When enterprises start working on
foundation model training, fine-tuning, or AI applications, one of the first
questions is often simple: Where does the data come from?
At first glance, data does not seem
particularly difficult to find. The public web contains vast amounts of text,
while enterprises have accumulated images, audio, video, and various types of
business content internally.
But once a project gets underway, many
enterprises quickly discover that having data is not the same as having data
that is suitable for AI applications.
The sources may be extensive, but poorly
aligned with the actual business scenarios. Some datasets may appear relevant
but contain little variation in how information is expressed. Others may
include duplicates, missing information, noise, or inconsistent quality,
creating a significant amount of work later in the process.
That is why the real challenge in AI data
collection is not simply finding data.
What matters more is knowing what to
collect, where to collect it, and how to continuously obtain data that matches
the model task and the business scenario.
1. AI Data Collection Starts with Collecting the Right
Data
Data collection may look like a
straightforward process of acquiring information. In practice, its value
depends heavily on decisions made before collection begins: What problem is the
model expected to solve? Which scenarios need to be covered? Which types of
data are worth investing in? And which datasets, regardless of their size,
would add little value?
1.1 Why Doesn't Data Volume Equal Data Value?
When evaluating an AI data project, data
volume is usually the most obvious metric.
Collecting one million records certainly
sounds more substantial than collecting 100,000. But if those one million
records are highly repetitive or concentrated in only a few scenarios, they may
be less valuable to a model than 100,000 records with much broader coverage.
Consider a visual dataset designed for road
environment recognition. If most of the images were captured during the day, in
clear weather, with unobstructed roads, a large number of samples does not
necessarily mean the dataset adequately represents real-world conditions.
Once deployed in real environments, the
model may encounter nighttime driving, rain or snow, backlighting, road
construction, vehicles blocking one another, and areas with heavy pedestrian
traffic. These scenarios may account for only a small share of the overall
dataset, yet they can be among the situations that most strongly test the
model's recognition capabilities.
So the first questions in data collection
should not be: How much data can we collect?
They should be: What problem will the model
ultimately solve? Which scenarios need to be covered? Which situations are
common, and which edge cases are easy to overlook?
For AI data collection, volume is a measure
of scale. The value of the data also depends on its relevance,
representativeness, and scenario coverage.
1.2 Why Are Real-World Scenarios More Valuable Than
“Standard” Data?
Much of the data enterprises actually need
comes from real-world environments, where variation is unavoidable.
Text may contain colloquial language,
abbreviations, typos, or missing context. Speech can be affected by background
noise, differences in recording devices, speaking speed, and multiple people
speaking at the same time. Images may contain occlusions, changes in
perspective, and variations in lighting. Video combines visual and audio
information, while some applications also depend on the relationship between
events over time.
For convenience, a project may be tempted
to retain only the clearest, most standardized, and easiest-to-classify
samples. The resulting dataset may look exceptionally “clean,” but that does
not necessarily make it representative of the environment in which the model
will actually be used.
That leads to a very practical question
during collection:
Which imperfections should be preserved,
and which should be removed?
An image captured in the rain may represent
an important real-world scenario. Background noise in a recording may need to
be retained for one task and removed for another. A piece of text containing
colloquial language and spelling mistakes may be valuable for a language model,
while offering little value for a different application.
This is why data collection and data
cleaning need to be designed together. If collection standards are not
established properly at the beginning, trying to compensate through data
cleaning later will usually cost more time and resources.
1.3 Why Do Specialized Domains Require Data Collection to
Start with Business Needs?
Once a project enters specialized fields
such as healthcare, finance, manufacturing, legal services, or intellectual
property, data collection becomes even more demanding.
In these fields, it is not enough to find
content that simply appears relevant.
Healthcare data, for example, can vary
significantly depending on the medical specialty, disease type, and stage of
care. Financial projects may need to cover customer inquiries, risk
disclosures, product information, and specific business processes. Intellectual
property projects may involve patent specifications, claims, technical
disclosures, and other document types.
Without an understanding of the underlying
business context, it is easy to collect large volumes of content that look
relevant but fail to cover the scenarios the model actually needs.
That is why data collection in specialized
domains often needs to work backward from the business task: What problem is
the model expected to solve? What data is required? Which scenarios must be
represented? Which content should be considered invalid or low-value?
At this point, data collection is no longer
simply a matter of gathering information. It requires a practical understanding
of the industry and its real-world use cases.
2. Multilingual and Multimodal Data Make “Collecting the
Right Data” More Complex
As AI data expands beyond text to images,
audio, and video—and as AI projects increasingly serve multiple languages and
international markets—the challenges become more complex. Different data
modalities require different quality standards, while different languages come
with their own patterns of expression and usage.
2.1 Why Do Different Data Modalities Require Different
Collection Standards?
AI applications increasingly rely on text,
images, audio, and video, but the methods used to collect and evaluate these
data types are not interchangeable.
For text, key considerations may include
content completeness, natural language, and scenario coverage. For images,
quality assessment involves not only the content itself but also resolution,
viewing angles, occlusion, and object distribution. For audio, factors such as
language, speaker characteristics, recording environment, and sound quality all
matter. Video requires consideration of both visual and audio information, and
some tasks also depend on maintaining the complete temporal relationship between
events.
This makes it difficult for multimodal
projects to apply a single set of standards across all data.
Tools such as OCR and ASR can improve the
efficiency of data acquisition and initial processing, while also reducing a
significant amount of repetitive work. But these tools primarily address how
data can be processed more efficiently. They cannot replace the judgment
required at the beginning of a project.
What should be collected, what should be
retained, and what requires further processing still depend on the specific
model task.
2.2 Why Can't Multilingual Data Collection Simply Be
Replaced by Translation?
For AI projects targeting international
markets, multilingual data introduces another layer of complexity.
Chinese, English, Japanese, German, and
other languages differ in patterns of expression, use of colloquial language,
terminology, and cultural context.
For example, suppose an enterprise needs to
build a dataset of English-language user interactions. Translating Chinese data
directly into English can quickly produce a large volume of English text, but
that does not necessarily make it equivalent to data generated by real
English-speaking users.
Real users use different expressions,
abbreviations, and habitual phrases. They may also raise different questions
depending on the market in which they operate.
For this reason, multilingual AI data
collection is not simply about converting content into the target language. The
goal is to preserve, as much as possible, the authentic usage scenarios and
linguistic characteristics associated with each language.
For multilingual models, cross-lingual
applications, and global AI products, these differences can directly affect the
representativeness of the dataset.
This is also where linguistic expertise
becomes especially valuable in AI data services: it is not only about
understanding the language itself, but also about understanding the environment
in which that language is used.
3. Large-Scale AI Data Collection Is a Long-Term
Capability
Collecting a few thousand records and
collecting hundreds of thousands are not difficult in exactly the same ways.
As scale increases, new problems tend to
emerge. The same types of data may appear repeatedly, some scenarios may become
overrepresented while others remain underrepresented, and collection standards
may drift between batches. Changes in data sources can also lead to
fluctuations in quality.
At that point, data collection is no longer
a one-time exercise in “finding data.”
It becomes an ongoing effort to build and
maintain a data resource.
Teams need to continuously assess which
types of data are already sufficient and where gaps remain. They also need to
determine which scenarios require further expansion, which sources are no
longer worth the investment, and whether quality remains consistent from one
batch to another.
At scale, the real challenge is the ability
to produce data consistently and adjust the collection strategy continuously.
Technology is certainly important, but
tools alone cannot solve every problem involved in large-scale data collection.
Web scraping, OCR, ASR, automated
deduplication, classification, and filtering can all improve efficiency. But
the key questions still require human judgment:
- Does this source meet the project's requirements?
- Is this batch of data actually what the model needs?
- A sample may be complete in format, but does it lack essential context?
- A data source may appear reliable, but does it cover the critical scenarios found in real business environments?
These questions require experienced
professionals to assess data against the specific requirements of each project.
Large-scale AI data collection is therefore
a collaborative process involving technology, data resources, and human
expertise. Tools improve processing efficiency. Teams establish collection
standards, evaluate data value, handle exceptions, and continuously refine
collection strategies based on project feedback.
These are also capabilities that Glodom has
been building through its long-term AI data services work.
As a provider of multimodal data solutions
for AI foundation models, Glodom offers services covering data collection, data
cleaning, OCR/ASR, structured data processing, annotation and data processing,
quality assurance and review, and dataset delivery, depending on the
requirements of each project. Data collection is one of the core areas in which
Glodom has accumulated extensive project experience.
Years of project experience have given the
team practical knowledge across different data types, languages, and industry
scenarios, along with a growing base of relevant data resources.
Today, Glodom has a data services team of
more than 1,000 professionals, with core team members averaging more than eight
years of industry experience. The team has long supported multilingual,
multimodal, and specialized-domain data projects. For large-scale projects,
this team structure enables Glodom to continuously manage the full process,
from source selection and collection standards to exception handling.
Drawing on its accumulated data resources
and project experience, Glodom continues to support large-scale data collection
for AI training, model fine-tuning, evaluation, and related applications.
4. Meet Glodom at Big Data Expo 2026 in Guiyang, August
28–30
As AI applications continue to move into
real business environments, enterprise expectations for data are also changing.
The focus is no longer simply on whether
data is available. Enterprises also need to consider whether the data matches
the model task, whether scenario coverage is sufficient, whether the required
scale can be sustained, and whether downstream data processing can be built on
a stable foundation.
These questions ultimately become part of
specific data projects, and every enterprise has different data types,
application scenarios, and levels of data readiness. For data service
providers, building project experience is only part of the work. It is equally
important to communicate directly with industry partners, understand their
practical requirements, and identify new challenges emerging across different
scenarios.
From August 28 to 30, 2026, the China
International Big Data Industry Expo 2026 (Big Data Expo 2026) will take place
in Guiyang. The event is officially listed under this English name by
government sources.
Glodom will be exhibiting at Booth W2F11 at
the Guiyang International Conference and Exhibition Center, where it will
showcase AI data service solutions, multilingual and industry-specific
datasets, and its experience in data collection, annotation, quality assurance,
and dataset development.
During the event, Glodom's professional
data services team will be on site to exchange ideas with industry partners on
practical topics such as multimodal data collection, multilingual data
development, specialized-domain datasets, and quality management for
large-scale data projects.
Glodom will also be offering a limited
number of complimentary admission tickets for Big Data Expo 2026 on a
first-come, first-served basis. Those planning to attend can leave a message
through Glodom's official WeChat account or website to request a ticket.
About Glodom
Glodom Language Solutions Co., Ltd. is an
innovative language technology solution provider specializing in multilingual
translation, interpretation, localization, desktop publishing, multimedia
services, data services, and AI technology and applications. The company serves
industries including software, ICT, gaming, finance, patents, and life
sciences.
Glodom provides high-quality data solutions
for foundation model training and AI applications. Its services cover data
collection, cleaning, processing, annotation, structuring, and dataset
development, supporting use cases such as model training, SFT, DPO, RAG, ASR,
multimodal models, and industry-specific AI applications.
With a data services team of more than
1,000 professionals and core team members averaging more than eight years of
industry experience, Glodom has built substantial expertise in data quality
assurance, project delivery, and data security management.
By combining language expertise, AI
capabilities, and data services, Glodom helps enterprises build reliable
datasets for increasingly complex AI applications and multilingual business
environments.
