Manage Users and Roles

AutoAnalytics uses role-based access control to ensure that the right people have the right level of access at the right time. This helps organizations maintain governance, accountability, and security while multiple teams work on the platform.

Access control is managed from the Admin Dashboard and applies across all projects.

Role Management Overview

With Access Control, Admins can:

• Onboard new users
• Assign roles based on responsibilities
• Control what actions users can perform
• Maintain clear ownership and auditability

Info: Only users with Admin access can manage users and roles.

Role Assignment Steps

1. Navigate to User Management from the Admin Dashboard
2. Click Add User
3. Enter the user’s name and official email address
4. Select the appropriate role
5. Save to grant access

Roles take effect immediately and apply across the platform.

Assigning Roles

• Roles are assigned during user onboarding
• Roles can be updated later by Admins
• A user has one primary role that defines their access level

Warning: Changing a user’s role may immediately restrict or expand what they can see or do.

Built-in Roles

AutoAnalytics comes with predefined roles designed for common enterprise use cases.

What Each Role Can Do

Admin

• Add, edit, and remove users
• Assign and update user roles
• Access configuration settings
• View all projects and reports
• Oversee audits and platform usage

Editor

• Create and manage projects
• Define goals and KPIs
• Initiate deployments
• Run validations and audits
• Download reports

Viewer

• View dashboards and project status
• Access audit and validation results
• Download available reports
• No ability to change data or run actions

Tip: Assign Viewer access to stakeholders who only need visibility, not control.

Permission Coverage (Conceptual)

Access control governs the following areas:

• Project creation and editing
• Goal definition
• Deployment initiation
• Validation and audit execution
• Report downloads
• User and configuration management

Permissions are role-based, not project-by-project, ensuring consistency and clarity.

Best Practices for Access Control

• Assign at least one Admin per organization
• Limit Admin access to essential users only
• Use Editor roles for hands-on analytics teams
• Use Viewer roles for leadership and review-only users
• Review user access periodically

Why Access Control Matters

Proper access control ensures:

• Data integrity
• Clear accountability
• Reduced risk of accidental changes
• Enterprise-ready governance

It allows teams to collaborate confidently while maintaining control.

Overview

This section explains how AutoAnalytics works behind the scenes, in simple business terms, so customers can confidently understand how data flows, where actions happen, and what each part of the system is responsible for.

5.1 Architecture at a Glance

AutoAnalytics is designed as a modular platform, where each major function is handled by a dedicated layer. These layers work together to ensure analytics setup, checks, and audits happen in a structured and reliable way.

At a high level, the platform consists of:

• A User Interface Layer (what you see and interact with)
• A Workflow & Control Layer (how actions are coordinated)
• A Processing Layer (where checks and audits happen)
• A Data & Records Layer (where results and history are stored)
• An Access & Governance Layer (who can do what)

Each layer has a clear role and does not overlap responsibilities.

5.2 User Interface Layer (What You Interact With)

This is the front-facing part of AutoAnalytics that users work with every day.

From the interface, users can:

• Create and manage projects
• Define goals and KPIs
• Initiate deployments
• Run validations
• Trigger audits
• View results and reports

The interface is organized around the Design → Deploy → Validate → Audit flow, so users always know:

• Where they are
• What is completed
• What needs attention next

Info: All actions taken in the interface are tracked at the project level for visibility and accountability.

5.3 Workflow & Control Layer (How Actions Are Managed)

This layer ensures that actions happen in the correct order.

For example:

• Goals must be defined before deployment
• Deployment must exist before validation
• Validation and audit results are always linked back to a project

This prevents:

• Skipped steps
• Inconsistent results
• Confusion across teams

The platform automatically manages dependencies so users don’t have to manually coordinate steps.

Warning: Skipping steps outside the recommended flow may result in incomplete or misleading results.

5.4 Processing Layer (Where Checks and Audits Happen)

This layer is responsible for doing the actual work once an action is triggered.

It handles:

• Goal-based checks
• Deployment verification
• Validation runs
• Analytics audits
• Health scoring

Each action runs independently per project, ensuring:

• One project does not affect another
• Results are consistent and repeatable
• Past runs remain available for reference

From a user perspective, this appears as:

• Status updates
• Progress indicators
• Completion results

5.5 Data & Records Layer (Results, History, and Tracking)

AutoAnalytics keeps a structured record of:

• Projects
• Goals defined
• Deployments initiated
• Validations performed
• Audits completed
• Downloaded reports

This ensures:

• Historical visibility
• Easy comparison across time
• Audit-ready records for internal or external review

Users can revisit past actions without rerunning them unless required.

5.6 Access & Governance Layer (Who Can Do What)

This layer ensures that only the right users can perform sensitive actions.

It controls:

• User roles
• Access permissions
• Admin-only actions
• Visibility of settings and management screens

This is especially important for enterprises where:

• Multiple teams use the same platform
• Accountability and control are required
• Changes must be governed

Info: User access and permissions are managed by Admin users through the Admin Dashboard.

This section explains how to configure AutoAnalytics correctly after onboarding, so teams can start using the product smoothly and avoid setup issues later.

Configuration in AutoAnalytics is project-driven and role-controlled. Most configuration activities are performed by Admin users.

7.1 Configuration Overview

Configuration in AutoAnalytics covers:

• User access and roles
• Master data required for goal definition
• Project-level setup readiness

These configurations ensure that:

• Goals can be defined correctly
• Projects follow a consistent structure
• Deploy and Validate steps work without rework

Info: Configuration is typically done once and reused across projects.

7.2 User Configuration (Admin Only)

User management is handled from the Admin Dashboard.

Admins can:

• Add new users
• Assign roles
• Edit user details
• Remove users when required

Each user is assigned a role that determines what actions they can perform inside AutoAnalytics.

Warning: Only Admin users can manage users and access configuration screens.

7.3 Project Configuration

Every project in AutoAnalytics represents one website or digital property.

When a project is created:

• It becomes available across Design, Deploy, Validate, and Audit
• Its configuration drives all downstream actions

Key project attributes include:

• Website domain
• Industry
• Platform
• Analytics tool

These attributes help AutoAnalytics:

• Apply the correct goal structure
• Maintain consistency across stages
• Display accurate project status

Info: Projects cannot be merged. Create separate projects for separate domains or applications.

7.4 Industry Configuration

Industries help structure goals and KPIs logically.

Admins can:

• Create industries
• Edit industry names
• Associate industries with projects

Industry selection ensures:

• Relevant goal frameworks
• Consistent reporting across similar projects
• Easier comparison across initiatives

7.5 Goal & KPI Configuration (Master Data)

Before defining goals at the project level, AutoAnalytics relies on master goal structures.

Admins manage:

• Goals
• KPI Categories
• KPIs
• Metrics
• Dimensions
• Events

These elements form the building blocks used during the Design step.

Info: Business users select from predefined structures instead of creating everything from scratch.

7.6 Mapping Configuration

AutoAnalytics supports structured mappings to maintain clarity between business intent and measurement.

Key mappings include:

• Goal ↔ Industry mappings
• Goal ↔ KPI mappings

These mappings ensure:

• Goals appear correctly during definition
• KPIs remain aligned to business outcomes
• Consistent interpretation across teams

Warning: Incorrect mappings may lead to missing or incomplete goal definitions during the Design step.

7.7 Configuration Best Practices

To ensure smooth usage:

• Complete all master configurations before onboarding teams
• Keep industry and goal structures simple
• Avoid frequent changes once projects are active
• Assign at least one Admin per organization

7.8 What Happens After Configuration

Once configuration is complete:

• Teams can start defining goals (Design)
• Deployments can be initiated confidently
• Validation and audits will reflect correct business context

Configuration acts as the foundation layer for everything that follows.

AutoAnalytics applies clear data governance practices to ensure that all information handled within the platform is controlled, traceable, and used responsibly.
Governance in AutoAnalytics focuses on analytics configuration data, audit results, and operational records, not customer end-user data.

This section explains what data AutoAnalytics governs, how access is controlled, and how accountability is maintained.

1. Governance Scope

Data governance in AutoAnalytics applies to:

• Project information
• Defined goals and KPIs
• Deployment, validation, and audit results
• User and role information
• Activity and usage records
• Downloaded reports

AutoAnalytics does not govern or store personal data of website visitors (such as names, emails, or transactions).

2. Data Classification Guidelines

AutoAnalytics organizes platform data into logical sensitivity categories to guide access and usage.

Classification Tiers

These classifications help determine:

• Who can view the data
• Where it is displayed
• Whether it can be downloaded or modified

Info: Classification is enforced through roles and platform controls, not manual tagging by users.

3. Data Access Control

Access to data is governed through role-based permissions.

• Admins can access configuration, user management, and all project data
• Editors can access and manage data for execution and analysis
• Viewers have read-only access to dashboards and reports

This ensures:

• Least-privilege access
• Clear accountability
• Reduced risk of unauthorized changes

4. Data Usage Rules

AutoAnalytics enforces the following usage principles:

• Data is used only to support analytics setup, validation, and audits
• Project data is visible only to authorized users
• Results from one project do not affect or expose another project
• Historical data remains unchanged once actions are completed

These rules help maintain data integrity and trust.

5. Audit Trails & Accountability

AutoAnalytics maintains activity records to support governance and internal audits.

Tracked records include:

• User logins and logouts
• Project creation and updates
• Goal definition actions
• Deployment, validation, and audit executions
• Administrative changes
• Report downloads

These records help organizations:

• Trace actions back to users
• Review historical decisions
• Support compliance and governance reviews

6. Data Export & Sharing Controls

AutoAnalytics allows users to export certain data, such as:

• Audit reports
• Validation summaries
• Project-level outputs

Governance controls ensure:

• Only authorized roles can download reports
• Exports reflect the user’s access level
• Sensitive administrative data is not exposed in exports

Warning: Downloaded reports should be handled according to the organization’s internal data policies.

7. Data Retention & Historical Records

AutoAnalytics retains:

• Project records
• Audit and validation history
• Activity logs

This supports:

• Trend analysis over time
• Internal reviews
• Audit readiness

Data retention follows contractual and organizational policies agreed with the customer.

8. Separation Between Customers

AutoAnalytics enforces logical separation between customers to ensure:

• One organization’s data is never visible to another
• Projects and reports remain private
• Access is limited to approved users only

This supports enterprise confidentiality and trust.

9. Governance Responsibilities (Customer Side)

To maintain strong governance, customers should:

• Assign Admin roles carefully
• Review user access periodically
• Remove inactive users
• Control internal sharing of downloaded reports
• Follow internal compliance and audit policies

10. Governance Value for Customers

Strong data governance in AutoAnalytics helps customers:

• Maintain control over analytics operations
• Ensure accountability across teams
• Reduce risk of misuse or confusion
• Support internal and external audits
• Build confidence in analytics outcomes

This section helps new customers set up AutoAnalytics correctly from day one.

2.1 First-Time Login Experience

When you log in to AutoAnalytics for the first time, you will land on the Dashboard.

A welcome message explains the three-step working model of the product:

1. Design – Define your business goals and KPIs
2. Deploy – Generate and apply analytics tracking
3. Validate – Check whether tracking is working correctly

This same structure is consistently followed across the platform screens.

You can complete these steps at your own pace. Progress is saved automatically.

2.2 Dashboard Overview

The Dashboard gives you a high-level view of everything happening in AutoAnalytics.

From this screen, you can:

• Create new projects
• See how many projects are active
• Track how many goals, deployments, and validations exist
• Quickly resume work on recent projects

The dashboard is meant to answer one question clearly:

“Where do we stand right now?”

2.3 Project Setup (Mandatory First Step)

Before using any feature, you must create a Project.

How to create a project:

1. Enter your Website Domain URL
2. Click Create Project

Once a project is created, it becomes visible across:

• Goals
• Deploy
• Validate
• Audit sections

All activities in AutoAnalytics are project-based. Goals, deployments, and validations cannot exist without a project.

2.4 User Roles & Access (Admin-Controlled)

AutoAnalytics uses role-based access to ensure proper control and governance.

From the Admin Dashboard, administrators can manage users.

Available Capabilities for Admins:

• Add new users
• View existing users
• Edit user details
• Remove users when required
• Export user lists

Each user is assigned a role, which determines what actions they can perform inside the platform.

Only users with Admin access can manage users and platform-level settings.

2.5 Managing Users (Admin Dashboard)

The Users screen shows:

• User name
• Email address
• Role
• Account creation date

Admins can:

• Search users by name, email, or role
• Add a new user using the New User button
• Edit or delete users using action icons
• Export the user list for records

This ensures:

• Controlled access
• Clear accountability
• Enterprise-ready governance

2.6 Why This Setup Matters

Proper onboarding ensures:

• Clean project structure
• Correct ownership and accountability
• Smooth collaboration across teams
• No confusion later during deployment or validation

Spending a few minutes setting this up correctly avoids major rework later.

This section outlines the hardware, software, and infrastructure prerequisites required to successfully access, configure, and use AutoAnalytics in an enterprise environment.

AutoAnalytics is a ready-to-use platform. Customers are not expected to build, host, or manage the product infrastructure. The requirements below ensure smooth usage, performance, and reliability from the client side.

6.1 Deployment Model

AutoAnalytics is provided as a configured platform instance.

• Customers do not install or deploy the platform themselves
• Customers access AutoAnalytics through a secure web interface
• Infrastructure responsibility lies with the platform provider
• Customers only need to ensure their internal systems and environments are ready to integrate and interact

Info: No local servers or platform builds are required from the customer side

6.2 End-User Hardware Requirements

The following are recommended for users accessing AutoAnalytics via browser:

Minimum Hardware Requirements

• Processor: Modern multi-core processor (Intel i5 / equivalent or higher)
• RAM: Minimum 8 GB (16 GB recommended for heavy usage)
• Storage (ROM): At least 10 GB free disk space (for downloads, exports, and reports)
• Display: Minimum screen resolution 1366 × 768
  (1920 × 1080 recommended for dashboards and tables)

These requirements apply to:

• Analytics teams
• Admin users
• QA and audit teams

6.3 Supported Browsers

AutoAnalytics is accessed via a web browser.

Recommended Browsers

• Google Chrome (latest version)
• Microsoft Edge (latest version)
• Mozilla Firefox (latest version)

Warning: Outdated browsers may result in incomplete page loads or reduced functionality.

6.4 Network & Connectivity Requirements

To ensure uninterrupted operation, the following network conditions are recommended:

• Stable internet connection
• Minimum 10 Mbps bandwidth per active user
• HTTPS traffic allowed
• WebSocket connections enabled (for live status updates)

This is important for:

• Running audits
• Viewing progress updates
• Downloading reports

6.5 Client Infrastructure Readiness (Website / Application)

Since AutoAnalytics interacts with your digital properties, customers should ensure:

• Access to the target website or application URLs
• Ability to allow analytics-related integrations as part of deployment
• Test and production environments clearly identified
• Basic coordination with internal IT or digital teams

Info: AutoAnalytics does not alter your website content. It only works with analytics-related configurations and checks.

6.6 User Access & Identity Readiness

Before onboarding teams, customers should prepare:

• Official email IDs for all users
• Defined ownership for:
  • Platform Admin
  • Project Owners
  • Review-only users
• Internal approval for user access and permissions

This ensures:

• Controlled access
• Clear accountability
• Smooth collaboration

6.7 Data Storage & Downloads

AutoAnalytics allows downloading:

• Audit reports
• Validation outputs
• Project summaries

Customers should ensure:

• Sufficient local storage on user machines
• Secure internal storage if reports are archived
• Compliance with internal data handling policies

6.8 What Customers Do NOT Need to Set Up

To avoid confusion, customers do not need:

• Dedicated servers
• Cloud accounts for hosting AutoAnalytics
• Database installations
• Backend services or schedulers
• DevOps or deployment pipelines

All of this is handled as part of the platform.

This section explains how to use AutoAnalytics end-to-end, from defining goals to validating analytics implementation.

The implementation follows a guided, step-by-step flow that is clearly reflected in the product UI.

AutoAnalytics implementation is divided into three mandatory stages:

1. Design – Define what needs to be measured
2. Deploy – Set up analytics tracking
3. Validate – Confirm tracking is working correctly

Each stage must be completed in order.

8.1 Implementation Flow Overview

Every project in AutoAnalytics moves through the following lifecycle:

Design → Deploy → Validate

You can see this flow clearly at the top of the product screens, where each step is highlighted as you progress.

Info: You cannot skip steps. Each stage depends on the previous one being completed.

Step 1: Design (Define Goals)

8.2 Design Stage Overview

The Design stage is where you define business goals and KPIs for your project.

This ensures analytics tracking is:

• Business-driven
• Structured
• Consistent across teams

All goal definitions are done from the Goals Dashboard.

8.3 Accessing the Goals Dashboard

From the left navigation:

• Click Define Goals

You will see:

• Total projects initiated
• Total goals defined
• A list of projects with their current goal status

Each project shows whether goals are:

• Not started
• In progress
• Defined

8.4 Defining Goals for a Project

To define goals:

1. Locate your project in the Goals list
2. Click Define Goals

You will then be guided to:

• Select relevant business goals
• Associate KPIs with each goal
• Review the overall goal structure

Info: Goals and KPIs are selected from predefined structures to maintain consistency.

8.5 Reviewing Goals (Preview)

Before moving to deployment, you can:

• Review the goals defined
• Ensure coverage aligns with business needs
• Confirm no critical goals are missing

This review step helps avoid rework later.

Warning: Changes to goals after deployment may require reconfiguration.

Step 2: Deploy (Configure & Get Code)

8.6 Deploy Stage Overview

The Deploy stage sets up analytics tracking based on the goals you defined.

Deployment activities are managed from the Deploy Dashboard.

8.7 Accessing the Deploy Dashboard

From the left navigation:

• Click Deploy

You will see:

• Number of deployments initiated
• Integration errors (if any)
• Successful deployments
• Project-level deployment status

8.8 Configuring Deployment

For a project:

1. Click Configure
2. Review deployment details
3. Confirm configuration readiness

Once configured, AutoAnalytics prepares the required setup for the project.

Info: Deployment configuration ensures tracking aligns exactly with defined goals.

8.9 Getting the Code

After configuration:

• Click Get Code

This step provides the required integration details for your digital property.

Important: Ensure the correct project and environment are selected before proceeding.

Step 3: Validate (Run Validation)

8.10 Validate Stage Overview

The Validate stage checks whether analytics tracking is:

• Active
• Accurate
• Aligned with defined goals

Validation ensures data reliability before audits or reporting.

8.11 Accessing the Validate Dashboard

From the left navigation:

• Click Validate

You will see:

• Validation initiated count
• Projects validated
• Project-level validation status

Statuses may include:

• In Progress
• Validated

8.12 Running Validation

To validate a project:

1. Locate the project in the Validate list
2. Click Validate
3. Click Run Validation

AutoAnalytics will then:

• Perform automated checks
• Update status as validation progresses
• Mark the project as validated when complete

Info: Validation can be re-run if changes are made to deployment.

8.13 What Happens After Validation

Once validation is complete:

• The project is ready for audits
• Analytics health can be assessed
• Issues (if any) can be identified early

Validation acts as a quality checkpoint before deeper analysis.

8.14 Implementation Best Practices

For smooth implementation:

• Complete goal definition carefully before deployment
• Avoid changing goals mid-deployment
• Validate after every major update
• Track project status regularly from dashboards

AutoAnalytics provides built-in monitoring and visibility features that help teams track project progress, execution status, and platform activity.
The focus is on operational transparency, not infrastructure monitoring.

This section explains what can be monitored today, how to interpret it, and how teams should use it effectively.

1. Monitoring Objectives

AutoAnalytics monitoring is designed to help customers:

• Track progress across Design, Deploy, Validate, and Audit stages
• Understand the current status of projects
• Identify stalled or incomplete activities
• Maintain accountability across teams
• Support internal reviews and governance

Monitoring is available through dashboards and status indicators across the platform.

2. Project-Level Monitoring

The primary form of monitoring in AutoAnalytics is project-level visibility.

Across the platform, users can see:

• Number of projects created
• Project status by stage:
  • Goals Defined
  • Deployment Initiated
  • Validation Completed
  • Audit Completed
• Projects pending action
• Recently updated projects

This allows teams to quickly answer: 
“Which projects are complete, and which need attention?”

3. Stage-Wise Status Monitoring

Each major stage provides its own monitoring view.

Design (Goals)
Users can monitor:

• Whether goals are defined for a project
• Projects where goal definition is pending
• Total goals defined across projects

This helps ensure that deployment does not begin without proper planning.

Deploy
Users can monitor:

• Deployments initiated
• Deployments completed successfully
• Projects awaiting configuration or code usage

Deployment status indicators help teams coordinate with internal stakeholders.

Validate
Users can monitor:

• Validations initiated
• Validation completion status
• Projects marked as validated

This confirms whether analytics tracking has been checked and verified.

Audit
Users can monitor:

• Audits yet to be started
• Completed audits
• Projects requiring further review

Audit status helps teams prioritize follow-up actions.

4. Dashboard-Based Visibility

The Dashboard acts as a central monitoring screen.

It provides:

• High-level counts and summaries
• Quick access to recent projects
• Immediate visibility into platform usage

This enables leadership and project owners to monitor progress without navigating into each project individually.

5. Activity Tracking & Logs

AutoAnalytics tracks key user and project actions to support transparency.

Tracked activities include:

• User logins
• Project creation and updates
• Goal definition actions
• Deployment, validation, and audit runs
• Administrative changes

These records help:

• Review who performed an action
• Support internal audits
• Investigate unexpected changes

Info: Activity tracking is automatic and does not require additional configuration.

6. Alerts & Notifications (Current Behavior)

AutoAnalytics currently relies on visual indicators and status changes rather than configurable alert rules.

Users are alerted through:

• Status changes visible in dashboards
• Completion states (e.g., Validated, Audit Completed)
• Action-required states (e.g., Not Started, Pending)

This ensures:

• Clear next steps
• Reduced notification noise
• Focus on actionable items

Note: External notifications (email, chat tools) are not configurable in the current version.

7. How Teams Should Use Monitoring Effectively

Recommended usage:

• Review dashboards daily or weekly
• Track projects stuck in intermediate stages
• Ensure validation is completed after deployment
• Use audit status to prioritize remediation
• Periodically review activity logs for governance

8. Monitoring Scope Clarification

AutoAnalytics monitoring does not include:

• Server or infrastructure health metrics
• Network or system resource monitoring
• Application performance metrics of customer websites

The platform focuses on analytics lifecycle monitoring, not infrastructure monitoring.

9. Monitoring Benefits for Customers

Using built-in monitoring helps customers:

• Maintain visibility across multiple projects
• Reduce missed steps
• Improve coordination between teams
• Support compliance and audit reviews
• Ensure analytics quality over time

10. Continuous Improvement

Monitoring capabilities may evolve over time based on:

• Customer feedback
• Enterprise usage patterns
• Platform enhancements

Updates will be communicated through release notes.

AutoAnalytics is committed to protecting customer and user privacy while ensuring transparency, control, and compliance across all data practices.

This Privacy Policy explains what data is collected, why it is used, how it is protected, and what rights users have, when using the AutoAnalytics platform.

The policy is designed around privacy-by-design principles and supports enterprise compliance expectations.

1. Scope of This Policy

This Privacy Policy applies to:

• All users accessing the AutoAnalytics platform
• All projects, audits, validations, and reports created within the platform
• All interactions through the AutoAnalytics user interface

This policy does not apply to customer websites or applications themselves. AutoAnalytics only works on analytics-related information.

2. Privacy-by-Design Commitments

AutoAnalytics embeds privacy considerations at every stage of the product lifecycle.

Core privacy commitments:

• Use data only for clearly defined purposes
• Limit data access based on user roles
• Maintain transparency on what is collected and why
• Support enterprise privacy and compliance requirements

AutoAnalytics is built to help organizations improve analytics quality without exposing or misusing personal data.

3. Data Collected by AutoAnalytics

AutoAnalytics collects only the data required to operate the platform effectively.

Types of Data Collected

Account & Access Data

• User name
• Official email address
• Assigned role
• Login activity

Project & Configuration Data

• Project names and identifiers
• Website or application URLs
• Industry and platform selections
• Defined goals and KPIs

Operational & Usage Data

• Actions performed in the platform
• Audit and validation run history
• Timestamps and status indicators
• Report download activity

Important: AutoAnalytics does not collect website visitor personal data such as names, emails, or payment information.

4. Purpose of Data Use

Collected data is used only to:

• Deliver platform functionality
• Enable analytics audits and validations
• Maintain project history and reporting
• Support troubleshooting and support requests
• Enforce access control and governance
• Meet enterprise audit and compliance needs

Warning: AutoAnalytics does not sell, rent, or trade customer data under any circumstances.

5. Data Retention & Deletion

• Data is retained only as long as required for platform usage and reporting
• Project records, audit results, and reports are stored for historical reference
• Users may request data removal through authorized Admin users
• Deletion requests are handled in line with contractual and regulatory obligations

Retention policies may vary based on enterprise agreements.

6. Access Control & Data Visibility

Access to data within AutoAnalytics is governed by role-based access control.

• Admins control user access
• Editors can act only within their permissions
• Viewers have read-only visibility

This ensures:

• Least-privilege access
• Clear accountability
• Reduced risk of unauthorized actions

7. Cross-Border Data Handling

AutoAnalytics may process data across regions to support global enterprise usage.

All such processing:

• Uses secure, encrypted channels
• Follows contractual and regulatory safeguards
• Adheres to enterprise data protection expectations

Info: AutoAnalytics does not expose raw customer data to unauthorized third parties.

8. Data Subject Rights

Where privacy regulations apply, AutoAnalytics supports user rights such as:

• Access to personal account data
• Correction of inaccurate information
• Deletion of account data (subject to contractual limits)

Requests should be initiated through the organization’s Admin user.

9. Audit Logging & Accountability

AutoAnalytics maintains audit logs to ensure transparency and accountability.

Audit logs capture:

• User logins and logouts
• Project actions (create, update, delete)
• Audit and validation runs
• Administrative changes

These logs help enterprises:

• Review activity history
• Support internal audits
• Meet compliance requirements

10. Security Practices (Privacy-Supporting)

To protect privacy, AutoAnalytics follows strong security practices, including:

• Secure access controls
• Encrypted data handling
• Role-based permissions
• Continuous monitoring for misuse or anomalies

Security measures are reviewed periodically to ensure ongoing protection.

11. Policy Updates

This Privacy Policy may be updated to reflect:

• Platform enhancements
• Regulatory changes
• Enterprise compliance needs

Continued use of AutoAnalytics indicates acceptance of the most recent version of this policy.

1.1 What is AutoAnalytics?

AutoAnalytics is a SaaS product based enterprise platform that helps organizations set up, check, and continuously improve their digital analytics implementation without manual effort or guesswork.

Instead of relying on multiple tools, spreadsheets, and manual checks, AutoAnalytics provides one structured flow to:

• Define what needs to be tracked
• Deploy tracking in a controlled way
• Validate that tracking is working correctly
• Audit overall analytics health across websites

The platform is designed to support large teams, multiple projects, and enterprise governance needs.

1.2 What Problems Does AutoAnalytics Solve?

Many organizations face common challenges with analytics:

• Tracking is implemented inconsistently across pages
• Business goals are not clearly connected to analytics data
• Tags fire, but data accuracy is uncertain
• Audits are manual, slow, and difficult to repeat
• Different teams interpret analytics health differently

AutoAnalytics solves these problems by introducing standardization, automation, and visibility across the entire analytics lifecycle.

1.3 Who Should Use AutoAnalytics?

AutoAnalytics is built for:

• Analytics Teams – to define goals, manage deployments, and validate data
• Marketing & Digital Teams – to understand tracking health and coverage
• QA & Audit Teams – to verify implementation quality
• Enterprise Stakeholders – to get a clear view of analytics readiness and gaps

The platform supports multiple projects at once, making it suitable for enterprises managing several websites or digital properties.

1.4 How AutoAnalytics Works (High-Level)

AutoAnalytics follows a simple three-step lifecycle, which is also reflected directly in the product UI:

1. Design – Define business goals and KPIs
2. Deploy – Set up analytics tracking based on defined goals
3. Validate – Check and confirm tracking accuracy

Each step builds on the previous one and ensures that analytics implementation is structured, auditable, and repeatable.

You can track the status of every project across these steps directly from the Dashboard.

1.5 What Makes AutoAnalytics Different?

AutoAnalytics focuses on business clarity, not just technical checks.

Key differentiators include:

• Business goals drive tracking decisions
• Clear visibility into project progress
• Automated checks instead of manual audits
• Consistent scoring and validation outcomes
• Enterprise-ready access control and governance

This ensures analytics teams spend less time fixing issues and more time using reliable data.

AutoAnalytics is designed with strong foundational security practices to protect platform access, project data, and operational activities. The platform focuses on access control, accountability, data protection, and auditability, ensuring customers can use it confidently in enterprise environments.

This section explains how security is handled in AutoAnalytics today, based on actual product behavior.

1. Security Approach

AutoAnalytics follows a security-by-design approach, where controls are embedded into everyday platform usage rather than added later.

Core security objectives:

• Ensure only authorized users can access the platform
• Control what actions users can perform
• Protect project and audit data from unauthorized access
• Maintain full traceability of user actions
• Reduce risk of accidental or unauthorized changes

Security controls apply consistently across users, projects, audits, and reports.

2. Access Security & Identity Control

AutoAnalytics uses role-based access control (RBAC) to manage who can do what on the platform.

Key characteristics:

• Users must log in using approved credentials
• Access is granted based on assigned roles (Admin, Editor, Viewer)
• Administrative actions are restricted to Admin users
• Sensitive actions are limited to authorized roles only

This ensures:

• Clear ownership
• Controlled access
• Reduced risk of misuse

Info: User roles and permissions are managed from the Admin Dashboard.

3. Data Protection Within the Platform

AutoAnalytics handles analytics configuration, audit, and validation data, not end-customer transactional data.

Data protection practices include:

• Secure handling of project information
• Controlled visibility of goals, audits, and reports
• Logical separation of data between customers
• Restricted access to sensitive configuration areas

Important: AutoAnalytics does not collect or store personal data of website visitors such as names, emails, or payment details.

4. User Activity Logging & Auditability

To maintain accountability, AutoAnalytics records key platform activities.

Logged activities include:

• User login and logout events
• Project creation and updates
• Goal definition actions
• Deployment, validation, and audit runs
• Administrative changes
• Report downloads

These logs help customers:

• Track usage history
• Investigate unexpected changes
• Support internal governance and audits

5. Platform Integrity & Change Control

AutoAnalytics ensures that platform usage remains stable and predictable by:

• Restricting configuration changes to authorized users
• Preserving historical project and audit records
• Preventing accidental overwrites of completed actions
• Maintaining consistent workflows across projects

This protects the reliability of audit and validation outcomes.

6. Session & Usage Security

User sessions are protected to prevent unauthorized access.

Security measures include:

• Secure session handling
• Automatic session expiry after inactivity
• Protection against repeated unauthorized access attempts

Tip: Users should always log out after completing work, especially on shared systems.

7. Monitoring & Incident Awareness

AutoAnalytics monitors platform activity to identify:

• Unusual login patterns
• Repeated access failures
• Unexpected usage behavior

If suspicious activity is detected:

• Alerts are reviewed
• Access may be restricted temporarily if required
• Corrective action is taken to maintain platform integrity

8. Security Alignment with Industry Practices

AutoAnalytics is designed in alignment with common enterprise security principles, without claiming formal certification at this stage.

Current alignment includes:

Info: These alignments represent design and operational intent, not external certifications.

9. Customer Security Responsibilities

To maintain a secure environment, customers are encouraged to:

• Assign Admin access only to trusted users
• Review user access periodically
• Remove inactive users promptly
• Download reports only to secure internal systems
• Follow internal data handling policies

10. Ongoing Security Commitment

AutoAnalytics is committed to:

• Continuously strengthening security controls
• Improving monitoring and accountability
• Supporting enterprise governance needs
• Enhancing security practices as the platform evolves

Security is reviewed regularly to keep pace with customer expectations and operational needs.

This glossary defines key terms, modules, and governance elements within the AutoAnalytics ecosystem. All entries correspond to actual features or controls used in the platform’s architecture and operations.

A

Access Auditing

Tracks and logs all access events, user actions, and data interactions within AutoAnalytics for compliance and traceability. Includes login attempts, SDR edits, deployments, and validation runs.

API Integration

Configuration that allows AutoAnalytics to connect to external systems such as Tag Management Systems (TMS) or schema sources using secure tokens, OAuth, or API keys.

C

Cache Layer

Performance optimization component that stores frequently accessed schema details, mapping rules, and validation results to speed up operations.

Compliance Mode

Platform setting that enforces stricter governance controls, such as mandatory dual approvals for deployments and extended audit logging for regulated environments.

Configuration Rules

Settings that define mapping logic, naming conventions, and deployment triggers across Design, Deploy, and Validate modules.

D

Data Classification Tags

Labels applied to schema fields, SDR variables, and validation payload elements that indicate sensitivity (e.g., Public, Confidential, Restricted), used to enforce access control, encryption, and export restrictions.

Data Governance Layer

The policy enforcement engine that governs schema ingestion, SDR creation, TMS deployment, and validation logging, ensuring compliance with enterprise and regulatory standards.

Deployment Engine

The AutoAnalytics module responsible for generating tag configurations, mapping variables, applying naming rules, and publishing tags directly to TMS environments.

Design Engine

Automated module that generates Solution Design References (SDRs) and technical specifications from business KPIs and schema inputs.

E

Environment Configuration

Per-environment (Dev, Staging, Prod) settings that control where and how tags are deployed, validated, and rolled back.

Export Controls

Permissions and workflows that restrict or approve the export of SDRs, deployment logs, and validation reports based on data classification.

F

Field Mapping

Process of linking KPI-defined events to schema variables in the Design module to ensure accurate data capture.

Full Validation Scan

A comprehensive site or app scan in the Validate module to check all configured events, tags, and payloads against the approved SDR.

G

Governance Policies

Rules configured in AutoAnalytics to control user access, deployment approvals, schema change handling, and audit log retention.

I

Integration Layer

The backend component that manages secure connections between AutoAnalytics and external systems like TMS platforms, schema sources, or CI/CD pipelines.

K

KPI Library

A pre-defined set of business KPIs with associated metrics and event definitions used to standardize SDR generation.

L

Log Retention Policy

Configurable rule that determines how long audit logs, deployment records, and validation results are stored before being archived or deleted.

M

Mapping Rules

Customizable logic for matching KPIs to schema variables, used during SDR generation and tag deployment.

Multi-Environment Deployment

Capability to publish tags to multiple TMS environments (e.g., Dev, Staging, Prod) from a single configuration.

N

Naming Conventions

Standardized formats for event names, tag IDs, and variables enforced by the Deployment Engine to ensure consistency across projects.

P

Payload Validation

A check performed in the Validate module to confirm that the event payload captured in production matches the fields and formats defined in the SDR.

Project Workspace

A dedicated container in AutoAnalytics for managing all Design, Deploy, and Validate activities for a specific implementation.

R

RBAC (Role-Based Access Control)

Granular access management system that restricts actions (e.g., SDR editing, deployment publishing, validation scheduling) based on user role.

Rollback

Feature that reverts a TMS configuration to the last known good deployment in case of validation failure or production issues.

S

Schema Drift Detection

Monitoring feature that alerts administrators when the source schema changes in a way that may impact mappings or deployments.

Solution Design Reference (SDR)

A structured technical document generated by AutoAnalytics containing all KPI definitions, mapped variables, triggers, and data layer specifications for an implementation.

Spec Approval Workflow

Review and approval process for SDRs before they can be used in deployments.

T

Tag Deployment

Process of publishing validated tag configurations from AutoAnalytics to a connected TMS.

TMS Integration Health

Monitoring feature that checks API connectivity, authentication validity, and rate limit status for connected Tag Management Systems.

V

Validation Engine

The AutoAnalytics module responsible for running automated scans, comparing live tag behavior to the SDR, and flagging pass/fail results.

Validation Report

Exportable report summarizing the results of a validation scan, including errors, warnings, and compliance percentages.

This guide helps users and administrators quickly identify and resolve common issues encountered while using AutoAnalytics across Design, Deploy, Validate, and Audit stages.
Use this as a first reference before reaching out to support.

Info: Most issues are related to incomplete steps, incorrect order, or missing prerequisites. Following the recommended flow resolves the majority of cases.

Common Issues & Resolutions

1. Project Not Visible in Design / Deploy / Validate

Possible Cause

• Project creation not completed
• Page not refreshed after creation

Resolution Steps

• Go to the Dashboard and confirm the project exists
• Refresh the page or re-open the section
• Ensure you are logged in with the correct role (Editor/Admin)

2. Unable to Define Goals for a Project

Possible Cause

• Required master configuration not completed
• Project not properly initialized

Resolution Steps

• Ensure the project is created successfully
• Check that industry selection is available
• Re-open Define Goals from the Goals Dashboard

Warning: Goals must be defined before moving to Deploy.

3. Deploy Option Disabled or Not Available

Possible Cause

• Goals are not fully defined or reviewed

Resolution Steps

• Go back to Define Goals
• Review and complete goal selection
• Ensure goals are saved successfully
• Return to Deploy and proceed

4. “Get Code” Not Accessible After Deployment

Possible Cause

• Deployment configuration not completed
• Incorrect project selected

Resolution Steps

• Verify you clicked Configure before attempting to get code
• Ensure the correct project is selected
• Re-open the Deploy page and retry

5. Validation Cannot Be Started

Possible Cause

• Deployment not completed
• Required setup not applied on the digital property

Resolution Steps

• Confirm deployment status shows as completed
• Ensure the deployment step was finished
• Re-open Validate and click Run Validation

Tip: Validation should be run after deployment changes are applied.

6. Validation Status Stuck or Not Updating

Possible Cause

• Validation still in progress
• Page not refreshed

Resolution Steps

• Wait a few moments and refresh the page
• Check validation status again from the Validate Dashboard
• Avoid triggering multiple validations simultaneously

7. Audit Cannot Be Started

Possible Cause

• Validation not completed
• Project not ready for audit

Resolution Steps

• Ensure validation status shows as completed
• Navigate to the Audit section
• Start the audit only after validation is complete

8. Audit Results Not Visible

Possible Cause

• Audit still running
• Audit not triggered for the project

Resolution Steps

• Refresh the Audit Dashboard
• Check audit status (Yet to Start / In Progress / Completed)
• Re-run the audit if required

9. Unable to Download Reports

Possible Cause

• Insufficient user permissions
• Audit or validation not completed

Resolution Steps

• Confirm your role allows report downloads
• Ensure the audit or validation is completed
• Retry download from the relevant section

Warning: Viewer users may have limited download access.

10. User Cannot Access Configuration or Admin Sections

Possible Cause

• User role does not permit access

Resolution Steps

• Contact an Admin user
• Request role update if required
• Log out and log back in after role changes

11. Dashboard Numbers Do Not Match Expectations

Possible Cause

• Recent actions not refreshed
• Filters or project selection mismatch

Resolution Steps

• Refresh the dashboard
• Check selected projects
• Ensure actions were completed successfully

Initial Release

AutoAnalytics was launched with its core analytics lifecycle capabilities, enabling teams to move away from manual analytics setup and audits toward a structured, repeatable process.

This release established the foundation for managing analytics across multiple projects from a single platform.

Stabilization & Adoption Phase

This release strengthened the end-to-end analytics lifecycle, ensuring users could clearly move step-by-step from planning to execution.

The product experience was aligned closely with how analytics teams actually work.

Currently Available

This milestone focused on helping teams understand analytics health, not just execution status.

AutoAnalytics began providing clearer signals on where analytics implementations are strong and where attention is needed.

In Progress

This phase focuses on making AutoAnalytics easier to manage at enterprise scale, especially for organizations handling multiple teams and projects.

Upcoming

The long-term vision for AutoAnalytics is to help organizations continuously improve analytics quality, not just set it up once.

Access curated guides, reference documents, and security materials to help you understand, adopt, and govern AutoAnalytics effectively within your organization.

This section is designed for:

• Business users
• Analytics teams
• Platform administrators
• Compliance and audit stakeholders

All resources are written in clear, business-friendly language and focus on using AutoAnalytics, not building or engineering it.

This glossary defines key terms, modules, and concepts used in AutoAnalytics.
All terms listed here map directly to features, screens, or behaviors visible in the product and described in the PRD and TAD.

A

Access Control

The mechanism that determines which users can view, edit, or manage projects, configurations, and reports based on their assigned role.

Admin

A user role with full access to AutoAnalytics, including user management, configuration, audits, and platform governance.

Audit

A structured review process that checks the health and completeness of analytics implementation for a project and highlights areas needing attention.

Audit Dashboard

A screen that displays audit status, results, and health indicators for projects.

C

Configuration

The setup of master data, user access, and mappings required before projects can be implemented smoothly.

Completed Status

A state indicating that a step (Goals, Deploy, Validate, or Audit) has been successfully finished for a project.

D

Dashboard

The main landing screen that provides a high-level overview of projects, progress counts, and recent activity.

Data Governance

The rules and controls that govern how project data, audit results, and user information are accessed, used, and retained within AutoAnalytics.

Deploy

The stage where analytics tracking is prepared and applied based on defined goals.

Deploy Dashboard

A screen that shows deployment status, progress, and actions available for each project.

E

Editor

A user role that can create and manage projects, define goals, run deployments, validations, and audits, but cannot manage users or platform-wide settings.

G

Goals

Business objectives defined for a project that determine what needs to be measured and validated.

Goals Dashboard

The screen used to define, review, and track goals for each project.

I

Industry

A classification used to organize projects and align goals and KPIs to specific business domains.

Implementation Guide

Documentation that explains how to use AutoAnalytics step-by-step from Design to Audit.

M

Monitoring

The ability to track project progress, stage status, and usage activity through dashboards and indicators.

P

Project

A logical unit in AutoAnalytics representing a single website or digital property.

Project Setup

The process of creating a project by providing the required basic information such as domain and context.

R

RBAC (Role-Based Access Control)

A system that restricts platform actions based on user roles such as Admin, Editor, or Viewer.

Reports

Downloadable outputs generated from audits or validations that summarize findings and results.

S

Security

The set of controls that protect access, data integrity, and platform usage within AutoAnalytics.

Stage

A step in the analytics lifecycle: Design, Deploy, Validate, or Audit.

Status Indicators

Visual labels (e.g., In Progress, Completed, Yet to Start) that show the current state of a project or action.

V

Validate

The stage where analytics implementation is checked to ensure tracking is active and aligned with defined goals.

Validate Dashboard

The screen that displays validation status and allows users to initiate or review validation runs.

Viewer

A user role with read-only access to dashboards, project status, and reports.

W

Workflow

The structured sequence followed in AutoAnalytics:
Design → Deploy → Validate → Audit.

Y

Yet to Start

A status indicating that an action (such as validation or audit) has not been initiated for a project.