Companies’ spend on technology to transform their businesses is a sky-high $3.8 trillion. Yet one thing is missing from that investment in digital transformation: a commitment to maintain workforce data and use it to build workforce capabilities.
With good data on the roles people play, how tasks get done and how those tasks map to jobs, you can focus on the redesign of work. Close digital skill gaps. Find more diverse teams. Level pay.
All strategic HR processes depend on this data, yet more often that not, companies don’t get and maintain people data in a way that’s usable to manage people-first transformations. And without a commitment to it, leaders severely limit how quickly they can evolve the skills base to meet the changing demands of their businesses.
It’s time for a more forward-looking approach. And that’s not a message just for HR. It’s a message for every business unit leader, CFO and CIO spending millions on strategic transformations that depend on a nimble, energized workforce.
It’s time for a more forward-looking approach. And that’s not a message just for HR. It’s a message for every business unit leader, CFO and CIO spending millions on strategic transformations that depend on a nimble, energized workforce.
Good foundations are hard to find
Today’s IT systems make it easier than ever to check to see if you have usable people data for business transformation—beyond the day-to-day transactions of HR.
Yet in our recent assessment of 100 companies’ employee-level data, more than half (55%) failed at having a consistent and complete approach to tracking the information that’s needed for strategic change.
Instead of being able to make fast, evidence-based workforce decisions, companies have to spend a significant amount of time structuring, cleaning or filling in holes in data. Analytics is on the periphery, not keeping up with the demands of what leaders need.
For example, one test showed that the number of unique titles for a manager-level role varies so widely that a single organization could have more than 660 different titles. Attempts to match people’s motivations and skills to the right jobs feels impossible with little or no comparability of roles and skills within just this one level of the company’s job structure.
Only a handful (4%) of companies in our analysis have their people and jobs data flowing in a clear and structured way so that analytics can run as they should.
Everyone else (41%) is partway there, but their data gaps and inconsistencies limit them from being able to take advantage of the insights people analytics can provide.
The result? Most people leaders say the state of people analytics capabilities is putting their future workforce capabilities at risk. Progress is not happening fast enough.
What to look for
Start with a structure
Structure
Common data structures allow for people and systems to seamlessly share and visualize data. A typical example is a clear and structured talent architecture. The architecture provides a consistent way to identify job levels, how people qualify for jobs, which people can move where and how work is compensated. With this comes consistent data formats for how this data is captured.
Track a complete view
Completeness
Good foundations start with complete data sets for organizational data and employee data. Complete data allows for calculation of more than 200 defined metrics that can be used to identify most people challenges. Operational data from applicant tracking systems or collaboration software, for example, can add more dimension to analysis or identify new metrics.
Spur the right behavior
Consistency
People and machines need the right behaviors to capture the right data for the specific goals of your people strategy. Gaps and inconsistencies can increase the time and costs for analysis.
What to consider for people-first transformation
When data foundations are in place, they underpin strategic talent processes.
Gain an edge in recruiting
Finding good talent remains a top and urgent priority, but too many companies have poor recruiting practices that drive candidates away.
One missing link for providing a better recruiting experience is systems integration. When companies connect their applicant tracking system (ATS) with their employee databases, they can blend candidate and employee data. This allows them to improve the recruiting process by analyzing:
- Where top employees come from and how to identify more diverse talent pools
- Which hiring managers or recruiters find the best people (or drive them away)
- How indicators from candidate assessments correlate with performance once people are hired
Recruiters can use this information to create optimal hiring profiles, so they can refine their tactics and identify the talent pools that deliver the best results.
Debias people processes, starting with pay
Creating a clean and consistent data environment is the first step to uncovering pay equity problems and ensuring everyone is paid fairly for the work they do.
Engagement studies we’ve done for more than 2,400 US companies show that only two-thirds of employees think they’re paid fairly—leaving a third feeling like they’re working harder for what they’re paid to do.
Almost half don't believe their pay and job performance are linked. Companies that strive for transparency around fair pay should find these numbers alarming.
Even with an equitable pay scale, lack of clarity in jobs and their roles means employees in the same positions are often paid and motivated differently. This limits compensation leaders’ ability to:
- Monitor pay equity
- Model pay into accurate ranges
- Build trust with employees who believe they’re compensated unfairly
We recently analyzed job data for a large organization and found that 15% of employees had erroneous or misleading job mappings. As a result, pay models didn’t catch the outliers. Spotting the problem early allowed compensation managers to adjust salaries before fair pay came into question.
Find, keep and inspire diverse teams
Inconsistent, unstructured and missing data prevent companies from tracking basic diversity metrics around representation, pay, development and time to first promotion.
And even those with mature diversity programs admit diversity continues to be a barrier to employee progression inside their ranks. As Gen Z floods the workplace the risk of standing still in this area may continue to bite, particularly when it comes to the talent pipeline—most Gen Zers hold strong beliefs that an increase in racial and ethnic diversity is good for society.
Tracking the diverse makeup of your workforce helps you show how diversity is a solution for many business goals. It also shows how your company cares for its reputation as a desirable employer.
More inclusive and diverse teams can play an important role in driving:
- Better outcomes for customers
- Better business returns
- More innovative teams that create more value
- The attractiveness of your company to potential and existing employees
Create a nimble and skilled workforce
One of the best ways to create a more effective and skilled workforce is to give people a range of work experiences to build their skills.
When we look at two key measures of this—rates of transfers and promotions—we see that one in every three companies cannot produce basic information on who is transferring or who’s been promoted. Nor can they tell whether these promotions make sense relative to a promotee’s performance and skills.
Managers lack data to shape career paths because company data can’t tell them which development opportunities are most advantageous. To compound this issue, job titles and job levels often aren’t comparable, making paths for development and career progression difficult to identify.
With better data foundations, organizations can:
- Identify skill advantages (or gaps)
- Create learning opportunities to close skill gaps
- Redeploy people quickly to new assignments and give them more experiences
Clean data and effective data management can solve these issues. Together, they enable better matching of current staff with opportunities for new work and new roles. As employees are given opportunities, managers learn more about their internal talent pool and how to draw from it.
Over time, as companies automate their workforce environment and deploy AI more broadly, clean data will be vital in predicting which jobs are likely to change and how companies get their employees ready for using AI at scale.
What you can do
Spell out why you need good people data for enterprise-level goals. Whatever business transformation project you plan to run for the next two years, make the structure and maintenance of your people data part of that plan. Learn why investing in these foundations often gets cut during misguided attempts to rein-in HR or IT budgets and then make the case for good people data, together with the CEO.
Start a targeted path for fixing your company’s data foundations. Focus on specific business units, roles or activities that can deliver a big and meaningful impact for the business. For example: Will linking your applicant tracking system to your HRIS help you hire more efficiently? Will tracking diversity data help you (finally) meet your diversity in management goals? Will a consistent job architecture streamline mobility and enable more effective training so you can recruit from within? How fast you go is up to you.
About the analysis
PwC’s Saratoga benchmarking program examined employee-level data from 100 multinational companies. Each company was scored along the following three dimensions:
Companies performed well in this category if their data was stored and available in a clear and structured manner. This often meant that the data had few issues requiring manual manipulation of different portions of the data to bring them into alignment with each other. For example, a clear sense of a talent architecture, such as level or pay information that was clear or not mixed. Mixed data for pay could mean that some data is annualized, while other data is collected biweekly or monthly. We checked for other common structures, such as recruitment data that could only be linked back to HRIS using names instead of an employee ID.
Companies scored high here if they were able to provide data that covered the majority of data used in human capital benchmarking. The result is broad metrics coverage across many topics and dimensions. Completeness of human capital data provides the basis for more advanced analytics.
Companies scored high in this area if their data did not have gaps in data coverage on an employee level (i.e., all employees had valid values for all fields).
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