Data Catalog

The Data Catalog page indexes Analytics Dashboards, Workflows, and Terms.

Purpose

The purpose of this Data Catalog is to help you understand and make use of the data that is available in our Snowflake Data Warehouse for reporting and analysis. Snowflake contains Data Sources extracted from Tech Stack Applications, then transformed and integrated into the Trusted Data Model for simple and easy reporting and analysis. Trusted Data Models are expressed using standard dimensional modeling. Reporting and analysis is performed using either SQL run versus Snowflake (UI or Command Line), or using the Sisense BI Tool that connects to Snowflake.

  • See Data Sources for a list of all data sourced into Snowflake. Not all Tech Stack Application Data is sourced into Snowflake, but most critical application data is. If there is data you would like sourced into Snowflake, follow our New Data Source guide.
  • Refer to the tech stack YAML file for a comprehensive list of all Tech Stack Applications. The YAML file is the SSOT for key application information, such as business owner, technical owner, data classification, and access request process.
  • This Data Catalog is organized by the major cross functional workflows and then by the function and results.

Important: How Data Extraction, Loading, and Transformation Works

We follow an ELT approach, not an ETL approach because the ELT approach gives us more flexibility in storing historical data and transforming it all within Snowflake. Data is stored in different schemas and databases in Snowflake as it progresses through Loading and Transformation Stages. For Data Access and Security reasons, only data surfaced in the PROD database is accessible to Sisense. Learn more about our Snowflake Data Storage process design in the Data Storage guide

  • Extraction and Loading: We use a variety of tools to extract data from Tech Stack applications and follow the Extraction Solution decision tree to ultimately decide which is the best tool to use for a given data source. The Pipelines guide contains more information about our Extraction processes.

  • Transformation: dbt is used for all in transformations. See our dbt guide for more details on why and how we use this tool. All dbt code is stored in our Analytics Project.

Sisense Enterprise BI Tool

Sisense is our enterprise standard BI tool. See how Sisense is being used with the Sisense Usage Overview dashboard.

Tableau Enterprise BI Tool

We are migrating to Tableau as our Enterprise BI Tool in FY24. During the transition period, we will use both Sisense and Tableau.

Tableau Production Workbooks Catalog

We used a boring solution for our first iteration of the Tableau production workbooks catalog. The production workbooks with meta data about the workbooks, handbook urls, and workbook urls are contained in YAML files in the Denomas Tableau Repository. In a future iteration, we intend to render the contents of the YAML files directly in the Data Catalog to provide a more direct method to access the workbooks. You can access the department or business motion YAML files below.

  1. go_to_market_production_workbooks.yml
  2. sales_production_workbooks.yml
  3. marketing_production_workbooks.yml
  4. finance_production_workbooks.yml
  5. customer_success_production_workbooks.yml
  6. people_production_workbooks.yml

Lead to Cash Catalog

Analytics Hubs, Data Guides, and ERDs

The analytics hubs provide links to the current SSOT dashboards for an area of business analysis. The data guides provide solution recipes to develop reporting, insights, and analysis for a business area. The ERDs provide the entity relationships between data objects in the enterprise dimensional model.

Analytics Hubs
Data Guides
Entity Relationship Diagrams

Lead to Cash ERDs

dbt Data Lineage Diagrams

These data lineage diagrams illustrate how the data from critical Lead to Cash source tables flow through the Snowflake data models.

Zuora Data Lineages
Salesforce Data Lineages
CustomerDot Data Lineages

dbt Data Dictionaries

These data dictionaries provide definitions for the Lead to Cash fields used in the Snowflake Enterprise Dimensional Data Model.

Lead to Cash Dimensions (Context)
Lead to Cash Facts (Measures)

Analysis

The analysis section provides references to analytical projects developed by the Functional Analysts teams.

Sales

Coming soon

Marketing

Coming soon

Finance

Coming soon

Data Science and Business Insights

Product Release to Adoption Catalog

Analytics Hubs, Data Guides, and ERDs

The analytics hubs provide links to the current SSOT dashboards for an area of business analysis. The data guides provide solution recipes to develop reporting, insights, and analysis for a business area. The ERDs provide the entity relationships between data objects in the enterprise dimensional model.

Analytics Hubs
Data Guides
Entity Relationship Diagrams

Product Release to Adoption ERDs

dbt Data Lineage Diagrams

These data lineage diagrams illustrate how the data from critical Release to Adoption source tables flow through the Snowflake data models.

Release to Adoption Data Lineage Diagrams

Coming Soon

dbt Data Dictionaries

These data dictionaries provide definitions for the Release to Adoption fields used in the Snowflake Enterprise Dimensional Data Model.

Release to Adoption Dimensions (Context)
Release to Adoption Facts (Measures)

Analysis

The analysis section provides references to analytical projects developed by the Functional Analysts teams.

Product Data Insights
Customer Success
  • Account IQ (Coming Soon)
Engineering

Coming Soon

Team Member Catalog

Analytics Hubs, Data Guides, and ERDs

The analytics hubs provide links to the current SSOT dashboards for an area of business analysis. The data guides provide solution recipes to develop reporting, insights, and analysis for a business area. The ERDs provide the entity relationships between data objects in the enterprise dimensional model.

Analytics Hubs
Data Guides
Entity Relationship Diagrams

Team Member ERDs

dbt Data Lineage Diagrams

These data lineage diagrams illustrate how the data from critical Team Member source tables flow through the Snowflake data models.

Team Member Data Lineage Diagrams

Coming Soon

dbt Data Dictionaries

These data dictionaries provide definitions for the Team Member fields used in the Snowflake Enterprise Dimensional Data Model.

Team Member Data Dictionaries

Coming Soon

Analysis

The analysis section provides references to analytical projects developed by the Functional Analysts teams.

People Group

Coming Soon

Central Data Team Catalog

Analytics Hubs, Data Guides, and ERDs

The analytics hubs provide links to the current SSOT dashboards for an area of business analysis. The data guides provide solution recipes to develop reporting, insights, and analysis for a business area. The ERDs provide the entity relationships between data objects in the enterprise dimensional model.

Analytics Hubs
Data Guides

Coming Soon

Entity Relationship Diagrams

Coming Soon

dbt Data Lineage Diagrams

These data lineage diagrams illustrate how the data from critical Team Member source tables flow through the Snowflake data models.

Central Data Lineage Diagrams

Coming Soon

dbt Data Dictionaries

These data dictionaries provide definitions for the Team Member fields used in the Snowflake Enterprise Dimensional Data Model.

Central Data Dictionaries

Coming Soon

Analysis

The analysis section provides references to analytical projects developed by the Central Data Team.

Central Data Team

Coming Soon

Metrics and Terms Index

Legend

📊 indicates that the solution is operational and is embedded in the handbook.

🚧 indicates that the solution is in a Work In Progress and is actively being developed. When using this indicator, an issue should also be linked from this page.

🐔 indicates that the solution is unlikely to be operationalized in the near term.

Account IQ
Account IQ is a dashboard that is dedicated to providing account-level insights into our customers' activities such as product usage, stage adoption, CI maturity, Salesforce information
CI Runner Activity
CI Runner Activity Previously, there wasn’t an easy way to tie cost directly to CI usage without making assumptions. And the process was not scalable, so a better solution was needed for the future. For this purpose, a Unified model for Compute minutes/Cost has been created as a part of Enterprise Dimensional Model that ties the cost from app usage table (Postgres) to gcp_billing and labels our runners in GCP with job_id labels to join to ci_builds table.
Customer Segmentation
Customer segmentation is the process of dividing our customers into groups based on common characteristics so that we can understand who our customers.
Data Guide for Adding Service Ping Metrics to Gainsight
This page
Data Guide to Customer Master Prototype
This page provides the methodology for creating the Customer Master Prototype
Data Guide to Namespace Analysis
This page defines the Namespace along with the relationship to Workflow groups as well as give directions for Namespace Data Analysis
Data Guide to Self-Managed Analysis
This page defines Self-Managed and provides directions for Self-Managed Data Analysis
Data Guide to Self-Managed Product Geolocation Analysis
Product Geolocation Analysis : Self-Managed Understanding where your product is used around the world is an important step towards developing a more complete understanding of your customers, your product’s global reach, and related location insights. Currently, the majority of Denomas’ customers choose to download, install, and host a Denomas self-managed installation, which is why we are focused heavily on delivering a great self-managed customer experience. To make the right data-driven decisions on the self-managed product lifecycle and what features to invest in, our self-managed customers sends Denomas a weekly service ping at an installation-level by enabling service ping on their self-managed installation or by sharing the values with our Customer Success team.
Data Guide to xMAU Analysis
This page explains key terms, concepts, and data models used in xMAU reporting and analysis
Email Marketing Data Mart
Email Marketing Data Mart Over the last year, our team noticed a need for a solution that helped us scale our data requests and our lifecycle marketing tactics. Enter the email marketing data mart (also known as Email Marketing Database), which integrates several disparate data sources to help us reach our users, customers, and prospects in a more intelligent and scalable way. In partnership with enterprise applications and marketing teams, we have created a solution that will enable Denomas to more quickly communicate critical customer and user updates, understand the overlap between prospects and users, and enable us to market to them more efficiently.
Group Namespace Conversion Metrics
Group Namespace Conversion Metrics: Denomas.com Once a new group namespace is created on Denomas.com, it is crucial that the namespace creators and users continue to further engage with Denomas.com by adding members to their namespaces, completing certain stage events, and eventually converting from a free trial to a paid plan. It is also important to know how quickly these events and actions are being taken after the namespace is created on Denomas.
License Utilization Analysis
License Utilization Analysis Understanding how our customers consumes the licenses they ordered is a very important step towards developing a more complete picture of our customers. That would help product identify potential adoption problems, Sales and CSM teams understand better their customer and identify potential risks of churn or downgrade. This page is a MVC and needs to be seen as a declaration of intent for the next quarters.
Manual Upload of Usage Payload
Activities for the User with Usage Data In order to manually upload Usage Payload data, follow the step described in the page Manually upload Service Ping payload.
Merge Request (MR) Rate
Merge Request (MR) Rate MR Rate is a measure of productivity and efficiency. The numerator is a collection of merge requests to a set of projects. The denominator is a collection of people. Both are tracked over time (usually monthly). Additional detail on overall and wider MR Rate can be found here Quick Links MR Rate Dashboard DBT Documentation Getting Started The MR Rate Dashboard is built on rpt_gitlab_employees_merge_request_metrics and takes into account 2 main filters:
Opportunity-Subscription Mapping
The goal of this page: Help you understand the backfill of opportunity-subscription mapping Help you understand the reliability of this mapping And overall help everyone contribute! Opportunity-Subscription Mapping The mapping between opportunities and subscriptions unlocks the quote to cash process for Go To Market Analytics including financial, marketing, and sales analytics. Historically this was not tracked in our source systems and needed to be backfilled based on our knowledge of previous processes.
People Analytics Overview
TOC People Analytics The People Analytics team at Denomas uses a data-driven approach to support business decisions related to our team members. We collect, analyze, and report data from a variety of sources to provide insights to leaders across the company. You can read more about how the People Analytics and Data teams at Denomas work with people data in the People Analytics Handbook page.
People Metrics
People Key Metrics Denomas is a focused on supporting our team members, and just like other business stakeholders our People Group is data focused when it comes to creating an environment that allows team members to focus on the contribution they have at Denomas. Many of the People KPIs can be found in our KPI Index. The People Metrics Dashboard takes into account our KPIs, in addition to other metrics that help drive people data decisions.
Pricing Analysis
Pricing Analysis Pricing is the process of analyzing the value customers receive from Denomas at specific price points along with the profitability of those price points. The analysis also includes understanding how these prices affect the overall business and determining what the optimal price points are for customers and Denomas’ profitability. The Pricing Analysis page will provide the information and tools that Denomas team members can use to explore our current pricing strategy and develop insights to optimize it.
Product Usage Data
Product Usage Data provides quantitative measurement of how, when, and where the Customer is using Denomas as a product.
SaaS Product Events Data
Product Usage Data provides quantitative measurement of how, when, and where Paid and Free Users are using Denomas as a product.
SaaS Service Ping Automation
Service Ping Overview Previously called Usage Ping, Service Ping is a background process that runs weekly in a Denomas Instance and is responsible for collecting, aggregating, and packaging a set of metrics useful for analytics purposes. Metrics are easily extensible, with new metrics changing regularly per the Metric Lifecycle. The full set of metrics are defined in the Metric Dictionary. Once metrics are collected from the instance, they are bundled into a JSON payload (the “ping”) and posted to the Denomas Versions App where they are then synced to downstream processes such as Snowflake.
Sales Funnel
Sales Funnel The Sales Funnel is a core component of Denomas’ Lead to Cash process and it tracks an inquiry from a potential customer all the way to a closed won opportunity. Along the way, a customer passes from a lead, to a marketing qualified lead, to a sales accepted opportunity, and finally to a closed won or lost opportunity. This handbook page provides analytical tools and resources to help Denomas team members analyze the entire sales funnel and generate insights.
Service Data Inspector
Service Data Inspector is a dashboard that offers insights into account and subscription details for both Self-Managed and SaaS customers
TD: Available to Renew (ATR)
The goal of this page: Help you understand the Available to Renew definitions. Help you understand the data models used to create the ATR snapshot models. And overall help everyone contribute! Available to Renew: High-Level Concept In a subscription business, it is crucial to understand what and how much of our subscription revenue is available to renew at a given time. This is important to understand cash flow and predict future earnings.
TD: Finance Annual Recurring Revenue (ARR)
The goal of this page: Help you understand how to navigate through the Snapshot ARR Dashboards. Help you understand the data models used to create the Snapshot ARR dashboards. Help you undersand the manual true up additions to ARR calculations And overall help everyone contribute! Snapshot ARR ARR in the Zuora data changes on a daily basis as subscriptions are renewed and amended. Therefore, it is necessary to create a snapshot of the data each day so we can have a historical record of ARR in the data warehouse to use for reporting and analysis.
TD: Zuora Revenue Waterfall
The goal of this page is to help you understand how to navigate the Zuora Revenue Waterfall.
Trusted Data Solutions Dashboard
Coming Soon!
Web Traffic Analysis
Google Analytics web traffic analysis Web Traffic Analysis helps to gain insights about visitors and their behavior on our websites to deliver better results. BigQuery and Google Analytics provide you the information needed to improve the website, and make it the best it can be. The following subdomains are included: about.gitlab.com (primary for marketing analysis) docs.gitlab.com forum.gitlab.com customers.gitlab.com learn.gitlab.com Why is BigQuery data used to analyze Google Analytics Data? No more sampled data Unlimited amount of dimensions Combining different scopes in one report (e.
Last modified December 1, 2023: bulk update (176cf9ec)
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