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Data Management

CryspIQ has been built to support the DAMA Framework. which is considered to be the Global Industry Standard for Data Management.

Data principles​

These are the common Data principles from DAMA Data Management Book of Knowledge are enforced when you utilise CryspIQ.


  • Data should be accessible in one place and be searchable.
  • Common understanding of Data across the business using terms defined in mapping process.


  • Timely and up to date data is available for use.
  • Governed Data is shared and available for use.


  • Captured one, used many times.
  • Issues are fixed at source.


  • Data must be retained independently of the source system.


  • Data is Open by default, restricted by exception


  • Must be usable, of good quality for both primary, secondary and tertiary uses.
  • Metadata about the Data must be maintained.

Data Roles​

The key Data roles outlined by DAMA are:

Data Owner​

Business Person who has approved financial authority for decision making about Data within their domain. The Data Owner is the Head of Business Function. Please note that this label can vary between organisations (some would call them General managers or Vice Presidents) Ultimately, its the decision maker within that Busienss Function that controls the approval of funding.

In CryspIQ, whilst adding a source message, the Business Function field is needs to be entered. In doing so, this directly assigns the ownership of the source Data to that business function.

Data Stewards​

Most often recognised as the Subject Matter Experts (SMEs) within their data domain.The Data Steward normally works within the Business Function and is considered to have a deep understanding of the Data. Stakeholders with questions on the Data would approach the Data Steward.

In CryspIQ, whilst adding a source message, the Data Steward name and email address is added. In doing so, this directly assigns the responsibility for the quality of the Data beind produced.

Technical Stewards​

Technical Professionals who operate within one of the technical knowledge areas (Data Integration Specialist, Database Administrator, BI specialists etc) for the Data. Normally the system owners in whic the Data resides and work under the direction of the Data Steward. They support the Data Stewards by doing the day to day work.

In CryspIQ, this role is not assigned, but they will be assigned the work by the Data Steward.

Data Management Lifecycle​

During it Life, the Data normally follows a Lifecycle which is very similar to a physical asset. CryspIQ supports these stages in the Data Management Lifecycle, which is shown in the diagram below:

Functional Flows

Data Security​

This provides some guidance to the levels of Data Security that can be applied in CryspIQ. Most business have their own Data Security Classifications, which can be implemented into CryspIQ.

General (Level 1)​

Information that is considered general and available to the public.

Official (Level 2)​

Information the organisation has chosen to keep confidential but the disclosure of which would not cause material harm.

Confidential (Level 3)​

Information could cause risk of material harm to individuals or the organisation if disclosed.

Top Secret (Level 4)​

Information would cause severe harm to individuals or the organisation if disclosed. Examples are:

  • Sensitive Personally Identifiable Information (PII). In Australia this is governed by the Privacy Act 1988.
  • Cardholder Data
  • Protected Health Information (PHI)
  • Bank Account Data

    “High Risk Confidential Information” 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, health and medical information, or data about the individual obtained through a research project.

“Confidential Information” refers to all types of data Levels 2-4. The higher the data level, the greater the required protection.

Data Quality​

Data Quality is measured in line with the six dimensions from DAMA Data Management Book of Knowledge, which are:


Data that attempts to model real-world objects or events. Examples are

  • Incorrect spelling of a person's name
  • Incorrect address for delivery


All required records and values should be available with no missing information. With completeness, the stored data is compared with the goal of being 100% complete. Example - An address on a membership form. If three forms out of 100 are missing addresses, the data, regarding addresses, is 97% complete.


This dimension is about a lack of difference when two or more data items are being compared. Items of data taken from multiple sources should not (in an ideal world) conflict with one another.

Example - a school’s database having a student’s date of birth showing the same format and value in both the school register and the documents sent from the school the student is transferring from.


The data’s actual arrival time is measured against the predicted, or desired, arrival time. Example


Data is properly identified and only recorded once. When data is unique, no record exists more than once within a database. Each record can be uniquely identified, with no redundant storage. Example


Data closeness to pre-defined business rules or a calculation. When these rules are applied, the data falls within defined parameters or conforms to the syntax of its definition. Examples

  • Based on Business Rules or Calculation
  • Based on Validity for range of values
  • Sequencing Order.

Example - Invoices order, receiving Invoice 1000 before Invoice 999 breaks the expected sequence.