Part 3: Data Governance Organization Decisions for MDM Success

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.

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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Part 2: Data Governance Decisions for MDM Success

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.

Part 3: Data Governance Organization Decisions for MDM Success

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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Why You Need Governance to Master Your Data

This is the first blog post in a three-part series discussing the importance and role of a data governance organization in a master data management (MDM) implementation.

MDM is about people and process, not just technology. Implementing MDM technology alone will not address operational and business process challenges. Rather, "mastering" data involves people taking the appropriate action through established data policies and processes.

Data governance is an important people and process component of an MDM strategy. It ensures that data in the MDM hub is of high quality and can be trusted by business users. Without data governance, organizations do not have consistent data definitions or know what constitutes a data problem, who is accountable, what decisions need to be made, or how to escalate and resolve issues.

MDM Implementation funnels_50 percent.png

In this way, data governance plays a vital role in an MDM implementation. The MDM hub provides the requisite data cleansing, duplicate detection, survivorship, hierarchy management and merge/unmerge capabilities. Essentially, it's the technology used to ensure the data is accurate, complete and can be trusted by business users. Data governance is the essential people and process component. It provides the processes, policies, organization and technology guidance required to manage and ensure the availability, usability, integrity, consistency, auditability, quality and security of the data in the MDM hub.

The data governance organization's (DGO) role is to understand and outline data requirements for the MDM hub. Once these requirements are understood, the DGO facilitates the creation and agreement of foundational elements--such as data models and data dictionaries--to support those requirements. Data governance creates a culture of accountability and ownership around the quality of data and provides escalation mechanisms to manage data quality.

The goal of the DGO is to ensure that the right resources, policies and processes are in place and that the data is available, usable and secure in the MDM hub. Bad data will no longer be ignored and can be addressed proactively and effectively. The DGO manages confidence in the data by ensuring that the data stays clean over time, that it is monitored and measured, and that data quality is continuously improved.

In order to deliver on its mandate, the DGO is responsible for guiding and making decisions concerning an MDM implementation. What MDM decisions does the DGO need to make? The next two blog posts will discuss the important data governance decisions that need to be made by the DGO for a successful MDM implementation.

Part 2: Data Governance Decisions for MDM Success

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 January 31, 2013 1:33 PM
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Why Even Bother with Big Data?

Why should organizational leaders pay attention to big data and big data management? Why must organizational leaders start identifying and assessing opportunities to capture and analyze big data? How do you unlock the value of big data to help your business?

In order to answer these questions, it is important to first look at a few challenges and roadblocks with the current business environment that all start with people, process or technology.

Most organizations today are faced with increasing data volumes and complexity. Data users are demanding quick access and an easy understanding of the data to address timely and important business questions.

Current data analysis technologies and data management structures are inflexible and no longer work in this new era of big data (e.g. they have reached or exceeded their maximum data volume size). Traditional analytical techniques and data management structures are lacking. When it comes to policies and processes, there is a lack of full clarity on data ownership and responsibility, usage and management. Rules and policies to track data security, privacy, retention, consumption and usage are limited.

And today customers are more knowledgeable and sophisticated. They demand organizations understand all their relationships, transactions and interactions across multiple channels.

Better information is required to compete in this current environment. High quality data is needed for effective decision making and to take action based upon newly revealed insights. Organizations should consider capturing, managing and analyzing big data to solve these business challenges and gain a competitive advantage in the marketplace.

According to research done by McKinsey Global Institute, big data will become a key basis of innovation, competition, productivity and growth. To aggressively compete and gain value from big data, executives must understand and address the implication of big data to the organization.

How does one derive and measure the value an organization could gain by investing in big data management and big data governance? What are the benefits of big data?

According to research done by McKinsey Global Institute, big data creates value in several ways. Organizations can use big data to achieve competitive advantage by harnessing it to perform more complex analyses, which provide an opportunity to find new insights in the data and content. Big data also enables organizations to be more agile with an ability to answer questions that were previously considered beyond reach. Executives and business stakeholders can now make better management decisions based on facts. Big data also enables executives to have more precise forecasts and adjust business levers accordingly because the organization now has the ability to understand every transaction versus analyzing a particular predefined data set from a traditional data warehouse. The insights from big data can truly help to increase operating margins, drive efficiency improvements and productivity gains, and improve customer satisfaction.

Big data can provide unique insights into customer behavior, satisfaction and preferences, allowing organizations to create specific customer segmentations and tailor products and services precisely to meet those needs. Access to this insightful, timely data allows organizations to identify changes in customer behavior quickly and launch targeted customer retention programs or marketing campaigns by creating specific messages or promotions. Organizations can also tailor new or existing products and services to more quickly and precisely meet customer needs.

Big data can enable organizations to identify new growth opportunities by using data obtained from external sources to improve the development of the next generation of products, drive more innovation and be alerted to alternative and additional supplier solutions. For example, manufacturers are using data obtained from the use of their products via social media data to improve the development of the next generation of products and to create innovative service offerings.

The use of big data is becoming a key way for leading organizations to outdo their peers. Executives and senior leadership need to embrace big data as a valuable and competitive asset.

How is big data adding value to your organization? Share with us your success stories!

Posted November 28, 2012 3:59 PM
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Big Data: Governance is the Critical Starting Point

As data volumes and complexities continue to grow and as organizations continue to acquire big data, this massive amount of data will be overwhelming without strong data governance.

Successfully managing big data often requires skill sets and technologies different than those used to manage "regular" data. It may also require a change in business processes. To ensure there is clarity on data access, integration, usage, management and ownership of big data, companies need to start with a big data governance program.

Adding a governance framework to big data establishes the organization, policies, processes and standards for effectively managing and ensuring the availability, usability, integrity, consistency, auditability and security of big data. Therefore, it is important to expand the scope and vision of your existing data governance program and organization to include big data or, if one does not exist, to establish a data governance program to support your existing enterprise data and your big data.

The existing FSFP Data Governance Framework, shown below, can be applied to big data to ensure that companies can truly harness and discover valuable insights from their big data. The framework consists of six component areas, which FSFP employs to help you determine a plan and roadmap for incorporating big data governance into your organization.

To get started with big data governance, the current data governance group within the company needs to think strategically about how to modify/extend the existing data governance structure, policies and processes to include big data. Based on the data governance framework above, the following are examples of how existing data governance components could be extended:

Strategy - Refine the existing data governance vision and mission statements, objectives and guiding principles to include big data as a data type. It is also important to articulate the business value, develop a business case and determine potential ROI, and then develop a roadmap/blueprint on how to implement a big data governance strategy.

Organization - Extend the operating model to include big data stakeholders (business steward leads, data stewards, steering committee, etc.). The charter, roles and responsibilities, ownership and accountability are also extended to encompass big data governance.

Polices, processes and standards (PPS) - Extend PPS to include big data privacy, security, risk, retention, archiving and regulatory compliance and data classification requirements.

Measurements - Extend data quality metrics and key performance indicators to include big data completeness, timeliness, accuracy, etc.

Technology - Extend data architecture to include big data and data governance technology such as: No SQL distributed processing engines, distributed file systems, advanced analytics and modeling tools, information lifecycle management (ILM) tools, etc.

This framework is used as a guide to create a big data governance strategy and to develop a plan to execute the strategy (with an agreed upon starting point and steps) and to define the criteria or metrics for success.

Organizational leaders must start identifying and assessing opportunities to harness big data. They need to have an understanding of the data assets that should be acquired or integrated as well as their priority. It's important to identify potential value creation opportunities and risk, build up internal capabilities to create data driven organizations and address data issues.

Most importantly, ensure solid alignment between the business and IT groups. In Forrester's most recent Global Big Data Online Survey, 70% of respondents said that big data is or will be a collaborative effort between business and IT. In addition to organizational alignment, executive sponsorship and buy-in for the project is critical. Leaders must understand the value of big data as well as how to unlock this value. You cannot "do" big data management without big data governance.

The data governance organization needs to ensure that the business stakeholders can trust the data. To ensure the success of big data governance, it is important to first understand the business stakeholder landscape--their needs and requirements--and then assess the potential changes required. What type of big data do they require? What are the sources? How often should the data be provided? Start with small changes and iterations and build on success rather than taking an all-or-nothing approach. Really know your data--what data is private? Public? Proprietary? And be sure to define big data governance requirements sooner than later. In order to measure the success and value of a big data governance programs, you need to clearly define measurable success criteria and then put processes in place to measure it over time.

Business users need accurate, clean, timely data about their prospects, customers, competition, etc. to meet business objectives and goals. Without a data governance foundation for small and big data, people often find themselves in a reactive mode when it comes to solving data related issues. big data governance will help ensure business users are getting the data they need while also mitigating the pain points and challenges associated with big data. So when you're not sure where to start with big data, be sure to start with a big data governance strategy and plan.

Posted November 12, 2012 7:26 PM
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