Data Governance during Migration sets companies up for long term success

Layering a data governance target operating model (TOM) over the migration process allows companies to beta test their operational and technical processes against the new and old systems, providing a sneak peek to a long-term future state solution. Often during the migration or conversion process, on-prem to cloud, or version to version, the idea of taking a minimal amount of time and effort to document, and design data governance processes is overlooked. Even for companies doing full scale chart of accounts updates, or mass dimension enrichment, the governance process and oversight of these new data domains is often left unnoticed until after go-live, if sometimes at all. Companies believing that their current process can be adapted need only to look at the hours being used to cleanse the legacy data to realize this is not a best practice, and implementing data governance can provide enterprise level return on investment.

Below outlines how the four major phases of a migration Prep, Cleanse, Map, integrate can influence the data governance people, process and technology:

Data Preparation

• Key stakeholders are engaged to provide insight into the reliability and scope of a data migration, which naturally reveals a data governance organizational structure.
• A data governance council members can easily be placed into three areas, Council members, Stewards, and Consumers to provide oversight and accountability for the migration and prove to be long term governance leaders.
• Potential data stewards will be fully engaged and spearheading the preparation of data, while engagement by data owners is key to providing the insight needed to scope the migration effort and these resources will be evaluated for data stewardship/owner long term roles within the organization

Data Mapping

• The other key principle is the data dictionary and catalog. During the cleansing process, table structures and associated where data will live in repositories will be revealed and could be documented at this point.
• The combination of the meta data needed for the cleansing activity and the structures create in the models become the basis of the data catalogue and dictionary that should be implemented in an MDM technology solution.
• The mapping of dimensions in the transactional data during a migration or transformation effort is a good time to create a data dictionary to clearly define the model attributes and create on common language.
• Each master data element and reference data element that will be part of the new system must be reviewed, it is a best practice to include an effort to define the elements, structures, relationships, and other meta data.
• The technical work for the mapping process and cleansing process are often tightly coupled, however, the review session and documentation of mappings, and the review of the data are often done prior to cleansing which are the basis to create data lineage diagrams.
• The data dictionary should influence the catalog. The detailed information will give a road map for the organization of the data and how the definitions can be propagated across the landscape.

Data Cleansing

• During the data cleansing and normalization phase of a migration effort two key data governance principals can be founded data quality and cleansing. The most notable governance practice to be created is data quality.
• The scripts that are written to normalize data from the legacy system can provide insight into the future state scripts that will become the foundation of the data quality rules to be implemented in a new MDM system. Although legacy systems will have different structures the output of scripts should be replicated by the new systems.
• Even if some data cleansing is done prior to the mapping, during the technical mapping process the normalized data should adhere to the definitions, and scripts should be written to ensure this happens.
• Data quality rules are the foundation, and no data migration can be successful without clear quality rules. Having these rules documented will be the key controls to ensure future data issues do not arise, give error handling and logging requirements.
• During the validation portion of data migration, the data quality and cleaning rules and activities can be tested to ensure viability, reliability and accuracy which give a dry run of that future sate BAU process

Integrate

• Pushing data from a legacy system or from a staging area where it has been mapped and normalized provides insight into the overall data flow, synchronization, and trusted source hierarchies.
• Creating a single source of truth for all domains will guide how survivorship and data accountability by system can be documented and put in place.
• As data is migrated to the new systems, documenting the systems which house the original records creation can be noted, as well as those systems which capture that data (i.e. Salesforce system of record for customer, ERP received Customer). Understanding where data is originated will guide governance processes and where the most integration controls are needed
• For those organization that are moving to data warehouses or implementing new ETL tools, moving to a hub and spoke model to centralize rules, quality issues, error logging, quarantining and other principles to stop bad data from propagating the systems.

One of the most difficult things when approaching data governance or master data management is being able to properly quantify or show the proposed value of good data governance within an organization. For organizations doing a finance transformation, data lake project, or beginning their AI journey there will ultimately be data migration or preparation activities which can provide insight into why data governance is needed to be successful, how to begin the data governance journey, and an overall ROI for investing time and resources during this activity as opposed to going through it stand alone. So leverage this opportunity, and ensure that your organization sets the foundation for governance and data quality going forward.

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Jason Proto