With the rise of AI, is your data foundation stable enough?

When 80% of AI prediction errors are caused by dirty data, here are three low-cost self-help solutions.

Industry illusion: AI's "garbage in, garbage out" law

According to a new report from Gartner: 73% of companies that try AI analysis tools have inaccurate predictions due to data quality issues. The experience of a cross-border e-commerce team is very representative:

● Spending money to purchase an AI inventory forecasting system.

● After entering 6 months of sales data, AI suggested "restocking down jackets in summer".

● After investigation, it was found that the promotional activity data was not labeled, and AI misjudged "limited time 10% off clearance" as a hot sale.

Even more cruel, when a SaaS company cleaned up historical data, it found that:

- 38% of the utm_source fields in user behavior logs were "null"

- There are 12 naming formats for payment success events (such as: pay_success/Payment_OK/subscription success)

These dirty data are like cracks in the foundation, and even the most advanced AI building will collapse.

Data cleanliness self-test: Is your foundation qualified?

Use website analysis tools to quickly diagnose:

1️⃣ Real-time traffic check

● Enter the real-time visitor list in Data4.

● Sample 10 records to check the completeness of key parameters (source/device, etc.).

● Danger signal: >20% of records are missing core fields.

2️⃣ Compare function consistency

● Select the same page in Data4 for comparison in two time periods.

● If the "average stay time" fluctuates >200%.

● Possible cause: Page embedding standard changes are not synchronized.

3️⃣ Check the team board for cognitive consensus

● Require members to write down the "payment success event name".

● Split alarm: more than 3 answers appear.

A customer used this method to find that there are 5 different definitions of "registration success" in technology/marketing/operation!

Three low-cost data cleaning techniques

▌The first one: Manually hunting for key dirty data

● Applicable scenarios: Startup teams with limited resources.

● Operation path:

a. Real-time module screening "TOP10 traffic pages".

b. Manually check the source tags of these pages (such as whether the utm parameters are complete).

c. Use sticky notes to record problem pages and promote technical repair and embedding.

● Case: After a blog team repaired the source parameters of 3 high-traffic pages, the accuracy of channel analysis increased by 65%.

▌Second move: Use existing tools to do data triangulation verification

● Core logic: cross-check/key indicators of advertising background/CRM.

● Diagnostic steps:

⚠️Note: Allow 5-10% reasonable error (such as cross-platform delay).

▌Third move: Establish team data convention

● Event naming iron rule:

a. Verb + noun (e.g.: "Click_Buy button" ❌ → "Button_Click" ✅)

b. All lowercase + underscore ("PaymentSuccess" ❌ → "payment_success" ✅)

● Parameter management:

a. Create a shared table to record all coded pages and parameters.

b. Changes must be announced on the team dashboard.

● Morning meeting accountability:

a. Check the integrity of core indicators on the overview page of the daily available analysis tools.

b. Locate the person responsible within 24 hours for field missing accidents.

How do Data4 users lay the foundation for the AI era?

Although we have not yet provided a data cleaning module, Data4 is becoming a "sentinel" for data quality:

● ✅ Real-time traffic monitoring: quickly discover large-scale missing fields.

● ✅ Comparison function: capture abnormal fluctuations in indicators (code failure signals).

Conclusion: Keep the bottom line of data in the face of AI illusions