Definition of Dirty Data: The Importance of Data Cleansing

What does dirty data look like? Let's peel back the skin.

Do data capture brothers have encountered this situation: obviously open proxy IP work, the result is either blocked, or data capture back like a dog chewed. At this time eighty percent isdirty dataIn the midst of a demon. The so-called dirty data, to put it bluntlyGarbage mixed in with the normal dataFor example, invalid proxy IPs, duplicate address segments, and request records with virus signatures.

For example, if you buy 1000 proxy IPs from a platform, 300 of them can't connect to the server at all, and 200 IPs have been blacked out by the target website - these uncleaned and dirty data are just like stir-fried vegetables without picking the sand, and your teeth will crumble when you eat them. Especially for e-commerce price comparison, public opinion monitoring, these need to be7×24 hours high-frequency operationbusiness, dirty data can cut your productivity right down to the bone.

Not cleaning your data? Waiting to get screwed until you cry

Last year, a customer doing overseas purchasing system complained to me that their team could not catch the price change of a luxury website for three consecutive days. In the end, they found that the proxy IP pool used was40%'s address has long since expired.The remaining IPs that work are all labeled as bot traffic by the official website. This is like opening a safe with a rusty key, which not only fails to open but also triggers an alarm.

Data cleansing is important at three main levels:
1. save money: A job that can be done with 1 valid IP may consume 3-5 IPs with dirty data.
2. save one's lifeDirty IP clusters are the first thing to be blocked when the target site discovers abnormal traffic
3. improve efficiency: After cleaning the precise IP pool, the success rate of requests can skyrocket by more than 60%!

A wild way to get cleaning with ipipgo

Many proxy IP service providers in the market only care about selling and not about raising, but our homeipipgoPlay with full process services. Our IP pool comes withTriple Filtration System::
- First off:survival testing(Automatically kicks out lapsed nodes every 15 minutes)
- Second off:behavioral portrait(Flagging IPs with anomalous access records)
- Third Pass:Geographic calibration(Ensure that the displayed IP geolocation matches the actual server)

For example, when doing social platform data collection, use ipipgo'sDynamic cleaning modeThe system will automatically skip the IP segments that have been marked by the platform. This function is measured to pull the account survival rate from 23% to 81%, which is much more reliable than the static IP pool commonly used by peers.

Data cleansing techniques that even a novice can perform

Even if you're not tech savvy, it's easy to take care of an IP pool with ipipgo:
1. Open in the background"Intelligent Stain Removal" Switch
2. SettingsMinimum Availability Threshold(Recommended not less than 85%)
3. HookingAutomatic spare tire replenishment IPfunctionality

In this way, the system will automatically filter out black IPs, dead IPs, and high-risk IPs like sifting soybeans. A friend doing cross-border e-commerce personally tested that after turning on the cleaning function, the account association risk of the Amazon store directly dropped by 7%.

QA Time: Have you stepped in any of these potholes?

Q: How can I tell if the IP pool has any dirty data?

A: Keep an eye on three indicators: a sudden spike in the request failure rate, duplicate content returned from the same IP, and an increase in the frequency of CAPTCHA appearing on the target site

Q: Does cleaning the data kill good IPs by mistake?

A: ipipgo'sAI learning modelIt will differentiate between business scenarios, such as crawler business will keep high stash IP, while data collection will prefer static residential IP.

Q: How are you different from other agency service providers?

A: We configure each customer individuallyIP Fresh StorageThe data cleaning rules for different businesses are completely segregated. For example, customer A, who is a cross-border e-commerce company, and customer B, who is a price comparison website, use two sets of cleansing programs at all.

In the end, data cleansing is notOne-off cleaning, rather it's an ongoing maintenance process. Brothers who use ipipgo remember to always look at the background of theIP Health ReportAfter all, a clean IP pool is your strongest card in the data battlefield.