Data Control and Quality

Clean data is the foundation of accurate analytics. Twinalyze helps you control event quality, identify tracking issues, and keep your reports, funnels, retention, user profiles, campaigns, and Ask AI insights reliable. This page covers:

Duplicate and missing events
Wrong event names
Invalid event properties
Bot and unwanted traffic
Test, internal, and spam traffic
Best practices before going live

Data quality overview

Area	What can go wrong	Impact
Duplicate Events	Same event is sent more than once	Inflated reports and wrong counts
Missing Events	Expected event is not received	Broken funnels and incomplete journeys
Wrong Event Names	Same action has multiple names	Split reports and confusing analysis
Invalid Properties	Missing or wrong property values	Filters, segments, and cohorts become unreliable
Bot Traffic	Non-human activity is tracked	Fake sessions, users, and page views
Internal/Test Traffic	QA or developer data mixes with live data	Production reports become inaccurate

Data quality checks

Duplicate Events
Missing Events
Wrong Event Names
Invalid Properties

Duplicate events happen when the same user action is tracked more than once.Common examples:

button_clicked fires two times for one click
screen_viewed fires again when the screen is already visible
purchase_completed is sent multiple times for one order
SDK auto tracking and manual tracking both send the same event

Duplicate events can increase event counts and create incorrect reports, funnels, campaign results, and revenue metrics.

How to prevent duplicate events

Use one tracking method

Avoid sending the same event from both auto tracking and manual tracking.

Use unique identifiers

Use values like eventId, orderId, sessionId, or screenId for important events.

Check sudden spikes

Review events that suddenly increase without a real product or marketing change.

Test critical events

Events like signup, checkout, payment, and purchase should be tested carefully before release.

Recommended duplicate-safe event

{
  "eventName": "purchase_completed",
  "eventId": "evt_10293",
  "orderId": "ORD_98765",
  "userId": "user_123",
  "sessionId": "session_789",
  "properties": {
    "amount": 2499,
    "currency": "INR",
    "itemCount": 3
  }
}

Missing events happen when an expected user action is not tracked.Common examples:

User opens checkout but checkout_started is not received
User signs up but signup_completed is missing
Screen opens but screen_viewed is not tracked
Notification is shown but notification_received is missing
Payment fails but payment_failed is not received

Missing events usually happen because of SDK setup issues, wrong trigger conditions, network failures, or code paths where tracking was not added.

How to detect missing events

Check	What to verify
Event flow	Expected events are received in the correct order
Platform coverage	Web, Android, and iOS send the same important events
Critical actions	Signup, checkout, payment, and purchase are always tracked
Session coverage	Events are linked with the correct user and session
SDK health	SDK is initialized before events are sent

Create a small test journey and verify every expected event from start to finish.

Example expected journey

page_view
product_viewed
add_to_cart
checkout_started
payment_started
purchase_completed

Wrong event names make analysis difficult because the same action appears as multiple different events.Bad examples:

AddToCart
add_to_cart
add-cart
product_added_to_cart
addtocart

Recommended format:

add_to_cart
checkout_started
payment_failed
purchase_completed
screen_viewed
notification_clicked

Event naming rules

Rule	Recommended
Case style	Use lowercase
Word separator	Use underscore
Event meaning	Use action-based names
Platform consistency	Use the same name across Web, Android, and iOS
Avoid	Spaces, random casing, unclear short names

Do not create multiple names for the same user action. It will split your reports and reduce data accuracy.

Invalid properties happen when event data is missing, wrongly typed, or inconsistent.Common issues:

price sent as text instead of number
currency missing from purchase events
userId missing after login
country sent as India, IN, and india
orderId missing from purchase event
screenName sent differently across platforms

Recommended property format

{
  "eventName": "add_to_cart",
  "properties": {
    "productId": "PRD_1001",
    "productName": "Gold Hair Pin",
    "category": "Hair Accessories",
    "price": 499,
    "currency": "INR",
    "quantity": 1
  }
}

Property validation checklist

Property check	Example
Required value exists	`productId` should not be empty
Correct data type	`price` should be number
Consistent value	`currency` should be `INR`, not `inr` or `Rs`
Clear meaning	`screenName` should describe the actual screen
Platform match	Web and app should send similar property names

Keep property names stable. Changing property names later can break filters, cohorts, and saved reports.

Bot and unwanted traffic

Bot traffic and unwanted traffic can make your analytics data inaccurate. Twinalyze should help you separate real user behavior from fake, automated, internal, or test activity.

Cleaner Reports

Remove bot-like activity from reports, funnels, retention, and user insights.

Better User Accuracy

Separate real users from fake, test, crawler, or automated traffic.

Improved Decisions

Avoid making product decisions based on invalid traffic.

Common unwanted traffic types

Bots
Internal Traffic
Spam Traffic

Bots are automated systems that visit your website or app without real user intent.Common examples:

Search engine crawlers
Scrapers
Automated testing tools
Headless browser traffic
Fake traffic generators

Bot traffic can increase page views, sessions, users, and event counts without real user activity.

Bot indicators

Signal	Example
Very high event frequency	Hundreds of events in a short time
No real interaction pattern	Page views without clicks or meaningful actions
Suspicious user agent	Headless browser, crawler, or automation tool
Repeated IP/device pattern	Same source creating many sessions
Invalid session behavior	Session duration too short or too repetitive

Internal traffic comes from your own team, developers, QA testers, support team, or admin users.This traffic is useful for testing, but it should not affect production analytics.Common examples:

Developer testing SDK events
QA testing checkout or signup flow
Admin team opening dashboard repeatedly
Internal users using staging or demo builds

Use environment properties like environment: "development" or environment: "staging" to separate test data from production data.

{
  "eventName": "checkout_started",
  "properties": {
    "environment": "staging",
    "isInternalUser": true,
    "testerRole": "qa"
  }
}

Recommended bot filtering rules

Use these checks to separate real users from unwanted traffic.

Check	What to detect	Recommended action
User agent	Headless browsers, crawlers, automation tools	Mark as bot or exclude
Event frequency	Too many events in a short time	Flag as suspicious
Session pattern	Very short or repeated sessions	Review or exclude
IP pattern	Repeated traffic from same source	Add to filter list
Environment	`development`, `staging`, `debug`	Exclude from production reports
Internal users	Team, QA, admin, developer traffic	Mark as internal
Invalid properties	Missing user agent, device, platform, session	Flag for review

Do not rely on only one signal to detect bots. Use multiple signals like user agent, IP pattern, event frequency, session behavior, and environment.

Suggested event structure for traffic quality

Send traffic-related properties with important events so Twinalyze can detect suspicious activity more accurately.

{
  "eventName": "page_view",
  "userId": "user_123",
  "deviceId": "device_456",
  "sessionId": "session_789",
  "properties": {
    "environment": "production",
    "platform": "web",
    "userAgent": "Mozilla/5.0...",
    "referrer": "https://google.com",
    "isInternalUser": false,
    "isTestUser": false,
    "source": "organic",
    "campaign": "summer_sale"
  }
}

Getting Started

Project And Organization

Data

Adminstration

Data Control and Quality

Data quality overview

Data quality checks

How to prevent duplicate events

Recommended duplicate-safe event

How to detect missing events

Example expected journey

Event naming rules

Recommended property format

Property validation checklist

Bot and unwanted traffic

Cleaner Reports

Better User Accuracy

Improved Decisions

Common unwanted traffic types

Bot indicators

How to reduce spam traffic

Recommended bot filtering rules

Suggested event structure for traffic quality

Getting Started

Project And Organization

Data

Adminstration

Documentation Index

​Data quality overview

​Data quality checks

​How to prevent duplicate events

​Recommended duplicate-safe event

​How to detect missing events

​Example expected journey

​Event naming rules

​Recommended property format

​Property validation checklist

​Bot and unwanted traffic

Cleaner Reports

Better User Accuracy

Improved Decisions

​Common unwanted traffic types

​Bot indicators

​How to reduce spam traffic

​Recommended bot filtering rules

​Suggested event structure for traffic quality

Data quality overview

Data quality checks

How to prevent duplicate events

Recommended duplicate-safe event

How to detect missing events

Example expected journey

Event naming rules

Recommended property format

Property validation checklist

Bot and unwanted traffic

Common unwanted traffic types

Bot indicators

How to reduce spam traffic

Recommended bot filtering rules

Suggested event structure for traffic quality