Business Intelligence Glossary

An interface that allows different software systems to communicate with each other. REST APIs are the current standard for data exchange. Fundamental for system integration and automation.

A set of strategies, technologies, and practices for collecting, integrating, analyzing, and presenting business data. The goal of BI is to transform raw data into actionable information that supports strategic decision-making.

A reference point or standard against which performance is compared. Can be internal (historical), competitive (industry), or aspirational (best practices). Fundamental for contextualizing metrics.

The total expense associated with an employee's departure and hiring their replacement. Includes direct costs (recruitment, training) and indirect costs (lost productivity, knowledge). Can reach 50-200% of annual salary.

A computing model that provides resources (servers, storage, databases) on-demand over the internet. Major providers: AWS, Azure, Google Cloud. Eliminates the need for own infrastructure.

An interactive visual panel that presents key business metrics and indicators (KPIs) in one place. Dashboards enable real-time business performance monitoring and facilitate the identification of trends and anomalies.

The process of examining datasets to draw useful conclusions. Includes statistical techniques, algorithms, and tools to discover patterns, correlations, and trends that inform business decisions.

A centralized data repository that integrates information from multiple sources for analysis and reporting. It's optimized for analytical queries (OLAP) rather than operational transactions (OLTP).

A centralized repository that stores data in its native format (structured, semi-structured, and unstructured) at any scale. Unlike a data warehouse, it doesn't require structuring data before storage.

An analysis technique that allows navigating from aggregated data toward more detailed levels. For example, from annual total sales to monthly, weekly, or specific product sales.

A formula language used in Power BI, Power Pivot, and Analysis Services to create custom calculations and measures. Similar to Excel formulas but optimized for relational data models.

The structure that defines relationships between tables in Power BI. A well-designed model (typically star or snowflake) is fundamental for performance and calculation accuracy.

A bridge that allows Power BI Service to securely connect to on-premise data sources. Enables scheduled data refreshes without exposing internal databases to the internet.

A series of processes that move data from a source to a destination, applying transformations along the way. Modern pipelines are automated, monitored, and scalable.

A type of analysis that answers 'what happened?' using historical data. Includes reports, dashboards, and basic statistics. It's the first level of analytical maturity in organizations.

Graphical representation of information to facilitate understanding. Includes charts, maps, tables, and infographics. Good visualization communicates insights clearly and memorably.

The art of communicating data insights through compelling narratives. Combines data, visualizations, and context to influence decisions. Goes beyond showing numbers to explaining their meaning.

The degree to which data is accurate, complete, consistent, timely, and relevant for its intended use. Poor data quality leads to incorrect decisions. 'Garbage in, garbage out.'

A framework of policies, processes, and standards that ensure effective data management in an organization. Includes data quality, security, privacy, and regulatory compliance.

The process of detecting and correcting (or removing) incorrect, incomplete, or duplicate records in a dataset. A critical step before any analysis. Also called data cleansing or data scrubbing.

The process of extracting data from multiple sources, transforming it for cleaning and standardization, and loading it into a destination system like a data warehouse. It's fundamental for enterprise data integration.

The sum of all interactions an employee has with their organization, from recruitment to exit. Includes culture, physical environment, technology, and work relationships.

A metric that measures the likelihood of employees recommending their company as a workplace. Calculated by subtracting the percentage of detractors from promoters. Typical scale: -100 to +100.

Full-time equivalent. A unit representing the hours worked by a full-time employee. Allows comparing workloads by normalizing part-time employees and contractors.

Also called People Analytics. The application of analytical techniques to human resources data to improve talent decisions: hiring, retention, development, and compensation. Transforms HR data into actionable insights.

The total number of employees in an organization at a specific point in time. Can be measured as physical headcount (people) or FTE (full-time equivalent). Base for many HR metrics.

A quantifiable metric that measures progress toward specific business objectives. Effective KPIs are Specific, Measurable, Achievable, Relevant, and Time-bound (SMART).

Google Cloud's Business Intelligence platform. Differentiates itself through its semantic layer (LookML) that centrally defines metrics, ensuring consistency across the organization.

In Power BI, a dynamic calculation defined with DAX that is evaluated at query time based on filter context. Unlike calculated columns, measures don't occupy space in the data model.

A quantitative measure representing an aspect of the business. Unlike KPIs, metrics are not necessarily linked to strategic objectives. Examples: number of sales, web visits, tickets closed.

Fundamental and consistent data about key business entities: customers, products, employees, suppliers. Master data management (MDM) ensures a 'single version of the truth.'

A branch of artificial intelligence where systems learn patterns from data without being explicitly programmed. BI applications: sales prediction, customer segmentation, anomaly detection.

Technology for multidimensional analysis of business data. Allows users to analyze information from multiple perspectives through operations like drill-down, roll-up, slice, and dice.

Microsoft's Business Intelligence platform that enables creating interactive dashboards, reports, and data analysis. Includes Power BI Desktop (development), Power BI Service (cloud), and Power BI Mobile.

The data transformation and preparation engine in Power BI and Excel. Allows connecting, combining, and refining data from multiple sources through a visual interface or M code.

A discipline that uses data, statistical analysis, and predictive modeling to understand and improve human capital performance. Includes productivity, engagement, retention, and organizational development metrics.

A type of analysis that answers 'what could happen?' using statistical models and machine learning on historical data. Examples: sales forecasting, turnover risk, inventory demand.

The most advanced level of analytics that answers 'what should we do?'. Combines predictive analytics with optimization to recommend specific actions. Requires data and algorithm maturity.

A versatile and popular programming language for data analysis, automation, and machine learning. Key libraries: Pandas (data), NumPy (calculations), Matplotlib (visualization), Scikit-learn (ML).

Row-level security in Power BI that restricts data access based on the user. Allows different users to see only the data relevant to them using the same report.

Technology that uses software robots to automate repetitive, rule-based tasks. RPA bots mimic human actions on user interfaces: clicks, typing, copying/pasting data between systems.

The process of generating, updating, and distributing reports without manual intervention. Includes automatic data extraction, transformation, visualization creation, and scheduled delivery to recipients.

A Business Intelligence approach that allows non-technical users to create their own reports and analyses without depending on IT. Tools like Power BI and Tableau facilitate this model.

A data model where a central fact table connects to multiple dimension tables. It's the recommended pattern in Power BI for its simplicity and optimal analytical query performance.

The number of direct reports a manager has. A wide span (many reports) may indicate efficiency but also risk of insufficient supervision. The optimal depends on the type of work.

Scheduled data updates in BI tools. Allows dashboards and reports to update automatically at defined times without manual intervention.

Information organized in a predefined format, typically in tables with rows and columns. Examples: relational databases, spreadsheets. Easy to search and analyze.

The standard language for managing and querying relational databases. Basic commands: SELECT (query), INSERT (add), UPDATE (modify), DELETE (remove). Essential for any data professional.

A Python framework for creating data web applications quickly. Allows analysts without web experience to build interactive dashboards and data tools with pure Python code.

The percentage of employees who leave an organization in a given period. Calculated by dividing the number of departures by the average number of employees, multiplied by 100. Includes voluntary and involuntary turnover.

The average time from when a job is posted until a candidate accepts the offer. A key efficiency metric for the recruitment process. Benchmarks vary by industry and position level.

Information without a predefined format: emails, documents, images, videos, social media posts. Represents ~80% of enterprise data and requires special techniques for analysis.

A technique for extracting data from websites automatically. Used for price monitoring, competitive analysis, and public information gathering. Requires legal and ethical considerations.

A sequence of steps or tasks that form a business process. Workflow automation eliminates manual tasks, reduces errors, and accelerates business process execution.