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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!


March 2013 Archives

In the last blog post, we discussed the first four elements a data governance organization (DGO) needs to consider in planning for and implementing a master data management (MDM) strategy. They included:
  • Entity Types
  • Ownership and Accountability
  • Policies, Processes and Standards
  • Data Integration (Inbound and Outbound)
In this blog post, we'll address the remaining five considerations:
  • Service Level Agreements
  • Data Quality
  • Match and Merge (Survivorship)
  • User Interface and Security
  • General Maintenance

Service Level Agreements

Service Level Agreements (SLAs) guide the standards and set expectations regarding the quality of service provided by the DGO. They are identified and developed to explain the level of service expected from the DGO. SLAs are typically defined and then implemented between key groups as described below.

The DGO and the business - For example, how are data quality exceptions handled? What is the duration of time needed to fix the problem? An SLA might specify that a response will be received from the DGO in two business days with a remediation plan and time line to fix the issue.

Producers of data and consumers of data - For example, to address data integrity, how will changes to data in upstream systems be addressed? The SLA might stipulate that producers of data must perform an impact analysis within a given time period and present the recommendations to the DGO before implementation.

The DGO and IT - For example, in regard to serviceability and the ease in which a service may be performed and completed on a system by the IT group, an SLA may state that 80% of service failures are recovered in less than 30 minutes.

DGO and IT_75 percent.png

Data Quality

Required data quality targets for each entity type and each data element that is to be measured should be defined. The DGO needs to monitor data issues and track progress over time to show the value of the MDM hub.


Typical questions to be addressed include:
  • How good does the data have to be?
  • How will the data be monitored?
  • What are the data quality measurements, metrics and key performance indicators (KPIs)?
  • What scorecards need to be created?

Match and Merge (Survivorship)

Survivorship rules attempt to create the best version of integrated data in cases where multiple systems can create and/or change a record that refers to the same record in production applications. They also serve to outline the required process when master data is deleted in a contributing source system.

The DGO must define survivorship rules to detect duplicate entities based on specific "match" rules. These rules may include:
  • 'find duplicate contacts'
  • 'exact match on full name, organization and email address'
  • 'fuzzy on full name'
  • 'fuzzy on organization'
  • 'exact email address'
The MDM hub must be able to automatically merge duplicates or set them aside for manual verification based on the configuration of the match rules.

User Interface and Security

The extent of the MDM user community and all associated security rights need to be defined and understood. Data stewards need access to the MDM hub, however not all have access rights to all the data in the hub. Some data stewards can only see and work on certain types of data within a certain subject area.


The following questions should be addressed:

  • What type of security is required around the data?
  • What user access rights and privileges are required by user type?
  • How is data security monitored and improved?


General Maintenance

The DGO also needs to define the requirements for job and system monitoring, maintenance, backup and recovery and system support.

Conclusion

Many decisions need to be made in the course of an MDM implementation, and those related to data management, in particular, should be made by the DGO. An MDM implementation has a much higher likelihood for success with an effective decision-making structure and process in place. That is the purpose and value of a well-defined data governance process and DGO.


Consequently, one of the most important factors in preparing for an MDM implementation is setting up the DGO to facilitate these critical decisions. The need for the DGO arises from the fact that data is now being shared at an enterprise level rather than used solely at the application level. With proper data governance practices in place, the MDM hub will deliver trusted data to the organization, and the organization will realize the full benefits of mastering data.


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 March 14, 2013 7:30 AM
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