Many enterprises focus first on models,
computing power, and applications when they begin exploring AI.
But once an AI project moves into
implementation, another reality quickly becomes clear: there is a substantial
amount of data work behind the model itself.
Model training requires data. Fine-tuning a
model for a specific business task requires more targeted, high-quality
datasets. Before and after deployment, evaluation data is needed to verify
model performance. At the same time, enterprises often find that their raw
materials are not ready for direct use. Product documentation may come in
inconsistent formats, customer service records may contain large amounts of
irrelevant content, audio may need to be transcribed, images and videos may
require annotation, and multilingual data may contain terminology or semantic
inconsistencies.
AI data services therefore go beyond simply
“preparing training data.” They address data needs across different stages of
AI development by collecting, organizing, processing, and validating raw data,
and turning it into data assets that can support training, fine-tuning,
evaluation, or real-world applications.
1. What Exactly Are AI Data Services?
Simply put, AI data services transform raw
data into datasets that meet the requirements of specific AI tasks.
Raw data can take many forms: text, audio,
images, video, or even 3D data. Before it can be used by a model, it typically
needs to go through a series of processing steps.
For example, a batch of customer service
conversations may need to be cleaned first, with irrelevant content removed and
user questions separated from agent responses. The data can then be classified,
annotated, and quality-checked. Audio may require transcription, segmentation,
speaker identification, and content annotation. Images and videos may need to
be labeled according to the task, whether that involves objects, scenes, or
semantic information.
A complete data services workflow typically includes:
- Data Collection: Collecting text, audio, image, video, 3D, and other data that meet specific task requirements.
- Data Cleaning and Processing: Removing duplicates and noise, handling missing content, and standardizing data formats and processing rules.
- Data Annotation and Enrichment: Classifying, labeling, revising, or structuring data according to the requirements of a specific task.
- Data Quality Control: Identifying incorrect or missing annotations, inconsistent standards, and other quality issues, then validating the resulting data.
-
Dataset Development: Organizing processed data into structured or semi-structured
datasets based on requirements for model training, fine-tuning, evaluation, or
specific business applications.
Data services, therefore, are not simply
about “finding people to label data.” They cover the entire process, from data
acquisition to processing and final delivery.
2. Where Is Data Used Across the AI Lifecycle?
Different stages of AI development require
different types of data and different quality standards.
Model Training: Large Volumes of High-Quality Data
Model training relies on large amounts of data to help models learn language, knowledge, scenarios, and information across different modalities.
At this stage, factors such as scale,
coverage, data quality, and consistency across samples are particularly
important. For multilingual models, enterprises must also consider how data
corresponds across languages and whether quality remains consistent between
them.
Model Fine-Tuning: More Targeted Data
Once a model has acquired general capabilities, enterprises often need targeted fine-tuning data to make it more suitable for a particular industry, task, or business scenario.
Customer service Q&A, professional knowledge Q&A, industry terminology, and multilingual instructions can all be developed into task-specific datasets.
Unlike training data, fine-tuning data does
not necessarily need to be extremely large. What matters more is how closely it
matches the intended task and real-world use case.
Model Evaluation: Data That Can Actually Test the Model
After training and fine-tuning, enterprises still need to determine how well the model performs.
That is where evaluation data comes in.
Evaluation datasets can be used to assess whether a model meets expectations for accuracy, relevance, language capability, and performance on domain-specific tasks. For multilingual and multimodal models, enterprises also need test data that reflects actual usage scenarios.
Data services therefore do not end when
model training is complete. Training, fine-tuning, and evaluation can all
require different types of data support.
3. What Problems Do Enterprises Commonly Encounter with AI
Data?
Once a data project moves into
implementation, several challenges tend to appear.
Inconsistent Data Quality
Raw data may contain duplicates, missing
information, errors, noise, and other issues. Without proper preprocessing,
these problems can affect downstream annotation and dataset development.
Difficulty Maintaining Consistent Standards
Teams need to agree in advance on what
should be annotated, how labels should be defined, and how edge cases should be
handled. When multiple people work on the same project without a unified
standard, the same type of data can easily end up being processed in different
ways.
Professional Content Is Not Always Easy to Assess
Industries such as healthcare, finance,
manufacturing, gaming, and intellectual property have their own terminology and
business rules. Content may appear straightforward on the surface, but making
the correct judgment often requires domain expertise.
Multilingual and Multimodal Data Add Another Layer of
Complexity
Multilingual data requires careful consideration of meaning, terminology, and cultural context. When text, audio, images, video, and even 3D data are involved, there may also be relationships that need to be maintained across modalities.
For example, in a video understanding task,
a text description must not only be accurate but also accurately reflect the
actual video content. Likewise, multilingual Q&A data involves more than
translating Chinese into another language. The questions, answers, labels, and
underlying intents all need to remain aligned.
This is why AI data services increasingly
depend on robust quality systems and specialized expertise.
4. Why Do Globalizing Enterprises Need Multilingual Data
Services?
For enterprises entering overseas markets,
data challenges often come with another layer of linguistic and market
differences.
A product expanding into multiple markets
may generate product documentation, customer service records, user reviews, and
marketing content in Chinese, English, Japanese, German, Spanish, and other
languages at the same time.
Simply translating these materials
separately does not automatically turn them into AI-ready data.
For example, if an enterprise wants to
develop multilingual Q&A datasets from Chinese customer service
conversations, the data may need to go through cleaning, classification,
translation, revision, annotation, and quality control. At the same time, questions,
answers, labels, and business intents must remain aligned across languages.
The same applies to speech data. Different
languages, accents, and recording environments can all affect downstream
transcription and annotation.
For globalizing enterprises, language is
therefore not just content that needs to be translated. It can also become data
that needs to be continuously collected, processed, validated, and maintained.
5. Do Enterprises Need to Build Huge Datasets from Day
One?
Not necessarily.
An enterprise can start with a specific,
clearly defined task.
For example, it might first build a
customer service Q&A dataset or organize a set of product materials. If it
is developing a speech-based application, it could begin with speech
collection, transcription, and annotation.
The priority is not to pursue massive data
volumes from the outset, but to establish clear data standards, processing
workflows, and quality requirements first.
As the project develops, annotation
guidelines, data standards, terminology resources, and quality rules can
continue to accumulate and support future data production and model
optimization.
6. How Should Different Types of Data Be Processed?
Different modalities naturally require
different approaches.
Text data may involve cleaning,
classification, annotation, Q&A revision, and multilingual processing.
Audio data may require collection, transcription, segmentation, speaker
identification, and quality control. Images and video need to be annotated according
to the task, whether that involves content, objects, or scenes. For 3D data,
enterprises need to establish appropriate standards for collection, processing,
and annotation based on the model and application scenario.
When professional or domain-specific
content is involved, industry standards and expert review may need to be added
to the general data processing workflow.
In real-world projects, AI and human
experts can also play different roles. Standardized tasks such as format
organization and initial classification can use AI to improve efficiency, while
complex judgment, domain-specific decisions, and final quality review require
human oversight.
The goal is not simply to replace human
work with AI, but to use the most appropriate approach at each stage of the
workflow.
7. What Does Glodom Provide in AI Data Services?
Glodom provides high-quality structured and
semi-structured data services for the data needs of foundation model training,
fine-tuning, evaluation, and related AI applications, covering five major
modalities: text, audio, image, video, and 3D data.
Depending on project requirements, Glodom
can support data collection, cleaning, annotation, speech transcription,
Q&A revision, data quality control, and dataset development, while
establishing corresponding data standards and delivery specifications based on
the project's needs. Glodom has built a comprehensive data services framework
covering data collection, data annotation, data quality control, platform
support, and domain-specific dataset development.
For example, in a multilingual Q&A data
project, Glodom can first clean and classify the source Q&A data, then
carry out translation, revision, annotation, and quality control to create
multilingual Q&A datasets suitable for training, fine-tuning, or
evaluation.
For professional and domain-specific data,
datasets can be developed according to industry requirements and
project-specific standards. Current areas include software, ICT, gaming,
finance, intellectual property, life sciences, Traditional Chinese Medicine,
finance and law, and question-bank development. Glodom's official English
website identifies ICT, gaming, intellectual property, life sciences, and
finance & law among its core industry areas.
For multilingual and domain-specific
projects, Glodom can also combine language expertise with human quality control
to process and review data as required. Relevant capabilities include
multilingual data processing, Q&A revision, speech transcription, and
multimodal data annotation.
Not every project needs to follow exactly
the same workflow. Data volume, modalities, application scenarios, and quality
requirements can vary significantly, which means collection methods, processing
standards, and quality control approaches may also need to change.
Ultimately, AI data services are not about
pursuing more data for its own sake. They are about making data more accurate,
more consistent, and genuinely fit for the needs of the model and the business.
8. AI Data Services Are Ultimately About One Question: Can
the Data Be Used?
From model training and fine-tuning to
evaluation and real-world deployment, AI systems require different types of
data at different stages.
For enterprises, the challenge is not
simply whether data exists. The more important questions are whether the data
is accurate, consistent, usable, fit for purpose, and capable of being reused
over time.
For globalizing enterprises, multilingual
requirements, domain expertise, and multimodal data add another layer of
complexity.
AI data services therefore turn fragmented
raw data into data that can genuinely support models and business operations,
step by step.
The China International Big Data Industry
Expo 2026 will take place in Guiyang from August 28 to 30. Glodom will showcase
its AI data services, multilingual data, and domain-specific datasets at Booth
W2F11 of the Guiyang International Conference and Exhibition Center, with a
focus on data collection, annotation, quality control, and dataset development.
The official English name of the event is China International Big Data Industry
Expo 2026 (Big Data Expo 2026).
Conclusion
AI models may continue to become more
capable, but what they learn and how well they perform still depend heavily on
high-quality data.
For enterprises, building data capabilities
does not necessarily mean launching a massive project from day one. Starting
with a clearly defined task, working with the right data, establishing
standards and quality controls, and expanding gradually based on actual needs
can often be a more practical path to implementation.
When the data is done right, AI has a much
stronger foundation.
