There have been some recent posts and articles related to advancing HR Analytics that grabbed our attention, especially those that propose a focus on maturing these capabilities to bring greater objectivity and impact on the businesses they help manage. What strikes us are two things in particular – first, that we in Human Resources have been talking about this for decades, and second, that combined or integrated business and HR metrics have been available but difficult to access by organizations for years. 

As frustrating as it seems, while many of the necessary technologies have been in place and the pundits have said “now is the time” for many years, the reality is that combining and accessing the data using those systems has never been as easy as it appeared. Basic issues continue to haunt corporate efforts to create a robust and informative capability with a lack of data dictionaries, agreement and alignment regarding calculation methods, restricted or difficult access to necessary datasets, poorly integrated systems, and continuing issues with HR quantitative skill levels. These are serious challenges to advancing HR analytics.  

One recent post by Dave Ulrich suggests a maturity model-style evolution that starts with Benchmarking and progresses through Best Practice to Predictive Analytics up to Guidance which focuses on generating tailored insights into stakeholder impact and drivers of desired results. What is appealing about this is that it assumes that basic HR metrics are in place and can be leveraged for more robust cause-effect analyses that integrate HR practices, programs, and initiatives with customer, financial, community, and talent outcomes. While many organizations aspire towards entering the latter stages of maturity, the struggles are holding them back. The model is not the newest proposition, but it is one that focuses the HR community’s attention on the application of advanced statistics to integrated business and HR data.

Another article by Josh Bersin discusses thinking on what he terms “Systemic People Analytics”, or the merging of “people data” with business data and connected HR analytics. In essence, he argues that companies continue to struggle to get quality data and analyses that provide useful insights to inform talent-to-business decision-making. And he goes further to suggest that the lack of such analyses has caused some of the largest and best-known technology companies to dig into the data and come to the conclusion that they were unknowingly overstaffed and in need of layoffs. He proposes a need for a new type of HR technology, the integrated People Analytics platform, which is designed to provide HR data integration from many sources, multi-dimensional analysis, and end-user reporting and analysis capabilities.  

Each of these leading experts offer solution sets, and yet the story remains the same – too many organizations continue to lag best practice in providing business critical insights into their talent and its deployment.  Why does this continue to be an issue after so many years of callouts and solution offerings by such experts?   

Our own experience supports the challenges as well - as HR leaders and consultants to leading companies for over 40 years, We’ve seen many well-known nameplate organizations struggle with creating meaningful and impactful measures and aligning HR analytics with key business strategies and operational initiatives. It has never ceased to amaze us given the investments in technologies and expertise that these organizations had seemingly made.

The reasons for this continued challenge are many 

HR teams often struggle to produce quality analytics due to several key reasons:

1. Lack of Data Quality.

HR data is often dispersed across various systems, such as applicant tracking systems, performance management tools, and payroll systems. Inconsistent data formats, missing information, and data entry errors can result in poor data quality, making it challenging to generate accurate and reliable analytics. Even today, the availability of integrated Human Resources systems is seen as a bit of an illusion when it comes to leveraging datasets that can be used for integrative analyses. The progression from databases to data warehouses to data lakes have created better repositories from which analyses can be conducted, but their use does not solve for the issues of low data quality, accuracy, and usability.

2. Lack of Academic and Professional Preparation.

While data is not readily available, experience suggests that most human resource professionals come from a combination of liberal arts or non-STEM backgrounds.  While those who majored in social sciences, business, and the sciences (physical and life) likely had coursework in mathematics and/or statistics, the typical HR roles they took on earlier in their career most frequently had limited applications and requirements for analytic work.  And the traditional HR functions that do rely on numerical analyses (e.g., compensation, benefits or even HR Information Systems (HRMS, HRIS, and HCM)) tend to involve smaller staff sizes, thus offering fewer opportunities for professionals to leverage such skills.

3. Insufficient Data Analysis Skills.

HR professionals may have limited expertise in data analysis and lack the necessary statistical knowledge to extract meaningful insights from the available data. They may struggle with selecting appropriate analysis techniques, interpreting results, and effectively communicating findings to key stakeholders. Even those with social, physical or life science degrees (particularly at the bachelor’s level) have had limited coursework in statistics as graduation requirements. A typical circumstance requires HR professionals with reporting needs to go to an HRIS or reporting team to request special reports or tailored analyses from existing dashboards. 

4. Limited Technology Infrastructure.

HR teams may face challenges in accessing and integrating data from different sources due to outdated or incompatible technology systems. Most frequently, the HR and business data are held in separate or non-integrated systems. Inadequate data storage, processing capabilities, and analytics tools can hinder their ability to perform complex analyses and generate actionable insights. An associated lack of quality data dictionaries and calculation guides, and the lack of awareness and knowledge of what is available often handicaps many HR professionals and teams. 

5. Lack of Clear Objectives.

HR analytics should be aligned with the organization's strategic goals. However, HR teams may struggle to define clear objectives and identify the key metrics that are most relevant to measuring progress and success. Without a well-defined focus, the analytics produced may lack direction and fail to address critical business needs. As a result, individual HR business partners (HRBPs) and Centers of Excellence (COEs) are left to develop their own reporting that is often developed in a relative vacuum and focused on the efficiency and effectiveness of the processes they own. Additional analyses of overarching talent measures such as those related to employee engagement and descriptive HR statistics (hiring, turnover, tenure, DE&I, etc.) are common as well.

6. Resistance to Change.

Transitioning to a data-driven HR approach requires a cultural shift within the organization. Some HR professionals may be resistant to change or skeptical about the value of analytics, preferring traditional methods. Overcoming this resistance and fostering a data-driven mindset throughout the HR department can be a significant challenge. Complicating matters is the research that has demonstrated that humans are skeptical of data and analyses that conflict with their previously held biases, viewpoints, and opinions.

7. Privacy and Ethical Concerns.

HR data often contain sensitive and personal information about employees. Striking the right balance between extracting valuable insights and respecting privacy rights and ethical considerations can pose challenges for HR teams. Compliance with data protection regulations, such as GDPR or CCPA, adds another layer of complexity to the analytics process.

8. Lack of Resources.

HR teams may face resource constraints in terms of budget, staff, or time. Investing in advanced analytics tools, hiring data analysts, or upskilling existing HR staff may not be feasible for all organizations. Limited resources can hinder the development of robust analytical capabilities. Too often there is an overreliance on IT and other centralized teams that are a further step away from the business compared to the HR teams and do not have the same “skin in the game” as the HR business partners or COE members.  

Actions steps to take

At a high level, some practices are key to the build-out of a robust HR analytics capability. Many of these can be found with detailed step-by-step guidance in Wowledge’s HR Metrics and Reporting progression. However, the primary practices that can be used to overcome many of the common challenges outlined above include:

1. Understanding strategic business needs and translating those into HR metrics.

The alignment of metrics to key organizational strategies and tactics is essential for the creation and development of quality metrics that are relevant to the business's needs. Core to this is the development of an understanding of the business strategies and their component parts, followed by the identification of the relevant HR goals or objectives linked to those. By conducting both steps, process, and talent outcomes that contribute to the successful execution of the business objective can be developed. The basis of such an assessment involves a combination of business knowledge and creative thinking related to the planned or desired people-related inputs to the strategy.

2. Identifying critical audiences and tailoring metrics and reporting to their unique needs. 

Critical audiences typically include leadership and management from different levels, functions, business units, and locations. Conducting structured interviews and focus groups to receive guidance on their key questions about how HR-related programs impact their business segments are invaluable to tailoring metrics and reporting. Such discussions generate the kinds of questions that those leaders are interested in, such as topics related to their team(s) performance, productivity, innovation, and contributions to overall objectives. Note that while the typical HR metrics (time to fill, average tenure, turnover, engagement, etc.) might be lower on the priority lists of managers, those measures that are related to HR efficiency or effectiveness still have an important place in the management of the HR function and capabilities.   

3. Leveraging multiple systems and sources to produce integrated metrics.

As leader requests grow in sophistication, an appetite for more complex data will rise.  This will most typically include data from numerous HR and business processes and systems, starting with the core HR system (e.g., HR Information System or HRIS), and/or the enterprise resource program system (ERP) and expanding to other HR and business systems. That means leveraging those systems’ ability to capture and retain data on HR and finance, manufacturing, retail, supply chain, and operations processes. The use of an integrated database, warehouse, lake, or other repository enables the constant refreshing of inputs to the needed fields used in analysis and reporting.

4. Measuring outcomes vs. processes to increase business insights and impact.

Leading practice calls for a shift from HR process efficiency (learners per course, cost per hire) and effectiveness (percentage of leaders completing leadership training program, time to fill) to the business reasons these processes exist – e.g., to secure, manage, motivate and improve workforce capabilities that drive desired business objectives. Measuring outcomes requires the use of more of the available data to create talent outcome metrics, and typically calls for the use of more advanced statistical techniques to ascertain the impact more objectively. A key piece of this is to measure what matters to the targeted stakeholders – the value and impact of leadership and management practices and HR interventions and investments on business drivers and outcomes.   

5. Create a dedicated analytics team to "own", manage, and evolve HR analytics capabilities.

Assign clear accountabilities and governance for the oversight and management of HR analytics and reporting.  Create a team (cross-functional) that has the necessary accesses, resources and skillsets to produce both standard, scheduled reporting as well as new and tailored, on-demand analyses. Such a team should have permissions to access sensitive HR and business data from across all functions that produce the raw data, including sales, revenue, marketing, supply chain, and people data. 

6. Train HR Staff members to leverage data and analysis capabilities. 

HR business partners (HRBPs), center of excellence (COE), and shared services (HRSS) leaders/teams should be trained and evaluated on their ability to leverage reporting systems, create queries, and identify key performance indicators. As many HR teams are populated with non-STEM individuals, extra development will be required to build a broader HR analytics capability and data culture. The training should focus on developing such staff members’ ability to create both talent and business-applicable insights and predictions. At a minimum, a subset of each team’s employees should have analytic expertise that can be leveraged for the larger group’s benefit.   

7. Communicate, evaluate, and update. 

Developing a quality data and reporting cycle is elemental to the management of an impactful analytics capability. Such a cycle or process should include the customer-driven creation of insights and reports, followed by a regular presentation schedule, and formalized steps for the evaluation of their utility, use, and adoption as decision support drivers. An annual process of reevaluation and updating of reports by major stakeholders should be conducted to ensure the durability and usefulness to line and HR leaders alike.

Create a core focus on business and talent outcomes

The core focus should be on creating a culture of data that supports decision-making. While many common HR metrics are useful and important for running the HR function, it has been pointed out by numerous experts that HR typically measures its own processes vs. those of the business.  When we think of common measures such as revenue, profitability, costs, productivity, etc., these focus on business performance issues and outcomes, which are subsequently used as proxies for departmental, functional, and corporate success. 

Similarly, HR should create a focus on identifying how its programs, policies, and practices impact those. The issue lies in the unfortunate situation where line managers do not see or understand the connection between the successful implementation of HR practices with successful business performance.  It is that lack of obvious and widely accepted connections that haunts and restricts HR efforts to become a driver of business direction and strategy. A quantitative linking of those HR initiatives and requirements to business outcomes can create a level of understanding and appreciation for managing the workforce according to the guiding values and principles that HR programming is designed to support.

An additional focus should be on reporting key HR outcomes that can be linked to desired and accepted employee or talent outcomes. For example, creating and reporting on the quality of hire measures the combined and integrated abilities of the company brand and employee value proposition (EVP), talent acquisition’s sourcing and screening capabilities, line manager’s assessment and selection successes, and the organization’s onboarding and culture management abilities. Likewise, employee engagement scores consider cultural fit, constructive managerial behaviors, job design, employee development, growth opportunities, and the employee experience of living with a wide variety of company policies, practices, and perks. Key to these examples again involves the linking of these measures to desired business outcomes such as productivity, profitability, innovation, growth, etc. that can demonstrate the value of attending to them as drivers of broader corporate success.

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