouse, focusing on specific business functions or departments.

Practical example of Datamart use

Picture your sales team relying on a datamart that stores and organizes all sales-related data. This includes customer information, transaction history, and market trends specific to their domain. This datamart allows the sales team to quickly access and analyze relevant data without sifting through the entire organizational database. It’s like having a dedicated shelf in the data warehouse, stocked with information customized for the sales department’s strategic decision-making.

Typical Datamart Issues

  • Data integration challenges: Difficulties in aligning and integrating data within each datamart.
  • Consistency maintenance: Challenges in maintaining data consistency within individual datamarts.
  • Adaptability to Business Needs: Potential issues in adjusting datamarts to evolving business requirements.
  • Scalability concerns: Difficulties in scaling datamarts to handle increasing data volumes.
  • Aligning with Business Goals: Challenges in ensuring that datamarts are in sync with overarching business objectives.

Data Model

What is Data Model

A Data Model is like the blueprint for understanding and organizing information within a business. It defines how data is structured, related, and accessed, serving as the foundation for effective data management and analysis.

Practical example of Data Model

Envision an e-commerce business structuring its product catalog. The data model outlines how product information is linked, including details like category, price, and availability. This structured model enables seamless navigation, helping the business understand customer preferences, optimize inventory, and enhance the overall shopping experience. It’s akin to the well-organized shelves in a store, making it easy to find and manage products effectively.

Typical Data Modeling Issues

  • Relationship complexity: Challenges in managing complex relationships between different data entities.
  • Adaptability to business changes: Difficulties in modifying the data model to accommodate evolving business requirements.
  • Data definition harmony: Issues in maintaining harmonious and consistent data definitions across the data model.
  • Performance optimization: Challenges in optimizing the data model for efficient query performance.
  • User understanding: Difficulties in making the data model understandable and accessible to a diverse range of users.

Data Quality

What is Data Quality

Data Quality is like the reliability meter for your information. It gauges the accuracy, consistency, and completeness of your data, ensuring that it meets the standards required for effective and reliable decision-making.

Practical example of Data Quality

Imagine you’re a Chief Data Officer (CDO), responsible for data governance. You actively lead initiatives to address issues like inaccurate customer contact details, inconsistent product codes across databases, incomplete sales records, and duplicated entries in your customer database. By implementing and overseeing robust data quality processes, you ensure that the organization relies on accurate and reliable data for strategic decision-making.

Typical Data Quality Issues

  • Incompleteness: The challenge of missing or insufficient data, hindering a comprehensive view and analysis.
  • Inaccuracy: Issues arising from incorrect or outdated data, leading to flawed insights and decisions.
  • Inconsistency: Divergence in data formats, units, or definitions, causing confusion and misinterpretation.
  • Duplication: Presence of redundant data, leading to inflated figures and inaccuracies in analysis.
  • Bias: Systematic errors in data collection, analysis, or interpretation, resulting in skewed outcomes.
  • Delayed Updates: Delays in data refresh, impacting the relevance and reliability of insights.
  • Non-Standardization: Lack of standardized formats or structures, causing difficulties in data integration.
  • Validation Gaps: Absence of proper validation processes, allowing erroneous data to go unnoticed.

Data Source

What is Data Source

Picture a data source as the well from which your information springs. It’s the origin point, a reservoir that provides the raw material for analysis and insights. Data sources can be diverse, including databases, files, applications, or external systems.

Practical example of Data Source

Imagine your sales database as a primary data source. It contains customer information, transaction details, and product data. This data source acts as the wellspring, supplying the necessary information for sales analytics, customer behavior analysis, and performance assessments. It’s like tapping into the well to draw the essential data needed for informed decision-making.

Typical Data Sources Issues

  • Integration complexity: Difficulties in seamlessly integrating data from diverse sources.
  • Data consistency: Issues with maintaining consistent data definitions and structures across different source systems.
  • Data accessibility: Challenges in accessing and retrieving data from certain source systems.
  • Security concerns: Risks associated with data security and privacy when dealing with multiple sources.
  • Varied quality: Issues with variations in data quality among different source systems.

Data Warehouse (DWH)

What is Data Warehouse

A data warehouse is a centralized repository that stores and integrates data from various sources within an organization. When a company wants everything at its fingertips for analysis, the Data Warehouse is the trustworthy keeper of information.

Practical example of Data Warehouse

Consider a comprehensive enterprise database that consolidates information from sales, finance, and customer service departments. This centralized repository empowers business analysts to extract valuable insights, optimize operations, and make informed decisions that impact the entire organization.

Typical Data Warehouse Issues

  • Data Integration Complexity: Navigating challenges in seamlessly integrating diverse data sources into the warehouse.
  • Performance Bottlenecks: Addressing potential slowdowns or bottlenecks affecting query and reporting performance.
  • Data Quality Assurance: Ensuring the accuracy and reliability of data within the warehouse to maintain trustworthiness.
  • Scalability Concerns: Handling issues related to the scalability of the data warehouse as data volumes grow.
  • Data Governance: Implementing effective data governance practices to maintain consistency and compliance.
  • Changing Business Requirements: Adapting the data warehouse to evolving business needs and analytical requirements.
  • User Training and Adoption: Overcoming challenges in user training and promoting widespread adoption of the data warehouse.

Digital Footprint

What is Digital Footprint

Imagine a digital footprint as a trail of personal information left across various online and offline activities. It’s a unique mark representing person’s interactions and engagements.

Practical example of Digital Footprint

In a B2B context, the digital footprint is manifested through the purchase history of a company’s customers. It encapsulates patterns in product preferences, buying cycles, and interactions. Analyzing this trail enables businesses to predict churn risk, tailor product recommendations, and implement targeted retention strategies. For instance, understanding the purchase history of a company’s customers can inform recommendations for complementary products, reducing churn and enhancing overall satisfaction.

Common Digital Footprint Challenges

  • Privacy Concerns: Navigating the delicate balance between utilizing digital footprints for insights and respecting customer privacy.
  • Data Security: Ensuring robust measures to protect the integrity and security of digital footprint data.
  • Data Accuracy: Addressing challenges related to inaccurate or incomplete digital footprint data.
  • Integration Complexity: Dealing with the complexities of integrating digital footprint data from various sources.
  • Ethical Use: Establishing ethical guidelines for the use of digital footprints to avoid misuse or harm.
  • Dynamic Nature: Adapting strategies to account for the dynamic and evolving nature of digital footprints.
  • Customer Consent: Obtaining and managing consent for collecting and analyzing digital footprint data.
  • Interpretation Challenges: Overcoming difficulties in accurately interpreting digital footprint data to derive meaningful insights.


What are Dimensions

Think of dimensions as categories or labels that help organize your data. They add context and make it easier to understand information.

Practical example of Dimensions

Imagine you have sales data. Dimensions, like product category, region, and time, act like labels that categorize and organize this data. Product category tells you what kind of product was sold, region gives you the location context, and time lets you see when the sales occurred. These labels help you analyze data from different angles and uncover insights.

Typical Dimensions Issues

  • Inconsistent Definitions: Dealing with variations in how dimensions are defined or labeled across different datasets.
  • Data Quality Issues: Addressing inaccuracies or missing information within dimension attributes.
  • Hierarchy Complexity: Managing complex hierarchies within dimensions, especially when dealing with nested or multi-level structures.
  • Change Management: Handling changes or updates to dimension values over time and ensuring smooth integration.
  • Compatibility: Ensuring compatibility and consistency in dimension structures across different systems or databases.
  • Data Integration Challenges: Overcoming difficulties in integrating dimensions from disparate sources for unified analysis.
  • User Understanding: Ensuring end-users have a clear understanding of dimension values and their significance.
  • Scalability: Addressing challenges related to the scalability of dimension structures as data volumes grow.

ETL / ELT (Data Pipelines)

What is ETL / ELT

ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) are processes that move and prepare data for analysis. ETL focuses on transforming data before loading it, while ELT loads raw data first and transforms it later.

Practical example of ETL / ELT

ETL in Retail Data Integration: Imagine a retail scenario where ETL is used to extract sales data, transform it by converting currency values, and then load it into a data warehouse for analysis. On the other hand, in ELT, raw sales data is loaded into the warehouse first, and transformations like currency conversion happen afterward during analysis. Both methods ensure the data is prepared for insightful decision-making.

Typical ETL / ELT Issues

  • Data Quality: Ensuring data is accurate and reliable.
  • Performance: Making data movement fast, especially with lots of data.
  • Transformations: Managing complex changes for better analysis.
  • Integration Bottlenecks: Fixing slowdowns when combining data.
  • Adaptability: Handling changes in how data is collected.
  • Security: Keeping sensitive information safe.
  • Resource Management: Dealing with tasks that use a lot of computer power.
  • Error Handling: Fixing problems that happen during data movement.

Historical Data

What is Historical Data

Think of historical data as a record of past events and states. It provides a timeline of how things have changed over time.

Practical example of Historical Data

Consider sales data spanning several years. Historical data in this context would show how sales have changed over time, revealing patterns, trends, and seasonality. It’s like flipping through a timeline to understand the story of business performance.

Typical Historical Data Issues

  • Data Accuracy Over Time: Ensuring that historical data remains accurate and reflects the actual state of affairs at the time.
  • Data Retention Policies: Establishing and maintaining clear policies on how long historical data should be kept.
  • Storage and Retrieval: Dealing with the challenges of storing large volumes of historical data and retrieving it efficiently.
  • Data Decay: Addressing issues related to the degradation of data quality over extended periods.
  • Data Interpretation: Overcoming difficulties in interpreting historical data, especially when context or documentation is lacking.
  • Versioning Challenges: Managing different versions of data and ensuring consistency across historical records.
  • Compliance: Ensuring that historical data management practices comply with relevant regulations.
  • Data Obsolescence: Handling issues related to the obsolescence of data formats or systems over time.


What are Insights

Think of insights as illuminating discoveries derived from data analysis. They provide a deeper understanding of trends, patterns, or opportunities.

Practical example of Insights

Imagine you’re a sales director. Insights, in this context, could be discovering that certain products perform exceptionally well during specific seasons. This revelation guides you in adjusting your sales strategy, optimizing inventory, and planning targeted marketing campaigns for maximum impact.

Common challenges with obtaining Insights:

  • Data Accuracy: Ensuring the accuracy and reliability of data to derive meaningful insights.
  • Data Complexity: Dealing with intricate and diverse datasets that may pose challenges in analysis.
  • Interpretation Ambiguity: Overcoming difficulties in interpreting insights, especially when multiple factors are at play.
  • Lack of Context: Addressing issues arising from insights that lack sufficient context or background information.
  • Inadequate Data Volume: Working with limited data, which may hinder the generation of robust and reliable insights.
  • Technology Limitations: Overcoming constraints in analytical tools or technologies that may impact the depth of insights.
  • Bias in Analysis: Identifying and mitigating biases that may inadvertently affect the interpretation of insights.
  • Data Integration Challenges: Handling difficulties in integrating data from various sources to derive comprehensive insights.


What is Join

Join is like connecting puzzle pieces in the data landscape. It combines rows from two or more tables based on related columns, creating a unified dataset.

Practical example of Join

Imagine having a list of customers and a list of purchases. Joining them is like connecting the dots to see which customer bought which product. It helps create a fuller understanding of customer behavior.

Key considerations for Joins

  • Join type selection: Choosing the appropriate join type (inner, outer, left, right) for specific data retrieval needs.
  • Performance impact: Addressing potential performance impacts when joining large tables or multiple datasets.
  • Join condition accuracy: Ensuring accuracy in defining join conditions to retrieve relevant and meaningful results.
  • Handling Null values: Navigating challenges related to null values and their impact on join operations.
  • Optimizing query plans: Strategically optimizing query plans to enhance the efficiency of join operations.

Machine Learning

What is Machine Learning

Machine Learning is like having a data-driven apprentice. It is a subset of artificial intelligence that enables systems to automatically learn and improve from experience without being explicitly programmed. It involves algorithms that can identify patterns, make predictions, and continuously refine their performance.

Practical example of Machine Learning

Consider a banking system using machine learning to detect fraudulent transactions. The system needs expert oversight to continually refine its algorithms and adapt to new fraud patterns. It’s akin to having a skilled assistant that, under expert guidance, evolves to address complex challenges.

Typical Machine Learning Issues

  • Data Quality: Challenges arising from poor data quality, impacting the performance and accuracy of machine learning models.
  • Overfitting: Issues when a model is too complex and fits the training data too closely, leading to poor generalization to new data.
  • Underfitting: Challenges when a model is too simple and fails to capture the underlying patterns in the data.
  • Bias and Fairness: Concerns related to biased training data, leading to biased predictions and potential fairness issues.
  • Interpretability: Difficulties in understanding and interpreting the decisions made by complex machine learning models.

Metric (Measure)

What is Metric

A Metric is like an excellent coach for a company. It measures performance in a specific area and tells how well you’re doing. When a company wants to improve its performance, a metric is like a personal fitness tracker for every aspect of business.

Practical example of Metric

Imagine tracking the total sales revenue in a retail business. The metric acts like a sum of measurements, offering a consolidated view of income from all products and transactions. It’s akin to adding up individual purchase orders to understand overall financial performance.

Typical Metrics Issues

  • Ambiguous definitions: Challenges arising from unclear or ambiguous definitions of metrics, leading to misinterpretation.
  • Inconsistent measurement: Issues when metrics are measured inconsistently over time or across different teams.
  • Relevance concerns: Difficulties in ensuring that metrics remain relevant to evolving business objectives.
  • Lack of benchmarking: Challenges in benchmarking metrics against industry standards or best practices.
  • Overemphasis on Vanity Metrics: Concerns related to focusing on metrics that might look good but don’t provide meaningful insights into business performance.

OLAP (Online Analytical Processing) (Cube)

What is OLAP

OLAP is like having a multi-dimensional view of your business. It allows the analysis of data from various angles and perspectives.

Practical example of OLAP

Imagine using OLAP to analyze sales data. It’s like slicing and dicing a dynamic cube, exploring sales performance across dimensions like time, products, and regions. OLAP allows for interactive, multidimensional insights within this virtual cube.

Typical OLAP Issues

  • Slow query performance: Challenges in experiencing slow query performance, impacting the speed of data retrieval.
  • Limited scalability: Difficulties in scaling OLAP systems to handle larger datasets or increasing user demands.
  • Complexity in cube design: Issues related to designing and managing complex multidimensional cubes.
  • Data consistency: Concerns about maintaining consistent and accurate data across OLAP cubes.
  • Integration challenges: Challenges in integrating OLAP systems with other components of the data architecture.

Pareto Principle

What is Pareto Principle

Think of the Pareto Principle as the 80/20 rule, where approximately 80% of effects come from 20% of causes. It highlights the imbalance in the significance of different factors.

Practical example of Pareto Principle use

Consider applying the Pareto Principle to product sales. It’s like recognizing that roughly 20% of the products contribute to around 80% of the overall sales. The principle emphasizes identifying and focusing efforts on the most impactful factors.


What is Perspective

Think of perspective in analysis as choosing your preferred view in a report or dashboard. It’s the selection of specific angles or dimensions to focus on, shaping how insights are presented.

Practical example of Perspective

Consider a sales dashboard with a regional perspective. It’s like selecting a lens that emphasizes regional sales performance, providing a focused view of how different areas contribute to overall sales. Perspective guides the presentation of insights in the analysis.

Power BI

What is Power BI

Microsoft Power BI is like an artistic tool for data visualization. It empowers users to create beautiful and interactive reports and dashboards for better comprehension of information.

Practical example of Power BI use

Imagine using Power BI on top of your Data Warehouse to build an interactive sales dashboard. It’s like becoming an information artist, arranging charts, graphs, and data visualizations to tell a story about sales performance. Power BI turns curated data into a visual masterpiece that enhances understanding.

Typical Power BI Issues

  • Data source integration complexity: Overcoming challenges in integrating diverse data sources seamlessly.
  • DAX formulas complexity: Dealing with the complexity of DAX formulas for advanced calculations.
  • Visual design constraints: Navigating limitations in visual design and layout customization.
  • Scheduled refresh issues: Addressing challenges related to scheduled data refresh for up-to-date reports.
  • Troubleshooting difficulty: Difficulty in efficiently troubleshooting errors and tracing them back to the specific data source.

Predictive Analytics

What is Predictive Analytics

Predictive Analytics is like the crystal ball of data science. It involves using historical data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes. It involves a broader approach, integrating various models and techniques to make informed strategic decisions.

Practical example of Predictive Analytics

Apply predictive analytics for strategic business planning. It’s akin to using a strategic forecasting tool to anticipate market shifts, customer behavior, and operational needs. Predictive analytics empowers organizations to make proactive decisions based on comprehensive insights.

Typical Predictive Analytics Issues

  • Data Quality: Ensuring high-quality data to improve the accuracy and reliability of predictive models.
  • Feature selection: Strategically selecting relevant features to enhance the model’s predictive performance.
  • Overfitting mitigation: Addressing the risk of overfitting by optimizing model complexity.
  • Interpretability: Balancing the need for accurate predictions with the ability to interpret and explain model outcomes.
  • Continuous model evaluation: Establishing processes for continuous evaluation and refinement of predictive models.

Predictive Model

What is Predictive Model

A Predictive Model is like a magical crystal ball for companies. It analyzes past data and predicts future events or trends. When a company wants to be prepared for the future, a predictive model is its visionary.

Practical example of Predictive Model

Picture yourself as a warehouse manager, and a predictive model is your guide to demand forecasting. It helps you plan inventory based on historical trends and expectations of future needs.

Prescriptive Analytics

What is Prescriptive Analytics

Envision prescriptive analytics as a GPS for decision-making, providing not just insights but also recommending the optimal course of action. It goes beyond predicting outcomes, suggesting the best steps to achieve desired results.

Practical example of Prescriptive Analytics

Apply prescriptive analytics to guide sales representatives for optimal performance. It’s like having a decision GPS that not only predicts potential sales opportunities but also prescribes the most effective strategies and actions for sales reps to maximize their success.

Key Prescriptive Analytics Considerations

  • Actionability: Ensuring that recommendations derived from analytics are actionable and align with business goals.
  • Dynamic adaptability: Adapting prescriptions to changing business conditions and evolving data.
  • Stakeholder collaboration: Involving relevant stakeholders in the decision-making process based on prescriptive insights.
  • Ethical considerations: Addressing ethical concerns related to the recommendations and actions prescribed.
  • Impact measurement: Establishing mechanisms to measure and evaluate the impact of prescribed actions on business outcomes.

Purchase Behavior

What is Purchase Behavior

Purchase Behavior refers to the actions and patterns displayed by consumers when making buying decisions. It encompasses the choices, preferences, and tendencies individuals exhibit when selecting and purchasing products or services.

Practical example of Purchase Behavior use

Suppose you’re an e-commerce manager, and analyzing Purchase Behavior is your task. It involves studying which products customers frequently buy together, understanding the factors influencing their purchase decisions, and identifying trends that can shape marketing strategies.

Key Purchase Behavior Considerations

  • Data accuracy: Ensuring accurate and reliable data on customer purchase behavior for meaningful analysis.
  • Predictive modeling challenges: Addressing complexities in developing predictive models to understand and forecast purchase patterns.
  • Segmentation precision: Achieving precise customer segmentation based on their distinct purchase behaviors.
  • Customer Journey mapping: Navigating challenges in mapping the entire customer journey to understand decision-making processes.
  • Incorporating external factors: Considering external factors such as market trends or economic changes influencing purchase behavior.

Recommendation Model

What is Recommendation Model

A Recommendation Model is like a friendly guide in the world of products or content. It uses data to predict and suggest items that a user might be interested in, enhancing the user experience.

Practical example of Recommendation Model

Picture yourself as an e-commerce manager, and a Recommendation Model is your personal shopper. It analyzes customer preferences and behaviors to recommend products, increasing the likelihood of successful sales.

Key Recommendation Model Considerations

  • Personalization accuracy: Striving for high accuracy in tailoring recommendations to individual user preferences.
  • Diverse recommendation: Balancing between providing familiar recommendations and introducing users to new content.
  • User privacy: Addressing concerns related to user privacy and data security in collecting and using preference information.
  • Scalability: Ensuring that recommendation models can handle the increasing volume of user interactions and preferences.
  • Feedback loop: Establishing a feedback loop for continuous refinement of recommendation algorithms based on user interactions.


What is Reporting

Envision reporting as the craft of assembling insightful storybooks from data. It involves organizing and presenting information in a clear and structured manner, transforming raw data into narratives that convey key insights.

Practical example of Reporting

Consider a monthly sales report as an example of reporting. It’s like crafting a storybook that outlines key sales metrics, trends, and performance indicators. Reporting turns complex data into a narrative that stakeholders can easily understand, facilitating informed decision-making.

Typical Reporting Issues

  • Data accuracy: Challenges arising from inaccuracies or inconsistencies in the data impacting the reliability of reports.
  • Timeliness: Issues related to delays in data processing, affecting the timeliness of report updates.
  • Clarity and Interpretability: Difficulties in presenting data in a clear and interpretable manner for diverse audiences.
  • Data Governance: Concerns related to maintaining data integrity, security, and compliance in reporting processes.
  • Adaptability to user needs: Challenges in tailoring reports to meet the specific needs of different stakeholders.


What is Retention

Think of retention as the art of nurturing ongoing relationships with customers. It involves strategies and efforts to keep customers engaged, satisfied, and loyal over an extended period, fostering long-term connections.

Practical example of Retention

Consider implementing a customer loyalty program as an example of retention. It’s like nurturing an ongoing relationship by offering rewards, discounts, and personalized incentives to encourage repeat business. Retention strategies aim to solidify the bond between the business and its customers.

Typical Retention Issues

  • Identifying key retention factors: Challenges in pinpointing the crucial factors that contribute to customer or employee retention.
  • Predictive modeling complexity: Difficulties in developing and maintaining predictive models for retention analysis.
  • Integrating diverse data sources: Issues related to integrating data from various sources to gain a comprehensive view of retention.
  • Monitoring engagement metrics: Challenges in effectively monitoring and interpreting engagement metrics for retention analysis.
  • Addressing churn predictions: Strategically addressing and mitigating factors contributing to customer or employee churn.

RLS (Row-Level Security)

What is RLS

Row-Level Security is like having a personalized security guard for your data rows. It restricts data access at the row level based on predefined rules, ensuring that users only see the data they are authorized to view.

Practical example of RLS

Picture yourself as a database administrator, and RLS is your security system. It helps you control access to sensitive data, ensuring that each user can only access information relevant to their role.

Seasonality Analysis

What is Seasonality Analysis

Picture seasonality analysis as the process of understanding and deciphering nature’s rhythms within data. It involves identifying and analyzing recurring patterns or trends that follow a seasonal or cyclical nature.

Practical example of Seasonality Analysis

Consider analyzing sales fluctuations throughout the year as an example of seasonality analysis. It’s like observing and understanding the natural ebb and flow of customer behavior, identifying peak seasons and adjusting strategies accordingly. Seasonality analysis helps businesses align their activities with predictable patterns.


What is Table

Think of a table as a meticulously organized ensemble of data. It’s a structured format that presents information in rows and columns, allowing for easy reference, comparison, and analysis.

Practical example of Table

Consider a product inventory table as an example. It’s like an organized data ensemble that lists products, their quantities, and other relevant details in a systematic layout. Tables facilitate a clear and efficient representation of information, enabling quick data retrieval and comprehension.


What is Tableau

Envision Tableau as an artistic data canvas, known for its creative flair in visualizations. It’s a tool that excels in offering a wide range of artistic freedom to users, fostering creativity in data presentation.

Practical example of Tableau use

Imagine using Tableau to craft a dynamic sales performance dashboard with artistic visualizations. It’s like being an artist on a canvas, expressing intricate sales trends creatively. Tableau’s strength lies in its ability to provide a platform for visually expressive and creative data storytelling.

Typical Tableau Issues

  • Data source compatibility: Ensuring seamless integration with various data sources for maximum functionality.
  • Advanced Calculations (LOD Expressions, Table Calculations): Managing the complexity of advanced calculations like Level of Detail (LOD) expressions and table calculations.
  • Dashboard interactivity: Balancing the need for interactive dashboards with the potential challenges in user experience.
  • Data Extract Refresh: Addressing issues related to refreshing and updating data extracts for up-to-date visualizations.
  • User-Level Permissions: Effectively managing user-level permissions and access controls within Tableau.


What is View

Think of a view as a focused lens that magnifies specific aspects of data without altering the underlying dataset. It’s a way to customize your perspective, emphasizing particular details while maintaining a dynamic connection to the original data.

Practical example of View

Consider a filtered customer purchase view as an example. It’s like using a focused lens to examine customer purchases within a broader dataset. Views provide a tailored way to analyze specific information without modifying the core data.

Materialized View

In some cases, views can be materialized, creating static snapshots of selected data. Materialized views offer the advantage of pre-computed results, enhancing query performance by storing and updating the view’s data at specific intervals.


What is Visual

Consider a visual as a single graph or chart on a report or dashboard that tells a story with data. It’s a straightforward representation that enhances understanding.

Practical example of Visual

Imagine a line chart illustrating monthly sales revenue. This visual provides a clear depiction of how sales have fluctuated over time, allowing quick insights into performance trends.

Typical Data Visualization Issues

  • Cluttered visuals: Challenges arising from overcrowded or cluttered visualizations, impacting interpretability.
  • Color selection: Difficulties in choosing appropriate colors to convey information without causing confusion.
  • Misleading representations: Issues related to unintentionally creating visualizations that may lead to misinterpretations.
  • Ineffective use of charts/graphs: Challenges in selecting the most suitable chart types for specific data sets and insights.
  • Accessibility: Ensuring that visualizations are accessible to all users, including those with visual impairments.