Preparing Data for AI and Machine Learning: A Production-Ready Playbook

Data is the backbone of any analytical system. Nothing has changed in this regard with the industry-wide adoption of AI technology. Drawing on our hands-on experience in delivering AI solutions across industries, this playbook walks you through every step of preparing data for AI, from collection and labeling to cleaning and augmentation, to help you build a reliable dataset that powers accurate, bias-free models.

What does data preparation mean for AI and machine learning? 

Data preparation for machine learning and AI means collecting raw data from internal and external sources, labeling it, and carrying out data quality improvement to produce a well-calibrated, bias-free dataset for training an ML model. It’s not a one-off step but a continuous process, since each time new data arrives, it must be labeled, cleaned, and checked for bias.

Why prepare data for machine learning and AI?

Data preparation is the most time-consuming part of any ML project, taking up to 80% of the overall timeline. But this initial investment in data discovery pays off manifold. 

• Grounded confidence in your data. At the AI scale, the old garbage-in-garbage-out adage evolves and takes the form of “garbage in, beautifully phrased/formatted/visualized garbage out.” Putting data preparation on the front burner saves you from falling into a trap of false confidence in the model’s outcomes without noticing that “something is rotten in the state of Denmark.”
• Highly precise decision-making. Clean, bias-free, use-case-relevant data leads to well-thought-out business decisions.
• Ability to deliver hyper-personalized user experience. In highly competitive domains, say, streaming services or ecommerce websites with AI-driven recommendation systems at their core, the level of data preparedness directly influences user experience, helping companies win new customers and retain existing ones.

Data readiness levels for AI & ML 

ML models are only as good as the data they’re fed. And that data needs to go from messy to clean and purpose-ready. 

• Raw data. Unstructured data in multiple formats and from various internal and external sources. It’s consolidated in one place, usually a data lake or a lakehouse, but hasn’t undergone any checks. 
• Clean data. Structured data that has been freed of duplicates, outliers, and missing values, making it usable for various projects. Clean data is typically stored in a data warehouse for easier access and management. At this stage, the intended use of the dataset isn’t yet defined.
• AI-ready data. Once the task is defined, data scientists get the clean and labeled data and ensure it fits the use case. For instance, they eliminate irrelevant data, such as dog images in a dataset for training a fare‑evasion detection model. At this point, they also determine whether the dataset needs to be reduced or artificially augmented with synthetic data.

Scanning your dataset for duplicates and missing values is a shortcut to understanding how much your data is messed up. For instance, you can use Python libraries like Pandas and Great Expectations to run an auto check. Even more than 3% of exact duplicates in your dataset is strong evidence that it’s nowhere near AI-ready. 

— Pavel Klapatsiuk, AI Lead Engineer, *instinctools

How to prepare data for machine learning and AI

Behind every thriving AI model is a lot of unglamorous preparation work. Here are our practice-proven tips on how to make each step of that groundwork count.

1. Data collection

The first thing to do for successful data collection is getting an experienced data scientist on board. Once the purpose of your ML project is clear, they will determine the right strategy to collect the data and prevent potential bias from slipping into a training dataset. 

Say, for a global online retailer that wants to analyze customer behavior, a data expert can anticipate the WEIRD bias (oversampling data from Western, Educated, Industrialized, Rich, and Democratic populations) and head it off by diversifying data sources to include inputs across regions, cultures, income groups, etc.

— Pavel Klapatsiuk, AI Lead Engineer, *instinctools

The same goes for data noise, which has to be filtered out in advance. For instance, in churn-prediction work spanning website, CRM, and ad platforms, not every event belongs in training. You’ll have to sift out the noise, such as test accounts, marketing email previews that look like real opens, competitors’ clicks, price-checkers’ activity, and other artifacts.  

If your business involves IoT devices, the physical world writes itself into your data (mechanical vibration, temperature spikes, electrical hum), turning real-world noise into data noise. In one of our oil and gas projects vibrations from drilling rigs were making it tricky to identify meaningful signals. Our data scientist had to go through the data fields filled in according to the info from sensors to determine the most informative ones and down-weight the rest to lower their noisy impact.

So where to collect the data from?

• Internal sources, such as databases and business operational systems (ERP, CRM, inventory software, etc.). 
• External sources, such as public databases, social media platforms, third‑party datasets, publicly available or purchased reports and statistics, etc.

If you’re a startup without rich internal data, check for valid publicly available datasets. Even if there’s no exact match, you can still resort to web scraping and assemble a solid dataset from free public sources.

— Pavel Klapatsiuk, AI Lead Engineer, *instinctools

Also remember to put a premium on data lineage from the very start of machine learning data preparation. When you can trace the path of any data point within your dataset end-to-end, fixing errors and auditing becomes a walk in the park. 

2. Data labeling

After collecting the raw data, you need to specify its context for the ML models by labeling it. The labels, or annotations, make data more consumable for a model and enable it to interpret the information correctly, contributing to the overall accuracy of the outputs.

While data labeling can be automated, our hands-on experience proves that if you want the ML model to masterfully imitate human perception, thinking, and judgment, at least some part of the labeling should be done by humans. 

— Pavel Klapatsiuk, AI Lead Engineer, *instinctools 

Here’s how to make the most out of the hybrid labeling approach while not spending a fortune:

• Create a ‘golden’ seed set. Have three human annotators cross-label 5-10% of the dataset (size-dependent). Use a brief guideline, measure inter-annotator agreement, and resolve disagreements. You don’t need senior data scientists here – trained annotators are enough.
• Train the auto-labeler, then loop. Use the golden set to train an AI-assisted labeling tool (passive learning), auto-label the rest, and spot-check samples. Route uncertain/low-confidence items back to humans (active learning) until quality stabilizes.
• Pick the right tooling. Available options range from open-source platforms like CVAT and Label Studio to SaaS solutions like SuperAnnotate and LabelBox.
• Run a final human check. Annotators from the first step validate auto-generated labels to ensure consistently high precision throughout the dataset. 

3. Data cleaning 

Once the whole dataset is labeled, clean it from duplicates, outliers, missing data, irrelevant or incorrect records. As we’ve mentioned earlier, you can leverage Python libraries like Pandas and Great Expectations to detect and flag all issues automatically. 

However, sometimes you do need to enrich your dataset with inconsistent and incorrect inputs on purpose. It applies to the conversational AI chatbots of all kinds, from general customer support bots to specialised ones like flight booking assistants, financial advisors, etc. You have to take into account user queries with typos and misspellings, syntax and grammar errors, to improve intent recognition rates. 

Further decisions like “should the outliers and missing values be removed, imputed, or corrected using domain knowledge?” require human judgment. 

Don’t rush to anonymize data at this stage! While encryption is a vital data protection mechanism, if applied to an uncleaned dataset, it only complicates spotting irrelevant and incorrect entries. It’s better to double down on sensitive data anonymization after you get a noise-free, clean dataset.  

— Pavel Klapatsiuk, AI Lead Engineer, *instinctools 

4. Data augmentation

It may happen that after all the cleaning, you’re left with too little data to train the ML model (“too little” being a spectrum that varies from tens of patient records for a niche medical research to thousands of user interactions for an ecommerce customer study). That’s where data augmentation comes in handy.

For example, a dermatology R&D lab is building AI-powered software to make a preliminary diagnosis based on skin photos. For a rare cancer like cutaneous T-cell lymphoma, early signs can resemble eczema or psoriasis, and examples are scarce. In this case, a data scientist can resort to image augmentation (zoom, flip/mirror, rotate, crop, slight lighting shifts) to expand the dataset. In less regulated contexts, synthetic images can be generated based on the originals as part of the machine learning data preparation.

If you still have data preparation-related questions, find an AI and ML consulting services provider to cooperate with.

AI/ML data preparation checklist

Here’s a short recap of the data preparation work that prevents rework. Do this before the modeling starts:

• Engage a data scientist early to design collection, cut noise, and preempt bias
• Apply a hybrid data labeling approach: create a human ‘golden set’ → train an auto-labeler → spot-check low-confidence items
• Automate the first pass of data cleaning, then apply human judgment to drop, impute, or correct with domain rules 
• Anonymize sensitive data after labeling and cleaning it
• Augment image and text data if the training dataset ended up being too small after the previous AI data preparation steps

Data preparation is the heavy lifting that accelerates every next step

Data preparation is like getting the soil ready before you plant. If the soil is full of rocks and weeds, the seeds won’t take. It’s the same with AI: clean, unbiased, balanced data gives your model the fertile ground it needs to perform well.

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