Guide to SQL Server data management and data quality
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Most guidelines for instituting data governance programs specify that a successful initiative must involve a combination of people, processes and technology. That prescription is intended partly to ensure against a blind reliance on data governance tools to accomplish the objectives of the initiative, and it implies the need to focus on establishing and clearly defining the required roles, responsibilities, policies and procedures.
In most organizations, numerous systemic, technical and organizational complexities create hurdles for the data repurposing and reuse enabled by well-managed governance efforts that result in consistent information and usage rules designed to keep it that way. Reviewing end-to-end data processing operations typically will expose many deeply ingrained data management challenges, such as variances in data models, inflexibility of existing data structures and broad inconsistencies in business terminology.
Gaining control over organizational data assets -- in spite of the often-disparate interests of different business units and departments and the variety of data platforms, business applications and data management approaches commonly found in companies -- starts by combining operational data stewardship procedures with documented best practices for creating and using data and appropriate technology. That sets the stage for collaborative data management and governance on steps such as defining and enforcing internal data standards, harmonizing business semantics in corporate data, embedding data validation controls in integration routines and developing shared sets of data rules reflecting the needs of business users throughout an organization.
In essence, data governance software and related technology should be used to supplement the methods by which policies agreed upon by a data governance council or a less formal group of business representatives are translated into information requirements and corresponding business rules. Various types of tools are available that support processes for improving data governance and information oversight. Let's look more closely at some of them.
Data governance templates. Often overlooked as a valuable tool, standardized templates can help in organizing the priorities, tasks and outputs of a data governance program. For example, providing a template for a data governance policy guides the members of a governance council in properly defining the policy, its scope, performance metrics and a process for escalating issues to higher levels as needed. Another example is an agenda template that spells out an orderly walk-though of data governance issues, proposals, standards and other items to be discussed at council meetings.
Data modeling tools. If some of the governance challenges faced by an organization are associated with variances and inconsistencies in data models, the way to address that is by rationalizing and standardizing aspects of the data modeling process. That can include steps such as defining common data entity types and data structures, ensuring referential integrity and maintaining data class hierarchies. Data modeling tools not only enable those and other improvements, they also help align data models with the overall enterprise architecture and influence the maintenance of data consistency.
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Data profiling software. Data usability is predicated on minimizing the severity of data issues, necessitating a means for doing data quality analysis and assessment as part of a data governance framework. That includes tools for profiling data (spanning data sets, records, elements and values), running statistical analyses and evaluating data models. Such technologies can help to identify data anomalies, determine their potential business impact and develop dimensions for measuring data quality level.
Data quality management software. A key objective of a data governance strategy is ensuring data accuracy, consistency and completeness. Data quality tools, such as parsing, standardization, enhancement and cleansing software, clearly have a role to play in supporting the implementation of a data governance model.
Metadata management tools. These are useful for creating and managing shared glossaries of business terms, definitions of data elements and internal standards on data architecture, data modeling, naming conventions and data exchange methodologies. They can also help in enabling better visibility into the information flows in corporate systems. In addition, semantic metadata management tools can be deployed as a central platform providing enterprise-wide views of data structures and a knowledge base of data definitions.
Master data management (MDM) hubs. While MDM is generally seen as a related but standalone activity, there are aspects of cross-functional data governance and management that can depend on some of the core data mastering and identity resolution capabilities often bundled into MDM software suites.
There's no doubt that such technologies can be an important part of an overall data governance and stewardship strategy. But don't be fooled into thinking that's all you need to succeed. The full value of data governance tools and supporting software can be realized only in the presence of well-defined data governance policies and processes, and a well-structured governance team that can effectively deploy and use the technologies to help support the adoption of data governance best practices in an organization.
About the author:
David Loshin is president of Knowledge Integrity Inc., a consulting, training and development services company that works with clients on big data, data quality, data governance, master data management and business intelligence projects. He also is the author of four books, including The Practitioner's Guide to Data Quality Improvement and Master Data Management. Email him at firstname.lastname@example.org.
David Loshin, President, Knowledge Integrity Inc. asks:
What are your plans for using packaged software in your data governance program?
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