For companies working on large language model training, model fine-tuning, or AI application development, data collection is often the first step in the broader data production process.
However, collecting data does not simply
mean increasing the size of a dataset. The real value of data depends on how
well it aligns with the model’s intended task, target users, and real-world
application scenarios.
Consider a company that has already
deployed an AI customer service solution in China and is preparing to expand
into international markets.
The company has accumulated several million
Chinese customer service records. At the start of the project, a seemingly
straightforward approach would be to clean the existing data, translate it into
English, Spanish, German, and other languages, and supplement it with selected
publicly available corpora.
This approach can quickly increase the
volume of multilingual data.
Yet once the model enters the testing
phase, limitations may begin to emerge.
Overseas users frequently use
abbreviations, colloquial expressions, spelling errors, and region-specific
language. Some users may combine several requests in a single message or change
their original question over the course of a multi-turn conversation. At the
same time, payment methods, logistics systems, product offerings, and
after-sales processes can vary significantly across markets.
As a result, a multilingual corpus derived
from Chinese business data may be large in scale while still failing to cover
the kinds of inputs that real users generate in international markets.
This points to a fundamental issue: AI data
collection should start with a clear understanding of what data the model
actually needs, rather than simply determining how much data to collect.
1. The Core of AI Data Collection: Define the Model Task
and Data Requirements First
A data collection project that starts with
a requirement such as “we need one million English customer service records” is
still missing several critical elements.
Before collection begins, teams should
define at least the following:
-
Target markets: Which countries or regions will the data support?
-
Target users: Which user groups will the model primarily serve?
-
Business scenarios: Should the dataset cover presales inquiries, order tracking,
after-sales service, complaint handling, technical support, or other scenarios?
-
Data formats: Does the project require text, audio, images, video, or multimodal
data?
-
Language characteristics: Should the dataset reflect colloquial language, abbreviations, spelling
errors, regional expressions, and other forms of natural user input?
-
Sample requirements: How much data is needed for each market, user group, and business
scenario?
Together, these dimensions determine the
scope, structure, and specifications of the data to be collected.
For example, English customer service data
for an e-commerce platform will have a different structure from data collected
for a SaaS product. E-commerce data may focus on orders, shipping, refunds, and
product inquiries, while SaaS-related data may center on accounts, permissions,
product features, and technical support.
An actionable data requirement therefore
needs to go beyond “how much data do we need?” and break the requirement down
into:
Target market → User group → Business scenario → Data format → Language characteristics → Sample requirements
The clearer the requirements, the easier it
becomes to align data collection, filtering, and quality control around a
consistent standard, reducing the risk of costly rework later in the project.
2. Why Is Long-Tail Data Difficult to Collect?
High-frequency data is usually easier to
obtain. Common requests such as order inquiries, refund requests, and password
changes often appear repeatedly in historical business data.
The real challenge lies in low-frequency
scenarios.
A user may enter only half a sentence,
misspell a product name, use a local abbreviation or slang term, raise several
questions in one message, or change their original request during a multi-turn
conversation.
These samples may account for only a small
share of the overall dataset, yet they can cluster around the areas where a
model is most likely to make mistakes.
This is a common long-tail data challenge
in AI projects.
If data collection simply follows the
natural distribution of historical business data, high-frequency scenarios will
continue to receive more samples while low-frequency scenarios may remain
underrepresented.
Data collection should therefore continuously examine the existing distribution and identify:
- Which scenarios already have sufficient coverage?
- Which scenarios are still missing?
- Which low-frequency scenarios are highly relevant to the model task?
- Which relatively small data categories could provide significant additional value?
- When expanding coverage of long-tail scenarios, teams also need to preserve as much real-world context as possible.
For example, an after-sales complaint may
include product information, order details, and records of previous
communication. Adding isolated sentences labeled as “complaints” may increase
the dataset size, but it does not necessarily help the model understand the
complexity of a real customer interaction.
Long-tail data collection therefore needs
to consider the samples themselves, the conditions under which they occur, and
the surrounding context. This allows newly collected data to address genuine
gaps in the model’s existing data coverage.
3. How Can You Tell Whether an AI Dataset Has Sufficient
Coverage?
Once a dataset reaches hundreds of
thousands or even millions of samples, total volume alone is no longer enough
to evaluate its quality. At this stage, teams need to look more closely at data
distribution, coverage, and the gaps that remain.
For a multilingual AI customer service
project, a basic data dimension matrix might look like this:
Language × Country/Region × User Type ×
Business Scenario × Expression Style
Suppose a company has one million
English-language samples, with 600,000 coming from the U.S. market,
concentrated mainly on order inquiries, and most written in complete,
standardized sentences.
In terms of volume, this is a substantial
dataset.
However, if the model is expected to serve
users in the United States, the United Kingdom, and Australia while handling
order inquiries, after-sales service, technical support, and multi-turn
conversations, significant coverage gaps may still remain.
AI data coverage can therefore be assessed
from four perspectives:
Scale: How
much data is available in total?
Distribution: Which markets, user groups, and scenarios account for most of the
data?
Coverage: How
extensively are key tasks and real-world application scenarios represented?
Gaps: Which
important scenarios still lack representative data?
Teams should also examine the incremental
value generated by newly collected data.
When new samples are highly similar to
existing data, the dataset grows in size while adding relatively little new
scenario coverage. By contrast, data from previously underrepresented
languages, regions, or business scenarios can provide a much more meaningful
expansion of coverage.
In practice, AI data collection often
follows a continuous cycle:
Collect → Analyze → Identify Gaps → Collect Again
Teams can then adjust subsequent collection
priorities according to changes in data distribution and the needs revealed
during model testing.
4. Why Should Data Sources Be Planned at the Collection
Stage?
Once data requirements have been defined,
the next question is where the data will come from.
Internal business data is usually closest
to real-world application scenarios, but historical data may contain
inconsistent structures, missing fields, or fluctuations in quality.
Publicly available data can provide
substantial volume, yet it may be less closely aligned with a specific model
task or business scenario.
Targeted data collection allows teams to
design samples around specific markets, users, and scenarios. It is
particularly useful for addressing clearly identified data gaps, although it
requires well-defined collection standards and execution procedures.
In real-world AI data projects, these
different sources are often used together.
Data sources also affect how usable the
data will be downstream.
For audio, images, video, and data
containing personal information, teams need to consider authorization, privacy
protection, de-identification, and data usage boundaries from the beginning of
the collection process.
Data sources therefore determine more than
where the data comes from. They also influence its authenticity,
representativeness, and compliant use throughout the rest of the project.
5. Why Can't Multilingual AI Data Rely on Translation
Alone?
As AI products enter global markets,
multilingual data adds another layer of complexity to data collection.
Translating Chinese data into English,
Spanish, German, and other target languages can quickly expand corpus volume.
However, translated data still differs from data naturally generated by users
in those target markets.
Real users use local expressions,
abbreviations, colloquial language, and slang. Their language is also
influenced by cultural background, product usage habits, and specific business
environments.
More importantly, business scenarios
themselves can vary across markets.
Take customer service as an example. The
same refund-related issue may involve different payment methods, logistics
systems, after-sales policies, and customer expectations in different
countries. Translating the same set of Chinese customer service records into
multiple languages increases the number of languages represented, but it does
not necessarily expand the underlying business scenarios.
Multilingual AI data collection therefore needs to consider both language and context:
Who uses the language?
In what situations do they use it?
How do they typically express themselves?
How does the local business environment
influence those expressions?
For AI products serving global markets,
data generated by users in the target market therefore provides important
value. Translation can form part of a broader data development strategy, but
when the goal is to improve a model’s ability to handle real user input,
localized data and targeted collection are still needed to capture
market-specific differences.
6. Why Does Domain-Specific Data Require Industry
Knowledge?
As AI applications move into specialized
fields, data collection must also address domain knowledge and data standards.
Healthcare, finance, legal services, and
ICT may all use text, audio, images, or video as data formats, but their
knowledge systems, terminology, and business processes can differ
substantially.
A term may have one meaning in everyday
language and another in a specialized context. Similarly, the same question may
correspond to different data structures, labels, and contextual requirements
depending on the business process involved.
Domain-specific data collection therefore
needs to consider three areas:
Language use: How do industry users actually describe problems, exchange
information, and use technical terminology?
Data structure: Which fields, labels, context, or metadata need to be included?
Domain requirements: Does the data align with the knowledge system and practical
requirements of the relevant industry?
This means domain-specific data collection
often requires language expertise, data capabilities, and industry knowledge to
work together. Only by understanding how the data will be used in a specific
business context can teams determine appropriate collection standards and data
structures.
7. Building a Strong Foundation for AI Data Development
From defining requirements and identifying
long-tail scenarios to planning data sources, collecting multilingual data, and
developing domain-specific datasets, an AI data project involves multiple
interconnected stages.
In practice, data collection is often part
of a broader workflow:
Requirements Analysis → Data Planning → Source Design → Targeted Collection → Data Filtering → Quality Control → Gap Analysis → Additional Collection → Dataset Development
Different projects may focus on different
stages.
Some companies need to build training data
from scratch. Others already have substantial business data but need cleaning,
additional collection, and multilingual expansion to address coverage gaps.
Some projects focus specifically on building targeted datasets for a particular
market, industry, or model task.
Glodom provides data services covering
text, audio, image, video, and multimodal data. The company supports
monolingual, bilingual, and multilingual data development, as well as targeted
collection based on specific markets, user groups, and business scenarios.
For specialized domains, Glodom also
supports Traditional Chinese Medicine datasets, Book datasets, Finance and
legal datasets, Question bank datasets, and Cross-domain datasets. Its services
extend through data cleaning, data annotation, quality control, and dataset
development, allowing collected data to be further processed and organized for
model training, fine-tuning, evaluation, and real-world applications.
For businesses, the ultimate purpose of AI
data collection is to determine what kind of data a model will encounter in the
future and whether that data adequately represents real users, real language,
and real business scenarios.
Data collection shapes what a model can
“see” at the beginning of its development and provides the foundation for
subsequent data production and model optimization.
