Common Enterprise Wide Data Governance Issues: #2 The quality of data entered by front-end staff is not as high as desired

This post is one of a series dealing with common Enterprise Wide Data Governance Issues.  Assess the status of this issue in your Enterprise by clicking here:  Data Governance Issue Assessment Process

Causes:

  1. Front-end staff are put under pressure to ‘sell business’ and sort out details later.
  2. Front-end staff have no incentive to enter high-quality data.
  3. Front-end staff are unaware of the downstream impact of poor data.
  4. Little realisation on the part of front-end staff that data entered at account opening may be re-used repeatedly for 30+years.
  5. Front-end systems may lack data validation software.
  6. Front-end systems may not enforce business rules between related fields.
  7. Reluctance to add data validation to legacy Front-end systems

Impact:

  • Incomplete or poor quality ‘informational’ or ‘Master data’ captured at data entry.
  • Risk that the enterprise and/or individual business units will fail to comply with regulatory requirements (e.g. the requirement to ‘Know your customer’).

Solution:
Agree and implement the following policies:

  1. Data owners must define the business rules to be applied to each data item at data entry time.
  2. Data owners must agree Data Quality requirements with the end users of the data, and the owners of the data entry processes.
  3. Data owners must agreee Data Quality Service Level Agreements with the end users of the data, and the owners of the data entry processes.
  4. Data Quality measurement must be implemented at each point of data entry to measure the quality of data being entered, and support the implementation of data quality Service Level Agreements.
  5. Front-end staff must be educated on the importance of data quality
  6. Front-end staff must be supported in taking the time and effort to “get it right first time”

Your experience:
Have you faced the above issue in your organisation, or while working with clients?  What did you do to resolve it?  Please share your experience by posting a comment – Thank you – Ken.

8 thoughts on “Common Enterprise Wide Data Governance Issues: #2 The quality of data entered by front-end staff is not as high as desired

  1. This is a great post. Ultimately the question is how far ahead in the data stream you implement data qualit? As rightly pointed out in this blogpost, closer you get to origin of the data for data quality checks, better it is (Cost, compliance, effort prospective). Some time back, I had a blog post with similar context, please do check it at http://vishagashe.wordpress.com/2009/07/17/inject-data-quality-before-it-becomes-data-quality-issue/

    Vish Agashe

  2. Pingback: Process for assessing status of common Enterprise-Wide Data Issues « Ken O'Connor Data Consultant

  3. I’ve seen that it’s pretty common to set up a real-time parse, geocode and match within a major enterprise application like SAP CRM or Siebel. This is more common in name and address data.
    For the end-user, they attempt to enter a bad address, it gets kicked down to a central server where it’s first parsed and the results are handed back to the user. It’s kind of like, you entered X, but did you mean Y? The user can choose the cleansed version, or the version they entered.
    Then, the major applications will typically send it back to the server for matching. The server essentially says, I see you entered X, by I think I already have a customer like it in my database.

  4. One more thing. This typically happens in subsecond speed when the business application vendor works in conjuction with the data quality tool vendors. An example of this is the open interface supplied by SAP called BC-BAS-PV and BC-BAS-DES. SAP working with the major DQ vendors to provide real time data quality.

  5. Pingback: Applying “Lateral Thinking” to Data Quality « Ken O'Connor Data Consultant

  6. Pingback: The Ryanair Data Entry Model « Ken O'Connor Data Consultant

  7. Pingback: Incomplete loan data puts €8.2billion at risk « Ken O'Connor Data Consultant

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