After decades of being overlooked or relegated to the deepest bowels of HR, people analytics has finally become front and center in many organizations over the last few years. More than anything, the drastic shocks to the workforce caused by the COVID-19 pandemic revealed just how important it is to know and understand what makes a workforce resilient, productive and healthy. The few organizations that have not already made some strides in people analytics have plans to do so soon.
As a consequence, many organizations are committing substantial resources and time to boost their people analytics efforts, investing in new software, consultants and data experts. For a medium-sized organization, the costs of cutting edge data analytics can strain resources, while for small organizations the costs quickly become prohibitively high. For the organizations that do follow through despite the high costs, many find that the return on their investment is far lower than expected. It is not unheard of that some people analytics efforts deliver no value at all.
In fact, most organizations are not getting the value they need from People Analytics. Why?
In our experience, there are a few common reasons why organizations struggle with people analytics, and there are some easy steps that can drastically increase the value generated. By far, the biggest source of failure is starting your data analytics journey in the wrong place. By focusing on foundational data work before investing in advanced techniques, organizations can generate immediate value, at low cost, and set themselves up for success in the long term.
Starting in the Wrong Place
HR thought leaders, particularly in the people analytics space, frequently champion the power of machine learning and AI in transforming HR, advocate for large HR teams dedicated to people analytics, and celebrate HR executives finally having a permanent seat at the C-Suite leadership table so that people analytics informs every decision about the organization. It is not hard to understand why organizations are tempted to start here. It is big, bold and “all in” on people analytics. And, it's expensive.
While the power of machine learning and highly specialized people analytics teams sound enticing, they are never the right place to start. There are many ways that an organization can overinvest or invest in the wrong place. For the sake of illustration, let’s break down the implications of heavily investing in machine learning before your organization is ready, and of hiring a large, highly specialized data analytics team too soon.
Machine Learning
Only organizations that are already very mature in people analytics should make substantial forays into machine learning. The reasons are many, but mainly because machine learning algorithms and systems are only as good as the data upon which they are based. Organizations that do not already have mature data systems and expertise will not support meaningful machine learning (no matter how much a vendor tries to convince you it will work fine if you just pay them a lot of money to set it up). Until the underlying data is of high quality and readily available, any machine learning output should be treated with extreme caution.
The other main reason is that it is a very expensive method for insight, compared to the value of starting with much lower-hanging fruit. If the organization is starting from scratch, it is far more important and more valuable to better understand the organization’s existing data, improve the quality of that data, and to begin creating new data sources.
For example, if the problem at hand is difficulty recruiting talent, understanding the basic parameters of what recruiting currently looks like should come before everything else. How long are we currently taking to recruit someone? How much is the per-recruitment cost? Is it hard to get candidates to apply, or are they applying and just rarely accepting an offer from us? Some simple descriptive analytics, achievable in a few days, for a fraction of the cost, are likely to yield immediate insights that leaders can act on. Best case scenario, machine learning would come to the same conclusion, but more likely than not, it won’t quite get there because what a human can glean with limited descriptive statistics and good subject matter expertise, would require tens of thousands of carefully cleaned data points for machine learning to achieve.
Going from zero straight to machine learning in people analytics is like a farmer buying a Formula One racecar to deliver tomatoes to the market. Sure, it can do the job, but at 50 times the cost, and with a much higher risk of catastrophic failure. If the farmer is not already an experienced racecar driver, and the delivery doesn't take place on a carefully constructed course with no unexpected challenges, the effort is more likely to end as a tomato-covered car crash than a market delivery.
People analytics teams
At face value, putting together a dream team of data experts to kickstart the rapid development of people analytics at an organization seems like an obvious advantage, and it is indeed a frequent way for organizations wanting to make quick progress to invest. However, most organizations inevitably experience the following problems:
First, data experts are extremely expensive to hire, and a team of 10-12 experts can cost more in salary than 50 other HR employees. The team leads will likely require salaries higher than the typical salary for Chief Human Resources Officer. On top of salaries comes the need to invest in equipment and platforms required to do advanced data science, which also require additional IT personnel to acquire and maintain.
Secondly, most of the team will be overqualified for all of the foundational work that has to be done to enable advanced people analytics, and once the foundations are laid it is more likely than not that the expensive, advanced hires are not actually the best fit for the particular area where the organization most needs to focus their people analytics attention. The risk is that the most skilled team members will not thrive and will leave the organization before they have had a chance to add any real value, or worse, they stay and consistently drag the people analytics development in the wrong direction because their competencies and tools are not aligned with the organization’s needs.
The Solution
In both of the above scenarios, the most effective solution is also the simplest. It’s a classic case of not putting the cart before the horse. Without getting the foundations of people analytics properly established, the only guaranteed power of advanced people analytics is the power to spend resources. First, make sure your data foundations are solid, and then over time you can build the data team, the data methods and develop the data maturity of the organization.
This does not mean immediate and high value cannot be delivered quickly - quite the contrary. If the organization does not already have a strong grasp of the available data, and basic insights into the composition of the workforce, the actionable insight from gaining this understanding will be more valuable than any other people analytics will ever be.
It is low cost, and with the right support, can be quick. It allows the HR function to demonstrate to the organization how valuable people analytics are and get buy-in from key stakeholders, laying the groundwork for more funding and a more rapid expansion of people analytics moving forward. It also increases the effectiveness of the data experts, and the data team as a whole, because the capabilities develop alongside the needs of the organization.
Start by asking yourself these questions instead:
1. Inventory what is already available:
What data do we already have?
Do we have the talent available to draw actionable insights from the existing data and understand its limitations? Does our talent also understand how to identify the data’s blind spots and how to develop new sources of data? Does our talent have the subject matter expertise in HR required to understand what data and insights deliver value in addition to the technical expertise?
If we do not have the talent, does it make most sense to hire someone (watch for upcoming free resources on hiring the right people analytics talent), or to work with an external expert like Opterion?
2. Understand the “blind spots” in existing data:
What major aspects of talent analytics do we not have any data available on?
3. Develop new sources of data:
Create meaningful and non-disruptive surveys or build other ways to collect data from the blind spots, and improve the data in existing data sources.
4. Create a comprehensive picture of the workforce and the organization:
Does the data reveal any problems we hadn’t even considered could exist? Does the data inform how we might address the problem?
Does the data reveal something that is contrary to what we thought was true for our workforce? Does it suggest some of what we are already doing is probably ineffective, or maybe even counterproductive?
In what areas does the data give us more confidence in what we are already doing? Does it suggest we should invest more heavily in those efforts? Or have we already maxed out the value we are likely to gain from that effort?
5. Determine where to dig deeper:
In areas that reveal something unexpected or unknown, can we design a data collection strategy that can help us understand why we missed it, how consequential it is, and what potential solutions are?
If we already have solutions in mind for new or well-known problems in the workforce, can we use the data to understand how successful these solutions are? Can the data help us understand when and how to tweak the solution to maximize its effectiveness or lower the cost?
How can we use the data to reveal problems before they develop, or before they become serious?
6. Recognize when capabilities are coming up short:
Does answering the questions above require more expertise than we currently have onboard?
If yes, does it make sense to hire someone or to work with an external expert?
Will bringing on more talent expedite or improve the quality of insights?
If yes, expand the team with the capabilities that would best augment the existing skills and allow the team to pursue insights in the highest priority area of analytics.
7. Repeat steps 5 and 6 continuously as your People Analytics capabilities advance.
With the right expertise, asking these questions and acting on them will provide an enormous amount of value for your organization. This is especially true when an organization currently has limited HR data maturity. Toward the end of the to-do list, it also becomes clear how the iterative nature of this setup allows the team and the methods to develop as the needs increase and the questions become increasingly complicated.
Download the steps in a convenient PDF:
Need More Help?
Partnering with an expert in HR data analytics can be crucial for organizations looking to get the most value out of their data. Opterion is uniquely positioned to provide the expertise and guidance needed to extract valuable insights and make informed decisions that drive real business outcomes.
Our team of experts has extensive experience working with organizations of all sizes and across a wide range of industries. We understand the specific challenges and nuances that come with each organization, and we tailor our solutions to meet their unique needs. Our team excels because we combine cutting edge technical data expertise with deep HR expertise, which means we can help you at any stage in your process, whether that is to create and implement technical systems, to problem solve or to drive strategic decisions that align with your organization's goals.
By partnering with Opterion, you can expect to:
Identify performance indicators and metrics that are tied to your organization's goals.
Gain valuable insights and a comprehensive understanding of your workforce, including blind spots and areas for improvement.
Develop and implement data-driven strategies that are tailored to your organization's unique needs and challenges.
Make informed decisions based on the insights gained from your HR data analytics, leading to improved performance, productivity, and employee satisfaction.
Leverage our expertise to build the case for the rest of your organization to buy into people analytics and the solutions and improvements revealed by those analytics.
We understand that every organization's needs are unique. That's why we take a collaborative, consultative approach to working with our clients, working closely with them to identify their specific pain points and develop tailored solutions that deliver tangible results. Contact us today to learn more about how we can help you unlock the full potential of your organization.
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