I’m not sure about you, but the prospect of reading an ISO standard does not always fill me with enthusiasm, however, the recent ISO 8000:150 presents an interesting read (at least for those interested in data management). In this blog post I will present an overview of the standard, subsequent related posts will look in more detail at specific areas and will consider the implications for data management and data governance generally. The actual standard title is “Master data: Quality Management Framework”, however, the approach is applicable to most business data quality management contexts. There are a few fundamental principles that underpin the usage of the standard:

  • People – data quality management is a people based activity and not merely a technology implementation
  • Process – effective management is based upon a number of key processes
  • Continuous improvement – as well as striving to continuously improve the quality of data, the processes used to achieve this should also be continually improved

These three principles are very important and, for me, the last principle is key in that it indicates that you do not need to establish large and complex processes and organisations before you can start to apply the approaches in this standard. The methodology defined by the standard is summarised by the following nine box model:

 

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This model is divided into three vertical ‘processes’ and three horizontal ‘roles’. The three key processes are as follows:

  • 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
  • Data Quality Monitoring defines 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
  • Data Quality Improvement process corrects data errors detected and eliminates the root causes of data errors
    • Data Stewardship and Flow Management is the process that analyses data flows and responsibilities and manages the stewardship of data operations
    • Data Error Cause Analysis is the process that identifies root causes of data errors in order to prevent them reoccurring
    • Data Error Correction is the process that corrects data that does not meet standards or data quality criteria

The standard also defines three generic roles – Data Manager, Data Administrator and Data Technician. For many organisations, these levels are probably too simplistic, however, they do provide an indication of whether the low level processes are strategic, tactical or operational. Subsequent blog posts will explore the standard in more detail:

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7 thoughts on “ISO 8000:150–A framework for Data Quality Management

  • 30th March 2015 at 2:49 pm
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    “Data Architecture Management” has a typo… nonetheless, a great article – Thanks 🙂

    Reply
    • 1st April 2015 at 2:06 pm
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      Stephen,
      Glad you like the article.

      Well spotted on the typo – clearly a problem with our data quality there! You would not believe how many times this has been viewed and not spotted before 🙂

      Reply
  • 14th April 2015 at 9:16 am
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    Interesting series of articles. I’ve been involved in several proof of concept projects in the development of these standards and the related ISO 22745 while working in MOD and now in a data specialist company in the private sector. Always interested to see others opinion’s on the use of the standards. I have some case studies of applied use if you would be interested.

    Reply
  • 19th July 2016 at 5:07 pm
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    Nice article Julian which could be improved by some real-world examples maybe? I spotted a typo I think, “Data Error Cause Analysis is the process the identifies…” should it be, “Data Error Cause Analysis is the process that identifies…”? Thanks, Paul

    Reply
    • 20th July 2016 at 12:09 pm
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      Paul,
      Thanks for pointing out the typo – good to know that you have gone into the details etc.
      Regarding real world examples – we have been involved with a number of rail organisations where the standard is being used to inform the approaches to data governance. There are some additional parts to ISO 8000 that are currently in production that will provide more prescriptive approaches to data quality management, with Part 150 providing the conceptual overview.

      Julian

      Reply
  • 31st March 2019 at 12:53 pm
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    Hello Julian

    what it takes and long it takes to become ISO 8000 150 and 61 certified?

    Reply
    • 1st April 2019 at 3:29 pm
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      The maturity assessment methodology we have developed is similar for both these parts of ISO 8000 is similar. For a directorate, business unit or smaller organisation typically, it will involve a few days interviews on site, document reviews and then analysis and reporting. Overall, two to three weeks to complete.
      The report and rich level of maturity outputs provides organisations with a powerful means to understand their current maturity and agree steps to improve maturity.
      We don’t do certification since this can tend to be a binary output i.e. pass/fail with a pass ending up as a certificate on a wall, but not necessarily improving the organisation.

      Reply

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