Inside Data Governance: Part 3 | The 7 Steps to Data Governance
In part one of this series, we defined data governance and looked at the missteps that lead to massive cleanup projects. In part two, we examined common data governance models and reviewed which work best for different types of organizations. In this post, we will walk through seven key steps to data governance.
Even if you are knowledgeable on the subject of data governance, knowing where to begin can still be a challenge. These steps will help set you on the right path towards an effective data governance framework:
1. Establish Data Governance Organization
The first step is to evaluate various data governance models and pick the one that best suits your organization. The role of data governance organization varies from one model to the other. However establishing ownership, and establishing processes and procedures are common to all the models. Here are some of the common responsibilities of data governance organizations:
- Develop master data maintenance procedures
- Clarify rules, issues etc. with the business functions such as sales, purchase, finance
- Specify and develop tools for supporting the master data maintenance
- Support the daily business process execution in managing master data objects
- The tasks of the master data governance organization can be completely operational or a mixture of operational and project based tasks with defined objectives
2. Identify Strategic Master Data Objects
Data governance certainly helps in improving the consistency of the data and keeps it in sync with the design of the system. However, it is not a good idea to govern each and every piece of data that is maintained. It is imperative to identify the data objects that need to be governed. Some of the key consideration for selection of data objects for governance include:
- Strategic importance to the company
- The master data object is used globally across the organization
- Large impact on business if data objects are not maintained correctly
- Data complexity
- Maintenance of the master data object is not a core activity for any of the users of the master data object
3. Allocate Ownership
One of the primary reasons that result in bad data over a period of time is not having a defined ownership of specific data elements. In data governance, one of the primary objectives is to eliminate this confusion by defining ownership of various aspects of governance.
The first step is to identify the ownership of various data elements at a global or local level. Strategic data objects and fields need to be owned by a global team and the rest can be handled at a local level.
The next step is to identify ownership for the following:
- Data fields – Ownership of data entry at a field level
- User guide – Document the purpose and meaning of individual field values to avoid misinterpretation
- Governance – Ownership to define and modify current field values
- Technical – Ownership to add/remove and update field values
4. Identify Master Data Maintenance Rules
This is an essential step and probably takes the longest time. Data migration mapping rules if, documented during the implementation, can be an excellent starting point. Typically you need to document the following:
- Field values – Rules for data maintenance of field values spanning across various business scenarios and business units
- Organizational dependencies – When there are multiple business units or organizational units involved, there is a need to document which field values apply to which business unit and which do not apply
- Data dependencies – Cross dependencies of data fields
- Use of Profiles (If automated tool is leveraged) – When an automated tool comes into play, grouping several rules and making them profiles can simplify the data maintenance and drive consistency
5. Establish Master Data Maintenance Procedures
Once the rules are documented, the next step is to build procedures that act as guides to the people who actually maintain the data. It is very important to build the procedures and to keep them updated based on the current situation. The data governance team should own these procedures and keep them updated based on the inputs from the business. Typically a procedure documents the following:
- Who maintains data?
- When/How often?
- Based on what?
- Special requirements?
- Organizational differences?
- Functional differences?
- Field selection?
- Field values?
6. Establish Tools for Master Data Maintenance
Building tools for maintenance and audit of data goes a long way in making sure the processes and procedures are being followed. The more difficult the maintenance process is, the higher the chance of not following it. It makes a lot of sense leveraging various tools, which can help with:
- Maintenance of data
- Maintaining work flows for approvals and hand offs from one to another
- Mass changes and mass uploads
- Periodic audits for the health check
There are various tools available in the market that can perform all these functions. SAP MDG, Itelligence it.mds, and SAP Information steward, all of which have built in capabilities to automate various governance processes and ensure compliance.
7. Establish Rules and Jobs for Master Data Archiving
While it is important to maintain the data correctly and catch errors quickly, the governance strategy is not complete without defining an archiving strategy. This completes the information life cycle and provide guidelines on when certain data elements need to retire. Various benefits of archiving include:
- This helps in maintaining the system performance at an optimal level
- Reduce the database size and reduce maintenance costs while hosting and using in-memory database devices
- Simplify searches and look-ups
Some of the key aspects that need to be defined for data archiving are:
- Which records to archive?
- Records, marked for deletion
- Records, not used for xx months
- When and how often to archive?
- Where to save the archive files?
- For how long?
Learn More About Data Governance
As you can see, data governance is an ongoing project that will take maintenance and diligence to keep up with. With a plan in place, your organization can stay on top of data governance, and itelligence can help!