In recent years, the focus of enterprise AI
discussions has been shifting.
Companies have moved from asking whether
they should adopt AI, to comparing different models, and now to a much more
practical question: can AI reliably solve problems in real business
environments?
At this stage, one factor that is easy to
overlook is becoming increasingly important: data.
Most companies are not short of data.
Historical documents, customer service records, product materials, knowledge
bases, terminology databases, and years of accumulated language content can all
provide a valuable foundation for AI applications.
But having a large amount of data does not
mean that the data is ready to use.
Terminology may be inconsistent. Annotation
standards may vary. Historical content may be outdated. Some language data may
even lack the context needed to interpret its meaning correctly.
One of the clearest things we have observed
in real-world projects is that many enterprises do not have a “lack of data”
problem. They have a problem determining which data is actually worth bringing
into an AI workflow.
That is one reason AI data services are
becoming increasingly important as AI moves from demonstrations into real
business operations.
1. As Models Get More Capable, Why Do Enterprises Still
Need Their Own Data?
General-purpose large language models can
already handle a wide range of language-related tasks.
But very few enterprise use cases are truly
“general-purpose.”
Healthcare companies have their own
specialized terminology. Manufacturers work with specific equipment, processes,
and technical systems. Financial institutions rely on defined business concepts
and rules. Game companies need to handle large volumes of context-dependent
content, including characters, skills, quests, and storylines.
A model may understand language, but it
does not automatically understand the business rules an enterprise has
accumulated over years.
Ultimately, the question for enterprise AI
is not simply how much the model knows. It is whether the model can make the
right decisions within the company’s own business context.
That is why high-quality business data
matters.
Companies can provide business knowledge to
models through knowledge bases, Retrieval-Augmented Generation (RAG),
terminology management, evaluation datasets, human feedback, and other
approaches. But whatever technical route they choose, reliable data remains the
foundation.
If terminology in historical data is
inconsistent, labels in training data vary from one sample to another, or a
knowledge base contains large amounts of irrelevant content, a model cannot
simply absorb and resolve all of these issues on its own.
So when model performance falls short,
companies should not only ask, “Is the model powerful enough?”
A more useful question may be: What is the
quality of the data going into the model?
2. Language Data Is About More Than Being “Accurate”
Assessing the quality of language data is
more complicated than checking whether the text contains errors.
A sentence can be linguistically correct
and still be poor training data for AI.
The same term may have different meanings
in different professional fields. The same sentence may need to be handled
differently in a product manual, a customer service conversation, or a game
interface.
These issues become even more apparent in
multilingual environments.
It matters whether the source and target
texts truly correspond, whether terminology is consistent, whether the language
follows the conventions of the target market, and whether enough context is
available for the data to be interpreted correctly.
This is what makes language data different:
it rarely exists as isolated text. It is closely connected to business
scenarios, industry knowledge, and context.
As a result, high-quality language data
cannot be produced through automated cleaning alone.
At the data collection stage, teams need to
understand what should be collected. During annotation, they need clear
criteria for what constitutes a correct result. During quality control, they
need to identify content that may appear acceptable on the surface but does not
actually fit the business context.
From this perspective, linguistic expertise
is not an add-on to AI data services. For many language-related data projects,
it is a fundamental capability.
3. Why Does AI Data Quality Need to Be Managed from the
Collection Stage?
In the past, companies often viewed data
cleaning and quality control as tasks to be handled later in a project.
For AI training data, however, quality is
often determined long before the data reaches the later stages of processing.
For example, does the collected data
actually cover the intended use cases? Are the samples representative? Are the
data sources appropriate? Are there large amounts of duplication, noise, or
low-relevance content?
If these issues are not addressed at the
beginning, later annotation and quality control efforts become attempts to fix
problems that have already been introduced.
A well-structured AI data service workflow
should therefore begin with data collection, followed by data cleaning,
annotation, and quality control.
Data collection answers one question: Do we
have the right data?
Data annotation answers another: What does
this data mean to the AI system?
Data quality control answers a third: Does
the data actually meet the requirements for use?
These stages are closely connected rather
than independent.
For multilingual and multimodal data in
particular, decisions made during data selection can directly affect the
difficulty of annotation and the cost of quality control later on.
Glodom’s data services follow this same
logic. They cover data collection across text, speech, audio, video, images,
and domain-specific scenarios, along with data cleaning, validation,
standardization, and quality control.
4. From Language Data to Multimodal Data, AI Data Services
Are Becoming More Complex
The growth of AI applications is also
changing the types of data enterprises need.
In the past, discussions about AI training
data tended to focus primarily on text.
Today, speech, images, and video are
becoming increasingly important across AI applications. A speech recording may
require transcription and annotation. An image may contain both textual and
visual information. A video may involve speech, subtitles, people, and scenes
at the same time.
This means AI data services are no longer
simply about organizing text.
Data collection, annotation, and quality
control need to be designed around the characteristics of each data type.
In multilingual projects, linguistic
capabilities and multimodal data capabilities are often closely intertwined.
For example, speech data may require both
transcription and linguistic processing. Video data may involve both speech and
text processing. AI products targeting global markets may also need datasets in
multiple languages.
Looking ahead, we believe enterprises will
need more than larger datasets. They will need AI training data that is better
aligned with real-world scenarios and specific business requirements.
This is also why industry-specific dataset
development is receiving increasing attention.
5. The Value of Industry-Specific Datasets Is Not in Their Size, but in Their Relevance
General-purpose data can help models
develop broad capabilities. Once AI moves into specialized business
environments, however, enterprises often need data that is much more targeted.
Healthcare, finance, manufacturing,
education, and other industries all have their own terminology, workflows, and
business scenarios.
The real challenge in industry-specific dataset development is not simply bringing together large amounts of content. It is starting from business requirements and determining:
- What data is worth collecting?
- Which content needs to be annotated?
- How should the data standards be defined?
- How can we verify that the final dataset actually fits the intended use case?
From what we have seen, a smaller dataset
that closely matches a specific business need can sometimes deliver more
practical value than a much larger dataset with limited relevance to the actual
application.
Industry-specific dataset development is
therefore a complete process that covers requirement analysis, data collection,
annotation, and quality control. It is far more than simply aggregating data.
Glodom also provides customized dataset
services for different scenarios and language requirements, covering everything
from requirement analysis and corpus collection to multilingual annotation and
dataset development. The goal is to help enterprises build training data assets
that are suited to specific AI applications.
6. Why Are Language Service Providers Moving into AI Data
Services?
The evolution of AI data services is also
changing the role of language service providers.
In the past, the primary deliverables from
language service providers were translated, localized, and proofread content.
Today, enterprises may need bilingual
corpora, terminology data, conversational data, speech data, evaluation data,
or datasets designed for specific industries and use cases.
These deliverables may fall under the
broader category of “data,” but many of the processes involved still depend
heavily on language expertise.
Is a term accurate? Does a conversation
sound natural in the target language? Do multilingual data points actually
correspond to one another?
These are not questions that can always be
answered through simple data-processing rules.
This is why language capabilities can
naturally extend into AI data services.
Glodom brings together three closely
connected capabilities: language, AI, and data. Language services provide an
understanding of content and industry context. AI technology supports data
processing and application. Data services cover data collection, annotation,
quality control, and industry-specific dataset development.
We believe the real value of this
integration does not come from simply placing several business offerings side
by side. It comes from helping enterprises turn fragmented language content and
business data into assets that AI systems can actually use.
7. What Enterprises Really Need to Build Is a Data
Mechanism That Keeps Running
For enterprises advancing AI initiatives,
model selection is certainly important. But data planning should begin just as
early.
At a minimum, companies need clear answers to several basic questions:
- Where does the data come from?
- What standards should be used for collection and annotation?
- Who determines whether the data meets the required quality level?
- Can issues be traced back to their source?
- Can the data be continuously updated as the business evolves?
These questions point to something larger
than any single data project: a continuous operating mechanism.
Data Collection → Data Processing → Data Annotation → Data Quality Control → AI Application → Feedback → Data Optimization
Once AI is deployed in real business
environments, the results themselves can help companies identify problems in
their data.
Which terms are frequently corrected? Which
scenarios are more prone to errors? Which types of data deliver the greatest
value?
All of these can provide input for the next
round of data optimization.
This means AI data services are becoming
increasingly difficult to define as a one-time “data processing” task.
They are evolving into an ongoing
capability that enterprises need to build and maintain.
Conclusion
Once AI enters the enterprise, the model
obviously matters.
But real-world adoption shows that the
model is only part of the picture.
The deeper challenge is turning an
enterprise’s knowledge, business rules, language content, and real-world
scenarios into data that AI can understand and use.
That is why the value of high-quality
language data is becoming increasingly apparent.
In the past, language content primarily
served people.
Today, it is becoming an important input
for AI training, knowledge retrieval, model evaluation, and intelligent
applications.
The value of language services, therefore,
may no longer lie solely in translating content accurately. Increasingly,
language service providers can help enterprises collect, organize, annotate,
and maintain language data that can become part of their AI ecosystem.
At the same time, as speech, images, video,
and industry-specific scenarios become increasingly important to AI
applications, enterprise data services will become more comprehensive.
From this perspective, AI has not
diminished the value of language services. It has brought linguistic expertise
into a much broader data ecosystem.
Models determine what AI can do.
High-quality data increasingly determines whether those capabilities can work
in the real world.
