GoodData terms and key terminology

    Project Hierarchy

     

    In the platform, your projects are organized into dashboards, dashboard tabs, reports, and the metrics that are contained within those reports. At the lowest level, facts, attributes, and source data represent the foundational components that are aggregated to form the metrics displayed in the dashboard reports. These terms will be explored in greater detail in the coming pages.

     

     

     

    Figure 1 The your project hierarchy.

     

    Key Terminology

     

     

    Term

     

    Definition

     

    Examples

     

    Data

     

    Raw records that your users load into project data sets for use in the project’s data model.

     

    1, IBM, $50000, 10/10/2012

     

    Facts

     

    Individual numerical measurements attached to each data set in the source

     

    Opportunity amount (i.e.

    $25,000)

     

     

     

    data.

     

    Facts are always numbers and are the smallest units of data.

    Campaign clicks (i.e. 212); Website views (i.e. 4,508)

     

    Attributes

    Non-measurable descriptors used to break apart metrics and provide context to reports’ data.

    Attributes dictate how metrics are calculated and represented.

     

    (by) month; (by) store;

    (by) employee; (by) region;  (by) department

     

    Metrics

    Aggregations of facts, or counts of distinct attribute values, presented as numbers in your reports. Metrics are defined by customizable aggregation formulas.

     

    Metrics represent what is being measured in a given report.

     

    sum of sales; average salary; total costs;

    count of Opportunity (attribute)

     

    Reports

    Visualizations of data that fall into one of three categories: tables, charts, and headline reports.

     

    All reports contain at least one metric (what is being measured), and often contain one or more attributes (dictating how that metric is broken down).

    A table that shows employee salaries (metric) broken down by quarter (attribute)

     

    A line graph that shows revenue (metric) generated across each month in the past year (attribute)

     

    A bar graph that shows sales figures (metric) broken down by region (attribute)

    Dashboard Tabs

    The pages upon which reports (either tables or charts) and other dashboard elements (lines, embedded content from the web, widgets, and filters) are

    ROI

     

    Funnel/Goals

     

     

     

    displayed.

     

    Dashboard tabs are typically used to organize reports within a given dashboard.

     

     

    Dashboards

     

    Groups of one or more dashboard tabs that contain reports belonging to a common category of interest.

     

    From Leads to Won Deals (marketing dashboard)

     

    Projects

     

    Groups of one or more dashboards containing tabs, reports, metrics, data models, data sets, and users.

     

    Projects are often provisioned for use by an entire team or department. In these cases, a change made by one editor would be visible to all.

     

    Sales Management Leads to Cash

    Subscription Management

     

    Data Set

     

    A collection of related facts and attributes, presented as a table.

     

    An opportunity data set, containing facts related to attributes like name, opportunityamount, and stage.

     

    Logical Data Model (LDM)

     

    A model of the relationship between all of the facts and attributes within a project.

     

    LDM Diagram in the form of an oriented graph (entity relationship diagram).

     


     

    Metrics – The Centerpiece of Reports

     

    For Editors and Administrators

     

    the primary mission is to help you gain business insights from your metrics – those numerical values that populate (or are visually represented within) your reports and widgets. Below we’ll provide a few more details on what we really mean by this term.

     

    Metrics Are Aggregations

     

    Metrics are fundamentally aggregations of facts. Here, when say fact we mean a numerical record that represents an individual business transaction. In other words, facts are the numbers you’d find in each cell of a spreadsheet:

     

    • Revenue earned from a sale
    • Cost of some item purchased
    • The hours worked by one employee
    • The number of items shipped in one delivery

     

     

    Facts can be aggregated by any of four operations, explained in further detail below:

     

     

    Operation

     

    Description

     

    Example

     

    SUM

     

    Adds facts together to form a metric.

     

    The metric defined by the SUM of the Sales fact would be computed by adding revenue earned from all individual sales transactions.

     

    AVG

     

    Calculates the mean of a set of facts to form a metric.

     

    The metric AVG of Sales would be computed by adding all sale transactions together and dividing by the number of sales transactions.

     

    MIN

     

    Identifies the lowest value from a set of facts.

     

    The MIN of the Sales facts is the lowest sales transaction value on record.

     

    MAX

     

    Identifies the largest value from a set of facts.

     

    The MAX of the Store Sales fact is the highest sales transaction value on record.

     

    A fifth aggregation operation for forming metrics is COUNT.

     

    COUNT is distinct from the other operations because it aggregates attribute values rather than facts.

     

     

     

    COUNT

     

    Counts the number of unique values belonging to an attribute.

     

    COUNT of Stores will return the number of different stores based on the number of distinct values of the Store attribute.

     

     

    NOTE: Because COUNT is a slightly different type of operation, there are some nuances you should understand before putting it to use. These are covered in a section that appears later in this guide: Creating Advanced Metrics: Forming Metrics With the COUNT Operation.

     

     

    Advanced Metric Definitions

     

    Metrics are fundamentally aggregations of facts and attribute values, but that’s not to say that an aggregation is all that goes into forming a metric. Metrics can also take on the following features:

    • Metrics can contain one or more predefined metrics, which can be combined or manipulated with various arithmetic operations.
    • Metrics can contain numerical variables that take on different values, customized for certain users or groups of users, impacting metric computations in reports displayed on those users’ dashboards.
    • Metrics can contain functions like absolute value, signum, and square root.
    • Metrics can contain one or more filters that specify which subsets of data should be included or excluded when computing metric values.
    • Metrics can contain complex conditional statements.
    • Metrics can be set to override report attribute and filter settings in various ways. As you can see, metrics are powerful tools for anyone seeking business insights from

    their data. This guide will cover a number of the features that will help you get the most

    out of your business dashboards, but some techniques for building complex metrics fall outside the scope of this document.

     

    For more on getting started with building custom metrics, see the section Creating Advanced Metrics that appears later in this guide. If you’re interested in using the MAQL data query language to write advanced metrics that take advantage of many of the features listed above, see the MAQL Reference Guide.