Real examples of HR analytics in action - and data scientists in HR
This is an interview with Ian Cook, Director of Product Management at workforce analytics company Visier. In this interview Ian discusses the most common and valuable use cases for data analytics in the modern HR department, to what extent the department will be staffed by data scientists in the future, and how HR directors can better build the business case for analytics software.
1) Can you give us some concrete examples of how companies can make better decisions by manipulating and using data and insight? What metrics are they using?
Here are some of the most common examples from our customers:
It is common for retention issues to have a stop/ start response where money is thrown at employees in the hope that it will change the outcome. More often than not, this approach does not work or has only a very limited and short-term effect. The money spent here does not deliver the outcomes expected.
Our customers report improved retention outcomes, as well as a better ability to focus resources and programs where they need them. They achieve this through the use of common metrics such as turnover, resignation, involuntary turnover, etc. However, the differentiator is the ability to compare trends over time, across business units or between key groups of employees to the overall organizational outcomes. It is not the standalone metrics that brings the insight, but the ability to quickly build comparisons, identify trends and find outliers that makes the difference.
In addition, these companies are using our clustering algorithms to determine the common features of employees that are related to higher or lower retention rates. This insight means the right approach can be taken with the right employees, leading to better results at a lower overall cost.
Pay for performance
There has been a steady and constant shift towards ensuring that what employees are paid is closely tied to their contribution to the organization. Some organizations are removing the artificial limits that kept high performers from earning more than their managers. For a number of the companies we work with the alignment of pay for performance is the number one shared agenda item between the CEO and the head of HR.
One of the best ways to demonstrate this practice is through a metric called the “Performance based compensation differential”. This metric expresses how much more high performers are paid compared to their average performing peers. For example, a score of 1.2 means that on average high performers receive 20% more compensation than average performers. Turning this critical question into a single number allows for powerful insight across the organization; it means that different locations, business units and groups of employees can be easily compared using simple visual analyses.
One powerful way this translates to business value is during the annual pay review cycle. Most HRIS systems allow you to enter changes in pay, however these systems do not enable you to analyse how these awards relate to performance and whether or not they are aligned to the goals of the organization. Most HR departments provide guidance and then trust their managers to get it right.
Being able to analyse all of these decisions in real time, report this back to the organizational leadership and then revise these adjustments before they are confirmed leads to a demonstrated ability to ensure that the budget increases going into labour costs are being applied in the optimal way.
It is one thing to know what has happened in HR - the majority of HR data to date has focused on the reporting of transactional outcomes - but another thing to know what will happen. For example, lots of HR groups report the percentage of people that had a performance review or who completed an engagement survey. This type of reporting relates to the process orientation of HR and, although interesting, does little to demonstrate the true value of the functions.
In addition, the opportunities to add value through HR practices come more from stopping the wrong outcome from happening, than from reporting on what has happened. For example, the cost of voluntary turnover has been established at approximately 1.5 times annual base pay for salaried employees (Source: PWC Saratoga and CEB). Therefore, if you prevent two high value employees, with salaries of 50,000GBP, from leaving the organization you have saved approximately 150,000GBP. In order to achieve this type of saving you need to know who will leave, before they have left.
This is where sophisticated algorithms, that use historical data to determine the likelihood that someone will resign, come into play. There are a number of known actions that will prevent someone from leaving like signing bonuses, formal agreements around career progression and learning opportunities. However, the crucial part is knowing to whom you should offer these incentives. When it is possible to focus on the right population, through powerful and validated statistical models, it leads to better outcomes for lower cost.
Another place where prediction is becoming valuable and important for the companies we work with is in relation to retirement. The pattern of behavior relating to retirement is changing with more and more people delaying retirement or shifting to contract or part-time work than ever before. Prediction here is important as often the people retiring are in key roles or hold key relationships and are critical for the business to ensure continuity of performance. However, it is also challenging to keep a potential successor waiting if the incumbent chooses not to retire at the time expected.
Instead of using the old indicators of age and tenure to estimate retirement behavior modern analytics technology applies algorithms that take into account many additional factors such as recent changes in role, pay level, rates of change in pay and incentive eligibility to refine the prediction of who will retire. This allows companies using this type of analytic approach to be more successful and effective in managing the retirement cycle and ensuring that key roles have a successor ready at the right time.
The above is just a small sample of the value that the companies we work with are realising from their use of workforce analytics and planning. For more examples on specific customer cases and to see the latest information on how workforce analytics is transforming HR practice please visit the Visier website.
2) What will the make-up of the HR department look like in 5 years? It won't be staffed entirely by data scientists, of course, but how will it be different to now?
We are seeing this change already. The number of Directors of Workforce Analytics and Planning has increased dramatically in the last two years. These people are building out specialist groups that cover three primary areas: data management, analysis and interpretation of data, driving the impact of the group on the overall business. These new groups are at the early stages of proving the value that can come from workforce analytics and planning.
The next change that will come is similar to the transformation in marketing. A decade ago the leadership in marketing needed to have a good idea and strong influencing skills. Now leadership in marketing is all about the tracking and analysis of which ideas worked, which ones did not, and identifying where data and results highlight that there are opportunities to meet targets.
In the same way HR leadership will need to be able to build a cohesive talent strategy that is founded on a robust and detailed analysis of the organisation’s people data. It will no longer be good enough to trust intuition, deliver what we delivered last year because no one complained, or to jump on the latest ‘buzz’ program. CEOs are already demanding that CHROs show up with an informed point of view on how to drive results for the business. Every leadership position within the HR group will need to be able to support this way of working throughout the HR organisation.
In the future, the HR organisation may or may not contain a data scientist; what will be true, however, is that those who lead the group will have to be well-versed in the interpretation and use of data and have the breadth of business understanding to link this to business outcomes. HR is moving away from being defined by its transactional volume and towards being valued for its strategic impact. This transformation cannot be achieved without a robust analytic orientation across the whole function.
3) If you're an HR director in the UK, how do you go about building the business case for investing in data analytics software?
The best way to build a business case these days it to prove value through small focused projects. The approach we recommend is to find one or two staff with the right skills and motivation and have them deliver an answer to a key business question such as: “Are we keeping the right people?.”
The project should be limited to a small part of the business and the timeline should be short so that you can demonstrate results quickly.
Once you have proven that there is value in this work it is much simpler to demonstrate that investments are required to deliver this value at scale. We constantly come across great projects that have been delivered on a spreadsheet. However, it is clear that the results cannot be repeated across the business, nor the range of questions answered be scaled if there is a reliance on people and spreadsheets. As soon as it is clear that there is value in scaling workforce analytics and planning across the enterprise then it becomes clear that an appropriate investment is required to put this into place.
The advantage of the pilot approach is that you can point to real value and justify how much more value can be created through building an appropriately resourced function, rather than asking for the function without any substantive claims on what it can deliver to the organisation.
We meet some organisations that are looking to change their transactional systems in anticipation that they will get the analytics piece once the transactional system is in place. This is not the right approach. Changing a transactional system takes 12-24 months and rarely does the historical data move from the old system to the new one.
Predictive insight requires at least 24 months of data. The total time frame following this approach means it is four years before you have any form of valuable insights. In four years your competition will be a long way ahead in the workforce analytics race. Even waiting two years to get the first round of reporting is missing the strategic value that workforce analytics can deliver to the business today.
The business case for analytics should be considered as separate from the transactional work and systems that support it. Making analytics a side effect of replacing a transactional system is risky at best and misses the nature of the transformation in thinking that is being requested from HR by the business.
4) What type of metrics that come from bringing insight into HR would the CEO be most interested in?
Many CEOs spend a large amount of their time on talent questions and ensuring that the right people are in place to drive the business and that there is sufficient depth in the overall organisation to continue delivering on the business plan.
It is common for CEOs and Boards to review compensation metrics to understand how pay is being used to reward performance and drive future performance, while not damaging overall profitability. It is also common for these groups to look at senior level succession or overall leadership bench strength down to the managerial level.
This senior group is not that likely to be interested in turnover in general but will be much more interested in resignations relating to people in critical roles or high performers and succession candidates.
CEOs and Boards will pay specific attention anywhere that headcount and people dynamics can be linked to cost or revenue outcomes. For example, in professional services, technology or natural resources organisation not having enough people has a direct impact on the revenue that can be generated. Hence the C-suite are very interested in overall recruiting effectiveness and the number of positions that are not filled relative to the overall plan.
One of the key ways to engage the C-suite in workforce analytics is to talk with them about the business strategy and identify with them how people make a difference in that strategy. From there it is an easier step to choose metrics and analyses that monitor progress and risks, than it is to try and persuade them that a certain set of numbers is interesting for its own sake.
5) How can HR ensure they link data back to organisational objectives instead of keeping the technology and capability siloed in HR?
The whole value of the HR function is to enable success in the organisation. In the same way that IT is there to enable productivity through the right tools and solutions, HR’s role in the business is to ensure the best people are in place to drive success.
Good IT looks at the needs of the business and shapes their services to deliver – at the lowest possible cost. Good HR services need to do the same thing as IT, and this means integrating the services with the business processes and people.
The simplest and most effective way to integrate workforce analytics with organisational objectives is to engage the business leadership in the design and assessment of the work being done. For example, an analysis of the effectiveness of the recruiting function is incomplete without input from hiring managers and the operational leaders of different business units. Alternatively, programs to identify high potential talent or prevent high performers from leaving have to engage the line of business. Without this the programs will fail and the analysis related to the programs forms a core part of this engagement process.
6) There aren't perfect decisions but there are decisions made on better rationale. What's the easiest way HR can make better decisions, before investing in data analytics technology?
Decision making is as much art as science. It has also been studied extensively and what we know is that decisions that rely on human interaction alone are most often flawed. The more complex the scenario, the more likely the decision is to be wrong, if it is based solely on the judgement of individuals. The best way to improve decision making is to look for and include data in the decision process. The inclusion of data can be done at a very basic level using simple reports or numbers. This change in approach and mindset does not require a large investment in technology.
Organisations that have taken this step quickly understand the benefit that data brings to their decisions and the value that these better, more precise decisions bring to the organisation. It is a very common progression along the workforce analytics path to then look at how more people can be enabled to support their decisions with data and how more decision areas can be covered by data. This is the point where the right technology solution will accelerate the change and provide the necessary catalyst to fully transform the way HR delivers value to the organisation.