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In this second blog post about ISO 8000:150 we will look at the Data Operations processes that form part of the overall data quality management approach defined in this standard. For an overview of the standard, please see ISO 8000:150–A framework for Data Quality Management.

The Data Operations processes focus on the factors that affect data quality and the usage of data:

  • Data Architecture Management manages the organisation wide data architecture
  • Data Design manages data standards and definitions, database and system implementation and configuration
  • Data Processing covers activities that create, modify, update and transfer data

Each of these can be considered as ‘leading’ indicators/ factors for data quality activities. We will look at each of these in turn…

Data Architecture Management

In most organisations, data is distributed across numerous data stores which means that data quality cannot be effectively managed if you do not approach data storage in a systematic way. This process includes activities such as:

  • Management of organisation wide conceptual data models, perhaps as part of an overall Enterprise Architecture approach
  • Management of organisation wide data standards including maintaining these as data requirements change
  • Interfaces and coordination between data systems and stores
  • Approaches to Master Data Management tools and approaches
  • Accessibility and data security considerations

These activities also link to other data processes:

  • Aligning Data Quality Planning to Data Architecture Management
  • Links to Data Stewardship/Flow Management activities to understand data interactions across an organisation
  • Input to Data Design activities to ensure that detailed data standards and conceptual models align to strategic ones

Data Design

Data quality errors can either arise from user errors or errors of data definition. The former can be corrected relatively easily, however, errors of data definition can be difficult to resolve, particularly if they have existed for some time.

Key data design activities include:

  • The development of logical and physical data models
  • Field level data standards and rules
  • Awareness of data requirements arising from the overall Data Architecture approach
  • Consulting other parties to understand the relationship of data to other systems/data stores and consulting users to ensure data standards meet data quality requirements
  • Maintaining system configurations to align with these data standards

These activities also link to other data processes:

  • Outputs of data design activities should influence Data Architecture Management
  • Ensuring that Data Processing is undertaken in line with data designs
  • Informing and being informed by the activity of creating data quality criteria

Data Processing

Data processing activities consider the actual processes of data update and creation. This is a key activity to manage effectively in order to get early warning that data quality problems are being created – using the ‘dirty lake’ analogy, this is like checking that the incoming water is clean.

Activities in this process include:

  • Monitoring data creation and update processes themselves
  • Logging data usage and update times
  • Awareness of the different roles undertaking data processing

These activities link to other data processes:

  • Processing should be based upon and tested against Data Design outputs
  • Results of data processing should be measured as part of Data Quality Measurement

 

Overall, these processes and activities should support organisations in setting a clearer framework for understanding and controlling the ‘leading’ indicators of data quality.

The next blog post in this series considers Data Quality Monitoring processes.

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