Angela Ignam Mathon is one of the HR Analytics ThinkTank Board advisors and has been working with our research teams and helping guide their work this year. To find out more about the current research projects please click here. In part one of this article Angela explores some of the reasons that you may not be seeing value from your HR and people analytics. Part 2 will be published next month.
People Analytics is the study of human capital with an aim to assess workforce performance, productivity, and profitability of the people in a business, market or industry. Human capital is an intangible asset with tremendous economic value to an organisation’s ongoing success. In The Wealth of Nations, Adam Smith suggests that incremental gains in human capital lead to profitable businesses and the collective wealth of societies as a whole.
How do employer’s make improvements in human capital? By investing in its people through education, training, well-being, values, and technology. Initially, employers will hire talent for their intelligence, skills, and capabilities. However, it is through proper investment in its workforce that it can leverage this talent for sustainable gain.
To achieve this goal, people analytics needs to look at data across multiple platforms in order to evidence the effectiveness of existing talent strategies within the organisation. The analysis of this data is carried out by data scientists with domain knowledge in human resource information systems, statistics, management consulting, and process rationalisation. People analytics is the science of HR.
Human capital cost can represent anywhere from 50 to 75% of an organisation’s operating expense. With such a sizeable investment, organisations need to ensure an adequate return on investment in order to compete and remain profitable. People analytics provides the meeting ground for HR and the business to discuss effective human capital strategies based on tangible evidence of workforce performance.
At its core, people analytics is about data. Data scientists working in HR rely on data held in HR systems, as well as data held in customer sales, finance, risk, and other operational platforms to assess the impact of people related interventions. This collection of data needs to share commonalities in order to enable effective analysis of this information across functions. Organisations with strong senior sponsorship and well-defined data strategies are much better positioned to derive value from people analytics versus organisations that are still unclear about their overall approach to data.
Figure: How well does the HR analytics sponsor understand HR analytics, and its impact on the HR analytics function overall. Source: HR Analytics ThinkTank's "How are organisations successfully building HR analytics functions" (2017).
As a growing HR specialism, people analytics is often delivered as a service through HR business partners, much like reward, learning, and recruitment. Firms who have recognised the value in building out people analytics functions will have likely invested in technologies and data science capabilities early on in their journey. However, without the proper understanding of data management and analytics, many companies can find themselves a few years into the journey without a meaningful return on their investment. Progress in this space is painfully slow, and the root cause for some can be hard to find.
It takes a lot of coordination to ensure that data remains an asset rather than a liability for your organisation. In 2017, the HR Analytics Think Tank published a study across leading organisations to understand how well HR sponsors understood the value of HR analytics across key dimensions. And not surprisingly, HR functions struggle to understand the value of their people data and how to leverage it effectively for use in analytics.
In my experience, there are a few common roadblocks that can emerge on the path to analytical greatness. Five big ones are; data quality, automation, the complexity of metrics, the commerciality of the insights and the comfort of the HR business partners with data. Each of these specific challenges presents unique symptoms that we will now explore in greater detail in two blog posts. If you find yourself struggling with one or more of these issues, the information in these blogs may help you to pinpoint and resolve your unique organisational challenges.
Symptom: hard to locate data, poor credibility, multiple versions of the truth, high cost
Common roadblock: Data Quality
Poor data quality is a major stumbling block onto the road of insightful people analytics. It makes the production of analysis extremely painful as teams spend an enormous amount of time locating the right data sources and then cleansing them for analysis. These activities can eat up precious capacity in already small teams making it difficult to build credibility amongst stakeholders.
If analytics is about providing clarity, poor data quality engenders uncertainty. Inconsistency around the appropriate data sources for reporting and or agreement on data definitions is often an indicator of operational discord. Competing messages start to circulate throughout the organisation causing managers to become defensive on their various positions. Disagreements on the data promote ambiguity amongst stakeholders making it difficult to locate opportunities for cost-saving synergies, growth initiatives and innovation.
Strong data sets enable teams to spend more time on interrogation rather than production, which leads to greater transparency and faster decision-making decision making. Poor data quality impacts your ability to industrialise workflows into an efficient process that is fit for purpose in a cost-efficient manner. If high costs are a major problem in your analytics function, assessment of the data quality could be a good place to start investigating.
Symptom: constant reconciliation, data is untimely, inability to scale, limited visibility of data
Common roadblock: Automation
A less obvious culprit is the impact of automation. Often, organisations with well-defined manual processes underestimate the benefits that automation can bring to critical pathways. For example, when new changes arise in a business environment, demands for more data can increase. Manually led operations will struggle to keep up with the pace of demand as new requirements start to emerge from the business. Processes originally established to cope with one set of requirements can find it difficult to pivot efficiently to another. Humans struggle with change in ways that machines do not.
In a similar vein, pre-existing reporting that was previously seen as sufficient to meet client needs is now viewed as stale and outdated. Shadow MI teams start to crop up in the hopes of trying to address the increased pace and scope of inquiry. These teams, although well intentioned, only serve to introduce more confusion as time is now spent reconciling between these new reports and the previously existing ones. The additional stress applied to the reporting cycle only serves to overwhelm further an already labour-intensive process leading to manual errors as teams try to reconcile between the different data elements within the reporting.
Automation is not just about implementing new technology. Instead, it is about rationalising business processes and incorporating new technologies into old systems. When done properly, automation enables organisations to experience tremendous gains in their operations. Firstly, in terms of speed as information becomes available in a timelier manner. Secondly, in terms of scale as the information can now cover wider data sets than a manual process. And finally, in terms of visibility as the information becomes available to a wider list of stakeholders.
Figure: The different types of HR Analytics functions or 'typologies. Source: HR Analytics ThinkTank's "How are organisations successfully building HR analytics functions" (2017).
Symptom: low usage of data, confusion amongst stakeholders, poor decision making
Common roadblock: Complexity and usefulness of metrics
As organisations accelerate their people analytics journey, they can find themselves at a critical stage whereby data quality across a list of key metrics is now powered by good automation. The flow of information throughout the organisation follows a natural cadence and clarity among a few key data points are well understood. Business critical processes are automated enough to start revealing valuable insights about people movements month on month.
Analytics teams now have access to a whole range of automated data points with which to start the exploration of new trends, building of basic models, and reporting assumptions. A flurry of new activity emerges, and a test and learn culture soon appears. It is at this point that data scientists start to develop more sophisticated metrics in the hopes of generating further interest in the data.
However, in doing so, data scientists also introduce a certain amount of complexity. Businesses looking to solve for simple operational data points like accurate employee email lists for corporate communications or the latest count of senior female leaders for invitations to executive learning programs are often baffled by the workforce dashboards and how to use them.
Thank you for reading - and please look out for part 2 of this article, which will continue to explore more common roadblocks to unlocking the potential for your HR analytics function. If you haven't already please sign up for the ThinkTank Newsletter.