Key Terms and Industry Comparisons

Understand Visier terms and the differences between our approach and those commonly used in the industry.

Analytic model

analytic object

a container that stores specific data and represents what you want to analyze or aggregate data for, like applicants, employees, pay change events, interviews, and even employee skills

Industry-related terms: data entity

any object, concept, or event that is represented and analyzed within a data system, including both tangible entities, such as employees, and intangible entities, such as promotions or terminations

Use cases: Data modeling, database design

Tools: Entity-relationship diagrams, data dictionaries

Examples: Employees, applicants, jobs, performance reviews, training courses

For more information, see Analytic Objects.

auxiliary mapping

the process of connecting a secondary data source to a target object, such as a subject or an event, to enrich it with additional attributes using an existing ID

Technical details:

Adds information without creating new objects.
Simplifies the data model by pre-integrating data from multiple sources, eliminating the need for additional joins or references.

Industry-related terms: foreign key relationship, left outer join

a SQL operation that combines rows from two tables

It returns all rows from the left table (the target in Visier's case), even if there are no matching rows in the right table (the auxiliary source).
For rows without a match in the right table, NULL values are used for the right table's columns, effectively augmenting the target data with supplementary information where available.

Use cases: Data integration and transformation, data enrichment, data warehousing, business intelligence

Tools: Studio

Examples: Consolidating employee data from multiple sources or systems into a single employee object in Visier, using the employee ID as the matching key.

event

something that happens at a specific moment in time and may be associated with one or more individuals, such as an employee getting hired, a salary change, or a performance review

Industry-related terms: fact table, transaction fact

a measurement or event that occurred at a specific point in time and is often linked to dimensions to provide context

Use cases: Data warehousing, business intelligence

Tools: Fact tables, ETL processes

Examples: Compensation Payout fact, linked to the Employee dimension, with attributes like effective date, payout amount, and currency

For more information, see Events.

forward reference

a connection that establishes a many-to-one relationship between objects

Technical details:

In Visier, queries are made on a single subject, allowing the reference of values from a different analytic object that matches the join criteria (key and time), if they exist.

Industry-related terms: foreign key relationship, inner join

a SQL operation that combines rows from two tables based on a related column between them

Returns only the rows where the join condition is satisfied in both tables, effectively filtering and combining data from both sources based on a shared characteristic.
Ensures that the result set includes only data points where a direct relationship exists between the two tables.

Use cases: Data warehousing, business intelligence, data analysis, reporting, querying

Tools: SQL, NoSQL, databases, data lakes, data warehouses

Examples: Analyzing employee demographics by joining employee data with department data to analyze the distribution of age, gender, or ethnicity across different departments.

For more information, see References.

lookup mapping

the process of connecting additional data to a target object, such as a subject or event, using a flexible key

Technical details:

The flexible key can be any attribute, not just a primary ID.
Requires formulas for business rules to define the join condition, allowing for complex matching logic beyond simple ID equality.
Allows you to search for and retrieve data from a separate source and add it to your main dataset based on matching criteria that you define.
Pre-joins the data during the data loading process, so you don't need to perform joins when analyzing the data.

Industry-related terms: lookup table with join condition, VLookup

a VLOOKUP function in Excel, but applied on a larger scale with more flexibility, similar to a left join in SQL, where data from two tables is combined using complex matching logic beyond simple column equality

Use cases: Data integration and transformation, data enrichment, data warehousing, business intelligence

Tools: Studio

Examples: Integrating data from a custom survey or performance management system into Visier by mapping custom fields to standardized values.

member (of a subject)

an individual instance of a subject

Industry-related terms: dimension record, dimension member

a single entry withing a dimension table, representing a specific instance of the dimension

Use cases: Data analysis, reporting, querying

Examples: A specific employee record within the Employee dimension.

For more information, see Subjects.

occurrence (of an event)

an individual instance of an event

Industry-related terms: fact record

a single entry within a fact table representing a specific instance of a fact

Use cases: Data analysis, reporting, data mining

Tools: Fact tables, ETL processes

Examples: A single record in the Compensation Payout fact table representing a specific employee's pay on a particular date.

For more information, see Events.

property

a quality that describes or adds information to analytic objects, with types including simple, calculated, and multi-value

Technical details:

Simple properties store one value at a time from the data.
Calculated properties takes a value that comes directly from the data (other properties) and creates a new value through a formula.
Multi-value properties are a collection of simple properties to describe a single attribute that can have several valid values at the same time.

Industry-related terms: attribute, property

a characteristic or quality that describes or provides additional information about an entity or event

Use cases: Data modeling, database design, reporting, analysis

Tools: Data dictionaries, database schemas, reporting tools

Examples:

Simple property: Employee's Date of Birth, Gender, Job Title
Calculated property: Employee's Age (calculated from Date of Birth), Tenure (calculated from Hire Date).
Multi-value property: Education History (allows for multiple degrees or certifications), Languages Spoken.

For more information, see Properties.

reverse reference

a connection that establishes a one-to-many relationship

Technical details:

Automatically created when a forward is established.
Allows for filtering and grouping data in the referenced object based on attributes in the referencing object.

Industry-related terms: foreign key relationship, left outer join

a SQL operation that combines rows from two tables

Returns all rows from the left table, even if there are no matching rows in the right table.
For rows without a match in the right table, NULL values are used for the right table's columns, effectively augmenting the left table's data with supplementary information where available.

Use cases: Data warehousing, business intelligence, data analysis, reporting, querying

Tools: SQL, NoSQL, databases, data lakes, data warehouses

Examples: Analyzing applicant pipelines by joining applicant data with open requisitions to understand how many applicants applied to each requisition, even if some requisitions have no applicants yet. This can help identify popular roles and potential bottlenecks in the hiring process.

For more information, see References.

subject

an entity that changes over time, for example, employees, applicants, requisitions, learning items, and sales opportunities

Technical details:

Data is often in a state table with members valid over specific intervals.

Industry-related terms: dimension table, slowly changing dimension type 2

a structure that categorizes and describes facts and measures for analysis, where the attributes of an entity change over time

Use cases: Data warehousing, business intelligence, OLAP analysis

Tools: Star schemas, snowflake schemas, OLAP cubes

Examples: Employee is a dimension with attributes like hire date, department, job title, and salary, which can change over time

For more information, see Subjects.

Aggregating data

concept

a design object that uses dimensions, properties, and other concepts to reclassify existing data into new groups, with types including process, movement, and calculation

Technical details:

Acts as an abstraction on top of properties and dimensions
Lets you standardize data and create reusable groupings for analysis, such as classifying employees into performance levels or defining the stages of a process.

Industry-related terms: derived attribute, categorization logic

a method for standardizing and grouping data using CASE statements or CTEs (Common Table Expressions) in SQL, enabling the creation of reusable categorization logic for analysis and reporting, such as grouping employees into performance levels or defining customer segments.

Examples: Classifying employees into generations using their birth dates by using data from the Birth Date property in selection concepts like Gen X, Millennial, and Gen Z to select particular birth years for each generation.

For more information, see Concepts.

dimension

a design object that organizes unique values of an attribute into a list or a hierarchical structure for use as a filter or group by in your solution, with types including simple, leveled, and range

Technical details:

Built from properties to help you organize and analyze your data in meaningful ways.
Differences between dimensions and properties:
- Properties are the raw characteristics or attributes of an entity or event (like Age, Department, or Salary), while dimensions are structured groupings or categorizations created from those properties to facilitate analysis and reporting (like Age Range, Department, or Salary Band).
- A property is a characteristic a single record, a dimension allows you to group or aggregate across multiple records.

Industry-related terms: categorization attribute, grouping attribute, grouping column

a database column, or set of columns, used to categorize and group data for analysis and reporting, similar to how a GROUP BY clause in SQL aggregates data by specific columns, allowing for the organization and summarization of data based on characteristics like department, location, or job title.

Use cases: Data analysis, reporting, filtering

Examples: Analyzing employee demographics by department, tracking headcount by job level, filtering data by age range, creating custom reports based on performance categories.

Simple dimension: A single property used for categorization, such as Department or Location.
Leveled dimension: A hierarchy with a fixed number of levels, such as Job Level: Individual Contributor > Manager > Director.
Parent-child dimension: A hierarchy with flexible parent-child relationships such as Organizational Structure.
Range dimension: Groups numeric values into ranges such as Age Range: 20-25, 26-30, Salary Bands.
Custom dimension: User-defined groupings of members from an existing dimension such as Performance Categories: High Performer, Mid Performer.

For more information, see Dimensions.

metric

a business concern that can be quantified as a number

Technical details:

Performs an aggregation over a set of subject members or event occurrences and returns a result.

Industry-related terms: measure

a quantifiable value representing a business metric or key performance indicator (KPI), often used in data analysis and reporting to track and evaluate performance

Can be calculated using various aggregation functions such as sum, average, or count, in SQL or other data analysis tools.

Use cases: Data analysis, reporting

Tools: SQL, Business Intelligence platforms (Tableau, Microsoft Power BI), spreadsheets

Examples: Headcount, Revenue, Employee Turnover

For more information, see Work With Metrics.

Time handling

data model

Visier's approach: Visier's multi-temporal data model tracks both valid time (when data is true) and transaction time (when it was recorded). This allows for detailed historical analysis and auditing. Using an interval event model, data is linked to specific time periods or points in time, providing a nuanced view of how information changes.

Industry approach: Traditional databases often struggle to handle temporal data effectively. Many are atemporal, storing only the current state and losing historical data as changes occur. While some offer basic temporal features, they often track only valid time, limiting the ability to understand the full history of data. More advanced approaches, like slowly changing dimension type 2, are less common due to implementation complexity. They are specifically designed to track changes in dimension attributes over time by adding new rows for each change, preserving a historical record of how data has evolved. This approach, while more complex, provides a robust solution for managing temporal data, especially in data warehousing environments.

Industry-related terms: slowly changing dimension type 2

Use cases: Data warehousing, business intelligence, data analysis, reporting, querying, filtering

Tools: SQL, NoSQL, databases, data lakes, data warehouses

Examples: Analyzing the career progression of employees, analyzing promotion patterns, tracking salary changes over time, reconstructing historical workforce data, tracking performance ratings through time.

querying

Visier's approach: Visier simplifies temporal queries with built-in support for analyzing data across any time period. Users can easily ask questions about the current state of data or investigate historical trends without needing specialized SQL knowledge. This ease of use makes temporal analysis accessible to a wider range of users.

Industry approach: Querying temporal data in traditional databases can be complex. It often requires using specialized SQL extensions and understanding temporal concepts like valid time and transaction time. This complexity can make it challenging for non-experts to effectively analyze historical data.

Use cases: Workforce analytics, HR reporting, capacity planning

Tools: SQL, Business Intelligence platforms (Tableau, Microsoft Power BI), HR reporting systems

Examples: Tracking employee headcount trends over time to identify growth or reduction areas. Analyzing headcount by department, location, or job level to understand workforce distribution.

storage

Visier's approach: Visier's "valid until it's not" approach to valid time maximizes storage efficiency. By storing only the changes in data, Visier avoids the need for redundant records, minimizing storage space and improving overall performance.

Industry approach: Traditional databases often rely on storing separate records for each period of valid data. This can lead to a significant increase in storage needs, especially when tracking data with frequent changes. This inefficiency can impact both cost and performance.

Use cases: Workforce analytics, HR reporting, capacity planning

Tools: SQL, Business Intelligence platforms (Tableau, Microsoft Power BI), HR reporting systems

Examples: Tracking employee headcount trends over time to identify growth or reduction areas. Analyzing headcount by department, location, or job level to understand workforce distribution.

valid time handling

Visier's approach: Visier directly integrates valid time into its data model. Each data record is considered valid until a new record indicates a change, eliminating the need to store separate entries for each time period. This "valid until it's not" approach simplifies data representation, reduces storage needs, and improves query efficiency.

Industry approach: In traditional databases, handling valid time often requires creating separate records for each period when data is valid or overwriting data. This can lead to increased storage needs, complex queries, or a loss of historical data, making historical analysis cumbersome and inefficient. Alternatively, slowly changing dimension type 2 offers a more effective alternative. Instead of overwriting data, they add a new row to the dimension table for each change, preserving previous versions for analysis and reporting. This can be implemented using valid time periods, version numbers, or current flag fields.

Industry-related terms: slowly changing dimension type 2

Use cases: Data warehousing, business intelligence, data analysis, reporting, querying, filtering

Tools: SQL, NoSQL, databases, data lakes, data warehouses

Querying data

querying for headcount

Visier's approach: Visier handles time granularity at runtime allowing for flexible headcount calculations at any desired level (daily, weekly, or monthly). You can query against other referenced subjects or events without explicitly writing joins. For example, the number of employees promoted in the 24th week of 2023 requires a scan of the Promotion events in the time period and a join to the employees who received the promotion in the time period and were still active at the end of the period.

Industry approach: Requires a complex temporal SQL query to handle time granularity. The finest granularity of the headcount calculation is limited by the granularity of the snapshots in the database. Joins need to be explicitly written in the query to combine data from different tables and time ranges.

Use cases: Workforce analytics, HR reporting, capacity planning

Tools: SQL, Business Intelligence platforms (Tableau, Microsoft Power BI), HR reporting systems

Examples: Tracking employee headcount trends over time to identify growth or reduction areas. Analyzing headcount by department, location, or job level to understand workforce distribution.