A Guide to Preparing Organizational Data for AI

The future of your business may hinge on how AI-ready your data truly is. The first step to integrating useful AI into your organization is data prep. In this guide, we explain how to prepare your data for machine learning (ML) models – which are essential to AI implementation.

Every AI journey begins with raw data, but transforming this data into structured datasets truly drives model performance. Missing data can be a hidden pitfall; without meticulous data preparation, even the most advanced ML models may falter. Aggregating insights and selecting suitable subsets are fundamental steps in this process. As organizations venture into AI, the value of thorough data preparation becomes increasingly evident.

With artificial intelligence and machine learning, quality data can be the difference between powerful outcomes and misleading reports. The quality and readiness of your information can make or break an AI project. Proper data preparation goes beyond just cleaning; it involves correctly classifying datasets so the system knows what public, internal, and confidential data is and adopting a robust Data Loss Prevention (DLP) strategy. However, navigating the intricacies of data preparation can be daunting. Challenges often arise, emphasizing the need for a systematic approach to unlock the true potential of AI.

If your organization wants to harness the transformative power of your data, this guide will help you understand the importance of data preparation and how it sets the stage for optimal AI implementation.

What Data Means to AI

The digital era introduced an abundance of big data. Now, with AI’s advanced tools, we can effectively use this data in ways we only dreamed of a few years ago. It’s not just about having vast amounts of figures; good data is essential for AI systems to function correctly.

Good training data directly enhances the reliability of an AI model. When an AI model can access accurate and high-quality data, it can generate precise insights. In contrast, the AI can produce errors and unreliable outcomes if the data is poor or incomplete.

While gathering large volumes of data is an initial step, the more critical action is converting this raw data into a format that AI can understand and use. Organizations have been modernizing and transforming their data for business intelligence purposes for years, and transforming data for AI is similar. The transformation process aims to optimize the data to improve the tool’s performance for both situations.

7 Foundations of Data Preparation

1. Data Collection: Accumulating Raw Data

What it means: Any AI endeavor cannot start without data collection. It is crucial to gather raw data from multiple sources. The project’s objects will determine the types of sources needed.

Example: If you’re a retailer wanting to improve customer experience, you might pull data from point-of-sale systems, customer feedback forms, online reviews, and even social media mentions.

2. Data Preprocessing and Profiling: Getting Acquainted with Your Data

What it means: With data in hand, it’s time for data preprocessing. Preprocessing and profiling is when you comb through your data to identify anomalies or missing values that could skew results.

Example: Upon sifting through customer feedback forms, you might find some without ratings or with unusually high or low ratings that don’t align with comments.

3. Data Cleansing: Fixing the Issues

What it means: Once issues are flagged, the next step is to address them. Cleansing ensures your data points are reliable and will not introduce errors into ML algorithms.

Example: For the feedback forms with missing values, you might input an average value or, depending on the extent, exclude those forms from your dataset.

4. Data Classification: Organizing Data Based on Importance

What it means: Data comes with varying degrees of sensitivity and relevance. Organizing or categorizing it according to its nature and importance is crucial. Common categories include:

• Public Data: Information that’s openly accessible and is sharable without restrictions. Like datasets that are available for academic research or open-source machine learning projects.
• Internal Data: Data specific to an organization that isn’t meant for public sharing but isn’t highly sensitive. For example, aggregate customer feedback (stripped of personal details) or internal performance metrics.
• Confidential Data: Highly sensitive information that needs rigorous protection due to its nature or the risk associated with its exposure. Like internal sales figures, personal client information, or proprietary research findings.

Example: An e-commerce company may categorize product reviews as Public Data, allowing all visitors to see them. The monthly sales performance could be considered Internal Data, shared only among teams for analysis. Meanwhile, individual customer purchase histories and personal information would be classified as Confidential Data, ensuring it’s strictly protected and only accessed by authorized personnel.

5. Data Transformation and Feature Engineering: Making Data Useful

What it means: Data often requires alterations or enhancements to be in the best format for ML algorithms to process. This step is where that format transformation takes place.

Example: If your organization records sales data hourly, but daily trends are more insightful for your project, you’d transform your data by aggregating hourly data to daily totals. If you wanted to look at first-year spend for your customers, feature engineering might involve aggregating data sets relative to each customer’s date of first purchase.

6. Data Validation: Quality Assurance

What it means: After cleansing, categorizing, and transforming your data, a second round of checks is essential to ensure consistency and that all data points meet the quality criteria for your project.

Example: A second look at the feedback forms provides an opportunity to address any overlooked missing values and ensure that any changes made retain data consistency.

7. Data Correlation: Linking Data Across Datasets

What it means: By examining different datasets, we can identify overlaps or connections between data points based on shared attributes or values, such as timestamps. Understanding these connections is essential for creating a more comprehensive view of the information and leveraging it for AI applications.

Example: Suppose dataset 1 contains timestamps of purchases and amounts spent, while dataset 2 provides timestamps and items purchased. By correlating these datasets using the timestamps, one could connect specific purchases with specific amounts, potentially revealing patterns or insights into purchasing behaviors.

Preparing data for AI is a precise and vital procedure. From collecting raw data to pinpointing correlations, every step ensures the AI system operates efficiently and delivers trustworthy insights. Properly prepared data means better results and more reliable decision-making for businesses and organizations.

Understanding the Datasets You Have

Data-driven organizations benefit from sharper decision-making, foresight through predictive analysis, and the ability to tailor customer experiences. However, being data-driven isn’t just about amassing data. Organizations need to be intentional about the datasets they identify and bring into their model, especially when the goal is to utilize it for machine learning. Now that we have a reasonable understanding of the fundamentals of data preparation, let’s focus on understanding your datasets and how to know if you have gaps in that data.

Common Types of Datasets in Organizations

• Categorical Data: This type of data represents categories or labels. You can further divide it into nominal (without any order) and ordinal (with a specific order). For instance, “red,” “blue,” “green,” or “first,” “second,” or “third”. Tools such as onehotencoder in the Python library scikit-learn can help transform categorical variables for machine learning models.
• Numerical Data: This represents quantifiable variables that can be discrete (countable, like the number of employees) and continuous (measurable, like temperature).
• Unstructured Data: Includes information not organized in a predefined manner, like emails, images, or videos. You can use neural networks and deep learning techniques to parse such data.
• Structured Data: This refers to data organized into defined fields, rows, and columns, such as in a CSV file or a database. Libraries like Pandas in Python make handling and analyzing structured data easier.

Characteristics of These Datasets

Understanding each dataset’s nature is essential in determining the data prep needed. For instance:

• Dimensionality: Some datasets have a vast number of variables or features. While more features might sound appealing, it can lead to overfitting. Techniques like dimensionality reduction can reduce the feature set while retaining most information.
• Outliers: These data points differ significantly from others in the dataset. Visualization tools can help spot outliers. Handling outliers is crucial as they can skew machine learning model results.
• Normalization: It’s often beneficial to normalize the data, especially for numerical values. Normalization ensures that each feature contributes equally to the distance calculations and, subsequently, the model performance.

Identifying Missing Datasets

Detecting and addressing gaps in datasets is crucial for a comprehensive analysis.

• Data Exploration: Before diving into data analysis, it’s crucial to understand the type of data you’re dealing with. Exploration involves examining the initial few rows, checking data types, and understanding the dimensionality.
• Comparison with Real-world Scenarios: A subset may be missing if the data doesn’t reflect specific real-world initiatives or workflows.
• Consistency Checks: This involves checking if structured data, like CSV files, maintains a consistent format. Any row or column deviating from the established pattern may indicate issues.

Collecting Data for Missing Sets

• Automated Collection: Using APIs can help in gathering new data seamlessly. Collection not only optimizes the process but ensures consistent data quality.
• Data Validation: Before integrating the collected data into machine learning projects, it’s vital to validate its integrity. Validation involves checking for inconsistencies or errors and addressing them through data cleaning.
• Diversify Sources: Actively source data from open and proprietary platforms for comprehensive data capture.
• Training and Test Sets: Always split your data into training and test sets. Splitting your sets ensures that your machine learning model does not just fit your training data (a problem known as overfitting) but also generalizes well to new data.

Common Hurdles in Data Preparation

Challenges often line the path to successful data preparation. Having someone who can help you navigate these hurdles efficiently reduces the resources needed to prepare the data for machine learning algorithms. While this article is no substitute for an experienced consultant, here are some of the most common challenges faced during this process:

1. Inconsistencies in Data

• What it means: Diverse data sources might employ varying formats or naming conventions, leading to inconsistencies when merged.
• Impact: These inconsistencies can hinder the ability to optimize predictive models, making them less accurate.
• Solution: Regular data audits and standardized data entry guidelines can ensure consistency.

2. Missing or Null Values

• What it means: Data often has gaps or null values, especially when aggregated from multiple sources.
• Impact: Missing values can distort the results of regression techniques and other predictive models.
• Solution: Methods like interpolation, mean substitution, or advanced feature selection techniques can be employed to address these gaps.

3. Duplicate Entries

• What it means: Redundant data entries can exist due to repeated data logging or other errors.
• Impact: Duplicates can bias the dataset, making some information appear more frequent than it should, potentially skewing linear regression results.
• Solution: Automating the deduplication process can help swiftly identify and eliminate these redundancies.

4. Challenges in Merging Data from Different Sources

• What it means: Integrating disparate data structures or formats can be complex.
• Impact: Data science projects often require holistic data views. Improper integration can lead to incomplete or misinterpreted insights.
• Solution: Using advanced data integration tools with automated features can ease this process, ensuring seamless data preparation for machine learning.

5. Scalability Issues with Large Datasets

• What it means: Certain tools or infrastructure may not effectively scale as data grows.
• Impact: Large datasets are standard in data science. Scalability issues can lead to performance bottlenecks or slow processing times.
• Solution: Embracing scalable architectures and periodically updating tools can address this concern.

Skills for Data Restructuring

If you have ever attempted a cloud migration, you know how valuable the right experience can be. Similarly, adequate data preparation for machine learning requires precision and understanding of the intricacies involved. Tackling this task head-on without the right tools or expertise can be daunting and resource-intensive.

Although this guide aims to equip you with fundamental knowledge, we cannot overstate the importance of having the right skilled personnel. Here, we will highlight essential skills that can aid the data restructuring process to make it more efficient and professional.

Essential Skills:

• Data Wrangling Techniques: Mastery of techniques like normalization, standardization, and data imputation is vital for optimizing predictive models.
• Understanding of Data Schemas and Structures: Data engineers must deeply understand data structures to ensure effective data transformation and integration.
• Proficiency in Data Cleaning and Transformation Techniques: Knowledge of feature selection, linear regression, and other advanced techniques ensures the data is ready for crafting predictive models.

The intricacies of data preparation for machine learning can be demanding. But with the right skills and guidance, data engineers can transform raw data into valuable assets, paving the way for data scientists to focus on successful AI and machine learning implementations.

After Data Preparation: Next Steps

Completing the data preparation phase can mark a significant milestone in an organization’s AI journey. With a clean, organized, and validated dataset, they are ready to unlock the true potential of their data science endeavors, including:

• Integration with Data Analysis Tools: With prepared data, organizations can seamlessly integrate it into various data analysis and machine learning platforms. This step facilitates the extraction of meaningful insights, the building of predictive models, and the generation of actionable intelligence.
• Leveraging Prepared Data for Decision-making and Insights Generation: The value of prepared data shines when it becomes the cornerstone for decision-making processes. Organizations can make informed choices, predict trends, and uncover nuanced patterns derived from their data.
• Maintenance and Continuous Improvement of Data Quality: Keeping the data in pristine condition is an ongoing endeavor. Organizations must continually monitor, update, and refine their data processes, especially as new data streams emerge and business needs evolve.

Navigating the complexities of data preparation can be daunting. The task may seem impossible, with endless challenges and pitfalls. However, reaching the finish line armed with a well-prepared dataset is immensely rewarding. Such progress lays the foundation for successful data-driven strategies. It reinforces the importance of diligence in the initial steps of any data science project.

The Power of Data Preparation in AI Success

Data is the fuel that powers AI and machine learning. But to harness the full potential of these technologies, you must ensure your data is curated, cleaned, and crafted with precision. This process, vital as it is, brings a unique set of challenges that can be daunting for many.

Organizations committed to optimizing their AI initiatives recognize the complexities of this journey. And while the process is an internal commitment, external expertise from a digital transformation consultancy like Core BTS can streamline the path.

At Core BTS, we help infuse a wide range of stand-alone and integrated AI services and solutions into every corner of our clients’ businesses. Learn more about how we can help you make the most of AI or contact our team today to get started.