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Data Classification

Classifying and assigning properties to your data is essential for effective data management—but this should focus only on the data that truly matters to your organisation, often referred to as critical data. Not all data within an application is relevant at the organisational level; in fact, typically only around 20% of application data holds strategic value, while the remainder supports internal application processes.

CryspIQ® is purposefully designed to help your organisation identify and concentrate on this important subset—ensuring that effort, governance, and analysis are applied where they deliver the most value.

Data Privacy​

In Australia, the handling of personal information is governed by the Privacy Act 1988.

Personal Information (PI) covers a broad spectrum of data or opinions that can be used to identify an individual. What qualifies as PI depends on the context—specifically, whether a person is identified or reasonably identifiable in the given circumstances.

Non PI Data​

Non PI Data that cannot identify an individual.

PI Sensitive​

PI Data is any data that could identify a specific person. Examples are:

  • name,
  • government-issued ID number,
  • date of birth,
  • occupation, or
  • address.

PI Highly Sensitive​

High Risk Confidential PI means an individual’s name together with any of the following data about that individual:

  • Medicare number,
  • bank or
  • other financial account numbers,
  • credit or debit card numbers,
  • driver’s license number,
  • passport number,
  • other government-issued identification numbers,
  • biometric data,
  • Protected Health Information (PHI)
  • data about the individual obtained through a research project.

Data Sensitivity​

Most organisations have defined their own processes for managing data sensitivity, and these can be seamlessly integrated into CryspIQ®.

To support secure data handling, CryspIQ® applies a default policy where all incoming data is inaccessible by default. Access is only granted once the data has been explicitly assigned to the appropriate security groups.

Types of Data​

The Five W’s and H framework is a structured approach for gathering information and analysing a situation by asking six fundamental questions: Who, What, When, Where, Why, and How.

In the context of analytics, three primary types of data are typically involved:

  • Master Data
  • Reference Data
  • Transactional Data

Effectively managing your data starts with correctly identifying which type of data you’re working with—each serves a distinct purpose and requires a tailored approach to governance, integration, and analysis.

Master Data​

Master Data refers to the core data that defines the key nouns used to answer three fundamental analytical questions:

  • Who – Identifies the people or entities involved, such as customers, employees, vendors, or suppliers.
  • What – Describes the objects or items of interest, such as assets, services, or cost codes.
  • Where – Pinpoints the physical or virtual locations relevant to the data, supporting questions like “Where is the event happening?” or “Where is the asset located?”

This foundational data provides the context needed for accurate analysis, reporting, and decision-making.

Master Data serves as the authoritative source record or object within an organisation, providing the definitive answer to three key questions: Who, What, and Where.

This type of data is typically slow-changing or infrequently updated, and should be managed and tracked accordingly to ensure consistency, accuracy, and historical integrity over time.

Reference Data​

Reference Data is used to link and align data across different business functions and applications. It typically takes the form of a code, number, or short text value that acts as a common reference point between systems.

A simple way to understand reference data is through an example: Consider the Payments and Invoicing functions within an organisation. A familiar piece of reference data is the Invoice Number. When processing a payment, including the invoice number ensures the payment can be accurately matched to the corresponding invoice—enabling clear reconciliation between functions.

Transactional Data​

Transactional Data refers to the individual records of business activities or events that take place within an organisation.

These records are created at a specific date and time, and can vary in frequency—ranging from high-frequency data like sensor readings or tag scans, to low-frequency events such as invoice payments.

Often described as factual data, transactional data captures the actual operations of the business and is typically immutable, providing a reliable historical record of what occurred.