A Guide to Data Preparation for AI/Machine Learning System

Data quality and quantity contribute significantly to the performance of AI and machine learning systems, as the algorithms rely heavily on large and accurate data to learn patterns and generate insightful predictions. Indeed, low-quality data generates inaccuracy in business decisions, leading to decreased business outcomes.

A study by Vanson Bourne and Fivetran, an independent market research firm, indicated that poor data quality drives a loss of 6% in an organization’s global annual revenue, or $406 million on average. This insight is based on organizations averaging $5.6 billion in annual global revenue.

Preparing data for Generative AI systems requires a comprehensive process of cleaning, transforming, simplifying, and training data to improve its quality. However, due to competitive pressure, resource constraints, and data complexity, businesses might lack patience in preparing data for AI solutions.

Regarding helping businesses fine-tune data for AI-driven outcomes, the article will provide a complete guide to processing data and clarify considerations during the AI implementation time. Read on to learn more.

What is AI Data Preparation?

AI data preparation involves a series of steps to sorting quality data for artificial intelligence models. The process includes collecting, cleaning, and organizing raw data to make it easy for machine learning models to understand.

Data preparation tasks can account for up to 80% of the total time investment in an ML project. Following the improvement of the AI model and new data acquisition, re-evaluating data preparation is essential to ensure optimal performance.

Benefits of Data Preparation in AI

Meticulous data preparation ensures quality and complete data for machine learning algorithms to learn effectively. Here are why businesses should emphasize this step more when implementing AI projects.

Fix Errors Before Processing

An advanced machine learning model can generate inaccurate and misleading outcomes while trained on poor-quality data. Regarding more visible issues, unreliable data can lower customer satisfaction rates and create other severe safety risks.

Therefore, businesses should manage data proactively, particularly for models on massive datasets, to identify and fix irrelevant data. For example, it’s essential to standardize currency formats before analyzing data from different countries to avoid erroneous insights.

Format Data for AI Model

Businesses often combine data from various sources to train AI models, including social platforms, websites, email marketing, and even third-party organizations. That’s why formatting data in the same format is essential to ensure smooth performance. Consequently, this practice requires proper data preparation before it’s used for the algorithm. For instance, data from Salesforce and HubSpot should be in the same format to help sales and marketing teams train the ML models for customer churn prediction.

Reduce Effort for Multiple Analyses

Preparing data meticulously the first time allows businesses to streamline the AI development process. This upfront effort involves formatting and cleaning data to make it suitable for training AI models. When completing this step, data scientists can repurpose the same high-quality data to train different models. This practice eliminates the need for repeated tasks, saving valuable time and resources.

Ensure Reliable Results

Businesses can enhance the quality and trustworthiness of their data-driven insights when preparing data. By sorting data thoroughly to remove errors and format data properly, organizations can eliminate inconsistencies and biases, guaranteeing dependable data for subsequent analysis and modeling efforts. Consequently, businesses can expect to receive insightful data analysis outcomes for strategic decisions.

Step-by-Step Data Preparation Process

How will an AI service provider support your businesses with the data preprocessing process? Practically, the process will follow a standard approach with six steps to ensure the model’s effective performance, regardless of the project’s nature.

Here are the six data preparation steps that data engineers use to refine data before training with AI algorithms:

1. Data collection
2. Data storage
3. Data cleaning
4. Data transformation
5. Data splitting
6. Data reduction

1. Collecting Data

Depending on individual project requirements, developers can choose relevant raw data sources to gather before preparing and refining data. Open-source databases, data from APIs, or other internal and external sources are just some of the available data sources you can look at.

Besides data sources, considering the format type is also essential. Typical data formats include:

• Structured data: Excel files or SQL data
• Unstructured data: Social media posts, audio recordings, emails, images, and videos.

Each data format requires different approaches to access and deploy it. While accessing internal structured data is as easy as clicking “export,” developers might need advanced web scraping techniques to gather high-quality data.

Unstructured data poses significant challenges due to its lack of organization, requiring extensive cleaning and preprocessing. Consequently, conducting a strategy to acquire and validate raw data is critical to guarantee quality outcomes.

2. Storing Data

Data storage indicates where you will store your data while maintaining security.

Depending on businesses’ specific needs, you can choose whether to use traditional relational databases or the latest cloud-based storage solutions. Cloud-based storage is more optimized than manual data infrastructure building and provides businesses a more convenient, scalable, and cost-effective solution.

Looking for solutions to secure sensitive data like customers’ personal addresses, phone numbers, or IP addresses is also important.

3. Cleaning Data

Cleaning raw data allows you to identify outliers, missing values, and inconsistent information. Effective data cleaning can significantly enhance data accuracy and reduce noise.

The data-cleaning process includes the following components:

• Handling missing values: Missing values are data points absent from the dataset. In most cases, the imputation technique is used to replace missing values with close estimates. For example, you can leverage forward-fill or backward-fill to impute missing values and maintain data integrity. However, replacing missing values is not always gainable due to the potential of biased data. In these cases, removing entire rows or columns from the dataset is necessary.
• Handling inconsistencies: Integrating data from diverse sources often causes inconsistencies in variable names and formats, such as state and country names. You should implement domain-specific rules to standardize all metrics to enhance data consistency, enabling more accurate and reliable analysis. For example, this solution is practical in aggregating data into a unified custom view to help track custom interactions across different platforms. Moreover, you can apply data validation techniques to identify any anomalies and inconsistencies for modification before affecting the model’s analysis.
• Handling outliers: Outliers refer to unexpected values generated from unknown data sources. For instance, you might identify high website traffic or purchase amounts as unusual events in marketing data. Most developers use Z-score normalization to detect outliers. This technique standardizes data to compare data points from different distributions, ensuring accurate predictions.

4. Transforming Data

This step involves practices in transforming raw data into a format that machine learning algorithms can understand. Practically, sentiment analysis algorithms rely on words’ numerical representations, not their literal meaning.

Here are some data transformation techniques to convert data into numerical values:

• One hot encoding: The process of converting categorical data like gender, blood type, and date into numerical representations.
• Tokenization: This technique breaks text into a sequence of individual words or tokens for further analysis.
• Normalization: This technique scales data values to a range between 0 and 1, facilitating better model performance and interpretation.

5. Splitting Data

Dividing the dataset into training, validation, and test sets is necessary for model development and evaluation. Proper data splitting enables the AI model to generalize unseen data, leading to more accurate and reliable predictions. Commonly, most developers use a 70-30 or 80-20 ratio for training and test sets. Genuinely, the training sets provide data for model learning, while the test ones measure the model’s predictive accuracy on new, unseen data.

6. Reducing Data

Data reduction enables AI models to access and analyze data quicker by simplifying data without losing its essence. Specifically, AI algorithms can identify patterns easily to generate insightful decisions for the project team. Another benefit of this step lies in its ability to accelerate the learning process of the AI algorithm without mitigating model performance.

Regarding real-world applications, you will need a professional partner to help you get through all the steps and achieve seamless data integration. Neurond AI consulting service provides a strategic approach that goes from evaluating a business’s current platforms to planning and deploying appropriate AI solutions. With multi-year experience in collecting and processing relevant data across various projects, our team guarantees data readiness for modeling and AI training.

Challenges in Preparing Raw Data for AI

Despite undeniable benefits, various businesses choose to skip or put less emphasis on this process due to multiple reasons. Some of the common challenges in approaching data prep include:

• Competitive pressure: The unstoppable development of AI technology has provoked various businesses to fear being left behind. Therefore, they attempt to integrate AI solutions rashly without a comprehensive strategy covering all potential risks. The lack of data preparation may cost them time and money in the long term, resulting from biased data-driven decisions.
• Unrealistic expectations and misunderstanding: Businesses have unrealistic expectations about AI effectiveness that can result in immediate results. However, they don’t recognize the importance of data preparation, ensuring clean and well-structured data for successful AI implementations.
• Resource constraints: Effective data preparation requires significant investments in skilled personnel, advanced tools, and substantial time.
• Underestimating data complexity: Businesses often underestimate the diversity and complexity of their data, failing to assess its readiness for AI utilization.

Prepare Quality Data for Your AI Models with Neurond Services

While careful data preparation accounts for most of AI’s success, businesses should emphasize this process to leverage the technology to its full potential. With a team of over 15 years of experience across different industries, Neurond supports businesses in assessing the data landscape and developing holistic data-driven AI strategies to maximize the implementation ROI.

Practically, Neurond’s expert team will source and prepare the necessary data to deliver cutting-edge solutions, whether mining real data or producing synthetic datasets. Our comprehensive approach ensures proper data for modeling and training, generating insightful outcomes that align with businesses’ objectives.

Contact Neurond AI now to revolutionize your method of processing data for AI and machine learning models.