The Ultimate Glossary of BI Terms

Agile: A methodology cribbed from software development that now sees application in many areas of business. Agile aims to help teams respond to unpredictability through incremental, iterative work cadences and shortened feedback loops (e.g. using short, daily meetings where project workers describe what they are working on.) Agile methodologies are an alternative to waterfall, or traditional sequential development.

Analysis Services: Also known as Microsoft SQL Server Analysis Services, SASS, and sometimes MSAS. Analysis Services is an online analytical data engine used in decision support and business analytics. It provides the analytical data for business reports and client applications such as Power BI, Excel, Reporting Services reports, and other data visualization tools. Analysis Services are used by organizations to analyze and make sense of information that could be spread out across multiple databases, or in disparate tables or files.

Analytics: The discovery, interpretation, and communication of meaningful patterns in data. They are essentially the backbone of any data-driven decision making.

Business Analytics (BA): Refers to the skills, technologies, and practices for investigation of past business performance to gain insight and drive business planning. It focuses on developing new insights and understanding of business performance based on data and statistical methods. While business intelligence (BI) focuses on a consistent set of metrics to both measure past performance and guide business planning, business analytics is focused on developing new insights and understanding based on statistical methods and predictive modeling.

Furthermore, while BI methods such as querying, reporting, OLAP, and alert tools answer questions such as:
– What happened?
– How many?
– How often?
– Where is the problem?
– What actions are needed?

Business analytics can address questions like:
– Why is this happening?
– What if these trends continue?
– What will happen next?
– How can we optimize?

Business Analyst: Someone who analyzes an organization or business domain (real or hypothetical) and documents its processes and systems, assesses the business model, and determines integration with technology. Their solutions can be the use of technology architecture, tools, or software application. In a BI project, this person is often responsible for determining business needs and translating them into architectural data and application requirements.

Back-End: In software, ‘back-end’ applications or programs interact directly with resources or databases without directly interfacing with an end user. To access back-end processes, users will usually do so via a user interface situated on the ‘front-end.’ The presentation layer is the front–end, whereas the access layer is known as the back-end.

BI application designer: Someone responsible for designing the initial reporting templates and dashboards in the front-end applications. They generally require a combined enthusiasm for data visualization, user experience design, and applications reporting. Typically, BI application designers become the source for ongoing front-end BI application support.

BI Project Sponsor: Ideally, a project sponsor is an executive level individual who understands the importance of BI projects, has compelling business motivation, and can help drive results. This person will be the project’s ultimate client and its strongest advocate. Not involved in the day to day of a project, but instead, they provide oversight, direction, and momentum.

Big Data: This term is rapidly achieving buzzword status—but colloquially, it refers to an amount of data so impossibly large that it cannot be parsed by traditional techniques. According to research firm Gartner, “‘Big Data’ is high-volume, high-velocity, and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision-making, and process automation.”

Business Driver: This term can refer to either a resource, process, or condition that is essential to the growth and continued success of a business. For an example in terms of a BI project, when the sponsor is too far removed from the project team, a business driver is helpful. The driver typically becomes responsible for the less strategic BI responsibilities. This role is usually filled by a middle manager, but possesses the same characteristics as the sponsor.

Business Intelligence (BI): A catchall term encompassing a variety of tools, applications and methodologies that enable organizations to collect data from internal systems and external sources. BI can be used to prepare data for analysis, develop and run queries, and create reports, dashboards and visualizations with the end goal of providing results to decision makers and end users.

Business Lead: A mid-to-senior level resource who understands both the business and technical side of a company well enough to communicate between the two. Within the context of a BI project, they understand the requirements, obstacles, and issues of each to make decisions on various courses of action. Additionally, this person should be highly involved in a BI project – communicating with the project manager constantly. Sometimes the business driver fills this role.

Business Owners: The business owner role needs to be filled from the business user groups by enthusiastic fans of the BI project who are also subject matter experts in their fields. Each set of business users from within the organization that will be using the BI tool (finance, operations, HR, etc.) should appoint a business owner. Business user involvement is critical and care should be taken to keep them involved from the very beginning of the project. Without business owners and users, a BI project is merely an academic technical exercise.

Business User: A user of a service or product, who may not necessarily have contact with the supplier/provider—therefore existing at the end of the data ‘supply chain’. i.e. a content management system (CMS) end user, or an accountant entering purchase orders into an enterprise resource planning (ERP) system.

Collaborative Business Intelligence, or Collaborative BI: Is the marriage of traditional business intelligence tactics with tools like social networking, wikis, or blogs, to enhance the collaborative problem-solving nature of BI. Microsoft SharePoint is an example of a popular collaborative BI product.

Cube: Multi-dimensional sections of data built from tables and fields in your database. Cubes contain calculations and formulae and are often grouped around specific business functions such as sales, finance, purchasing, inventory, etc. Each cube contains contextual, pertinent, and useful metrics for that particular area of the business.

Dashboard: Provides at-a-glance statistical analysis and historical trends of an organization’s key performance indicators (KPI’s), presented in easily digestible, graphical representations. For example, a human resources dashboard may show numbers related to staff recruitment, retention, and composition. Whereas a marketing dashboard may show numbers related to inbound web traffic, search volume, and lead velocity.

Data Architect: A practitioner of data architecture, an information technology discipline concerned with designing, creating, deploying, and managing an organization’s data architecture. This person is usually responsible for designing the Extract, Transform, and Load (ETL) process and building the structure (dimensional model) that the data will reside in after it goes through the ETL process. Data architects also tie together the required technical functionality for the BI project. Multiple skills are needed: expertise in dimensional modeling is required, as well as a deep sensitivity to the requirements of the business. Also imperative is expertise with ETL functions such as SQL Server Integration Services, and experience in conducting ETL tasks. Hardware infrastructure and supporting software skills are also necessary.

Data Architecture: A set of rules, policies, standards and models that govern and define the type of data collected and how it is used, stored, managed, and integrated within an organization and its database systems.

Database:In its most generic sense, a database is collection of information that is organized so that it can be accessed, managed, and updated. Usually stored in a computer or on a server, the data could be images, numeric, scripts, full-text, etc. characterizing almost any kind of information. In the context of business intelligence (BI), databases represent systems like Microsoft Dynamics, Excel, CRM, Salesforce, etc. that contain aggregations of data records or files, such as sales transactions, product catalogs and inventories, and customer profiles. Information is input and stored in the database, and a BI solution is required to get a meaningful, organized and informing format of that data out. See also relational database and multidimensional database.

Database Management System (DBMS): A computer software application that interacts with the user, other applications, and the database itself to capture and analyze data. A general-purpose DBMS is designed to allow the definition, creation, querying, update, and administration of databases.

Data Cleansing: The process of detecting and correcting faulty records, leading to highly accurate BI-informed decisions, as enormous databases and rapid acquisition of data can lead to inaccurate or faulty data that impacts the resulting BI and analysis. Correcting typographical errors, de-duplicating records, and standardizing syntax are all examples of data cleansing.

Data Feed or Live Data Feed: A mechanism for users to receive updated data from data sources. It is commonly used by real-time applications in point-to-point settings as well as on the Internet.

Data Intelligence: Focuses on internal data used for future endeavors and is sometimes mistakenly labelled as business intelligence. Whereas business intelligence involves organizing, rather than just gathering data to make it useful and applicable to the business’s practices, data intelligence focuses on extrapolating data to assess future services or investments.

Data Manager: Any team requires management, and a data science team is no different. On a data science or analysis team, the data manager acts as the middleperson between technical team members and strategic management. Because of this, it’s ideal that the data manager possess a technical IT background with strategic experience.

Data management: The process by which data is acquired, validated, stored, protected, and processed. In turn, its accessibility, reliability, and timeliness is ensured to satisfy the needs of the data users. Data management properly oversees the full data lifecycle needs of an enterprise.

Data Mining: Refers to the process of analyzing large batches of data to find patterns and instances of statistical significance. By utilizing software to look for patterns in large batches of data, businesses can learn more about their customers and develop more effective strategies for acquisition, as well as increase sales and decrease overall costs.

Data Model: Defines how data is structured, related, and standardized for the purpose of extracting meaningful insight. Businesses can utilize multiple data models (like you do with Microsoft’s Power BI) to ensure all relevant data is included.

Data Modeling: Refers to the process of defining, analyzing, and structuring data within data models.

Data Source: The source of the data. It can be a file, a particular database on a DBMS, or even a live data feed. The purpose of a data source is to gather all of the technical information needed to access the data—driver name, network address, network software, etc.—into a single place and hide it from the business user. The user should be able to look at a list that includes Payroll, Inventory, and Personnel, choose Payroll from the list, and have the BI application connect to the payroll data. This is completed without the user knowing where the payroll data resides or how the application got to it.

Data Visualization: The practice of structuring and arranging data within a visual context to help users understand it. Patterns and trends that might be unrecognizable to the layman in text-based data can be easily viewed and digested by end users with the help of data visualization software.

Data Warehouse: A large store of data drawing from a wide range of sources that can be processed, split, and analyzed to extract insights that guide management decisions. Data warehouses are typically relational databases that contain historical data and are designed for query and analysis.

Data Warehousing: The process of aggregating data from disparate sources for the purpose of building a data warehouse. Data warehousing involves design, development, testing, deployment, operations, impact analysis, and change management.

Data Warehouse Automation (DWA): Uses technology to gain efficiencies and improve effectiveness in data warehousing processes. The idea behind DWA is to automate each part of the data warehouse lifecycle that can be automated so that the project team can focus on the parts that require more intellectual input than raw technological horsepower. DWA can help reduce the time to implementation for BI projects, reduce annual expenditure on BI, as well as improve success rates. For more information on DWA, see this post.

Data Warehouse Developer: Primary role is to develop and deploy code. They receive direction from the ETL architect and directly build ETL functions. Expertise and experience in ETL are needed but that can vary depending on which ETL tool(s) are used.

Database Administrator (DBA): This role includes capacity planning, installation, configuration, database design, migration, performance monitoring, security, troubleshooting, as well as backup and data recovery. They use specialized software to store and organize data, and are often on-call to provide general database support to the BI delivery team.

Data Analysis Expressions (DAX): Provides a specialized syntax for querying Analysis Services. DAX includes some of the functions that are used in Excel formulas as well as additional functions that are designed to work with relational data and perform dynamic aggregation. DAX can compute values for seven different data types: Integer, Real, Currency, Date, Boolean, String and BLOB (binary large object).

Dimension: A category that can be used to arrange data by facts and measures for data dicing (grouping) and slicing (filtering) purposes. Commonly used dimensions are people, products, places, and time.

Dimension Table: In a data warehouse, a dimension table is a companion to a fact table. Dimension tables contain descriptive fields that are traditionally textual. Dimension tables are related to fact tables (which contain measures) through the use of keys (such as surrogate keys).

Drillthrough/Drill down: In BI terminology, ‘drill down’ refers to moving from high level to detailed, transactional data by focusing in on something (a particular number in a report, for example). In a visualization environment (such as Jet Reports), “drilling down” may involve clicking on some representation in order to reveal more detail.

Data Analysis Expressions (DAX): Refers to the process by which data is:
– Extracted from sources
– Transformed or standardized for storing in the proper heterogeneous format
– and Loaded into the final store or warehouse. The ETL process is commonly run in parallel with transformation processes executing as data is being extracted from sources. ETL systems commonly integrate data from multiple applications (systems) managed and operated by different employees. For example, a cost accounting system may combine data from payroll, sales, and purchasing.

Executive BI: The practice of collecting, analyzing, and visualizing data to deliver key insights to the executive level resources that help drive business change.

Executive Dashboard: Allows the executive team to gain instant insight into the big picture of an entire organization from finance and operations to sales and marketing. Executive Dashboards (such as those Jet Reports delivers through Jet Analytics) are fully customizable to display the data that matters to your executive team.

Extract, Transform, Load (ETL): Refers to the process by which data is:
– Extracted from sources
– Transformed or standardized for storing in the proper heterogeneous format
– and Loaded into the final store or warehouse. The ETL process is commonly run in parallel with transformation processes executing as data is being extracted from sources. ETL systems commonly integrate data from multiple applications (systems) managed and operated by different employees. For example, a cost accounting system may combine data from payroll, sales, and purchasing.

Fact Table: Consist of measurements, metrics, or facts of a business process. They are located at the center of a star schema or a snowflake schema surrounded by dimension tables. Fact tables provide the (usually) additive values that act as independent variables by which dimensional attributes are analyzed. Fact tables are often defined by their grain. The grain of a fact table represents the most atomic level by which the facts may be defined. For example, the grain of a sales fact table might be stated as “Sales volume by Day by Product by Store”.

Field Transformation: These enable an administrator to transform raw field input into standardized values that are more meaningful to an organization. Transformations are controlled by rules and can be configured for use in queries. For example, if a measurement variable does not fit a normal distribution or has greatly different standard deviations in different groups, field transformation may be necessary—the transformation of data at the field type level. Data transformations are an important tool for proper statistical analysis; to those with a limited knowledge of statistics, however, they may seem a bit fishy, a form of playing around with your data in order to get the answer you want. It is therefore essential that you be able to defend your use of field and data transformations, as well as document them.

Financial Reporting: The process of producing statements that disclose an organization’s financial status to management, investors, and the government. For Financial Reporting best practices, see our ‘Do This, Not That: Financial Reporting’ blog post.

Front-End: In software, contrary to the ‘back-end’, the ‘front-end’ is the portion of a program or device that interfaces directly with the end user via a user interface (UI), and communicates with the back-end.

Full Load: A means of reading and updating all records in a data source during warehouse loading. When it comes to loading data into a data warehouse, there are two main techniques: full load and incremental load.

Governed Data: Information and data processes that are managed, controlled and secured by a governing department, typically IT, to fit business rules and standards before users may access it. This helps to ensure data integrity; in effect, users will only be working with trusted, credible data.

Hierarchy: Refers to a means of organizing different levels of a dimension by granularity; usually from largest to smallest. For a date, a typical hierarchy would be organized by year, quarter, month and day. Country, state, city and customer is another example of hierarchy levels within a cube.

Human Resources Dashboard (HR dashboard): Analyzes and presents enterprise data for the purpose of displaying meaningful HR KPIs. Employee Satisfaction Index, number of FTEs, turnover rate, employee benefit usage, and cost per employee are examples of data points that can be visualized and displayed via an HR dashboard.

In Memory: A database management system that primarily relies on computer memory for data storage, contrasted with database management systems that employ a disk storage mechanism. The benefits of using an “in-memory database” system (or IMDB—not to be confused with the ‘Internet Movie Database’) are that it eliminates seek time when querying the data. This provides faster, and more predictable, performance than disk—highly beneficial when computing and analyzing vast amounts of data.

Incremental Load: Contrasted with full load, incremental load is a means of data warehouse loading that involves only loading new or updated records. Incremental loads are useful because they run very efficiently when compared to full loads and allow for more frequent updating of the data warehouse and cubes, particularly with large data sets.

Index: A data structure that stores the values for a specific column in a table. Indexing is a way of sorting a number of records on multiple fields. For example, creating an index on a field in a table creates another data structure which holds the field value, and pointer to the record it relates to. This index structure is then sorted, allowing rapid Binary Searches to be performed on it.

In-Memory Analytics: Refers to the process of querying data when it resides in computer memory (such as RAM) rather than on physical storage device, such as hard drives. In-memory querying is incrementally faster than the alternatives, resulting in expedited business decisions driven by data from business intelligence applications. As the cost of RAM continues to decline, large-scale in-memory analytics is becoming a more feasible option for many organizations.

Inmom Approach: A top-down methodology for data warehousing. It states that the data warehouse should be modeled using normalization rules. Tables are grouped together by subject areas that reflect general data categories (e.g., data on customers, products, finance, etc.). The normalized structure divides data into entities, which creates several tables in a relational database. When applied in large enterprises, the result is dozens of tables that are linked together by a web of joins.

Jet Data Manager (JDM): The Data Warehouse Automation (DWA) platform for Jet Analytics. With the JDM you can transform and validate data from different data sources, consolidate them in a data warehouse, and build Online Analytical Processing (OLAP) cubes using a drag-and-drop interface. When you execute or deploy a BI project, the Jet Data Manager also automatically generates the underlying SQL code of your solution. With the Jet Data Manager you can access data from a variety of sources including ERP systems (such as Microsoft Dynamics), CRM applications, SQL databases, spreadsheets, and plain text files. The data is stored in a data warehouse in Microsoft SQL Server and can be viewed in your preferred front-end application such as Excel, Microsoft’s Power BI, the Jet Analytics mobile dashboard builder, or other third party applications.

Jet Analytics (Previously Jet Enterprise): A corporate analytics and reporting platform that delivers fast and flexible dashboards and financial reports in Excel on the web. Offers pre-built cubes, data warehouse, dashboards, and more. Learn more about Jet Analytics.

Jet Basics (Previously Jet Express): A free extension included with Microsoft Dynamics NAV and GP to create basic reports inside of Excel (Jet Basics) or Word (Jet Express for Word). Created in collaboration with Microsoft. Learn more about Jet Basics or Word.

Jet Reports (Previously Jet Professional): A fast, flexible financial and business reporting solution inside of Excel. Allows users to drill down on the numbers directly from Excel and access reports anywhere on the web. Learn more about Jet Reports.
joins: A means for combining fields from two tables by using values common to each. It’s possible to combine fields by utilizing different kinds of joins, such as:
– inner join: Returns all rows from multiple tables where the join condition is met.
– left join: Returns all rows from the left table, and the matched rows from the right table.
– right join: Returns all rows from the right table, and the matched rows from the left table.
– full join: Return all rows when there is a match in ONE of the tables.

Joins A means for combining fields from two tables by using values common to each. It’s possible to combine fields by utilizing different kinds of joins, such as:
– inner join: Returns all rows from multiple tables where the join condition is met.
– left join: Returns all rows from the left table, and the matched rows from the right table.
– right join: Returns all rows from the right table, and the matched rows from the left table.
– full join: Return all rows when there is a match in ONE of the tables.

Kimball Approach A bottom-up methodology for data warehousing (contrasted with the Inmon approach, which is top-down). Using the Kimball approach, dimensional data marts are first created to provide reporting and analytical capabilities for specific business areas such as “Sales” or “Production”, then combined into a broader data warehouse. It is the most frequently used methodology, especially if you are using the Microsoft BI stack.

Key Performance Indicator (KPI): Business metrics used to evaluate factors that are crucial to organizational success. KPIs differ per organization; business KPIs may be net revenue or a customer loyalty metric, whereas HR KPIs may involve employee turnover rates, employee satisfaction and headcount.

Lead and Lag: Analytical functions that are used to calculate the difference between rows in a table. LEAD calculates the difference between the current row and the following row, while LAG calculates the difference between the current row and the previous row.

Level: A grouping within a dimension. For example, customers can be grouped by city or country. When grouped in this way, customer, city and country are categorized as different levels within a cube. Similarly, dates can have different levels in a BI cube (day, month, quarter, year, is a common example.)

Many-to-Many Relationships: Refers to a relationship between tables in a database when a parent row in one table contains several child rows in the second table, and vice versa. Many-to-many relationships are often tricky to represent. However, one or more rows in a table can be related to 0, 1, or many rows in another table. In a many-to-many relationship between Table A and Table B, each row in Table A is linked to 0, 1, or many rows in Table B and vice versa. A 3rd table called a mapping table is required in order to implement such a relationship.

Marketing Dashboard: Analyzes and presents enterprise data for the purpose of displaying meaningful marketing KPIs. Lead acquisition, website conversion rate, cost per lead, web traffic, and more are examples of data points that can be visualized and displayed via a marketing dashboard.

Multidimensional expressions (or MDX): A querying language for OLAP or relational databases, with syntax similar to spreadsheet formulae. Due to its simplicity and straightforward syntax, it has quickly become the standard for OLAP systems over the more complex SQL.

Measure: A calculated numerical value. It can be a sum, a count, an average, a percentage, etc. Examples of measures would be gross sales, profit, profit percentage. In cubes, many measures are pre-calculated, providing extremely fast performance when analyzing data. Examples of measures in a sales cube may include Sales Amount, Profit YTD, Average Unit Cost, Document Count, etc.

Member(s): Users with preconfigured access to a database based on roles.

Metadata: A set of data that gives information about other data. Examples of relational-database metadata include:

– Tables of all tables in a database, their names, sizes, and number of rows in each table.
– Tables of columns in each database, what tables they are used in, and the type of data stored in each = column.
In database terminology, this set of metadata is referred to as the catalog. The SQL standard specifies a uniform means to access the catalog, called the information schema, but not all databases implement it, even if they implement other aspects of the SQL standard.

Metrics: Quantifiable measures used to track, monitor, and gauge the results and success of various business processes. Metrics are meant to communicate a company’s progression toward certain long and short term objectives. This often requires the input of key stakeholders in the business as to which metrics matter to them.

Microsoft BI Stack: Microsoft’s suite of business intelligence tools that cater to the individual, team, and enterprise level depending on the needs of the organization. This encompasses SQL servers, OLAP cubes, SharePoint, PerformancePoint, Excel, and Dynamics. In conjunction, these tools can be combined to collect, store, analyze, segment, and visualize data and generate meaningful insights to drive business decisions.

Mobile Analytics: Refers to the accessibility of meaningful and pointed data, dashboards, and reports for end users on mobile devices, such as tablets and smart phones.

Mobile Dashboards: A business intelligence dashboard that can be accessed via a mobile device. Given the gravitation of business to an always-on, anywhere mentality, business leaders are embracing mobile dashboards and mobile analytics as a means of effectively accessing, analyzing, and optimizing data from mobile devices. Jet is an example of a leading mobile dashboard solution.

Multidimensional Database (MDB): A type of database that is optimized for data warehouses and online analytical processing. Multidimensional data solutions utilize cube structures for analyzing data across numerous dimensions. They are composed of cubes and dimensions that can be annotated and extended to support complex query constructions. BI developers create cubes to support fast response times, and to provide a single data source for business reporting.

Online Analytical Processing (OLAP): A powerful technology for data discovery, including capabilities for limitless report viewing, complex analytical calculations, and predictive “what if” scenario (budget, forecast) planning. OLAP performs multidimensional analysis of business data and provides the capability for complex calculations, trend analysis, and sophisticated data modeling. Typical applications of OLAP include business reporting for sales, marketing, management reporting, business process management (BPM), budgeting and forecasting as well as financial reporting.

OLAP Cube: A method of storing data in a multidimensional form, generally for reporting purposes. In OLAP cubes, data measures are categorized by dimensions. OLAP cubes are often pre-summarized across dimensions to drastically improve query time, and accuracy, from relational databases.

One version of the truth (or ‘single version of the truth’; or SVOT: A technical concept describing the business analysis ideal of having either a single centralized database (data warehouse), or at least a distributed synchronized database, which stores all of an organization’s data in a consistent and non-redundant form. A combination of software, data quality, and strong data leadership can help enterprises and organizations achieve SVOT.

Operational Reporting: Tactical, real-time reporting that reflects and supports day-to-day activity at an organizational level. Examples of operational reporting include bank teller window balancing reports, daily production records, and transaction logs.

Power BI: Microsoft’s flagship business analytics data visualization tool. Power BI includes graphically robust dashboards and advanced analytics on both mobile and desktop devices.

Project Manager: An individual who is responsible for the planning, organization, resource management, and discipline pertaining to the successful completion of a specific project or objective. Our experience is that project managers are most effective when they come from the IT organization – technical skills being critical to understanding the level of effort and time demands of data warehousing tasks. Communication and PM skills are critical as well as a comfort level around senior executives.

Relational Database: A database structured to recognize relations among stored items of information. Microsoft Dynamics NAV, AX, GP, and SQL are all examples of relational databases. Relational databases include tables and fields which are joined together by keys. They are optimized to get information into a system in a cohesive manner; however, they aren’t optimized for getting the information out.

Report Distribution: Refers to the methodology used to circulate relevant information to appropriate stakeholders via manual or automated means. To learn more about how to automate your time-consuming report distribution process, see our whitepaper ‘Intuitive Reporting’.

Sales Dashboard: One of the most widely utilized examples of business intelligence, sales dashboards collate and present relevant sales data including opportunities, pipelines, lead funnels, revenue, product performance, forecasting, customer profitability, and more. By persistently displaying organized sales data, businesses can leverage their notoriously competitive sales departments to drive customer acquisition. To learn more about sales dashboards and the KPIs that can improve your bottom line, download the Jet white paper ‘How to Build KPIs That Actually Drive Revenue.’

Schema: Refers to the organization of data as a blueprint of how fact and dimension tables are arranged and constructed to form a relational database. A database schema specifies, based on the database administrator’s knowledge of possible applications, the facts that can enter the database, or those of interest to the possible business users.

Scorecard: A graphical representation of the progress over time of an enterprise, employee, or business unit, toward some specified goal or goals highlighting relevant KPIs. Performance scorecards are widely used in many industries throughout both the public and private sectors.

Services Delivery Manager (BI delivery team): Has accountability for overall success of BI project, within a BI delivery team. The Services Delivery Manager is responsible for staffing and managing the vendor delivery team. The SDM also manages communications with the organization – usually working with product sponsor and business drivers to do so. For more on the roles involved in a BI project and how they impact overall data quality, download our white paper, ”A New Twist on Data Governance from Jet .”

Slowly Changing Dimensions (SCD): Refers to data dimensions that change slowly and unpredictably, rather than on a static or fixed schedule.

Snowflake Schema: An arrangement of tables in a multidimensional database such that the physical model resembles a snowflake shape. The snowflake schema consists of centralized fact tables which are connected to multiple dimensions.

SQL Server Analysis Services (SSAS): Microsoft SQL Server Analysis Services (or SSAS) is an OLAP data mining and reporting tool in Microsoft SQL server. SSAS is used to analyze, access and present information spread across multiple databases or in disparate tables. See also Analysis Services.

SQL Server Integration Services (SSIS): An enterprise data integration, transformation, and migration tool that is built into Microsoft’s SQL Server. It is used for a variety of integration-related tasks, such as analyzing and cleansing data or running ETL processes to update data warehouses. SSIS can consolidate data from multiple relational databases as well as sources such as XML data files and flat files.

Star Schema: The simplest style of data mart schema and most common approach to developing data warehouses and dimensional data marts. The star schema gets its name from the physical model’s resemblance to a star shape with a fact table at its center and the dimension tables surrounding it representing the star’s points.

Stored Procedure: A batch of SQL statements that can be used to perform a specific task and shared across a network by several clients using different input data. Stored procedures reduce network traffic, increase performance, and protect against SQL injections.

Subject Matter Expert (SME): A person who is an authority in a particular area or topic. In some cases, this person can become the go-to resource for ad hoc analysis. The number of subject matter experts your implementation team needs will be determined by the number of user groups from within the organization that will be using the BI tool (finance, operations, sales, HR, etc.).

Surrogate key: Within a database, the surrogate key is a unique identifier system wide. The value of a surrogate key generally has many attributes including never being manipulated by a system or a user, contains no semantic meaning, and not being composed of multiple values. The surrogate key is not derived from application data, unlike a natural (or business) key which is derived from application data. Having the key independent of all other columns insulates the database relationships from changes in data values or database design (making the database more agile) and guarantees uniqueness.

Table Relations: Are a key component in data set building by matching common fields in tables that are related. To ensure data accuracy and limit redundancy, data is split into subject-based tables so that each fact is represented only once; table relationships are then defined and common fields mapped to paint a complete picture.

View: The result set of a stored query on the data, which the database users can probe just as they would in a persistent database connection object. This pre-established query command is kept in the database dictionary.

Visual Studio: Microsoft Visual Studio is an integrated development environment (IDE). It is used to develop Windows programs, as well as web sites, web applications, and web services. It is also used to create local database files for SQL.