What Data Juice Would You Like To Have Today?

Mix it up, blend it right,

Smoothie vibes feeling so light!

Passionfruit, peach, or pomegranate,

Every sip feels so fantastic!

Just like crafting the perfect smoothie, data elements need to be carefully selected and categorized based on their purpose.

Whether it's passionfruit, peach, or pomegranate, every ingredient contributes to the final blend, just as business data elements fall into four key categories according to Dr. Prashanth Southekal, Guru of Data Governance for 20 years, and author of “Data Quality” books and other books on business performance and analytics:

• Storage

• Integration

• Regulation

• Insights

These data elements primarily serve three key purposes: 

• Operation

• Regulation

• Decision Making

The application of these elements depends on the objective, determining whether Data Management or Data Governance is required.

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STORAGE DATA

In most organizations, stored both structured and unstructured data. The type of data that requires higher storage capacity depends on the nature of the business and its operational needs.

Structured data is primarily stored in relational databases or spreadsheets, where it follows a predefined schema with organized rows and columns. 

In contrast, unstructured data includes images, audio, and video files, which do not have a predefined format and cannot be easily stored in traditional databases. Instead, they often require specialized storage solutions such as data lakes, NoSQL databases, or cloud-based repositories.

INTEGRATION DATA

It is common for most organizations to have multiple systems and formats for their core source systems, which feed into multiples downstream data warehouses. However, all data stored across the enterprise is integrated to support business operations. These data types include:

• Master Data – Core business entities shared across systems (e.g., customers, products, employees, supplier).

• Reference Data – Standardized values that help maintain consistency (e.g., country codes, currency codes).

• Transactional Data – Business activities and events captured in operational systems (e.g., sales transactions, order status).

All data type above would need Metadata – Information about data that provides context and governance (e.g., data lineage, schema definitions).

REGULATION DATA

From a compliance perspective, data is classified based on its sensitivity level, and appropriate data protection controls are implemented to ensure its security and confidentiality. The key classifications include:

• Public Data – if disclosed, poses little to no risk to the organization. (e.g., Financial Statements, products specifications)

• Personal Data – Disclosed could result potential harm to organization or individual. (e.g., Identification Number, Demographics)

• Confidential Data – Information that, if exposed, could compromise the organization's competitive advantage. (e.g., employee salaries, profit margins)

• Restricted Data – Highly sensitive information that, if disclosed, could cause significant reputational or financial harm to the organization. (e.g. payment card numbers, trade secret)

INSIGHTS DATA

These data are often the result of Integration Data, which consolidates information from various sources to provide insights into business performance.

• Labeling or Categorizing Data – Organizing data based on predefined classifications (e.g., customer segments, product categories).

• Numeric or Quantitative Values – Providing measurable metrics used for business analysis (e.g., revenue figures, customer churn rates).

  
  

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I hope this topic provides a comprehensive overview of the different types of data from various perspectives (Storage, Integration, Regulation, and Insights).

The key message I want to convey is that, despite the existence of various data types viewed from different angles and requirements, Master Data remains a fundamental component within the broader "Data Juice" Menu. It serves as the core building block, integrating with other data types to support business operations effectively. 

Just like a full-course meal—starting with an appetizer, followed by the main course, and ending with dessert—data within an organization follows a structured flow.

Each type of data plays a crucial role in the broader "Data Juice" framework:

• Appetizer (Reference & Metadata) – Provides foundational context and categorization, setting the stage for deeper insights.

• Main Course (Master & Transactional Data) – Forms the core elements that drive business processes and decision-making.

• Dessert (Integration & Analytical Data) – Delivers valuable insights and enhances strategic outcomes.

In the next topic, let's explore the key components that form the entire Master Data Management (MDM) architecture!