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Kelle O'Neal

Thanks for joining our data conversation! This blog is an opportunity to share the real life challenges, opportunities and approaches to improving the quality and value of data in your organization. We will write about everything data related from translating "data" speak into "business" speak, to governance models, to the real differences among the myriad software tools available. But there's one catch: we all have to agree to toss out the fluff. That's right, no 30,000 foot, theoretical strategies that leave you wondering how to execute and actually improve performance. Visit regularly to learn from peers and partners on how they are managing and improving data, and we hope you'll also share your views and experiences.

About the author >

As Founder and Managing Partner of First San Francisco Partners, Kelle O’Neal manages specialist data governance and data management consulting services to complex organizations that deliver faster time to results. Kelle can be reached at kelle@firstsanfranciscopartners.com or through the First San Francisco Partners website.

Follow First San Francisco Partners on Twitter at @1stSanFrancsico.

Editor's Note: Find more articles and resources in Kelle's BeyeNETWORK Expert Channel. Be sure to visit today!

In part one of this series, we covered the importance of setting up a data governance organization. Now let's identify and review the types of decisions made by this organization.

During the requirements gathering phase of a master data management (MDM) implementation, the data governance organization (DGO) is involved in defining the scope of requirements for data that will be managed in the MDM hub. Several categories need to be considered, including:
  • Entity Types
  • Ownership and Accountability
  • Policies, Processes and Standards
  • Data Integration (Inbound and Outbound)
  • Service Level Agreements
  • Data Quality
  • Match and Merge (Survivorship)
  • User Interface and Security
  • General Maintenance
We'll cover the first four of these categories in this blog post. Our last blog post in this series will review the remaining five decisions that a DGO must consider for a successful MDM implementation.

Entity Types

One of the first decisions the DGO must make is to determine the entity types that are in the initial scope of the MDM implementation. The entity type (or master data type) to be managed in the MDM hub may include, for example, client, product, supplier, legal entity, etc.

The hierarchies, relationships and associations among these entity types that will be managed by the MDM hub must also be defined. Again, these may include client, account and product hierarchies, as well as the association of an individual to a company, a party to an address, a product to a supplier, a part to a finished good, etc. Additional entities, hierarchies, relations and associations can be added as needed.

Ownership and Accountability

Identifying ownership and accountability ensures that there are people in place to drive decision-making and execute data related tasks, such as determining match/merge rules and handling exception reports. A responsible, accountable, consulted and informed (RACI) matrix must be developed and agreed upon. This matrix should outline data owners (by data element) and data custodians who can create, view, update/change and delete the data.

Policies, Processes and Standards

Policies are business rules or guidelines that need to be in place in order to manage and govern the core set of data elements in the MDM hub. Policies ensure that consistency exists around how data is managed. Policies, processes and standards should be clearly defined, followed and enforced by the DGO.

Policies are business rules used to manage the data. Business rules fall into the categories of data management, data integrity, data lifecycle, data access and retention.

Processes are workflow processes that define "how" the business rules will be implemented. Workflow processes can be integrated into a data governance workflow tool. Foundational processes include:
  • Issues identification, escalation and resolution
  • Data changes, change control and new elements
  • Data quality management approach
  • Standard operating procedures (SOPs)
  • Performance baselines
  • Data reconciliation and synchronization
Standards define a means of maintaining consistency. Standards are created to help reduce the risk of multiple data definitions as a result of financial, operational, and compliance related inefficiencies. Definitions of each entity type must be agreed upon. In addition, the data's purpose, the usage of each data element and an authoritative source for each data element must clearly be articulated.

Data Integration (Inbound and Outbound)

The DGO should not only define the type of entity data to be integrated into the MDM hub but also which systems will supply the data. The DGO needs to determine which are the authoritative and most trusted sources of the data, the frequency of updates to the data and the timeliness of the data. These definitions inform the development of service level agreements between the producers of data, consumers of data and other business groups.

Inbound Data Sources
  • What type of data does each source supply?
  • Why is this needed?
  • At what frequency should they supply it?
  • Who owns the quality of the data?
Outbound Data Sources
  • Which applications will receive master data directly from the MDM hub?
We've reviewed the first four decisions that must be made by a data governance organization (DGO). Our next blog post will cover the remaining five necessary considerations.

Want to learn more about Data Governance as a key to MDM Success? See Kelle O'Neal present live at the Data Governance & Information Quality (DGIQ) Conference June 17-20, 2013 in San Diego, California!

Posted February 22, 2013 5:27 PM
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