Prepare organizational data

This article introduces key concepts about compiling and uploading organizational data in Workplace Analytics. After reading this, you will know what kind of organizational data to provide, what that data can help you discover, and how to upload the data.

About organizational data

Organizational data is information about employees that your company provides to Workplace Analytics. Workplace Analytics combines your organizational data with Office 365 to provide rich, actionable insights into your company’s communication and collaboration trends to help you make more effective business decisions.

What you can do with organizational data

  • Know how people communicate across job functions, department groups, and management hierarchies by enabling the grouping and filtering of organizational data attributes.
  • Enable metrics customization to quantify group relationships. For example, collaboration time between marketing and sales.
  • Enable specific metrics calculations, such as insularity and redundancy.

Sources of organizational data for your employees

  • Human resources (HR) information systems
  • Other line-of-business (LOB) data stores with business outcome data, such as:
    • Performance review data from specific work groups
    • Employee engagement scores captured by HR outside of HR information systems
    • Sales or other quota attainment data that provide additional views into performance
    • Employee survey data

Examples of organizational data include: job family, job role, organization, line of business, cost center, location, region, layer, level, number of direct reports, and manager. This data is supplied to Workplace Analytics at the individual level, meaning these attributes are used to give context to each person that makes up the data set.

Internal teams required to supply Workplace Analytics with organizational data

  • The team that manages your organization's HR information system needs to provide you a data export of HR attributes for individual employees.
  • The line-of-business owners who have data that your analysts want to use.

How often to upload

You can update organizational data as frequently as you like, but it will be refreshed upon the latest update of email and calendar data. Since email and calendar data is updated once a month, it might make sense to provide updates for this data on a similar cadence.

Whom to include in the data

HR data

We recommend that you include every person in your company as part of your data upload, even if you only plan to gather collaboration data for a sub-group (target population) within your company.


If you upload data for everyone, you can analyze who everyone is collaborating with, even if they are outside your target population.

For example, if the people in Marketing communicate frequently with the people in Product Development, but Workplace Analytics only has HR data about the Marketing organization, you won't be able to create reports to show how much time Marketing is spending with Product Development.

If you can't include every person in your organization, the minimum to include is all people for whom collaboration data is being gathered. This minimum enables you to analyze collaboration patterns between groups within this population, but not between groups outside this population.

Line-of-business data

Unlike HR data, for line-of-business data you might not need to include every person in your company as part of your data upload. Knowing the scenarios you want to analyze will help you to decide.

For example, suppose you want to compare collaboration patterns between employees in the Sales organization who have high engagement as compared to those who have low engagement. Although you will want HR data for all employees so you can characterize broader collaboration patterns, you only need engagement score data for employees in the sales organization, because you will use the score values to group and filter specific report outputs.

What to include in the upload

To get full functionality from Workplace Analytics, you will need to supply several required attributes as described in the following Export data section. Additionally, you can supply up to 65 total attributes to group and filter data in interesting and custom ways.

The specific requirements for which organizational data attributes you want to provide will depend on the specific business scenarios that data analysts want to explore. You can use the following steps to help you think through all the relevant factors in determining what data to provide.

In your analysis, you will look at collaboration across different employee segments. These employee segments can be groups as defined by organizational data, groups made up of organizational hierarchy levels, or groups made up of performance, engagement, or other line of business data. The following are some common analyses examples.

Collaboration between groups

A common analysis scenario is to find patterns of collaboration between different groups of employees. For example, you might want to know how much your product marketing team is talking to your sales team.

Attributes for segmenting populations can be helpful to consider in defining patterns of collaboration:

  • Job family or role attributes, such as profession, function, discipline, job code
  • Organization, line of business, or cost center, such as HR, Finance, Sales, and Marketing
  • Location attributes, such as city, state, country, and regions, as defined by your organization
  • Attributes that describe the type of worker, such as remote, exempt/non-exempt, FTE/vendor, part time/full time, tenure in organization, and tenure in current role.

Most of these attributes are available within HR information systems.

Hierarchical collaboration

It is also common to want to define patterns of collaboration behavior in reference to the hierarchy in your organization, as well as to quantify collaboration between managers and individual contributors, and between higher and lower levels and layers in the organization.

Concepts that are helpful in this analysis are:

  • Whether an employee is an individual contributor or a manager.
  • Organizational hierarchy. For example, the names of all managers above the employee in that employee's reporting structure; each manager can be stored as a separate attribute.
  • Layer. For example, the position of the employee in the organizational hierarchy where layer 0 = the top leader in the company.
  • Span. For example, the number of direct reports assigned to an employee.
  • Level. For example, senior manager, VP, director, CVP, and so on.

Most of these attributes are also found in HR information systems.

Collaboration, engagement, and outcome data

Finally, you many want to consider tying collaboration behavior patterns to employee engagement scores or other performance outcome data such as sales quota attainment or high/low performance ratings. These data are often found outside of traditional HR information systems, either in separate HR data repositories or in line-of-business systems.

Step two – Avoid common pitfalls

Strive to avoid the following pitfalls when choosing the organizational data you want to provide:

Too many unique values

Sometimes an attribute will have too many unique values to be used in grouping and filtering. For example, if a job function or code is very narrowly defined, it might not give a useful view of the overall group. If it is likely that an attribute could have hundreds of unique values resulting in a small population group per value, the attribute may not be useful.

Too few unique values

Conversely, sometimes an attribute is too broadly defined to allow for useful filtering. For example, if your organization resides entirely in the United States and your HR records per employee contain a country code that always equals US, that attribute would not be useful to include.

Redundant attributes

Some attributes may represent the same data and provide unnecessary redundant data for analysis. For example, HR data could contain both a cost center ID and a cost center name for an employee. Since both represent the same information in a slightly different format, you may want to include only the one with the more “user friendly” name.

Attributes that only exist for a population subset

When choosing attributes to include, be aware of cases where some attribute values might be populated for one organization but not others. For example, if you bring in sales quota attainment data that only applies to your sales organizatio, you must be mindful that this data cannot be used for filtering and grouping employees outside of sales.

Dirty data

In addition to some attributes only being applicable for certain employees in the population, you may find that data from HR and other line-of-business systems can often be dirty or inconsistent. As noted later in this topic, key organizational data attributes must be supplied in order for Workplace Analytics to accept the data. If your systems have gaps in these special attributes, you will have to perform data cleansing to provide accurate values for the missing entries or suitable defaults.

Step three – Export data

After you have identified the data to provide, you need to export it into a format to upload in Workplace Analytics. This section explains how to correctly format the data into a comma-separated value (.csv) file that will successfully upload into Workplace Analytics.

Required attributes and file format

The data must be supplied in a UTF-8 encoded .csv file and contain a set of required attributes for the population. The following table provides details on the required attributes and the data coverage requirements.

Required attributes must be supplied with the following exact column headers (case sensitive) in the .csv upload:

  • PersonId
  • EffectiveDate
  • LevelDesignation
  • ManagerId
  • Organization

Reserved optional attributes are reserved column headers for attributes that are currently used only to filter and group data. In the future, they will be used for additional metric calculations.

  • FunctionType
  • HireDate
  • HourlyRate
  • Layer
  • SupervisorIndicator
  • TimeZone

Although these attributes are not required, if included, they must meet the following coverage requirements as described in the table that follows:

Custom attributes are any additional attributes you want to define to use in filtering and grouping data.


  • The maximum number of total attributes allowed in the system is 65 (including the attributes mentioned above).
  • All dates should be in the MM/DD/YYYY format.

Attribute description and data coverage requirements

Attribute (column header) Description of data / data validity Data coverage requirements
PersonId Unique identifier for the employee record. This identifier can be the employee's primary SMTP address or email alias. It must be in a simplified format that contains no spaces. For example:
  • Allowed:
  • Not allowed: <Name, Person> (
  • Each row must contain a valid PersonId. Each upload file can have only ONE record with the same PersonID / EffectiveDate pair.
    EffectiveDate Date for which the given attribute value applies for the employee. The attribute applies until another record for the same attribute with a different effective date is specified. Each row must contain a valid EffectiveDate. Each upload file can have only one record with the same PersonID / EffectiveDate pair.
    LevelDesignation The employee’s level, represented as a string. The level will be specific to your organization and can represent an employee’s experience or management level, or seniority within the organization. This data is needed to correctly calculate metrics for redundancy and insularity. Each row must contain a LevelDesignation value.
    ManagerId Unique identifier for the employee’s manager. This data is needed to correctly calculate metrics for time spent with managers and their direct reports.
    This identifier can be the manager's primary SMTP address or email alias. It must be in a simplified format that contains no spaces. For example:
  • Allowed:
  • Not allowed: <Name, Person> (
  • Each row must contain a valid ManagerId.
    Organization The internal organization the employee belongs to. An employee’s organization will be specific to your individual needs and could be identified by the leader of the organization, or other naming convention. This data is needed to correctly calculate metrics for redundancy and insularity. Each row must contain an organization value.
    FunctionType The job function the employee performs. This is specific to your organization. This data is used to filter and group reports, and for grouping of data in Explore-the-metrics features. This attribute column is not required. If it is included, then each row must contain a function value.
    HireDate The date the employee began employment. This date determines the beginning date for calculating metrics of a measured employee. If an employee has multiple hire dates (for example: first hire date, most recent hire date), we recommend using the most recent hire date. Each row should ideally contain a valid HireDate. If not included, metrics will be calculated from the start date of the data collection period.
    HourlyRate The salary of the employee, represented as an hourly rate (if you have annual rate, divide each record by 2080). Note: The value can be formatted as a whole number, or include two decimal places, and cannot include any special characters such as a dollar sign. The value can represent pay only, or include the full value of benefits, as long as that choice is consistently applied for all employees. This is used in calculations and can be used to filter and group employees. Note that the Explore-the-metrics feature uses a fixed default HourlyRate of $75. When the default rate is changed, it applies retroactively to anyone without an effective hourly rate for the next scheduled (usually set monthly) refresh of your organizational (HR) data. This attribute column is not required. If it is included, then each row must contain a floating point or integer value with no special characters (such as a dollar sign).
    Layer The place within the organizational hierarchy where the employee belongs. The layer is represented as an integer and expressed as distance from the top leader of the organization. For example, the CEO, is at layer 0. This data is used to filter and group reports, and for grouping of data in Explore the metrics features. This attribute column is not required. If it is included, then each row must contain an integer value.
    SupervisorIndicator Use this attribute to view the habits of people managers in your organization in Power BI visualizations. It powers the Overview table as well as the Generated Workload charts that are generated when you use the Manager Impact Power BI template.

    This attribute indicates the manager status of each employee as IC (individual contributor), Mngr (manager), or Mngr+ (manager of managers); however, note that if different nomenclature is used in your file, you must update the Power BI chart filters accordingly. If you include SupervisorIndicator, you must also include the values IC, Mngr, or Mngr+ in your organizational data.

    This attribute is required only if you want to use the Manager impact dashboard in Power BI.
    TimeZone Time zone in which the employee performs work. This must be one of the time zones in Time zones in Workplace Analytics. If you do not have a time zone available for each employee, the system will use the default, which is Pacific Standard Time. This attribute column is not required. If not included, the default will be used.
    Any user-defined columns Additional columns can represent any data that you want to use in queries to group and filter employee records. No coverage requirements.

    Supplying data over a time period

    By default, Workplace Analytics includes meeting and email data for measured employees for one year.

    Organizational data is provided to Workplace Analytics with an effective date associated with each row in the upload file, as described in the table.

    If you do a point-in-time export of organizational data from your HR information system as of the current date, you will get a picture of your employee population for that single point in time; therefore, for greatest data fidelity, during provisioning you should provide organizational data exports for each of the last 13 months. This can be supplied in a single file or in a sequence of files.

    This means that for each measured employee, you would have 13 separate rows for each employee, with an effective date for each month in which data was pulled. If this is not possible, then you can provide one single point in time. In this case, the effective date should be set to the first day of the current month, one year back. For example, if provisioning occurred in October 2018, the effective date for all rows should be set to 10/1/2017.

    Example .csv export file

    This is an example snippet of a valid CSV export file:

    PersonId,EffectiveDate,HireDate,ManagerId,TimeZone,LevelDesignation,Organization,Layer,Area,10/1/2017,1/3/2014,,Pacific Standard Time,5,Sales,8,Southeast,11/1/2017,1/3/2014,,Pacific Standard Time,5,Sales,8,Southeast,12/1/2017,1/3/2014,,Pacific Standard Time,4,Sales,7,Northeast,10/1/2017,8/15/2015,,Pacific Standard Time,6,Sales,9,Midwest,11/1/2017,8/15/2015,,Pacific Standard Time,6,Sales,9,Midwest,12/1/2017,8/15/2015,,Pacific Standard Time,6,Sales,9,Midwest


    Numerical fields (such as "HourlyRate") must be in the "number" format and cannot contain commas. Also, the .csv file must use UTF-8 encoding. For more information about saving a file in UTF-8 format, see Solution.

    Allowed time zones

    The default time zone for Workplace Analytics is Pacific Standard Time (PST). Visit Time zones for Workplace Analytics for a complete list of the times zones that you can use.

    Step four - Upload data

    After you create your source .csv file, you can upload it to the Workplace Analytics service. If this is the first time that you will upload organizational data, see Upload organizational data (first upload). If this is not the first time, see Upload organizational data (subsequent uploads).

    After your data has been successfully uploaded, Workplace Analytics performs additional validation and processing to complete provisioning. If any problems arise, the Workplace Analytics team will contact your Workplace Analytics administrator.