In this post, expert panelist Ken O'Connor outlines a proven approach for assessing and governing enterprise wide data issues.

Ken also provides a list of 10 common data management issues that can beset corporate initiatives.

How To Create A Data Issue Assessment Process: Expert Interview With Ken O'Connor

Data Quality Pro: What is the Data Issue Assessment Process?

 Ken O'Connor: The Data Issue Assessment Process is a process for assessing the status of common Enterprise wide data issues. You can use it to assess the status of common data issues in your organisation, or that of a client. 

There are 7 levels on the scale, starting at level zero, and increasing to level 6.  The higher the score, the better prepared the Enterprise is to deal with the issue.  The worst case scenario is a score of zero, which means that management in the enterprise is not even aware that the issue exists.  To assess the actual status of an issue, ask for documentary evidence to prove that the Enterprise has actually reached that level.

Figure 1: Status of a (data) issue.

Data Quality Pro: What prompted you to develop the Data Issue Assessment Process?

Ken O'Connor: I first developed a process called the "Euro Programme Mobilisation Preparation Checklist".  I developed this to allow me to assess the readiness of UK financial institutions to mobilise a Euro Changeover programme, in the event that the UK decided to adopt the Euro.

The process proved to be flexible, and I have since used it to assess the status of project and programme risks, issues and requirements. I tailor the process for specific purposes – as in the case of “Enterprise wide Data Issues”.

Data Quality Pro: Your blog (kenoconnordata.wordpress.com) lists Common Enterprise Wide Data issues - how did you identify them?

Ken O'Connor: They are issues I have repeatedly encountered working on Data Migration and Data Population programmes, such as Euro Changeover, Single View of Customer and Anti Money Laundering (AML) programmes.

I refer to such programmes as "End of food-chain"programmes.  They share the following characteristics:

• Dependent on existing data
• No control over the quality of existing data they depend on
• No control over the data entry processes by which the data they require is captured.
• The data required may have been captured many years previously.

Many "end of food chain" compliance programmes like BASEL II and AML make use of "Best in breed vendor solutions".
The marketing pitch for these solutions typically says something like: “Populate our Compliance Data Repository and our ready made queries will satisfy the regulators’ requirements”. The solution vendors, quite reasonably say "The client is responsible for locating the data to populate the target repository, and the client is responsible for the quality of the data entered in the target repository".

In my experience, the ease with which an Enterprise completes "End of food chain" data dependent programmes directly depends on the status of the common Enterprise Wide data issues I have identified.

Data Quality Pro: You have recently identified ten common Enterprise Data issues. Which ones are the most serious?

Ken O'Connor: You’re right, to date I have listed ten issues on my blog (ed, see: kenoconnordata.wordpress.com). I will be adding more over the next few weeks.

Of course this is not “The definitive list of Enterprise Data Issues”. I would like others to join in and share issues that they have experienced.

In my experience, the most serious issues are:

3. No culture of Data as an ‘asset’ or ‘resource’

and

4. No clear ownership of data

The other issues are symptoms of the above. An Enterprise that treats Data as a valuable corporate asset understands the value of data and is likely to have addressed the issues I have identified. The ten issues I have covered to date are:

Common Enterprise-wide data issues:

1. Quality of informational data is not as high as desired

2. Quality of data entered by front-end staff is not as high as desired

3. No culture of Data as an ‘asset’ or ‘resource’

4. No clear ownership of data

5. Business Management don’t understand what “Data Quality” means

6. No Enterprise Wide Data Quality Measurement of Data Content

7. No SLAs defined for the required quality level of critical data

8. Accessibility of data is poor

9. Data Migration and ETL projects are Metadata driven

10. No Master repository of Business Rules

Data Quality Pro: Who will benefit from the Data Issue Assessment Process?

Ken O'Connor: I believe Information / Data Quality professionals will find the process useful. Specifically:

1. Solution Vendors (CRM, AML, BASEL II, MFID, SOX, etc.)
   The success of a CRM or compliance programme depends on more than the quality of the vendor’s solution. The duration, cost and overall success of the programme will be influenced by how well the Enterprise has dealt with the common data issues I have highlighted. To manage the risk of the client not being in a position to successfully populate the target repository, compliance solution vendors should advise prospective clients to use the process, or engage an independent consultant to use the process to verify that the client is aware of the issues and has addressed them successfully.
2. In-house Data Quality professionals
   In housed professionals often struggle to convince senior management of the need for an on-going Data Quality improvement programme. This process can help them to identify which issues the Enterprise has addressed, and which ones need attention.
3. Data Quality Solution Vendors
   There’s an old saying “If it ain’t broke, don’t fix it”. Data Quality Solution vendors fix problems. They can use the process to identify which issues a prospectivte client Enterprise has addressed, and which ones need fixing.

Useful Resources

The Ken O'Connor Blog : http://kenoconnordata.wordpress.com

Process for assessing status of common Enterprise-Wide Data Issues (original entry on Ken's blog)

15 Publicly Available Data Quality Frameworks