The key to successful AI projects! Everything about "data preparation" explained in an easy-to-understand way for beginners
Hello, I'm John, a veteran blogger. Recently, you've probably heard the words "AI" and "machine learning" a lot. I'm sure there are many of you who feel that they sound difficult. But don't worry! These technologies have the potential to make our lives richer and more convenient. But for AI and machine learning to truly demonstrate their power, there are actually some very unsung but important "unsung heroes" who are needed. That's the one I'm going to introduce to you today."Data preparation"This article will surely help you take your first step into the world of AI in a fun way!
Basics: What is AI, Machine Learning, and "Data Prep"?
First, let's briefly review what each term means.
- What is AI (Artificial Intelligence)?
Simply put, AI is "technology that enables computers to think and learn like humans." For example, voice assistants on smartphones and functions that automatically identify people in photos use AI technology. - What is Machine Learning (ML)?
One method for achieving AI is "a technology in which computers find patterns from large amounts of data and learn by themselves." Its unique feature is that it finds patterns from data, rather than being taught rules one by one by humans. Machine learning involves "learning from data with correct answers and labels."Supervised learningThere are various techniques, including "supervised learning," which involves learning multiple layers of information at once, and "unsupervised learning," which involves finding patterns in unlabeled data. - The role of data preparation (data preprocessing) – at the heart of any AI project
And now, the main topic of today's article, "data preparation." This is the process of preparing data so that AI, especially machine learning models, can learn effectively.Collecting the original data, cleaning it up, and arranging it in a usable formIt refers to the whole. If we use cooking as an analogy, it is like selecting fresh ingredients, washing them, peeling them, and cutting them into appropriate sizes to make a delicious dish. Without proper preparation, no matter how skilled a chef (high-performance AI algorithm) is, he or she will not be able to make a delicious dish (accurate AI predictions). Data preparation is the very "heart" that determines the success of an AI project.
Apify's search results also emphasize the importance of "data preprocessing enhances data quality" and "gathering, cleaning, and structuring data."
The Value of Data: Quality and Quantity in AI Projects
AI, especially machine learning, thrives on "data." However, quantity is not enough. Quality is just as important, if not more so.
- Why quality data is essential
There is a saying that goes, "Garbage in, garbage out." This is also a golden rule in the world of AI. If you teach AI with inaccurate, biased, or old data, the AI may learn the wrong patterns and become useless or make the wrong decisions. For example, if you train an AI with customer data from only one region, it may make irrelevant suggestions to customers in other regions.Improving data quality through data preparationThis is the first step in ensuring the accuracy and reliability of AI. - What happens if there's not enough data?
While high-quality data is important, a certain amount of data is also necessary. This is because AI needs to learn from a variety of cases in order to find patterns in data. If the amount of data is too small, the AI will not be able to learn sufficiently and will not be able to respond to unknown situations. However, rather than blindly collecting large amounts of data, it is important to collect data that is appropriate and diverse for the problem you want to solve.
Like oil, good data is a precious modern resource, and data preparation is the refinery that turns that oil into valuable energy.
Technical Mechanism: The Path to AI "Learning" and Data Preparation Steps
Now, let's take a look at the "learning" process that makes AI smarter, and what data preparation specifically does in that process.
How does AI learn?
A machine learning model looks at a large amount of input data and the corresponding "correct" data (in the case of supervised learning), and tries to find the relationship between them using a mathematical formula (algorithm). Even if it makes a lot of mistakes at first, by repeating learning many times while correcting the mistakes, it will gradually be able to derive the correct answer. The role of data preparation is to maximize the efficiency and accuracy of this "learning".
Specific steps for data preparation
Data preparation is a series of small steps. Let's look at the main steps:
- Data Collection
Collect data related to the problem you want to train AI on. Collect data from various sources, such as in-house databases, public datasets, and information from sensors. At this stage, it is important to clarify what data you need and where to collect it from. - Data Cleaning
Collected data is often unusable as is. It is also called "dirty data" and may contain missing values, abnormal values, input errors, duplicate data, etc. These can be corrected or appropriately processed to increase the reliability of the data. For example, if the age field in a questionnaire says "200 years old," that's clearly an error. - Data Transformation/Structuring
Data is formatted or transformed to make it easier for AI models to understand. For example, free-form responses in a survey (such as "very satisfied" or "somewhat dissatisfied") can be converted to numbers (such as 5 points or 2 points) (this is called categorical data encoding), or the range of numbers can be made uniform (normalized or standardized). This makes it easier for AI to learn relationships between data. - Feature Engineering
This is a particularly creative and important step in data preparation. It involves creating new information (features) from the original data that will help improve the AI's prediction accuracy. For example, creating new features such as "average purchase amount" and "number of days since last purchase" from customer purchase history data will enable the AI to perform more advanced analysis. - Data Splitting
The prepared data is divided into three parts: training data for learning the AI model, validation data for evaluating performance during learning, and test data for evaluating the performance of the final model. This allows us to objectively evaluate whether the AI works correctly even with unknown data.
It is only after going through these steps that the data becomes "edible" for AI. It takes time, but it is no exaggeration to say that this careful preparation is what determines the success or failure of an AI project.
Heroes behind the scenes: The people and tools behind data preparation
Data preparation is so important, but who is doing it, and what tools are they using?
- What experts are involved?
Data preparation involves people with a variety of expertise.- Data Scientist:We use our knowledge of statistics and machine learning to analyze and design what kind of data is needed and how it should be processed to improve the performance of the AI.
- Data Engineer:We build and operate systems (data pipelines) for efficiently collecting, storing, and processing large amounts of data. We are truly the craftsmen who build the foundation for data preparation.
- Other subject matter experts (domain experts) also work together to make sense of the data and take appropriate action.
- Helpful tools and libraries
Luckily, there are many powerful tools available to help with data preparation.- Programming Language:Python is extremely popular in the world of data science and has a wealth of libraries that are useful for data manipulation and analysis.
- Library:
- Pandas:The go-to library for working with tabular data in Python. Great for loading, cleaning, and transforming data.
- NumPy:A library for performing high-speed numerical calculations.
- Scikit-learn:This is a comprehensive library for machine learning, and also has extensive data preprocessing functions.
- Data processing platform:Cloud-based platforms like Databricks provide an environment for efficiently preparing large amounts of data and building machine learning models (as you can see in the Apify results, "preparing data for machine learning using Databricks").
- Other specialized software such as ETL (Extract, Transform, Load – a process for extracting, transforming, and storing data) tools and data quality management tools are also used.
These experts and tools work together to support the complex process of data preparation.
Data Preparation Use Cases and Future Outlook
Data preparation is essential in any field where AI is used.
Data preparation in various fields
- medical care:We organize and analyze patient charts and medical images (X-rays, MRIs, etc.) to help with early detection of illnesses and the development of treatments. In the case of image data, noise removal and contrast adjustment are also important parts of data preparation.
- finance:We analyze customer trading history and market data and use it for fraud detection, loan screening, and personalized financial product proposals.
- manufacturing:We collect and analyze sensor data in factories to predict machine failures and optimize production processes (smart factories).
- Retail/E-commerce:We analyze customer purchasing data and browsing history to display recommended products (recommendations), forecast demand, and optimize inventory management.
- Autonomous driving:It is used to process huge amounts of data from cameras, LiDAR and other sensors to recognize the surrounding environment, which requires fast data preparation in real time.
The future of data preparation technology
Data preparation is a time-consuming task, but as its importance is recognized, technology is evolving.
- Increased automation:There is an increasing number of tools available, including "AutoML (automated machine learning)" technology, which allows AI to automatically perform some of the data preparation, as well as tools to assist with data cleaning and feature engineering.
- Increased focus on data quality:There is increasing importance being placed on creating mechanisms for continuously monitoring and maintaining data quality (data governance).
- Leveraging synthetic data:In order to protect privacy and compensate for data shortages, research is also underway to generate "synthetic data" that has properties similar to real data and use it for AI training.
In the future, it is expected that more efficient and advanced data preparation techniques will further accelerate the speed of AI development.
Good Data Prep vs. Bad Data Prep: How Does it Change Your Results?
How much difference will there be in AI performance if data preparation is done properly versus if it is done poorly?
- Quality data preparation leads to:
- Improving AI model accuracy:This increases the accuracy of predictions and gives you more reliable results.
- Reduced development time:This reduces future rework and allows development to proceed more efficiently.
- Reducing bias:By consciously correcting for bias in data, we can achieve fairer AI.
- Discover new insights:Through careful data analysis, you can sometimes discover business opportunities and issues that you may not have noticed before.
- Risks of not preparing your data:
- Low performance of AI models:This could result in incorrect predictions or useless AI.
- Incorrect decision making:Inaccurate AI analysis could lead to wrong decisions that could harm your business.
- Project failure:This can easily lead to the result that "AI was introduced but was ineffective," resulting in a waste of time and money. In fact, many AI projects fail due to data problems.
- Ethical issues:AI trained on biased data can lead to unfair judgments against certain groups, which can cause social problems.
As such, data preparation is an important process that affects not only the performance of AI, but also the success or failure of the entire project, and even its impact on society.
Caveats and risks: Data preparation pitfalls
Data preparation is very important, but there are also some caveats and potential risks.
- Data bias issues:
If the collected data reflects only certain aspects of the real world or is biased towards certain groups, the AI will also learn that bias. For example, an AI trained on past recruitment data may unconsciously treat certain genders or age groups unfavorably. It is necessary to be aware of such biases during the data preparation stage and make efforts to correct them as much as possible. - Privacy and Security:
In particular, when handling data that includes personal information, it is essential to comply with privacy laws and regulations (such as the GDPR and Japan's Personal Information Protection Act). Security measures such as anonymizing or pseudonymizing data and thorough access management are also essential. - Recognize that "perfect data" does not exist:
No matter how hard you try, it is difficult to prepare "perfect data" that is completely free of noise and bias. Data preparation is not something you do once and then do, but rather it is important to think of it as a process (part of MLOps) in which you continuously review and improve the quality of the data while operating the AI model. - Time and cost:
Quality data preparation takes time, expertise, and money, so understanding the importance of data preparation early in project planning and allocating sufficient resources is key to success.
Understanding these risks and dealing with them appropriately will enable safer and more effective use of AI.
What the experts say: Why do so many AI projects fail?
As mentioned at the beginning, unfortunately, not all AI projects are successful. One of the main reasons for this is, of course, problems related to "data." The InfoWorld article I referred to (by Matt Asay) also pointed out this point sharply.
According to the article, the reason why many corporate AI projects fail before reaching practical use is that "unclear goals,Insufficient data readiness, and a lack of in-house expertise.Garbage in, garbage outNo matter how advanced an AI algorithm is, if the training data is biased, incomplete, or outdated, the output of the AI model will be unreliable.
According to a Gartner survey:Around 85% of AI projects fail due to poor data quality or lack of relevant data.That's a shocking number, isn't it? Companies often find that their data is siloed, riddled with errors, or simply not relevant to the problem they're trying to solve. Models trained on idealized or irrelevant data sets are powerless when faced with real-world inputs.
Successful AI/ML efforts, in contrast, treat data as a top priority. This means investing in data engineering pipelines, data governance, and domain expertise before spending on advanced algorithms. As one expert put it, data engineering is the "unsung hero" of AI, and without clean, well-curated data, "even the most advanced AI algorithms are rendered powerless."
For developers, this means focusing on data preparation. It's important to ask yourself, "Do I have the data my model needs? And do I really need the data I have?" If you're trying to predict customer churn, do you have comprehensive, up-to-date customer interaction data? If not, all that neural network tuning will be for naught. Don't let your enthusiasm for AI blind you to the importance of the hard work of ETL (extract, transform, and load), data cleaning, and feature engineering.
As you can see, experts are unanimous in their emphasis on data preparation, and it is this "mundane" task that must be taken seriously in order for AI projects to succeed.
Current and Future Trends: The World of Data Preparation is Evolving
As the importance of data preparation becomes more and more recognized, new techniques and thinking are emerging in this field.
- The rise of automation tools and AutoML:
Tools that automate parts of data cleaning and feature engineering, as well as AutoML (automated machine learning) technology that automates model selection, are evolving. This allows data scientists to focus on more creative tasks. However, it is not possible to fully automate it, and human judgment and domain knowledge are still essential. - The Importance of MLOps and Data Pipelines:
MLOps is a combination of machine learning (ML) and operations, and is a concept and mechanism for streamlining and continuously improving the entire process from AI model development to operation, monitoring, and re-learning. In this process, great importance is placed on building and operating a "data pipeline" to ensure stable data supply and quality control. Data preparation is a core element in the early stages of this MLOps cycle. - Data-Centric AI:
Until now, attention has tended to be focused on improving the algorithms of AI models, but recently an approach called "data-centric AI" has been gaining attention, which says, "The model is fixed, and AI performance is improved by thoroughly improving the quality of data." This is a way of thinking that further emphasizes the importance of data preparation. - Explainable AI (XAI) and Data:
Advances are also being made in "explainable AI" technology, which allows humans to understand why AI made a certain decision. To realize this XAI, highly transparent data preparation is required, so that we can track and understand what data was used for learning and which features influenced the decision.
These trends demonstrate that data preparation is no longer just a pre-processing task, but a key strategic element that should be addressed throughout the AI lifecycle.
AI/ML and Data Preparation FAQs
Here we answer some common questions beginners have about AI, machine learning, and data preparation!
- Q1: What is the difference between AI, machine learning, and deep learning?
- A1: AI (artificial intelligence) is the broadest concept, and refers to all technologies that realize human-like intelligence in computers. Machine learning is one method to realize AI, and is an approach to learning from data. Deep learning is a more specific method of machine learning, and it learns using multi-layered neural networks that mimic the neural circuits of the human brain. In other words, there is an inclusive relationship between AI ⊃ machine learning ⊃ deep learning.
- Q2: How long does it take to prepare the data?
- A2: It depends on the scale of the project, the state of the data, and the desired accuracy of the AI, but generally it takes about60% ～ 80%It is said that about 100,000 people spend their time on data collection and preparation. It is a very time-consuming and labor-intensive task, but that just shows how important it is.
- Q3: Can people new to programming learn data preparation?
- A3: Yes, you can! Of course, it is advantageous to have programming skills (especially Python) and knowledge of statistics, but recently there has been an increase in learning materials for beginners and tools that allow you to manipulate data relatively easily. It is a good idea to start by learning the basics of how to handle data little by little. The important thing is to be interested in the data and ask yourself, "Why is it like this?"
- Q4: What exactly is "dirty data"?
- A4: "Dirty Data" refers to data that is inappropriate for use in AI training. Examples include:
- Missing values:A value that should be entered is missing (e.g. age is left blank in a survey)
- Outliers:An unusual value that is significantly different from other values (e.g., a product's price is negative)
- Variation in spelling:The same meaning is written differently (e.g. "A Co., Ltd." and "(Co., Ltd.) A")
- Duplicate data:The same data exists multiple times
- Conflicting data:Logically impossible data (e.g. the cancellation date is before the membership registration date)
etc. Cleaning these is an important step in data preparation.
- Q5: What is the most important thing in data preparation?
- A5: It's difficult to choose just one, but "Clarify the problem you want to solve and prepare appropriate, high-quality data for it." is the most important thing. There's no point in just collecting data randomly. It's important to have a sense of purpose, carefully select the data you need, and proceed with preparations carefully. And always thinking about "why is this data processing necessary?" leads to better data preparation.
Summary: The key to AI success is diligent data preparation
This time, we have explained in detail the "data preparation" that supports AI and machine learning, its importance, specific steps, related technologies, points to note, etc. Although it is not flashy, data preparation is the "unsung hero" that builds the foundation of an AI project and greatly determines its success or failure.
Just as it is necessary to select good ingredients and carefully prepare them to make a delicious dish, it is essential to carefully prepare high-quality data in order to develop a smart AI. The first step in utilizing AI is to remember the saying "Garbage in, garbage out" and to face data sincerely.
I hope this article will help you deepen your understanding of AI and data preparation and get you excited about their possibilities. The world of AI is deep, so it's important to keep learning, but that exploration will surely satisfy your intellectual curiosity!
Links
- Data Preprocessing in Machine Learning: Steps & Best Practices – Data pre-processing steps and best practices are explained in detail.
- What is Data Preparation for Machine Learning? – A summary of what data preparation is and why it's important.
- Machine Learning Tutorial - A tutorial to learn the basics of machine learning.
Disclaimer: This article is intended to provide general information about AI technology and does not recommend any specific products, services, or investments. When using or learning about technology, please check the latest information at your own discretion and responsibility, and consult with experts if necessary.