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  Damned If She Does, Damned If She Doesn’t: rethinking the rules of the game that keep women from succeeding in business

By Lynn Cronin & Howard Fine, 272 pages, Prometheus Books, 2010. ISBN 978-1-61614-174-5
  Retooling HR: using proven business tools to make better decisions about talent

By John W. Boudreau, 200 pages, Harvard Business Press, 2010. ISBN 978-1-4221-3007-0
  Workforce of One: revolutionizing talent management through customization

By Susan M. Cantrell and David Smith, 260 pages, Harvard Business Press, 2010. ISBN (not available)
  Training on Trial: how workplace learning must reinvent itself to remain relevant

By Jim D. Kirkpatrick, Ph.D. and Wendy Kayser Kirkpatrick, 240 pages, AMACOM Books, 2010. ISBN 13: 978-0-8144-1464-4
  One Page Talent Management: eliminating complexity, adding value

By Marc Effron and Miriam Ort, 176 pages, Harvard Business Press, 2010. ISBN (not available)
  What’s Next GenX? Keeping Up, Moving Ahead, and Getting the Career You Want

By Tamara Erickson, 244 pages, Harvard Business Press, 2010. ISBN 978-1-4221-2064-4

  Workforce Solutions Review Online  
  Data Governance in Process Design  
  By Wes Wu, Knowledge Infusion  

The problem with process excellence in human resources organizations is that often when processes are broken, we are not always able to identify the root causes. The reason is that processes are usually defined as discrete sets of tasks that result in an output, rather than end-to-end transactions resulting in a business outcome. The effect is that process breakdowns often occur before or after the documented set of tasks and are identified when the attendant business outcome is not what was expected. The solution is to look at business process as an end-to-end transaction.

Ten to fifteen years ago, core HR systems could contain most of the HR, benefits, and payroll transactions that were needed. Within modern HR technology ecosystems, these core components are just a fraction of the total transactions that must be considered. Emerging talent management systems and employee and manager direct access creates additional transactions from a user population that HR did not have to worry about a decade ago.  Many of these newer transactions, e.g., online performance reviews, are used by every employee within an organization. Additionally, HR service centers that have been created support HR knowledge-base functionality and case management tools that also did not exist for HR in the 1990s. The cyclical trending of outsourced services and having transactional systems, some of which are hosted on-premise and others that are hosted by third-party providers, adds additional complexity. Each of these systems must be part of the entire HR ecosystem and not exist as an island.

When we talk about high-quality processes from an HR systems point of view, we are often talking about data quality. High-quality processes should be defined by more than just efficiency and engineering to make them faster and cheaper. In HR systems, we must talk about data governance and the impact of how we define and utilize data throughout all end-to-end processes. A high level of data quality must also be achieved or the process efficiency is meaningless.

Defining Data

Data governance is, at the same time, the old joke and the newest urgent project in many organizations. It’s the old joke because consultants have been talking about data governance for years, but most organizations still have not implemented it well. The governance of data is often an afterthought during HR system implementations rather than included in the design of the processes.  As a result, organizations often find themselves trying to retroactively implement data governance policies for processes that are already in use.  But data governance consists of multiple components that must occur to make it effective, and to positively impact all resultant HR data processes.

At the core, data governance is a set of definitions and rules that govern your utilization of HR data within the organization. In the world of multiple, disparate, operational, and transactional systems within the HR ecosystem, defining across all of these systems what each individual data element means and providing common language is the first imperative. The most basic example is the utilization of Job and Position data.  With these data elements, HR practitioners can easily become confused based on their role in the organization, whether they are in HRIS, HR, or a Center of Excellence. Crafting definitions that can be rolled out to the user population eliminates the confusion behind the naming conventions for data elements that each individual HR system uses by creating a common organizational language regardless of multiple HR systems.

Secondly, data governance applies rules to the data elements in the HR system ecosystem. The first set of rules will identify systems of record. When multiple transactional systems exist, including employee direct access, there are often multiple capabilities where an employee name can be updated. By ensuring that systems of record have been identified for each data element, the appropriate transactions can be made through the appropriate systems, and system conflicts are avoided.

Defining Utilization

Process quality is impossible without thinking about who are the process participants and clearly defining roles and permissions. Within HR transactions, the possibility of decreased quality increases with larger numbers of participants since each participant has a margin of error and requires extensive process training. But, within HR technology transactions, there are multiple-tiered roles ranging from those who have access to configuration tables that have downstream process impacts, to those who process transactions at the employee level. Complexity is added as transactions become increasingly cross-functional and access rights within functional areas must be combined with cross-functional transaction processing.

In the best governance models, access rights are assigned for each data element. This is one of the core problems in many organizations where access permissions are too broad. Going back to the job or position example, many organizations have found it to be a simpler process to allow end users to add jobs or positions to the database as needed, rather than control these data elements more centrally. While allowing HR end users to add or update jobs and positions at the point of need may seem appropriate, the result is often that the organization has more job codes than they have employees after five years of transactions. Access rights control who has the ability to configure tables, who is able to update those tables, and who is able to update employee information within the tables. By making these distinctions, organizations are able to control quality of data at these multiple tiers rather than allowing the tactical need at the time of entry to govern the whole transaction.

Guiding Process Transactions

Data governance also provides guidance for engineering transactions. As with the discussion about access rights, process quality is about the total number of users and the control that the organization has of the users. I’ve been in organizations where the people who can create new hire records and edit positions in position management number in the hundreds (high hundreds, too). When you have this many people in the system editing the core tables and making revisions to employee records, the loss of controls is significant and the impact on data quality reverberates all the way through to end-state analytics (which are now off by a multiple of the total users). The number of total users you have at the beginning of the process will be directly proportional to the error rate for the employee data, but also directly proportional to the disaster you have in the core HR tables. Whether it’s simply finding ways to limit the population or thinking about implementing shared services, organizations that can limit the total user population will often find increases in quality.

Data governance may also provide guidance on process principles such as pushing quality audits to the end user. Human Resources, by nature, has always been transactional to an extent. Practitioners excel at executing transactions, but data quality issues are usually discovered far into the future when analytics are produced or downstream processes are discovered to have errors. Organizations that wait until the records are saved cannot control data quality until it’s too late. Once rows have been added to the job table or the employee records are committed, organizations cannot simply update data to correct the problems. Post entry audits are also critical, but they should only be used to spot systematic issues with the process or audit macro level data. Process users at the entry point should be given tools to audit their own entries, or be given audit tools that can run queries prior to commit.

The farther forward you can push data quality to the beginning of the process, and the more you can control the initial steps, the higher your end-state quality will be. Saving yourself auditing on the back end spares you from the clean-up tasks and, ultimately, allows you to do the high-value work around creating meaningful analytics, which now makes sense because your quality is better.

Defining System Transactions

In an HR world of growing enterprise needs, centers of excellence and divisions should no longer own data and systems. An enterprise-level HR ecosystem guides cross-functional and end-to-end processes that flow within the HR domain but also transact external data from outside of HR. As HR increasingly reaches for business relevance, systems are increasingly collaborative and transparent. Human resources transactions and data are no longer the domain of a specific area, and all data contributes to employee insights that are delivered at functional, divisional and enterprise levels. End-to-end processes cannot be contained for the same reasons that systems should be viewed at the enterprise level.

This, however, poses data security issues as HR data transactions now must understand the distribution of data as that data is replicated through multiple sub-systems within and without the HR ecosystem. It’s simple for HR to restrict access to data within its own functional areas, but without sufficient preplanning, sensitive data replicated in enterprise-level business systems may not have the same levels of restriction. Human resources data security is often a stated priority, but when process transactions are impacted, trade-offs can be made that preclude good data security.

Crafting Process

All too often, when processes are crafted, organizations think of specific events that act as a starting point. However, good process design actually contains a fundamental framework of definitions, rules, and guiding design principles that have to exist for processes to be truly end-to-end and achieve the objectives of efficiency and effectiveness. Data governance is the foundation of good process design, it answers many fundamental questions about how process should be crafted, and should precede all process design projects. Without good governance, processes may be crafted without organizational and enterprise context. With good governance, processes can focus on optimizing specific transactions with the foundation of enterprise end-to-end insights.


Wesley Wu is a senior consultant with Knowledge Infusion, based in San Francisco, California USA. He works with major corporations, helping them improve the effectiveness of their human resource programs and services. Wu’s areas of expertise include HR strategy, service delivery, function effectiveness and HR technology. He can be reached at

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