Agile: A methodology derived from software development that is now used in many areas of business. Agile methods are designed to help teams respond to unpredictability through incremental, iterative workflows and shortened feedback loops (e.g., through short, daily meetings in which project team members describe what they are working on). Agile methods are an alternative to the waterfall method or traditional sequential development.
Analysis Services: Also known as Microsoft SQL Server Analysis Services, SSAS, and sometimes MSAS. Analysis Services is an online analytics data engine used for decision support and business analysis. They provide 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 enterprises to analyze and make sense of information that may be distributed across multiple databases or in different 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: Refers to the skills, technologies, and practices used to examine past business performance to gain insights and drive business planning. It focuses on developing new insights and understanding 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 drive business planning, business analytics focuses on developing new insights and understanding based on statistical methods and predictive modeling.
In addition, BI methods such as queries, reports, OLAP and alert tools answer questions such as
- What happened?
- How many?
- How often?
- Where is the problem?
- What actions are required?
Business analysis can answer questions like
- Why is this happening?
- What happens if these trends continue?
- What will happen next?
- How can we optimize?
Business Analyst: Someone who analyzes a company or business unit (real or hypothetical) and documents its processes and systems, evaluates the business model, and determines integration with technology. Their solutions may consist of using technology architectures, tools, or software applications. In a BI project, this person is often responsible for identifying business requirements 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 a direct interface to the end user. Back-end processes are usually accessed via a user interface located on the "front-end". The presentation layer is the "front-end", while the access layer is referred to as the "back-end".
BI Application Designer: someone responsible for designing the initial report templates and dashboards in the front-end applications. They typically need a combined enthusiasm for data visualization, user experience design, and application reporting. Typically, BI Application Designers become the source of ongoing support for front-end BI applications.
BI project sponsor: Ideally, a project sponsor is an executive-level person who understands the importance of BI projects, has compelling business motivation, and can drive results. This person is the actual customer of the project and its strongest advocate. He or she is not involved in the day-to-day operations of the project, but provides oversight, direction and momentum.
Big Data: This term is quickly gaining buzzword status - but colloquially it refers to a volume of data so large that it cannot be analyzed using conventional techniques. According to market research firm Gartner, "Big Data" refers to high-volume, high-speed and/or high-variant information assets that require cost-effective, innovative forms of information processing that enable improved insights, 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 example, in a BI project, if the sponsor is too far removed from the project team, a business driver is helpful. The driver typically takes responsibility for the less strategic BI tasks. This role is usually filled by a middle manager, but with the same characteristics as the sponsor.
Business Intelligence (BI): A collective term that encompasses a variety of tools, applications, and methods that enable organizations to gather data from internal systems and external sources. BI can be used to prepare data for analysis, develop and execute queries, and create reports, dashboards, and visualizations with the goal of providing results to decision makers and end users.
Business Owner: The role of Business Owner must be filled from the business user groups with enthusiastic fans of the BI project who are also subject matter experts in their field. Each group of business users within the organization that will use the BI tool (finance, operations, human resources, etc.) should appoint a business owner. Business user involvement is critical, and care should be taken to include them in the project from the beginning. Without business owners and users, a BI project is merely an academic technical exercise.
Business User: A user of a service or product who is not necessarily in contact with the supplier/vendor and is therefore at the end of the data "supply chain," e.g., an end user of a content management system (CMS) or an accountant entering purchase orders into an ERP system.
Collaborative Business Intelligence or collaborative BI: is the combination of traditional business intelligence tactics with tools such as social networks, wikis or blogs to enhance the collaborative problem-solving nature of BI. Microsoft SharePoint or Teams is an example of a popular collaborative BI product.
Cubes (data cubes): Multidimensional data areas built from tables and fields in your database. Cubes contain calculations and formulas and are often grouped around specific business functions, such as sales, finance, purchasing, inventory, and so on. Each cube contains contextual, pertinent, and useful metrics for that business function.
- Also Online Analytical Processing (OLAP)
Dashboard: Provides at-a-glance statistical analysis and historical trends of an organization's key performance indicators (KPIs), presented in easily digestible, graphical representations. For example, a human resources dashboard may include figures on recruitment, retention, and composition. A marketing dashboard, on the other hand, may display figures on inbound web traffic, search volume, and lead velocity.
Data Analysis Expressions (DAX): Provides a special syntax for querying Analysis Services. DAX includes some of the functions used in Excel formulas, as well as additional functions designed for working with relational data and performing dynamic aggregations. DAX can be used to calculate values for seven different data types: Integer, Real, Currency, Date, Boolean, String and BLOB (binary large object). Refers to the process through which data is
:- extracted from sources-
transformed or standardized to store them in the correct heterogeneous format- and loaded into the final storage or warehouse. The ETL process usually runs in parallel with the transformation processes performed during the extraction of data from sources. ETL systems typically integrate data from multiple applications (systems) that are managed and operated by different personnel. For example, a cost accounting system may combine data from payroll, sales, and purchasing.
Data Architect: A data architecture professional, an information technology discipline concerned with the design, creation, deployment, and management of an organization's data architecture. This person is typically responsible for designing the ETL (extract, transform, and load) process and building the structure (dimensional model) in which the data will reside after the ETL process. Data architects also assemble the required technical functions for the BI project. Several skills are needed: Expertise in dimensional modeling is required, as is a deep understanding of business requirements. Knowledge of ETL functions such as SQL Server Integration Services and experience in performing ETL tasks are also essential. In addition, knowledge of hardware infrastructure and supporting software is required.
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.
Data mining: extracting previously unknown data from databases and using it to make important business decisions, in many cases leading to new insights.
Database: In the most general sense, a database is a collection of information organized so that it can be retrieved, managed and updated. The data is usually stored in a computer or on a server and can be images, numbers, scripts, full texts, etc., containing almost any type of information. In the context of business intelligence (BI), databases are for systems such as Microsoft Dynamics, Excel, CRM, Salesforce, etc., where records or files such as sales transactions, product catalogs and inventories, and customer profiles are grouped together. The information is entered and stored in the database, and a BI solution is required to output this data in a meaningful, organized, and informative format. 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 collect and analyze data. A general-purpose DBMS is designed to define, create, query, update, and manage databases.
Database Administrator (DBA): This role includes capacity planning, installation, configuration, database design, migration, performance monitoring, security, troubleshooting, and 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 deployment team.
Data Blending: Provides a fast and straightforward way to extract values from multiple data sources to find patterns without using a traditional data warehouse architecture.
Data cleansing: The process of finding and correcting erroneous data sets that leads to highly accurate BI-driven decisions, as huge databases and rapid data collection can lead to inaccurate or erroneous data that affects the resulting BI and analytics. Correcting typos, removing duplicates, and standardizing syntax are all examples of data cleansing.
Data democratization: Allows users across the enterprise to access data so they can perform analytics to answer any question at any time.
Data Discovery: User-driven process of searching for patterns in a data set, enabling self-service and data democratization. Data Discovery has been called "modern business intelligence" by Gartner.
Data Governance: The management of the availability, usability, integrity, and security of data stored within an organization. The process by which data is captured, validated, stored, protected, and processed. In turn, it ensures its accessibility, reliability and timeliness to meet the needs of data users. Data management oversees the entire lifecycle of a company's data.
Data integration: the combination of technical and business processes used to combine data from disparate sources into meaningful insights.
Data Intelligence: focuses on internal data used for future endeavors and is sometimes incorrectly referred to as Business Intelligence. While business intelligence is about organizing, not just collecting data to make it useful and applicable to business practices, data intelligence focuses on extrapolating data to evaluate future services or investments.
Data Lake: A repository where a large amount of raw data is stored in its native format until it is needed.
Data Lineage: Refers to the lifecycle of data, which includes the origin of the data and its evolution over time, and describes what happens to the data as it goes through various processes.
Data Mart: A collection of reports, metrics, and other stored data on a particular topic. Think of this as an organization of similar information that makes it easier to find.
Data manager: Every team needs management, and a data science team is no different. In a data science or analytics team, the data manager acts as an intermediary between the technical team members and strategic management. For this reason, it is ideal if the data manager has a technical IT background with strategic experience.
Data migration: The process of moving data between two or more storage systems, data formats, warehouses, or servers.
Data mining: Refers to the process of analyzing large amounts of data to find patterns and instances of statistical significance. By using software to search for patterns in big data, companies can learn more about their customers and develop more effective customer acquisition strategies, as well as increase revenue and reduce overall costs.
Data model: Defines how data is structured, linked, and standardized to produce meaningful insights. Organizations can use multiple data models (such as Microsoft's Power BI) to ensure that all relevant data is included.
Data Modeling: Refers to the process of defining, analyzing and structuring data in data models.
Data source: The source of the data. It can be a file, a specific database in a DBMS, or even a live data feed. The purpose of a data source is to collect all the technical information needed to access the data - driver name, network address, network software, and so on. - to be collected in a single location and hidden from the business user. The user should be able to look at a list of payroll, inventory, and personnel, select payroll from the list, and have the BI application connect to the payroll data. This is done without the user knowing where the payroll data is or how the application got to it.
Data quality: Refers to the contextual quality of a company's data collection. The more relevant, available, complete, and accurate the information, the greater the chance that profitable business insights can be gained.
Data replication: the frequent copying of data from one database to another so that all users can use the same level of information, resulting in a distributed database that allows users to access data relevant to their own specific tasks.
Data Science: A field of study concerned with the processes and systems used to extract knowledge from data in all its forms. The field is seen as a continuation of other areas of data analysis, such as statistics.
Data Staging: A temporary location to which all data is copied from external resources.
Data Warehouse: A system used for data analysis. It is a central location where integrated data from other, more disparate sources are stored, both current (real-time) and historical data, which can then be used to generate trend reports. In multidimensional data sets, drilling is the process of navigating between data levels ranging from the most summarized (up) to the most detailed (down).
Data visualization: the transformation of numerical data into a visual or pictorial context to help users better understand the data.
Data cube: A database structure with multiple dimensions that can be stacked, combined, and manipulated to enable browsing.
Data feed or live data feed: A mechanism by which users can receive updated data from data sources. It is commonly used by real-time applications in point-to-point environments as well as on the Internet.
Data visualization: The practice of structuring and arranging data in a visual context to facilitate user understanding. Patterns and trends that are not apparent to the layperson in text-based data can be easily recognized and processed by end users using data visualization software.
Data warehouse: A large repository of data from a variety of sources that can be processed, partitioned, and analyzed to produce insights that serve as the basis for management decisions. Data warehouses are typically relational databases that contain historical data and are designed for querying and analysis.
Data Warehousing: The process of combining data from disparate sources for the purpose of building a data warehouse. Data warehousing includes design, development, testing, deployment, operation, impact analysis, and change management.
Data Warehouse Automation (DWA): Uses technology to improve the efficiency and effectiveness of data warehousing processes. The idea behind DWA is to automate any part of the data warehouse lifecycle that can be automated, allowing the project team to focus on the parts that require more intellectual input than raw technological power. DWA can help shorten time to implementation for BI projects, reduce annual BI spend, and improve success rates.
Data Warehouse Developer: Your main task is to develop and deploy code. They receive instructions from the ETL architect and create ETL functions directly. ETL expertise and experience are required, but may vary depending on the ETL tool used.
Dimension: A category that can be used to organize data by facts and scales, to group and filter them. Commonly used dimensions are people, products, places and time.
Dimension table: In a data warehouse, a dimension table is a supplement to a fact table. Dimension tables contain descriptive fields that are traditionally textual. Dimension tables are linked to fact tables (which contain key figures) through the use of keys (e.g. surrogate keys).
Drill-Through/Drill-Down: In BI terminology, "drill down" refers to moving from higher-level to more detailed transactional data by focusing on something (such as a specific number in a report). In a visualization environment (such as Jet Reports), "drill down" can mean clicking on a specific representation to see more detail.
Layer: 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 layers within a cube. Similarly, data in a BI cube can have different layers (day, month, quarter, year is a common example).
Executive BI: The collection, analysis, and visualization of data to provide key insights to senior leadership that drive business change.
Executive Dashboard: Provides the leadership team with instant insight into the big picture of the entire business, from finance and operations to sales and marketing. Executive Dashboards are fully customizable to display the data that matters to your leadership team.
Extract, Transform, Load (ETL): Refers to the process by which data is extracted from sources and transformed or standardized
to be stored in the correct heterogeneous format -
and loaded into final storage or warehouse. The ETL process usually runs in parallel with the transformation processes performed during the extraction of data from sources. ETL systems typically integrate data from multiple applications (systems) that are managed and operated by different personnel. 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 in the center of a star or snowflake schema surrounded by dimension tables. Fact tables provide the (usually) additive values that act as independent variables against which the dimension attributes are analyzed. Fact tables are often defined by their structure. The structure of a fact table represents the most atomic level at which facts can be defined. For example, the structure of a fact table for sales could be as follows: "Sales volume by day by product by store".
Field transformations: These allow an administrator to transform raw field inputs 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 conform to a normal distribution or has widely varying standard deviations in different groups, a field transformation may be required - the transformation of data at the field type level. Data transformations are an important tool for proper statistical analysis; however, for those with limited knowledge of statistics, they may seem like a way to play around with your data to get the answer you want. Therefore, it is important that you are able to justify and document your use of field and data transformations.
Financial reporting: the process of preparing reports that disclose an organization's financial status to management, investors, and the government.
Front-end: In contrast to the "back-end", the "front-end" in software is the part of a program or device that is directly connected to 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 when loading the data warehouse. There are two main techniques for loading data into a data warehouse: full load and incremental load.
Governed Data: Information and data processes that are managed, controlled, and secured by a senior department, usually IT, so that they comply with business rules and standards before users can access them. This helps ensure data integrity; users work only 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 by year, quarter, month, and day. Country, state, city, and customer are other examples of hierarchy levels within a cube.
Human Resources Dashboard (HR Dashboard): Analyzes and presents company data with the goal of displaying meaningful HR KPIs. Employee satisfaction index, number of full-time positions, turnover rate, benefits utilization, and cost per employee are examples of data points that can be visualized and displayed through an HR dashboard.
Incremental loading: Unlike full loading, incremental loading is a form of data warehouse loading in which only new or updated data sets are loaded. Incremental loads are useful because they are very efficient compared to full loads and allow for more frequent updates of the data warehouse and cubes, especially for large data sets.
Index: A data structure that stores the values for a specific column in a table. Indexing is a way to sort a number of records by multiple fields. For example, when you create an index for a field in a table, another data structure is created that contains the field value and a pointer to the record to which it refers. This index structure is then sorted so that quick binary searches can be performed on it.
In Memory: A database management system that primarily uses computer memory for data storage, as opposed to database management systems that use a disk storage mechanism. The advantages of an "in-memory database system" (or IMDB - not to be confused with the "Internet Movie Database") are that it eliminates the search time required to retrieve data. This provides faster and more predictable performance than hard disks - a big advantage when computing and analyzing large amounts of data.
In-memory analytics: Refers to the process of querying data that resides in computer memory (e.g., RAM) rather than on a physical storage medium such as a hard disk. In-memory queries are incrementally faster than the alternatives, resulting in faster business decisions based on data from business intelligence applications. As the cost of memory continues to drop, large-scale in-memory analytics is becoming a more viable option for many organizations.
Inmom approach: A top-down method for data warehousing. It states that the data warehouse should be modeled using normalization rules. Tables are grouped by topics that reflect general categories of data (e.g., data about customers, products, finances, etc.). The normalized structure divides the data into entities, creating multiple tables in a relational database. When used in large companies, this creates dozens of tables connected by a network of joins.
Joins A means of combining fields from two tables using common values in both tables. It is possible to combine fields using different types 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 matching rows from the right table.
- right join: Returns all rows from the right table and the matching rows from the left table.
- Full join: Returns all rows if there is a match in ONE of the tables.
Kimball approach A bottom-up approach to data warehousing (as opposed to the Inmon approach, which is top-down). In the Kimball approach, dimensional data marts are created first to provide reporting and analysis capabilities for specific business areas, such as "sales" or "production," which are then combined into a more comprehensive data warehouse. This is the most common approach, especially if you are using the Microsoft BI stack.
Key performance indicator (KPI): Business metrics used to evaluate factors that are critical to the success of the business. KPIs vary from company to company; business KPIs may be net sales or a customer retention metric, while HR KPIs may be turnover rates, employee satisfaction, and headcount.
Lead and Lag: Analytic functions 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.
Many-to-many relationships: Refers to a relationship between tables in a database when a parent row in one table contains multiple child rows in the second table and vice versa. Many-to-many relationships are often difficult to represent. However, one or more rows in one table may 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. To realize such a relationship, a third table, called the mapping table, is required.
Marketing Dashboard: Analyzes and presents company data with the goal 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 query language for OLAP or relational databases with a syntax reminiscent of spreadsheet formulas. 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 number, an average, a percentage, etc. Examples of ratios would be gross sales, profit, profit percentage. In Cubes, many ratios are pre-calculated, which allows for extremely fast performance in data analysis. Examples of ratios in a sales cube are sales amount, profit YTD, average unit cost, receipt count, etc.
Member(s): User with preconfigured access to a database based on roles.
Metadata: A set of data that contains information about other data. Examples of metadata in relational databases are:
- Tables of all tables in a database, their names, sizes and the number of rows in each table.
- Tables of columns in each database, in which tables they are used and what kind of data is stored in each column.
In database terminology, this set of metadata is called a catalog. The SQL standard specifies a uniform means of accessing 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 evaluate the results and success of various business processes. Metrics are intended to convey an organization's progress toward specific long- and short-term goals. This often requires the organization's key stakeholders to indicate which metrics are important to them.
Microsoft BI Stack: Microsoft's suite of business intelligence tools that can be deployed at the individual, team, and enterprise levels, depending on the needs of the business. These include SQL Server, OLAP cubes, SharePoint, PerformancePoint, Excel and Dynamics. These tools can be combined to collect, store, analyze, segment and visualize data to provide meaningful insights that inform business decisions.
Mobile Analytics: Refers to the accessibility of meaningful and pointed data, dashboards, and reports to end users on mobile devices such as tablets and smartphones.
Mobile Dashboards: A business intelligence dashboard that can be accessed from a mobile device. With businesses being accessible anytime, anywhere, executives are turning to mobile dashboards and mobile analytics to effectively access, analyze and optimize data from mobile devices.
Multidimensional Database (MDB): A type of database optimized for data warehouses and online analytical processing. Multidimensional data solutions use cube structures to analyze data in numerous dimensions. They consist of cubes and dimensions that can be annotated and expanded to support complex query constructions. BI developers create cubes to enable fast response times and provide a single source of data for business reporting.
Online Analytical Processing (OLAP): A powerful data discovery technology that provides capabilities for unlimited report viewing, complex analytical calculations, and predictive planning of "what-if" scenarios (budget, forecasts). OLAP performs multidimensional analysis of business data and provides the capability for complex calculations, trend analysis, and sophisticated data modeling. Typical applications of OLAP are business reports for sales, marketing, management reports, business process management (BPM), budgeting and forecasting, and financial reports.
OLAP Cube: A method of storing data in multidimensional form, generally for reporting purposes. In OLAP cubes, data is categorized by dimension. OLAP cubes are often pre-summarized across dimensions to dramatically improve the query time and accuracy of relational databases.
Operational Reporting: Real-time tactical reporting that reflects and supports day-to-day operations at the enterprise level. Examples of operational reports include bank counter reconciliation reports, daily production records, and transaction logs.
Power BI: Microsoft's flagship tool for visualizing business data. Power BI includes graphically robust dashboards and advanced analytics on both mobile and desktop devices.
Project Manager: A person responsible for the planning, organization, resource management, and discipline related to the successful completion of a specific project or goal. It has been our experience that project managers are most effective when they come from the IT organization - technical skills are critical to understanding the effort and time requirements of data warehousing tasks. Communication and PM skills are important, as is a confident demeanor when dealing with senior executives.
Relational database: A database that is structured to recognize relationships between the information it stores. Microsoft Dynamics NAV, AX, GP, and SQL are all examples of relational databases. Relational databases contain tables and fields that are linked by keys. They are optimized to transfer information into a system in a coherent manner; however, they are not optimized to return the information out.
Schema: Refers to the organization of data as a blueprint for 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 be included in the database or those that are of interest to the possible business users.
Scorecard: A graphical representation of a company's, employee's, or business unit's progress toward a specific goal or goals, highlighting relevant key performance indicators (KPIs). Performance scorecards are widely used in many industries in both the public and private sectors.
Services Delivery Manager (BI Deployment Team): Is responsible within a BI delivery team for the overall success of the BI project. The Services Delivery Manager is responsible for staffing and managing the vendor team. The SDM also handles communications with the business, typically working with the product sponsor and business leaders. For more information on the roles involved in a BI project and how they impact overall data quality, read our whitepaper, "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 so that the physical model resembles the shape of a snowflake. The snowflake schema consists of centralized fact tables associated with multiple dimensions.
'Single version of the truth' or SVOT: A technical concept that describes the ideal of enterprise analytics, which is to have either a single central database (data warehouse) or at least a distributed, synchronized database that stores all of an organization's data in a consistent and non-redundant form. A combination of software, data quality and strong data governance can help companies and organizations achieve SVOT.
SQL Server Analysis Services (SSAS): Microsoft SQL Server Analysis Services (or SSAS) is an OLAP tool for data analysis and reporting in Microsoft SQL Server. SSAS is used to analyze, access, and present information that is distributed across multiple databases or in different tables. See also Analysis Services.
SQL Server Integration Services (SSIS): An enterprise data integration, transformation, and migration tool integrated with Microsoft SQL Server. It is used for a variety of integration-related tasks, such as analyzing and cleansing data or performing ETL processes to update data warehouses. SSIS can consolidate data from multiple relational databases as well as from sources such as XML data files and flat files.
Star schema: The simplest form of data mart schema and the most common approach for developing data warehouses and dimensional data marts. The star schema takes its name from the resemblance of the physical model to a star shape, with a fact table at its center and the surrounding dimension tables representing the points of the star.
Saved Procedure: A batch of SQL statements used to perform a specific task that can be shared across a network by multiple clients with different input data. Stored procedures reduce network traffic, increase performance, and protect against SQL injections.
Surrogate key: Within a database, the surrogate key is a system-wide unique identifier. The value of a surrogate key typically has many properties, including never being manipulated by a system or user, not containing semantic meaning, and not consisting of multiple values. The surrogate key is not derived from application data, unlike a natural (or business) key, which is derived from application data. The independence of the key from all other columns isolates database relationships from changes in data values or database design (making the database more flexible) and guarantees uniqueness.
Table Relationships: They are a key component in creating records by matching common fields in tables that are related to each other. To ensure data accuracy and limit redundancy, data is divided into topic-based tables so that each fact is represented only once. Subsequently, table relationships are defined and common fields are matched to create a complete picture.
Report distribution: Refers to the method by which relevant information is distributed to the appropriate stakeholders in a manual or automated manner. For more information on how to automate your time-consuming report distribution process, see our whitepaper "Intuitive Reporting".
Sales Dashboard: One of the most commonly used examples of business intelligence, sales dashboards collect and present relevant sales data such as opportunities, pipelines, lead funnels, revenue, product performance, forecasts, customer profitability, and more. By continuously displaying organized sales data, companies can help their notoriously competitive sales departments acquire customers.
Visual Studio: Microsoft Visual Studio is an integrated development environment (IDE). It is used to develop Windows programs as well as websites, web applications and web services. It is also used to create local database files for SQL.