Data Conversion

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Common Enterprise wide Data Governance Issues #10: No ‘Master’ repository of business rules

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

Business rules provide critical details about data fields, including the ‘business’ name of the field, the business purpose of the field, the values it may hold, the business meaning of each value, and interdependencies with other data.

An example of a business rule could be ‘Account Type must be consistent with Account Fee Rate, both must be BUSINESS, or both must be ‘PERSONAL’.  Such a business rule would be critical on an Anti Money Laundering Programme, where you must apply different alert rules to personal and business accounts.

In some organisations, there is no ‘Master’ repository of business rules.  Business rules are not easily researched, not formally documented, and not subject to change control.

Impact: Projects dependent on existing data must research business rules governing that data from first principles, and face the risk of not finding them, or finding inconsistent business rules.  This leads to duplication of effort, project delays, and the risk of making incorrect business decisions based on incorrect business rules (e.g. generating False Anti Money Laundering Alerts on accounts you treat as PERSONAL, when in fact they are BUSINESS.)

Solution:
Agree and implement the following Policies:

  1. Overall ownership for business rules governing data within the Enterprise lies with the CIO.
  2. Ownership for business rules within each Business Unit lies with the CIO and the head of the Business Unit.
  3. Business rules must be formally documented and subject to change control (Enterprise-wide, and Business Unit specific).
  4. The CIO must appoint a person (TitleX) with responsibility for Enterprise wide business rules.
  5. TitleX is responsible for the definition and maintenance of Enterprise-wide business rules, in consultation with business units.
  6. TitleX must provide a single point of contact to handle requests for business rule details.

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.

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Common Enterprise wide Data Governance Issues #9: Data Migration and ETL projects are Metadata driven

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

Too often, Data Migration and ETL projects are built on the basis of Metadata, without measuring what is actually contained in the source data fields.  This happens when the IT function build data ‘pipes’ on the basis of what the metadata says the source fields should contain, and don’t perform data content analysis, or data profiling, to find out what the source fields actually contain.

Impact:
The IT function turn the  ‘tap’ on, the data flows through the ‘pipes’ and the business express surprise, followed by denial, when expectations cannot be met due to data quality issues.  This is known as the ‘Load and Explode’ approach to data.

Solution:
To prevent ‘Load and Explode’ impacting the success of your data dependent projects, agree and apply the following policy:

Before building, or purchasing a system that is dependent on existing data, projects must complete the following process:

  1. Define what data is required.
  2. Define the quality requirements of the required data.
  3. Identify the source of the required data.
  4. Specify the data quality metrics to be captured.
  5. Measure the quality of the available source data.
  6. Understand the implications of the quality of available source data for the proposed system.
  7. If necessary, and if feasible, implement data quality improvement measures to raise the quality to the required level.
  8. Worst case – if the facts tell you data quality is too low and cannot be improved – Cancel the project and save yourself a ton of money!

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.

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Process for assessing status of common Enterprise-Wide Data Governance Issues

If you work with data in large enterprises, you will be aware that the data, and the ability of the business to access that data is seldom as “good” as it should be.  But just how “good” or “bad” is it?

This post outlines a process for assessing the status of common Enterprise-Wide Data Governance  issues within your enterprise, or that of a client.  I use it as the basis for my “Data Governance Health Check”.

These issues can impact your ability to deliver the underlying data required for meaningful CRM, Business Intelligence, etc.. More seriously, they can impact your ability to satisfy regulatory compliance demands (e.g. GDPR, BCBS 239, Solvency II, Anti Money Laundering, BASEL II etc.) in a timely cost effective manner.

Do issues like these affect your enterprise?  If not, how have you resolved or prevented them?  Please share your experience by posting a comment.

Common Enterprise-wide data governance 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


11. No ownership of Cross Business Unit Business Rules


12. No Enterprise Wide Data Dictionary


13. Islands of Data

14. No Enterprise Wide Data Model

Explanation of the scale and the process for using it:

There are 6 levels on the scale, starting at level 1, and increasing to level 6.  The higher the score, the better prepared the organisation is to deal with the issue.  The worst case scenario is actually 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 illustrate that the Enterprise  has actually reached that level:

Figure 1: Status of a (data governance) issue.

1. Aware Senior Management is aware that the issue exists.e.g. Data Quality is not measured, or measured in ad-hoc manner.#Evidence: Captured in Issues Log or Requirements document.
2. Understands Senior Management fully understands the issue; the impact of not addressing it; options available to address it, complete with the pros and cons of each option.e.g. Issue paper explains the impact of no Data Quality Metrics on downstream data dependent projects etc.Evidence: Issue Paper, Rationale paper or Point of View paper(s).
3. Policy defined Senior Management has a clearly stated policy/strategy identifying the selected option.e.g. Data Quality Measurement must be performed by each Business Unit, using a standard Enterprise Wide Data Quality Measurement process….Evidence: Policy document / Design Principles/ Communications/ education material
4. Process defined The organistaion has a clearly defined process detailing exactly how the policy / strategy will be implemented, which common services / utilities must be used, and exactly how to use them.E.g. The standard Enterprise Wide Data Quality Measurement process will use ‘off the shelf tool X’, to produce a standard set of Data Quality metrics….Each BU must train N staff in the use of the tool.  Training will take place……Evidence: End To End Process documentation / Education and Training material.
5. Infrastructure in place Infrastructure (systems / common services / utilities) needed to implement the process is in place.E.g. ‘off the shelf tool X’ has been licenced and installed Enterprise Wide.  Staff have been trained …Pilots have been run…Evidence: Programme Infrastructure document / Utility user manuals.
6. Governance in place Governance is in place to ensure that the defined policy is implemented in accordance with the defined process.E.g.  The stakeholders are…The Data Steering Enterprise includes the CIO and ….The reporting process is….. The following controls are in place….Evidence: Programme Governance document / Education / completed sign-offs

Your experience:
How do you assess Data Governance within your organisation, or that of a client? Please share your experience by posting a comment – Thank you – Ken.