In this third blog post about ISO 8000:150 we will look at the Data Quality Monitoring 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. Data Quality Monitoring processes define a systematic approach to assess the levels of data quality:

  • Data Quality Planning sets the objectives of data quality management to align with organisational objectives
  • Data Quality Criteria Setup sets the measures and methods to assess data quality
  • Data Quality Measurement is the process that utilises these data quality criteria in order to assess data quality levels

Unlike the Data Operations processes which focus on the ‘leading’ factors for data quality, these processes focus on the ‘lagging’ indicators i.e. identifying the level of quality.

Data Quality Planning

Whilst many people and organisations would ideally like ‘perfect’ data, the reality is that the time/ effort/ costs to achieve this are unrealistic. Therefore, the Data Quality Planning process sets the objectives for Data Quality Management to achieve overall organisational aims. This includes ensuring that there is a consistent approach to data quality supported by a detailed Data Quality Plan. Specific activities include:

  • Agreeing and managing the organisational objectives for data quality based on internal and external requirements
  • Managing the assurance processes for data quality management
  • Planning of the activities to deliver the required level of data quality including specific tasks, timescales, resources and budgets
  • Control of factors affecting data quality
  • Gaining executive support for the Data Quality Plan

This activity also links to other data quality processes:

  • Interface to the Data Architecture Management process to inform this process
  • Results of the Data Quality Plan will also inform the Stewardship/Flow Management process
  • Data quality planning provides the framework for the Data Quality Criteria Setup process

Data Quality Criteria Setup

In order to deliver the Data Quality Plan you need to be able to assess current levels of data quality. This involves the establishment of a number of Data Quality Criteria (which can also be known as Data Quality Rules) that details specific tests of the validity, completeness, uniqueness and accuracy of data. The two main activities of this process are:

  • Identifying the data quality criteria, the target data and the measurement method
  • Refining data quality criteria based upon the results of data quality measurement

This process links to some of the other processes in the framework:

  • Criteria will be informed by the overall Data Quality Plan
  • The agreed criteria are a key input to the Data Quality Measurement process
  • Data Designs will also be a key input to the process of developing and agreeing data quality criteria

Each Data Quality Criteria should include:

  • A description of the Data Quality Criteria
  • What the Criteria represents
  • The consequences of data failing the Criteria
  • Status of the Criteria – e.g. Proposed, Regular, Ad Hoc
  • Key stakeholders for the Criteria
  • Relevant data source(s) and method of analysis
  • Frequency of reporting
  • Target quality levels by organisation and function

Data Quality Measurement

So far we have set the overall quality objectives for the organisation and have developed the supporting data quality criteria, however, we have not yet assessed the actual quality of the data! The Data Quality Measurement process utilises the Data Quality Criteria to measure current quality which could be done manually or by specific tools. There should also be suitable analysis and presentation of the data quality measurements in order to inform wider organisational processes and stakeholders. This process is informed by the Data Quality Criteria Setup process and is a key input to both the Data Error Cause Analysis process and the Data Error Correction process.

Overall, these processes should allow an organisation to monitor its current data quality levels against overall target levels. As such these can be considered the ‘lagging’ indicators of data quality. The next blog post in this series will consider Data Quality Improvement.

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