Data Export

This document presents a brief description of the different datasets Worklytics is able to export to its customers.

Each file will hold a week worth of data. We consider weeks in ISO format, so weeks start on Monday.

From the Data Export Connections section in the Worklytics Web App, you can export any of the available datasets to the Cloud Provider of your choice. For detailed instructions specific to each provider, refer to the corresponding documentation.

Table of Contents

1. Sample complete output structure

2. How to import the data

3. Weekly Aggregates

   1. List of metrics

4. Collaboration Graph

5. Collaboration Graph Split By Tool

6. Employee Events

7. Items

8. Export data to Cloud Storage Providers

   • Amazon Web Services (AWS) S3

   • Azure Blob Storage (beta)

   • Google Cloud Storage (GCS)

Disclaimer

Worklytics assumes no responsibility for Customer use or management of exported data. In particular, such data is no longer subject to our Privacy Policy, Terms of Service, or any applicable agreements between Worklytics and the Customer Organization.

By initiating or requesting a data export, Customer agrees to ensure that all subsequent usage, management, and retention of exported data by the Customer is consistent with all applicable laws, data protection regulations, and labor/employee/contractor agreements. Customer acknowledges that data exports may include Personally Identifiable Information (PII), which may impose particular data protection requirements on the data.

Additionally, data exports may include information that could be considered sensitive, confidential, or private. Please take care in your usage of the data to avoid inadvertent disclosures of such information. The simplest way to do this is to avoid exposing any non-aggregated data from the Items/Events dataset to end users (ie, humans), and in particular any text fields. Should you choose to expose data from that set to end users, we recommend that you endeavor to respect the privacy models of the underlying data sources.

Sample complete output structure

This is how an output folder with all types might look depending on the format and compression options selected.

• bucket/folder/

  • aggregates/

    • acmeorg_ANONYMIZED_AGGREGATES_20230327Z.parquet

  • collaboration-graph/

    • acmeorg_ANONYMIZED_COLLABORATION_GRAPH_SPLIT_20230327Z000000000000.avro

    • acmeorg_ANONYMIZED_COLLABORATION_GRAPH_SPLIT_20230327Z000000000001.avro

  • collaboration-graph-split/

    • acmeorg_ANONYMIZED_COLLABORATION_GRAPH_SPLIT_20230327Z00000000000N.avro

    • acmeorg_ANONYMIZED_COLLABORATION_GRAPH_SPLIT_20230327Z00000000000N.avro

  • events/

    • acmeorg_ANONYMIZED_EMPLOYEE_EVENTS_20230327Z.json.gz

    • acmeorg_ANONYMIZED_EMPLOYEE_EVENTS_20230327Z.json.gz

  • items/

    • acmeorg_ANONYMIZED_ITEMS_20230327Z000000000000.avro

    • acmeorg_ANONYMIZED_ITEMS_20230327Z000000000001.avro

How to import the data: tips

Each export will contain a week's worth of data and a week column. Given that we occasionally need to reprocess and re-send data, the simplest way to ensure that all the data is up-to-date is to automate the following steps via your internal systems:

1. Look in the bucket for new files to import since the last successful import.

2. Load the files in a temporary table.

3. Delete all the data in the main table for the weeks found in the temp table.

4. Copy all the contents of the temp table into the main table.