Data Preparation for AI Initiatives: The Essential Steps

The success of any machine learning project comes down to the quality and quantity of data used, with the former carrying the biggest significance. Any inconsistencies within the training data could lead to several pitfalls, potentially undermining the success of the project.

This is why any organization looking to create accurate, well-performing machine learning models must first clean and transform raw data into structured datasets that drive model performance.

However, proper data preparation isn’t just limited to cleaning and transforming raw data – it involves a meticulous, systemic approach designed to improve the quality and readiness of the data.

This guide will dive deep into the data preparation process for AI initiatives with a focus on the various steps and strategies applied to properly navigate the process. We will also explore some of the most common challenges in data preparation and how to overcome them effectively.

What is data preparation for AI?

Also called data preprocessing, data preparation is the process of cleaning and transforming raw data to create suitable datasets for use in artificial intelligence applications. The data preprocessing process involves several steps, which, if not done correctly, could impede the successful creation of accurate and reliable artificial intelligence and machine learning models.

This notion remains true for all artificial intelligence projects, regardless of the size and complexity of the problem at hand.

While organizations may have some leeway when it comes to the specific strategies and technologies applied, the process typically comes down to two crucial steps: data exportation and cleansing.

These two processes are generally quite time-consuming and may account for a majority of the preparation workload. For instance, the more unstructured or fragmented the data is, the greater the time and effort required to successfully export and cleanse it.

Machine learning algorithms use data to identify structures and correlations. However, without sufficient data input, these models can’t provide accurate outputs. Therefore, for a model to perform as intended and provide accurate outputs, the data needs to be available in large quantities, complete, and of a high quality.

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As such, any successful data preparation strategy should address several key factors regarding how it deals with data. These factors include:

Storage

Your data storage mechanism plays a crucial role in determining how secure your data is. Losing any crucial data during the preparation process could completely impede the project or lead to poorly performing artificial intelligence systems.

According to recent estimates, 75% of data loss instances occur due to human error [1]. While automating some processes could reduce the possibility of human error, the most effective way to mitigate this challenge is creating proper backup channels. Depending on organizational needs, this could be done either in-house or through cloud service providers.

Compatibility

How compatible is your data with existing systems? For any data preprocessing strategy to work, you must first be able to export existing data into the various preparation tools you’re using. This necessitates the need for a system that facilitates smooth data exportation to various systems.

That said, you should select an ideal system at the early stages of the process. The ideal system should be compatible with the data formats available and be able to integrate seamlessly with various machine-learning programs and service providers.

Scope

When it comes to the diversity and volume of data, more is usually better – as long as you’re dealing with properly-labeled, high-quality data. Therefore, as long as your data source is accurate, you’re good to go.

Take KPIs, for instance. These systems are generally more accurate and informative the further back they go in line. Essentially, even seemingly outdated historical data may prove beneficial when running a machine learning algorithm.

Read more: Product Management KPIs & Metrics in AI Development

What is the importance of data preparation for AI?

Machine learning algorithms are only as good as their training data. They’re also only able to learn effectively if the data is clean and complete.

Here are some other reasons why data preparation is such a crucial step in any successful artificial intelligence project:

Data quality

Without low-quality data, even the most advanced machine-learning algorithms can produce inaccurate or misleading results. Proper data preparation ensures that the data used in the project is accurate, clean, and up-to-date.

This is particularly important when dealing with big data applications where it is crucial to identify any faulty or irrelevant datasets before putting the model into a production environment.

Feature selection

Machine learning and AI projects typically rely on data collected from diverse sources. Some of this data may not be necessarily valuable to the project. There’s also the issue of inaccurate or misleading information within the datasets.

During the preparation process, data scientists select important features necessary to the project. This way, they are better able to build more accurate and better-performing models from high-quality, relevant data [2].

Data transformation

Data comes in different formats, especially if it is derived from different sources. Dealing with the data in disparate formats can be problematic and might make the model harder to train. Data transformation can solve this issue by enabling data scientists to transform and normalize the data, making it easier to incorporate it into AI systems.

Model Training

Data preprocessing is a crucial step in model training. Before data can be incorporated into a machine learning model, it must first be prepared by cleaning and removing any redundancies and inconsistencies. This way developers can better create more accurate models.

Time savings and cost reduction

AI projects can take anywhere from three to 36 months, depending on complexity and intended use case [3]. Most of this time is spent training and fine-tuning the model to meet project requirements. Production costs can also exceed $500,000 for more complex projects.

Data preparation can significantly reduce the cost and time taken to put an AI system into a production environment. By ensuring that only clean, relevant data is used, the process can effectively reduce the number of resources required to train and develop models.

Proper preparation may also help save time by reducing the amount of manual effort required to clean and prepare data. Ultimately, this leaves more time for developers to fine-tune and develop the model more effectively.

Read more: How Does Gen AI Reduce Operational Costs: Safety Tips

Improved performance and scalability

The performance of a model all depends on the quality of data used during production. By adequately preparing the data, developers are better able to create more accurate and efficient models that perform significantly better than models created with un-prepared data.

Comprehensive guide to preparing data for AI

Data preprocessing serves as the groundwork for any machine learning project. The process has a fairly standard approach, regardless of the nature of the project, with each step designed to refine data, making it a reliable input to facilitate more accurate predictions and better model performance.

The typical steps in the preparation process include:

Data collection

Before you get to preparing and refining data, you first need to collect it. Data collection sources can vary widely depending on individual project requirements. For instance, you might pull data from APIs, open-source databases, or even scrap it from websites. Some artificial intelligence projects may also require real-time data.

Regardless of the data source you choose, you should always ensure that you only collect data relevant to the problem you’re trying to solve. Low-quality or irrelevant data can lead to several bottlenecks during the development process and poor model performance.

Data cleaning

After collection, data must be cleaned to identify and handle any missing values, outliers, or inconsistent information. When done right, data cleaning can help reduce noise and provide a more accurate representation of the data.

Here’s a breakdown of each component in the data-cleaning process:

• Handling missing values

Missing values occur when there are blanks in certain numerical values in your dataset. The problem is quite common and can be pretty challenging to handle. However, it is still manageable.

One of the most effective ways to handle missing values is imputation. The imputation method involves replacing missing values with close estimates. Essentially, the goal here is to estimate the value of the missing values based on available information.

For instance, if you’re working with time series data where sequence and continuity are essential, you may implement imputation methods like forward-fill or backward-fill to replace missing numeric values.

Conversely, if you’re dealing with a dataset containing random values or values that don’t follow a specific pattern, you could consider replacing the missing values with a mean or median of the column.

However, in some cases, value replacement might be unattainable, particularly in cases where replacing missing values carries the potential to introduce a bias. In such cases, it is better to delete entire rows or columns altogether.

For instance, when dealing with a dataset with missing market campaign information and critical data like conversion rates and click-through rates, it might be more beneficial to remove those records to avoid the potential for biased analysis.

• Handling outliers

When dealing with a distribution of values, you may encounter unexpected values, especially when working with data from unknown sources, which may lack data validation controls. For example, in a marketing context, outliers may present themselves as unusually high website traffic or purchase amounts on a particular day.

If left unchecked, outliers may skew the model’s analysis, leading to inaccurate predictions. One of the most effective techniques to point out outliers is z-score normalization. This is a statistical method that calculates the number of standard deviations of a data point from the dataset’s mean.

It helps identify how abnormal a data point is compared to the average. Once you have identified outliers, you can cap them at a certain level to minimize their impact or remove them altogether to prevent them from skewing your model.

• Handling inconsistencies

When dealing with combinations of data from disparate sources, you may end up with several variations in variables like names and states. If left unchecked, these inconsistencies can throw off your analysis, resulting in misleading information.

Say, for example, you’re tracking customer interactions across different platforms like your website, email, and social media accounts. In that case, inconsistent tagging or naming can make it difficult to aggregate the data into a unified customer view.

To fix this, you can consider employing domain-specific rules that standardize naming or other metrics to correlate any inconsistencies.

You could also apply data validation techniques by setting up automated checks that flag any anomalies or inconsistencies, allowing you to correct them before they impact the model’s analysis.

Data transformation

Your artificial intelligence model’s learning capabilities depend on how well you prepare your data. That’s why it is crucial to transform your data into a format suitable for machine learning algorithms. This can involve several techniques, such as:

• Feature scaling

Sometimes, you might have data in variable scales. In marketing, for example, you may have to deal with customer age and monthly expenditure. In that case, feature scaling can help normalize these variables to prevent either variable from negatively influencing the model [4].

Read more: Top Generative AI Solutions: Scaling & Best Practices

• Feature encoding

Categorical values like product categories and customer segments must be converted into numerical format before being fed into the ML algorithm. In that regard, you can utilize feature encoding techniques like label and one-hot encoding to transform the categorical variables into a numerical format that can be understood by the model [5].

Data reduction

Data reduction is the process of simplifying your data without losing its essence. By simplifying your data, you can enable the model to identify patterns easily and make accurate decisions quicker. Data reduction techniques also make your datasets more manageable and increase the speed of your machine-learning algorithms without sacrificing model performance.

Data splitting

Before you can load data into the ML algorithm, you first need to split it into different sets, including training, validation, and test sets. This is also the final stage in the data preparation process.

Splitting your data correctly makes it easier for the ML model to generalize well to new data, ultimately making its predictions more accurate and actionable.

The most common practice in data splitting is using an 80-20 or 70-30 ratio for training and test sets. Essentially, the train set is used to train the model while the test set is used to evaluate the model. You can also use a subset of the training set, called a validation set, or another separate set to fine-tune model parameters.

How time-consuming is the data preparation process?

Data preparation is one of the most essential yet time-consuming aspects of model creation. It accounts for a huge percentage of the total time taken to develop a machine-learning model.

And contrary to popular belief that data preparation is a one-time process, it is actually an ongoing process. As your ML model evolves or new data becomes available, you may need to revisit and refine your preparation process.

Final thoughts

The success of any artificial intelligence project all depends on the quality of data used. This makes data preparation for AI a crucial step in the development process. While organizations may need to contend with several complexities during the preparation process, doing it right significantly improves model quality, leading to more accurate and actionable insights.

References

[1] Enable.com. How to Reduce Human Error in Rebate Management. URL: https://tiny.pl/d4mmk. Accessed on July 12, 2024
[2] Learn.microsoft.com. Feature Selection. URL: https://learn.microsoft.com/en-us/analysis-services/data-mining/feature-selection-data-mining?view=asallproducts-allversions. Accessed on July 12, 2024
[3] Connect.comptia.org. Business Considerations Before Implementing AI. URL: https://tiny.pl/d4mms. Accessed on July 12, 2024
[4] Datasciencedojo.com. Feature Scaling: A Way to Elevate Data Potential. URL: https://datasciencedojo.com/blog/feature-scaling. Accessed on July 12, 2024
[5] Analyticsvidhya.com. Types of Categorical Data Encoding URL: https://www.analyticsvidhya.com/blog/2020/08/types-of-categorical-data-encoding. Accessed on July 12, 2024