Core HR data is critically important to the success of company programs — even those that are not directed by Human Resource departments. So why do they continue to mess it up? Unfortunately, core HR data represents the Achilles heel for most Human Capital Management (HCM) programs. There are five common mistakes businesses make when managing their core HR data:
- Relying on outdated HCM practices — business success in a post-industrial society requires more sophisticated practices that depend on core HR data.
- Ignoring change and the impact it can have — success in a complex, ever-changing environment requires an agile and responsive HCM program with core HR data at its root.
- Allowing for HCM silos — today's HCM programs operate in silos causing core HR management processes to fracture, underscoring the need for a more integrated model.
- Tolerating poor quality data — traditional HR data management processes fail to deliver the quality core HR data required by today's businesses.
- Relying on an outdated data governance framework — many governance frameworks have become outdated and isolated from the business.
Mistake #1: Relying on Outdated Practices
"Post-industrial society" is a term coined by Daniel Bell in his 1973 work, "The Coming of Post-Industrial Society". Excerpting from Wikipedia: A nation becomes post-industrial when it passes through (or dodges) a phase of society predominated by a manufacturing-based economy, and moves to a structure of society based on the provision of information, innovation, finance, and services.
As the term has been used, a few common themes (not limited to those below) have begun to emerge.
- The economy undergoes a transition from the production of goods to the provision of services.
- Knowledge becomes a valued form of capital (e.g., the knowledge produced through the Human Genome Project).
- Producing ideas is the main way to grow the economy.
- Through processes of globalization and automation, the value and importance to the economy of blue-collar, unionized work, including manual labor (e.g., assembly-line work) decline, and those of professional workers (e.g. scientists, creative-industry professionals, and IT professionals) grow in value and prevalence.
- Behavioral and information sciences and technologies are developed and implemented. (e.g. behavioral economics, information architecture, cybernetics, Game theory and Information theory.)
The model below illustrates this shift from industrial to post-industrial society. Based on the 3 sector hypothesis of Primary Sector (raw materials), Secondary Sector (manufacturing), and Tertiary Sector (services) it is easy to see the shift to a services-based economy dominating the developed world today. A Quaternary sector has also been proposed with a focus on research and development and information technology.
Many companies have not adapted to the realities of our new economic paradigm because they continue to rely on what they know best — HCM practices from an industrial era, limited by silo-focused processes and technologies. When HRIS first came on the scene in the 1980s, the focus was core HR processes such as payroll, benefits, time, and labour. Therefore, core HR data processes were driven primarily by the needs of payroll, and this continued to be the case until the late 1990s. At the turn of the century, talent management processes such as recruiting, competency management, and performance management began entering the HRIS world. Core HR data requirements became more complex as organizations began needing job, competency, and positional data in order to function effectively.
Given the shift from the industrial (manufacturing) era, it is time to ensure our HCM functions make the transition as well. We now need to think about a lot more than just ensuring pay cheques are distributed on time!
Mistake #2: Ignoring Change
In our post-industrial time, many companies struggle to adapt to the new paradigm, where change is truly constant. Today, organizations of every size must: anticipate and adapt to increasing uncertainty; respond to a new economy that produces requirements for new and increasingly divergent jobs and skills; perform in an increasingly global economy where the supply of talent is subject to fewer geographic boundaries; manage additional talent shortages resulting from an aging population; respond to increasing demands for process efficiencies and cost reductions; and keep pace with system automation and integration demands in both HR and non-HR functions.
The rate of change in our post-industrial society is dramatic:
Unfortunately, companies have not adjusted their core HR data management processes to keep pace with changing data requirements. On-premise HRIS (versus SAAS) is an increasing burden because customizations and upgrades often take years to implement when a change is needed. This is especially problematic for talent management processes in which can changes might occur annually. Consequently, companies struggle with flawed and inconsistent HCM processes, corrupted HCM data, and worse: misleading HCM intelligence from which they make critical business decisions and resource allocations. This means companies have a harder time finding, attracting, developing, and retaining the talent needed to execute their business strategy. If they cannot find the right people with the right skills, at the right time, in the right places, at the right price, then their business will not succeed. Our new era requires a paradigm shift to more fluid, agile, resilient, and quick-to-mobilize talent pools. To achieve this companies require agile and responsive human capital management (HCM) programs lead by HCM expertise and driven by best-of-breed processes, systems, content, and quality core HR data.
Mistake #3: Allowing HCM Silos
HCM disciplines like payroll have evolved in silos for many decades. As separate HCM disciplines, historically they have had their own teams and systems, and they have generated their own variations of core HR data. For example, talent acquisition is generally lead by a talent acquisition team, which manages the process with a specialized recruiting software system, and they use a competency framework different from that of the talent management teams (for succession and performance). This causes huge inefficiencies, added expense, added administrative burden, redundant data management, and duplication of effort.
There is a clear need for integration of processes, systems and data across HCM disciplines — and ONE core HR data model that scales to support all HCM disciplines (not just payroll!). This will streamline processes, maximize availability of data, and save costs (time savings and software license costs).
Mistake #4: Tolerating Poor Data Quality
HRIS systems are the source for core HR data and yet they are often too complicated or confusing for typical users and administrators to use properly. These primary users are not given adequate training and/or adequate functionality, controls, or procedures for most core HR data management processes (e.g., career mapping, job design and evaluation, competency modeling, and position management/control). As a result, the quality of the core HR data generated and managed by these processes is marginal at best — damaging at worst (given the business decisions that are generated from this information).
Mistake #5: Relying On Outdated Data Governance Frameworks
Historically, ownership of HCM systems and data has resided with IT departments (as the default stewards of 'all things IT') and/or HR departments (because they create the data). Until recently, this has been an acceptable business practice as any challenges associated with process or data quality have been managed (or swept under the rug) through administrative response. Administrators have been cleaning up data, over-riding procedures, manually assembling reports from disparate databases, and performing whatever other workarounds are necessary to help the system moving along. The result is an attitude of complacency — accepting 'administrative necessities' and further entrenching poor practice as 'the way things are done here'. Because of our new paradigm, workarounds are no longer an acceptable option. Efficiency demands new technologies within and beyond the core HR domain (e.g., talent management, purchasing, provisioning, finance) because so much depends on the quality of core HR data. Real-time workforce decision making demands reliable, up-to-the-minute HR data. Business leaders and human resource professionals must take the reins of the HCM agenda and demand the right solutions if they are to remain effective and relevant in delivering their HCM mandate.
Examples of Organizational Mismanagement of Core HR Data
To illustrate the need for change, Position Management (or Position Control) is a good example to review. Positions are one of the most critical HR data elements in a company. Not only are they used in most HR processes, they are also key to driving permissions and workflows in non-HR functions such as Purchasing, Finance, and User Provisioning.
Typically, when an approval is received for a new position an HRIS Analyst creates a position in the HRIS. The Analyst's primary motive (historically) is to ensure a position is in place so that once an incumbent is found he/she is paid on time for their first pay period (and thereafter). The Analyst will use whatever means are at his/her disposal to support this end. This could mean creating a net new position, repurposing a dormant position or moving and renaming an existing position (keeping it tied to the original incumbent). Unfortunately, this myopic focus often triggers a negative chain reaction: inconsistent practices producing inconsistent and corrupt data that in turn corrupts downstream processes, and ends with the erosion of business confidence in the HR organization. The data becomes irrelevant, not to be trusted, and therefore poses a threat to business execution. Examples of downstream processes that might be affected include: succession planning (dependent on the accuracy of a positional hierarchy), workforce planning (positions used for movement analysis, headcount analysis and planning), purchasing (budget authority levels determined by positions), and system provisioning of users (positions determine appropriate software licenses, approvals, and workflow notifications assigned to a user). Governance of this core HR data should be firmly in the hands of HR given its criticality to the downstream systems and processes that depend on it.
Jobs represent another example of how companies often mismanage core HR data. Similar jobs are generally created across organizational boundaries, in isolation of one another, resulting in too many variants: causing a terrible waste of scarce HR resources (needing to develop unique job descriptions, evaluate each job), and additional problems with compensation calibration and competency modeling. Control of this core HR data should also be firmly in the hands of HR given its criticality to the operational efficiency of corporate human resource departments.