Data Annotation
Precise annotation of various data types – text, image, audio, video
We have delivered projects built on natural language processing, machine translation, speech recognition and image analysis. We know what modern algorithms actually need – and how to prepare data so it delivers results.
What is data annotation, and how can you start your own project?
Data has become the fuel of modern technology. Without it, the AI models we now rely on every day would simply not exist. But owning data is not enough – for it to have real value to an algorithm, it has to be properly prepared. Data annotation is the process of preparing raw information according to clearly defined guidelines for machine learning.
What is more, at a time when almost every company is experimenting with artificial intelligence, it is increasingly the quality of the data – not the algorithm itself – that decides who pulls ahead. Capable models are widely available today; the real edge belongs to whoever can feed them well-described, trustworthy examples. High quality labels help systems recognize patterns and train models more effectively. And that is precisely what good data annotation delivers. 80% AI project failures stem from poor data quality.
It may sound technical, but the idea is intuitive: we show the machine what it “sees”, “hears” and “reads” so it can draw conclusions. This article explains, without the jargon, what data annotation actually is, its main types, how to run a project step by step, and what to watch out for – plus why linguistic experience makes such a difference here.
What is data annotation?
In the simplest terms, data annotation is the process of labelling, tagging and describing information – text, such as images, audio or video – in a way that makes it understandable to artificial intelligence systems. Imagine you have thousands of photos of rabbits and hamsters, but the computer has no idea which is which. To help the algorithm learn, you have to tell it – marking one image as a rabbit and another as a hamster. This annotated data lets ai systems recognize patterns and train ai more reliably. Only then does the model start to spot patterns and recognise similar cases on its own.
It is worth separating two terms that often get confused. Data annotation is the broader activity – adding labels, notes and metadata to text, paratext (titles, footnotes, table descriptions), images, audio and video. Data labelling, sometimes called data tagging, is a narrower subset, focused mainly on classification and assigning content to predefined categories. In practice both serve the same goal: the machine gains context and begins to “understand” what it is analysing.
Let’s put that into business terms. Say you want a system to sort incoming correspondence by itself. Someone first has to show the model hundreds of examples: this document is an invoice, this email is a complaint, that message is a routine request for a quote. That is data annotation – patiently building a set of described examples from which the machine derives a rule and starts working on its own. The more carefully we handle this stage, the fewer surprises await us after go-live.
Annotation is therefore an essential step in preparing training data for AI and machine learning systems – the moment raw, unstructured information turns into material an algorithm can genuinely learn from. Well-described datasets can also protect a model from overfitting. Without it, even the most advanced model is like a student without a textbook: full of potential, but with nothing to work from.
Why does data annotation matter?
Imagine a translator handed a document with no punctuation, no formatting and no cultural context. That is exactly how an algorithm “feels” when raw data lands on its desk. Annotation is a kind of translation: it tells the model what content means, what to focus on, and how to interpret an image, a sound or a sentence – whether something is meant literally or as irony.
A great deal depends on that quality: the accuracy of the model, the soundness of its decisions, the time it takes to deploy, and the overall cost of the project. Errors in the annotation process can introduce bias and even model hallucinations. The relationship cuts both ways – well-described data shortens the path to a working product, while careless data can undo months of a data science team’s work.
A customer-service chatbot is a good example. If user intents – a complaint, a question about order status, a refund request – are labelled inconsistently, the model confuses these situations and sends customers to the wrong place. Get the annotation right, though, and the very same model starts to read needs accurately and genuinely takes the load off your team. The difference between a frustrating assistant and a helpful one is very often settled at the labelling stage, and it’s important that intent labels match the task.
It is also worth looking at the economics. Data science teams can spend the bulk of their time not building the model, but cleaning and correcting poorly described data, especially when the data annotation process weakens quality and slows deployment. Every hour invested in thoughtful annotation up front pays back many times over later – in a faster rollout, fewer fixes, and a model you can simply trust.
What are the main types of annotation?
The type of annotation you need depends on your data and your goal. In practice we work with four main categories, though projects that combine several of them at once play an ever larger role today.
Text annotation
Used in chatbots, customer-opinion analysis and machine translation, among others, in natural language processing. Here we mark user intent, the tone of a statement, named entities or product categories. An example? In the sentence “I’m flying from Amsterdam with Lufthansa”, we tag “Amsterdam” as a city and “Lufthansa” as a company name – so the model learns to tell places apart from brands. This is the basis of named entity recognition. Another common task is text classification. This is also where annotation extends to paratext: elements such as titles, subheadings, footnotes or image captions. In the financial sector, text annotation helps identify fraud.
Image annotation
This works in object-recognition systems used in computer vision – from autonomous vehicles, through medical diagnostics, to e-commerce. The annotator marks specific elements in photos: vehicles, faces, products or changes in a medical image, as well as road signs and the road in autonomous driving scenarios. In an online store, this kind of annotation makes it possible to build visual search, where a customer finds a product from a photo rather than keywords. The techniques range from a simple label on the whole image, through bounding boxes; bounding box annotation uses a rectangle to localize an object for training. Polygon annotation is a more precise way to trace an object’s contour. Bounding box annotation is faster, on the other hand polygon annotation works better when object boundaries matter. For more advanced tasks, teams also use instance segmentation.
Audio annotation
This helps with speech recognition, and annotated audio helps models recognize speech more accurately, alongside voice assistants and the analysis of ambient sound. Here we label words, moments of silence, emotion in the voice, intonation, and other data types such as text, images, video or audio – often including the characteristics of the speaker too. In practice we describe voice personas: individual speakers, their approximate age and gender, dialect or accent, and the way they speak (pace, tone, pauses). It is precisely this level of detail that makes natural, expressive synthetic voices possible. Such datasets are used to train models that recognize emotion and speaker patterns.
Video annotation
This combines image and audio annotation across time – video annotation is the process of labelling frames in motion over time, analysing successive frames, marking objects in motion to track the object across frames and detecting specific behaviours. It is more complex than image annotation because objects and context change from frame to frame. Data like this is invaluable in surveillance systems, sports analysis or autonomous vehicles, and keypoint annotation is often used here for motion analysis. Increasingly we talk here about multimodal data, which combines text, image, sound and video into a single coherent set – and describing it calls for experience in each of those areas at once, and much more.
In most real-world projects these categories overlap. A voice assistant blends audio and text annotation; a content-moderation system blends image, video and description. So when planning, think less about a single data type and more about the decisions your model must make – then work back from that goal to the annotations you need.
How to prepare a data annotation project?
A well-organised annotation project is half the battle. Below are the key stages worth going through in the right order – whether you are labelling a few hundred sentences or hundreds of thousands of images.
1. Define your goal
Start with a simple question: why do I actually need data annotation? Is it about better product classification, sentiment analysis in customer reviews, or a system that recognises faces or speech? Different annotation choices also depend on the task the model must perform. The more precisely you define the goal, the easier it is to choose the methods, tools and scope of labelling. The goal shapes everything that follows – deciding which elements of the data matter and which can be left out. Describe that goal through concrete use cases.
2. Collect and prepare your data
There is no annotation without data. Gather all the files you need – texts, recordings, images, video – and check their quality. Make sure, too, that they comply with GDPR and other regulations, especially if they contain personal data. Even at this stage it is worth weeding out low-quality material and ensuring variety, because the model will only ever learn what you show it.
3. Choose tools and platforms
The market offers plenty of annotation tools – from simple open-source tools to advanced platforms with automation and a human-in-the-loop approach, where a person supervises and corrects the algorithm’s work. Choose the one that fits your scale and data type, whether that means a lightweight app or a full data annotation platform for larger projects. A good tool will not replace a well-thought-out process, but it can speed up the work considerably and reduce errors, and large language models are increasingly used for pre-annotation or draft labels. In computer vision projects, a dedicated computer vision annotation tool can make review and collaboration easier.
4. Create annotation guidelines
This is the key document – a brief for annotators, the people who do the labelling. It must clearly state what to annotate, how to do it (for example with a bounding box, a label or a code), what to avoid, and show both correct and incorrect examples. Well-prepared guidelines reduce mistakes and improve consistency, and in complex projects a short demo video walking through the process can be a real help.
5. Build an annotation team (or outsource it)
You have three routes here: build an internal team, use data annotation services, or combine the two in a hybrid model where your experts work side by side with an external company. You’ll need dedicated reviewers, clear guidelines, and enough operational capacity to make the in-house model work well. Bear in mind that annotation is time-consuming and demands precision. If pace and repeatable quality matter to you, it is worth entrusting the work to an agency with experience and proven people. The market for data annotation services is projected to reach $10.07 billion by 2032.
6. Put quality control in place
No annotation is perfect on the first attempt – even with manual annotation, which is why quality control is not an add-on but the foundation of the project. In practice we assess labels along several axes: accuracy (is the label correct), consistency (are similar cases described the same way), clarity and completeness. In fact, small inconsistencies scale quickly across large datasets. We often use a five-point rating scale as well, which lets us capture nuances in quality rather than reducing it to a simple “right or wrong”. On top of that come random checks, peer review between annotators, fact-checking of disputed cases and regular feedback for the team. A process like this – with a human overseeing the key decisions, while teams also train annotators to apply the rules consistently – is what makes your data genuinely valuable and stops hidden errors from carrying through into the finished model.
What to watch out for?
The most common source of problems is a vague project goal. Without a clear direction it is hard to judge whether the annotation succeeded at all, and the team wastes time labelling things that turn out not to matter. The second trap is overly general guidelines – if annotators interpret the rules their own way, discrepancies creep into the data that are very hard to catch later on.
The next risk is a lack of quality control. Skipping verification means the model may be built on flawed data, and no amount of later fine-tuning will fix that foundation. Finally, a frequent mistake is too few or too uniform examples – an algorithm will not learn to discriminate without rich enough input. Being aware of these four risks is already half the way to avoiding them.
A simple scenario makes the point well. Two people label the same customer reviews. One treats the remark “Oh great, it crashed again” as positive, because they see the word “great”; the other recognises the irony and labels it as negative. If the guidelines do not settle cases like this, the model learns contradictory signals and loses accuracy. That is exactly why unambiguous guidelines and quality control matter so much – they turn each person’s intuition into a repeatable standard.
can a translation agency help with annotation?
Absolutely – and, contrary to first impressions, it is a very natural fit. Translation agencies increasingly take on text-annotation projects: for language models, multilingual chatbots, sentiment analysis across markets, and OCR and machine translation systems. Translators and linguists are right at home in tasks that demand linguistic precision and a feel for cultural context – which is crucial in text annotation.
If you work with multilingual datasets, data labelling calls not only for linguistic correctness but also for semantic sensitivity – the awareness that the same word can mean different things depending on market and culture. Linguistic experience comes into play in more advanced tasks too: in assessing machine translation quality (for instance in A/B tests comparing two engines) and in optimising models with methods such as RLHF and fine-tuning, where people rate and improve the responses an AI generates. In every one of these cases it is human knowledge that gives the data a value the algorithm cannot produce on its own. For a business this also means convenience: a single, trusted relationship instead of juggling several suppliers – the very people who safeguard your translations can safeguard the data behind your AI.
Need help with data annotation?
If you already have data and want to make the most of it in your AI project, we are here for you. We will help you plan the project, choose the methods, carry out the labelling and look after its quality – whatever the language, industry or data format. You can hand the whole thing to us, build a hybrid team together, or scale the work with our resources exactly when you need it. You do not need a ready-made strategy or a technical backend – just the data and a goal. The rest, from the annotation guidelines, through team selection, to multi-level quality control, is on us. Get in touch, and together we will turn raw data into real value for your business.
Data security and confidentiality
We take the protection of personal data and compliance with GDPR very seriously. As a data controller, Skrivanek processes personal data in line with applicable law, with particular regard to Regulation (EU) 2016/679 of the European Parliament and of the Council – the GDPR – and national data protection legislation. We apply widely accepted information-security and technology standards to protect the data entrusted to us against loss, unauthorised access, alteration or destruction.
When we act as a data processor, we do so under a separate data processing agreement concluded with the client in accordance with Article 28 of the GDPR. The information we receive during a project is treated as confidential and shared only with the employees, associates and subcontractors directly involved in it. Importantly, we do not contact our clients’ clients directly unless agreed otherwise in advance. In annotation projects we pay particular attention to sensitive data – where personal data or protected content appears, we flag it and handle it under agreed security rules.
Summary
Data annotation is a crucial step in building effective AI-based solutions. Whether you are working on a chatbot, an image recognition system, sentiment analysis or machine translation, you need data that “speaks the language” of your algorithm. It is well-executed labelling that decides whether a model lives up to the hopes pinned on it.
At Skrivanek, we combine linguistic expertise, precision and technological know-how to deliver data perfectly prepared for training AI and machine learning models. We support many languages, various data types – text, audio, image, video – and over 100 file formats, and we handle every project with a strong focus on quality, consistency and information security. If you value a professional approach, flexible collaboration and real technological support, we are ready to help. Turn raw data into real value for your business with the help of Skrivanek’s experts.