Data Cataloging vs Data Productization: Understanding the Differences and Complementary Roles

Does your data bring your teams and business concrete value? Or has it gotten so complex you don’t know where to start—or would prefer not to think about it? Two different but complementary processes can help companies maximize the value of their data assets: Data cataloging and data productization. 

What is data cataloging?

Data cataloging enables efficient asset management by making it easy for users to find and access relevant data. Data cataloging organizes and categorizes data assets using metadata like tags, descriptions, and classifications. When data is cataloged, it’s faster and easier for catalog users to search for data. When the data catalog is accurate and up-to-date, organizations make better, more informed decisions and avoid the pitfalls of outdated or incomplete data.

What is data productization? 

Data productization transforms raw data into a usable and valuable asset that solves a customer problem. You can sell your data products to third parties, enhance your internal operations, or even provide added value for your customers. A data product helps businesses extract insights to inform data-driven decisions that drive company growth, improve customer experiences, and optimize business processes. 

The data cataloging process

Data cataloging is useful for effective data management and for companies seeking data productization. A catalog provides a comprehensive view of data assets, makes data more accessible, and supports data governance and compliance efforts. There are a number of steps required to render data cataloging effective:

1. Identify data to catalog
2. Gather metadata
3. Standardize metadata
4. Categorize data
5. Document data
6. Test and validate
7. Publish

Let’s review what each step entails.

1. Identify data to catalog

The first step involves identifying and documenting all available data assets within an organization. It also requires identifying the specific data elements and types of data the organization deems most important to track and manage. 

Organizations may need to catalog data sources like:

• Transactional data like sales, inventory, and customer data
• Analytical data like data warehouses, data marts, or data generated by BI tools
• Master data that defines and manages key business entities like customers, products, and suppliers
• Reference data like currencies, languages, and time zones

Organizations must then identify specific data elements they want to track and manage. This may include element names, descriptions, data types, relationships, and other essential attributes. 

It is important that the identification process be as thorough as possible to ensure the accuracy and completeness of the catalog.

2. Gather metadata

Metadata provides information and context about the data being cataloged. Metadata can include data definitions, formats, types, sources, data lineage, and quality. 

Types of metadata to gather during the cataloging process include:

• Technical metadata, such as data types, formats, and structures
• Business metadata, such as the intended use of the data, its relationship to other elements, and its importance to the organization
• Operational metadata about how data is used and maintained, like who has access to the data and how it is updated
• Relationships between different data across different sources and within each source

Gathering metadata is usually a combination of manual and automated work. Automated tools can extract technical metadata. However, gathering business and descriptive metadata may require manual work and gathering information from subject matter experts like data scientists or data engineers. 

3. Standardize metadata

Establish enterprise-wide, standardized guidelines for metadata management. This includes how metadata is defined and documented. Consistent definitions and documentation of data ensure others can easily understand and use the data in the catalog.

This will consist of standardized formats for naming conventions, data formats, data types, and how metadata is stored and managed.

4. Categorize the data

Data categorization organizes data into groups or categories based on their characteristics and intended use. Categorization makes it easier for users to find, filter, and understand data based on their desired criteria.

Category criteria include data sources, data type, business function, or department. Data can also be categorized through tags or keywords that specify data attributes like geographic location, time period, or confidentiality level.

Standardized metadata and well-categorized data promote consistency, reduce duplication, and facilitate enterprise-wide data integration and collaboration.

5. Document the data

Data documentation captures and records important information about data in the catalog. Documentation relies on technical data, business metadata, and descriptive metadata.

Like with all aspects of cataloging, it’s crucial to establish consistent standards and guidelines for documenting data. Important information to document includes:

• Data lineage like data source, how it was collected, and transformations or processing the data underwent
• Data quality such as accuracy, completeness, limitations, and known issues with the data
• Data security such as confidentiality level and applicable security or access controls

The more complex the data set, the more time consuming documenting can be. However, it is a worthwhile investment that ensures data is understood and accessible to users across the organization. 

6. Test and validate

As with anything data-related, testing and validating are crucial to ensuring a functioning cataloging process. This process includes:

• Search functionality testing to ensure the catalog is easy to query and provides the correct results
• Metadata accuracy validation ensures data is correctly and consistently described. This may include spot-checking data elements and comparing them to original data sources
• Data lineage review to ensure data sources and or transformations are accurately documented
• User feedback evaluation ensures the catalog meets user needs

These steps can identify issues or problems and ensure it is accurate, reliable, and user friendly.

7. Publish the catalog

Once the catalog is complete, it should be published and available to users. The catalog can live in a centralized data portal, data management system, or other enterprise data catalog systems.

Catalog management

Once the catalog is published, it will require maintenance and management. Establishing a governance framework to manage the catalog and outline policies and procedures for adding, updating, or removing data is important.

The data productization process

Data productization involves transforming raw data into a valuable asset. The asset may help business stakeholders make better decisions and improve business outcomes. The data productization process involves the following steps:

1. Identify the business problem
2. Explore data options
3. Prepare the data
4. Analyze and model the data
5. Create the data product
6. Deploy and maintain the product

1. Identify the business problem

While it’s ideal first to identify the business problem you’d like your data to solve, it’s not necessarily required. You can search your business data catalog to identify data that could potentially solve a problem and release that, making you a “bulk data provider.” These types of data products place the burden of utility and value on the data consumer. However, the most successful and usable data products solve a known problem for real customers. 

If you know the business problem beforehand, collaborate with stakeholders to identify the biggest opportunities to drive business value.

2. Explore data options

Working with a subject matter expert or with stakeholders throughout your organization, identify the data sources available. Then explore each source to understand the data’s characteristics, relationships, and potential value. If you know the business problem you want to solve, you can identify sources and specific data that meet your qualifications. If you don’t, it’s the perfect opportunity for fact-finding.

From there, you can scope the potential product and identify its sources and the insights or outputs it will deliver. 

3. Prepare the data

The next step is to gather and clean the data. First, extract the data from various sources, then transform it into a usable format. Analyze the data distributions, identify missing or null values, and look for inconsistencies, errors, or anomalies.

Then standardize the data into a common format with universal measurement standards to ensure consistency across sources. Then transform the data to correct quality issues. This can involve inputting missing values, removing duplicates, and correcting any inconsistencies or inaccuracies.

Finally, validate the data for accuracy, completeness, and reliability. 

4. Analyze and model the data

With the newly cleaned data, you can now identify patterns, trends, and insights. The process typically involves:

• Exploratory data analysis to identify patterns and trends through visualizations, identify correlations between different variables, and calculate summary statistics
• Data modeling through statistical or machine learning models that identify patterns and make predictions. This can involve regression analysis, decision trees, neural networks, and more
• Performance evaluation and accuracy validation. This entails testing the model on a hold-out data set or using cross-validation to ensure the model is robust and accurate
• Feature selection and engineering to identify the most important data variables or features and create new features. This can involve principal component analysis (PCA) or feature scaling
• Data visualization to communicate insights and findings from analysis. This may look like interactive dashboards or reports for stakeholders to explore the data and understand its insights and findings

5. Create the data product

The insights and findings from the previous steps can now inform the actual creation of the data product. The steps include:

• Defining the product to solve a specific problem or opportunity for a defined target audience
• Designing the product to have all the features and functionality the target audience requires
• Building the product with preferred tools, frameworks, and libraries
• Testing and validating the product through user acceptance testing to ensure the product is robust and reliable
• Deploying and maintaining over time. This includes monitoring performance and maintaining the product to ensure it continues to meet user needs 

This creation phase ensures insights and findings from data analysis translate into solutions that support better decision-making.

Deploy and maintain the product

Now is the time to make the data product available for the target audience. The data product might be hosted on a server, part of a web application, or integrated into business systems and workflows. Configuring security measures to protect the product from unauthorized access and data breaches may also be necessary. 

After deployment, the data product should be monitored for performance to identify issues that arise and enact updates as necessary. 

Comparing data productization vs data cataloging

Although data cataloging and data productization are related, they are two distinct processes that serve different purposes. 

Data cataloging focuses on organizing and documenting existing data assets. This makes data assets easier to find and use so teams can access the data they need when they need it. Data cataloging is a preparatory step that makes data accessible. 

Data productization is the next step. It involves turning cataloged data iinto a product that can be monetized and/or leveraged internally for better decision-making.

How data cataloging and data productization work together

Though data cataloging and data productization are different processes, they are complementary. 

Data cataloging provides the foundation for data management and sharing. As an organized, central metadata repository, the data catalog makes data more accurate, discoverable, and shareable. 

Data productization turns raw data into a usable product after a long process of data analysis, product design, development, and testing. 

These two processes are interdependent. A well-organized data catalog ensures data is accurate and accessible, making it more discoverable and positioning it for data productization. Data productization can identify gaps in metadata and highlight areas where further cataloging is necessary. When these two processes work together, an organization can turn its data into actionable insights and another revenue stream. 

Data cataloging and data productization are two essential, interdependent processes that make data accurate, accessible, and actionable. Effectively enacting these processes is key for organizations that want to drive innovation and harness their data for growth. Organizations can gain a competitive advantage by developing a data-driven culture and delivering category-defining innovations.