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Organizing and classifying data into different categories, groups, or segments.

Data categorization definition:

Data categorization involves organizing and classifying data into different categories, groups, or segments.

By grouping related data together, data engineers can more easily analyze, access, and utilize data.

Why Categorize Data?

  • Efficiency: Sorting data into categories makes it easier to process and manage. Instead of dealing with a single massive pool of data, you can work with smaller, related chunks.
  • Security: Different data categories may have different access requirements. Sensitive data might require stricter access controls compared to public data.
  • Regulation Compliance: Some regulations might require certain categories of data to be stored or treated differently.
  • Improved Analytics: Categorizing data can also lead to improved analytics. For example, sales data can be categorized by region, allowing for regional sales analytics.

Examples of Data Categorization:

Based on your use case, you might want to organize data into different "buckets" or categories, such as:

  • Private vs. Public: Private data might be internal company financials, while public data might be publicly available stock prices.
  • Sensitive vs. Non-sensitive: Data categorized as "sensitive" might include personal information, while non-sensitive data might be aggregated statistics that don't identify individuals.
  • Temporal Data: Data can be categorized based on time periods, such as real-time, historical, or forecasted data.

Approaches to the categorization process:

As data engineers, we aspire to automate the process of data categorization, yet we find many manual classification steps are stil in use, often involving human experts—or even relatively unskilled contractors—sorting and classifying data by hand

This said, intelligent process automation continues to replace manay of the manual approaches. Automated classification uses algorithms or tools to automatically categorize data. This might involve machine learning or rule-based systems.

For more complex tasks, a hybrid approach is sometimes adopted in which a combination of manual and automated methods is used.

Poor data quality or changes in the data formats are the main challenges in fully automating a categorization process.

Categorization strategies

One key factor in a successful categorization process is deciding which categories to create in the first place. It's essential to strike a balance between granularity and simplicity. If categories are too broad, they may not provide meaningful differentiation. On the other hand, if they are too specific, the categorization system might become overly complex and hard to manage.

Here are some best strategies to consider:

  1. Understand the Use Case:

    • Purpose: Determine the primary purpose of categorizing the data.
    • Stakeholder Needs: Engage with stakeholders, such as analysts, data scientists, and business users, to understand their data needs and how they plan to use the data.
  2. Start with a High-Level Categorization:

    • Begin with broad categories and then refine into sub-categories if necessary.
  3. Use Existing Standards and Frameworks:

    • There might be industry standards or frameworks available that provide guidelines on data categorization. For example, in healthcare, there's the International Classification of Diseases (ICD) for categorizing diseases.
  4. Consistency is Key:

    • Ensure that whatever categorization scheme you adopt is applied consistently across the board. This aids in easy understanding and minimizes confusion.
  5. Adopt a Hierarchical Structure:

    • A hierarchical categorization allows for more refined data querying and can be especially useful for drill-down analyses. For instance, data could be categorized by region > country > state > city. It also makes your data structure more adaptable (see below).
  6. Future proof your categorization:

    • The categorization system should be designed to adapt to changing business needs or data types. It should be scalable to accommodate growing data volumes without requiring a complete overhaul. See the section "making your categorization structure adaptable" below.
  7. Automate Where Possible:

    • Using machine learning or rule-based algorithms can aid in automatically categorizing data, especially if you're dealing with vast amounts of it.
  8. Review and Iterate:

    • Periodically review the categories to ensure they remain relevant. Remove obsolete categories and add new ones that emerge with changing business scenarios or data sources.
  9. Avoid Over-Categorization:

    • While granularity can be helpful, overly specific categories can be counterproductive. It can make the system complex and harder to manage.
  10. Include Metadata and Documentation:

    • For each category, it's beneficial to have metadata and documentation that describes its purpose, the kind of data it holds, any associated rules, and other relevant details. This aids in clarity and helps users understand the categorization system better.
  11. Engage and Train the Team:

    • It's essential to engage with the data teams and provide them with training on the categorization system. Their feedback can be invaluable in refining categories. This is especially true in a hybrid approach where you have manula categorization steps.

Remember, the primary goal of data categorization is to make data more manageable, accessible, and valuable to users. The categorization strategy should align with this goal. This might sound obvious but many teams become myopic over the steps of classifying data and lose sight of what the end goal was originally.

Making your categorization structure adaptable

Adaptability in data categorization is crucial given the ever-evolving nature of data sources, business needs, and analytics requirements. Ensuring that your data categorization system can adapt over time is essential for long-term data management success. Here are some strategies to make data categorization more adaptable:

  1. Modular Design:

    • Build your categorization system in a modular manner, so adding or removing categories doesn't disrupt the entire system.
  2. Hierarchical Structure:

    • As recommended above, hierarchical categorization allows for introducing sub-categories or new levels without necessarily overhauling the entire system.
  3. Use of Tags and Labels:

    • Instead of (or in addition to) rigid categories, use tags or labels. They can be easily added, removed, or changed. This system can adapt to evolving needs, especially for unstructured data.
  4. Flexible Data Structures:

    • Consider using databases or data platforms that support flexible schema or schema-less data structures, like NoSQL databases. These can easily adapt to changes in categorization.
  5. Automate with Machine Learning:

    • As mentioned above, Machine learning algorithms can be trained to categorize data the first time around but can also help adjust to changes over time, especially when new data patterns emerge.
  6. Versioning:

    • Implement a versioning system for your categorization schema. This way, you can introduce changes without disrupting existing categorizations and roll back if necessary.
  7. Documentation and Metadata:

    • Maintain thorough documentation for the categorization schema and any changes made. Metadata management tools can be used to provide context and history.
  8. Periodic Reviews:

    • Schedule regular reviews of the categorization system to identify necessary updates or improvements.
  9. Centralized Data Asset Catalog:

    • Use a centralized data asset catalog that provides a unified view of all data assets and their categorizations. This central view makes it easier to implement and track changes, especially across several teams.
  10. Standardize Processes for Change:

    • Have a standardized process in place for introducing changes to the categorization system. This could include procedures for proposing, reviewing, approving, and implementing changes.
  11. Backward Compatibility:

    • Whenever possible, ensure that new categorizations are backward compatible with old ones. This ensures that historical data analyses and systems don't break.
  12. Prototype and Test:

    • Before rolling out major changes, prototype the new categorization system and test its impact. This can provide insights into potential pitfalls and areas of improvement.
  13. Decouple Systems:

    • Ensure that your data categorization is not tightly coupled with critical systems. Loose coupling ensures that changes in categorization don't have cascading effects on other parts of the system. Again, category ids can help.

By integrating adaptability into the very design and maintenance processes of data categorization, you can ensure that your system remains relevant and effective over time, even as business needs and data sources evolve.

An example of data categorization in Python

Let's consider an example of using the pandas library, along with a basic usage of scikit-learn for automated data categorization.

Say you have a dataset of customer transaction records. These records include details like transaction_amount, transaction_date, and product_id. We want to categorize customers into segments based on their transaction behavior: High Value, Medium Value, and Low Value.

Here's a hypothetical approach:

  1. Compute the total transaction value for each customer over the past year.
  2. Use KMeans clustering (a machine learning algorithm) to segment customers based on their transaction value.
  3. Label the clusters as High Value, Medium Value, and Low Value based on cluster centroids.
import pandas as pd
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler

# Sample dataset
data = {
    'customer_id': [1, 2, 3, 4, 5],
    'transaction_amount': [100, 1500, 250, 1200, 650],
    'transaction_date': ['2023-01-10', '2023-01-15', '2023-01-20', '2023-01-25', '2023-01-30'],
    'product_id': [101, 102, 103, 104, 105]

df = pd.DataFrame(data)

# Aggregate transaction values by customer
customer_value = df.groupby('customer_id')['transaction_amount'].sum().reset_index()

# Use KMeans clustering to segment customers (assuming 3 segments for simplicity)
scaler = StandardScaler()
scaled_values = scaler.fit_transform(customer_value[['transaction_amount']])

# Explicitly setting n_init to its current default value of 10
kmeans = KMeans(n_clusters=3, n_init=10, random_state=0).fit(scaled_values)
customer_value['segment'] = kmeans.labels_

# Sort segments by their centroids and label
sorted_labels = customer_value.groupby('segment')['transaction_amount'].mean().sort_values(ascending=False).index

label_mapping = {
    sorted_labels[0]: 'High Value',
    sorted_labels[1]: 'Medium Value',
    sorted_labels[2]: 'Low Value'

customer_value['segment_label'] = customer_value['segment'].map(label_mapping)


In this example, the customer_id is grouped by its transaction amounts, and then those aggregate amounts are scaled and clustered into segments using KMeans clustering. The centroids of these clusters are used to define which cluster corresponds to "High", "Medium", or "Low" value customers.

The output from our exercise above is as follows:

   customer_id  transaction_amount  segment segment_label
0            1                 100        2     Low Value
1            2                1500        1    High Value
2            3                 250        2     Low Value
3            4                1200        1    High Value
4            5                 650        0  Medium Value

An experienced data engineer can further refine and expand on this example by incorporating more features, fine-tuning the clustering algorithm, or using other advanced techniques for segmentation.


In summary, data categorization in data engineering is about bringing order to data, making it more manageable, secure, and useful. Proper categorization is foundational for many data-related tasks and is especially crucial for organizations dealing with large or diverse datasets.

Other data engineering terms related to
Data Management:


Move rarely accessed data to a low-cost, long-term storage solution to reduce costs. store data for long-term retention and compliance.
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Add new data or information to an existing dataset to enhance its value.
An image representing the data engineering concept of 'Augment'


Create a copy of data to protect against loss or corruption.
An image representing the data engineering concept of 'Backup'
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Batch Processing

Process large volumes of data all at once in a single operation or batch.
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Store expensive computation results so they can be reused, not recomputed.
An image representing the data engineering concept of 'Cache'
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Identify and remove duplicate records or entries to improve data quality.
An image representing the data engineering concept of 'Deduplicate'


Deserialization is essentially the reverse process of serialization. See: 'Serialize'.
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Analyzing the number of features or attributes in the data to improve performance.
An image representing the data engineering concept of 'Dimensionality'
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Enhance data with additional information from external sources.
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Extract data from a system for use in another system or application.
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An operation that produces the same result each time it is performed.
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Create an optimized data structure for fast search and retrieval.
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Combine data from different sources to create a unified view for analysis or reporting.
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Understand of how data moves through a pipeline, including its origin, transformations, dependencies, and ultimate consumption.
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Executing a computation and persisting the results into storage.
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Store the results of expensive function calls and reusing them when the same inputs occur again.
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Combine data from multiple datasets into a single dataset.
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Create a conceptual representation of data objects.


Track data processing metrics and system health to ensure high availability and performance.
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Named Entity Recognition

Locate and classify named entities in text into pre-defined categories.
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Interpret and convert data from one format to another.
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Divide data into smaller subsets for improved performance.
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Transform your data so it is fit-for-purpose.
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Transform raw data before data analysis or machine learning modeling.
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Create a copy of data for redundancy or distributed processing.


Increasing the capacity or performance of a system to handle more data or traffic.
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Schema Mapping

Translate data from one schema or structure to another to facilitate data integration.


Ensure that data in different systems or databases are in sync and up-to-date.
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Check data for completeness, accuracy, and consistency.
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Maintain a history of changes to data for auditing and tracking purposes.
An image representing the data engineering concept of 'Version'