The Data Trust Framework

This document outlines the design and rollout plan for the Data Trust Framework. The goal is to move from a reactive, chaotic "fire drill" approach to a structured, proactive system that builds and maintains trust in the company's data assets.

1. Problem Definition: The State of Data Integrity

The current process for handling data quality issues is undefined, creating a high-risk environment where data cannot be consistently trusted for critical decision-making. This erodes confidence and leads to significant operational inefficiency.

The Recurring Scenario: The "Red Alert" Report

A senior leader flags a critical KPI on a widely-used dashboard just before a major weekly business review. A metric shows an alarming and illogical shift. A message is posted in a public channel:

"Can someone from the data team confirm if this number is correct? We need to know before the 11 AM review."

This single question triggers a predictable pattern of chaos:

• The Scramble: Multiple team members simultaneously begin investigating, often duplicating efforts. One person checks the dashboard logic, another dives into the raw data, and a third starts reviewing recent pipeline runs. There is no coordination.
• The Investigation Black Box: Stakeholders have no visibility into the investigation's progress. Lacking a central status point, they send direct messages to individual engineers, creating constant interruptions and increasing pressure.
• The Inevitable Discovery: After significant time and effort, the root cause is often found to be a silent failure in an upstream data source: an API change, a corrupted file, or a pipeline that failed without an alert.
• The Short-Term Fix: The immediate issue is resolved, but the underlying vulnerability remains. The incident is not documented, no preventative actions are assigned, and the team is left to hope it doesn't happen again.

Core Pain Points

a) Detection & Alerting:

• Reactive, not Proactive: Issues are discovered by stakeholders, not by internal monitoring.
• Silent Failures: Critical data pipelines fail without generating clear, actionable alerts.
• Lack of Data Assertions: No automated checks exist within pipelines to validate data freshness, completeness, or accuracy.

b) Response & Triage:

• No Clear Ownership: No designated "Incident Commander" leads to a chaotic response.
• Ineffective Communication: Stakeholders interrupt engineers for updates due to a lack of a standard communication protocol.
• Difficult Root Cause Analysis: A lack of data lineage and observability tools makes investigation slow and manual.

c) Resolution & Prevention:

• Missing Runbooks: Every incident relies on individual heroics and institutional knowledge.
• No Post-Mortem Culture: The team never performs a blameless post-mortem to understand the root cause and contributing factors.
• Untracked Improvements: Preventative measures are discussed but never formally tracked, ensuring problems reoccur.

2. Identify Stakeholders & Their Needs

To design an effective process, we must understand the needs and frustrations of everyone involved.

Stakeholder Role	Current Frustrations (Pain Points)	What They Need in a New Process
Data Engineers	Pulled from planned work for "urgent" fire drills. High stress from being on the spot. Blamed for data issues that may originate elsewhere. Wasted time investigating the same recurring problems.	Clear, actionable alerts with specific error messages. Well-defined on-call responsibilities and runbooks. A blameless process for investigating failures. Protection from constant stakeholder interruptions.
Data/BI Analysts	Their dashboards and reports are the first to be flagged as "wrong". Lose credibility with business stakeholders. Blocked from doing their own work while they wait for data to be fixed.	Proactive notification when a data source they depend on is "stale" or "unreliable". A status page or channel to check for known issues. Clear data lineage to understand upstream dependencies.
Product and Project Managers	Can't trust the data needed for feature analysis or decision-making. Unsure who to contact or where to get updates during an incident. Product experiments may be invalidated by bad data.	A reliable, central source of truth for data. Clear, non-technical communication on the impact and ETA for a fix. Confidence that data powering their features is monitored.
Business Stakeholders (e.g., Marketing, Finance)	Make wrong decisions based on faulty data. Lose trust in the data team and the systems they build. Don't know the status of an issue after reporting it.	A simple way to report a suspected data issue. Timely acknowledgment of their report. Regular, easy-to-understand updates on the resolution. Confidence that the data they use for critical decisions is accurate.
Engineering/Team Lead	Team velocity on the roadmap is constantly derailed. Has to manage stakeholder anxiety and team burnout. Spends time coordinating the fire drill instead of managing the team.	A predictable process that they can manage. Data on incident frequency and type to justify headcount or tool investment. A system that empowers the team to solve issues without constant oversight.

3. Designing the New Process: The "To-Be" State

This section outlines the new, structured process for managing data quality incidents. It is designed to be predictable, transparent, and focused on continuous improvement.

Core Concepts

• Incident Severity Levels: To ensure the response matches the impact, all incidents will be classified:
  • SEV-1 (Critical): Major impact on key business metrics, executive-level visibility, or customer-facing data. Requires immediate, all-hands response.
  • SEV-2 (High): Significant impact on internal operations or important dashboards. Requires a response within business hours.
  • SEV-3 (Medium): Minor data issue with a known workaround or limited impact. Can be handled as regular planned work.
• Key Roles:
  • Incident Commander (IC): The single point of contact responsible for managing the incident response. This is a temporary role, assigned to the on-call engineer by default. The IC coordinates, communicates, and delegates tasks but does not necessarily fix the problem themselves.
  • Subject Matter Expert (SME): The engineer(s) with deep knowledge of the affected system. They are responsible for the technical investigation and resolution.
• Communication Channels:
  • #data-incidents: A dedicated public Slack channel for all incident communication. This replaces DMs and ad-hoc messages.
  • Incident Ticket: A single source of truth (e.g., in Jira) for every incident, tracking status, impact, and resolution.

The New Workflow

Roles & Responsibilities (RACI Chart)

Activity	On-Call Engineer (IC)	Data Engineer (SME)	Eng/Team Lead	TPM/PM/PO
Declare Incident	R	A	C	I
Communicate Status	R	C	I	R
Investigate Root Cause	C	R	A	I
Deploy Fix	C	R	A	I
Conduct Post-Mortem	C	R	A	R
Track Action Items	I	C	A	R

4. Rollout & Implementation Plan

A phased approach will be used to introduce the Data Trust Framework, ensuring smooth adoption and allowing for iterative improvements.

Phase 1: Foundation & Tooling (Weeks 1-2)

• Objective: Prepare the necessary infrastructure and documentation.
• Key Activities:
  • Create the #data-incidents Slack channel and document its purpose.
  • Configure Jira with an "Incident" issue type and the workflow defined above.
  • Draft initial runbook templates for common data source failures (e.g., "Third-Party API Failure," "Stale Data Warehouse Table").
  • Communication: Announce the upcoming initiative to the Data team and Engineering Leadership, explaining the "why" behind the project.

Phase 2: Pilot Program (Weeks 3-4)

• Objective: Test the new process with a single, high-visibility data asset.
• Pilot Candidate: The "Weekly Customer Acquisition Cost (CAC)" data pipeline and dashboard.
• Key Activities:
  • Conduct a focused training session with the Data Engineers who maintain the CAC pipeline.
  • Run a simulated SEV-2 incident ("fire drill") to walk through the process in a safe environment.
  • Apply the full framework to any real incidents that occur for the pilot asset.
  • Feedback: Hold a feedback session with the pilot team to identify friction points in the process.

Phase 3: General Availability & Training (Weeks 5-6)

• Objective: Roll out the framework to the entire organization.
• Key Activities:
  • Refine documentation and runbooks based on pilot feedback.
  • Conduct formal training sessions for all Data Engineers on the new process and their roles (IC, SME).
  • Host a company-wide or department-wide session for Analysts, PMs, and Business Stakeholders on how to report issues and where to find status updates.
  • Communication: Announce the official launch via email, Slack, and in relevant team meetings. Clearly articulate the benefits for each stakeholder group.

Phase 4: Measure & Iterate (Ongoing)

• Objective: Embed the framework into the team's culture and continuously improve it.
• Key Metrics to Track:
  • Time to Detection (TTD): The time from when an issue occurs to when it's formally declared.
  • Time to Resolution (TTR): The time from incident declaration to resolution.
  • Number of Recurring Incidents: Are our post-mortems effectively preventing repeat failures?
  • Source of Detection: What percentage of incidents are found by internal alerts vs. user reports? (Goal: Increase internal detection).
• Activities:
  • Review incident metrics monthly.
  • Ensure every SEV-1/SEV-2 incident has a completed post-mortem with tracked action items.
  • Hold quarterly reviews of the framework to make adjustments as needed.