Dirk Petersen of Insight222 shares his sage advice on data ethics and how people analytics teams can successfully build up this capability.
In late July 2017, a Who’s Who of Silicon Valley people analytics leaders came together at Intel. No one foresaw that Cambridge Analytica would soon raise the profile of data ethics to a new level or urgency. In fact, the leaders that day confirmed what similar groups had found at meetings earlier that month in London (hosted by Cisco), Amsterdam (ABN AMRO), and New York (Pfizer): people data ethics is important, but it’s not urgent.
At that Intel meeting, the people analytics leaders from 55 companies came to the realization that people data ethics is critical because of its foundational importance for people analytics success. No organization can build a sustainable, successful people analytics function without clear understanding of what it should and shouldn’t do. Data ethics, participants realized, is the foundation on which all analytics work builds.
An Insight222 survey with 57 companies* shows the extent of the challenge: 81% of companies report that their workforce analytics projects were sometimes or often jeopardized by data ethics/privacy concerns.
Even the most sophisticated companies face challenges in this area: project delays because of uncertainty whether the data gathered complies with GDPR; projects cancelled because works councils are not on board; analyses weakened because of lack of alignment among company leadership on whether ‘it’s ok’ to run certain algorithms. Those who’ve worked for some time in this field will recognize–and have likely faced–all these issues.
No Data Ethics, No Sustainable Analytics Value
This data ethics uncertainty partly explains why so many companies struggle to show tangible value from their data analytics work. In our conversations with well over 100 of the Fortune 500 companies, a consistent feature was the difficulty people analytics leaders face in showing real value.
The Insight222 survey showed that 9% of companies strongly disagreed or disagreed when asked whether people analytics projects had produced tangible value over the past 12 months. In all, 30% of respondents couldn’t give an affirmative answer that their people analytics work had produced tangible value. It is one of the key reasons why we see companies restructuring their analytics functions or taking a step back from their initial commitments.
What is the solution? We believe that every company needs to have clear guidance and alignment around the employee data it should collect, especially how it should collect, analyze, store, use, and distribute it. Typically, such alignment takes the shape of a data ethics charter. However, the Insight222 survey uncovered that almost half of companies do not have a data ethics charter in place.
Over the past six months, Insight222 worked with 15 leading Global 500 companies (the “i222 group”) in a co-creation effort that unveiled a set of principles and led to a universal data ethics charter. The participating companies invested countless man hours, traveled to meetings in the US and Europe, and shared their expertise and know-how because they understand that to build a sustainable and successful function, both management and employees need to trust the data and analytics methods and process.
The People Analytics Data Ethics Charter
The data ethics charter developed by the i222 group and the tools to help users go from insight to action are described below. They are built on a set of six recommendations:
1. Define what’s important to you
The tenets of your approach to collecting, analyzing, and sharing people data need to be clearly defined and the people analytics team is the right team to take the lead. Other stakeholders in the organization, such as data privacy officers and the legal team, have a risk-focus when it comes to data. The people analytics team also understands the benefits that both management and the employees can derive from the proposed data analytics project and weigh risk and benefit.
2. Align key stakeholders
One of the more surprising findings of the i222’s group’s work is the sheer number of stakeholder groups: 27! Each of the 27 stakeholders has legitimate interests in the analytic work. One of the values of the co-creation is the creation of an intelligent grouping methodology to make alignment practically feasible. Once you know your key stakeholders, engage with each to assure your principles address their key concerns. If not, iterate on your principles or iterate on your messaging.
3. Demonstrate/communicate the specific individual benefit
The power of the charter is that it enables organizations to drive analytic projects with greater speed. However, this only works if the charter is well-known and accepted. A proactive and sustained communication approach is key to socializing your charter internally. The i222 group suggests including case examples where the charter was used to protect the stakeholders and create value for the employee. In fact, the i222 group’s work revealed that a key question has to be: what is in it for the employee? If no specific benefit that can be derived for employees, be careful.
4. Create a process to get to your goal
The charter creation is not a one-time conversation, a single day-long meeting, or one off-site. A truly effective charter creation is also a change management process (i.e. a multi-round conversation). This is another reason why the co-created charter is so valuable: it can speed up the initial stages, includes the credible insight from 15 highly respected, world-class organizations, and shows what best practice looks like.
5. Develop an implementation plan
Your implementation plan is adjacent to the multi-round creation process. This plan must contain clear actions steps for each phase of your people analytics project. The i222 group suggests a three-step decision model with go/no-go stage gates at each phase. These gates assure that your project stays within the boundaries of your ethical guidelines.
6. Translate your charter into action questions
A charter and decision model becomes actionable when it can be tested. Create specific questions for each stage of the analytics project to test whether the project conforms to your agreed ethical norms. Insight222 and its co-creation partners developed a set of specific, practical questions** that enable quick implementation.
Call to Arms
You will by now have noticed that taking these six steps doesn’t tackle one key problem: urgency. How do you get management to take people data ethics seriously before your organization has, for lack of a better term, “a Cambridge Analytica moment”?
Large organizations have a natural aversion to take on complex questions. We recommend two actions that have proven effective to create a window of opportunity: first, use the GDPR implementation. With GDPR now in place for six months, this is a great opportunity to review whether implementation is actually effective. Volunteer to take this review on and expand its remit to include data ethics. Second, if GDPR is not an option, develop a story based on your organization’s data ethic “near misses” or the (near) misses of organizations that are well regarded by senior management. Your research should point to a potential pattern and vulnerability (i.e. something that requires a systemic solution).
People analytics will only grow in importance over the coming years. Tackling data ethics now will assure that your house of data ethics is built on a rock solid foundation and has lasting positive impact.
All images in this article are used with permission from the author.
According to Aon’s 2017 U.S. salary increase survey, more than two-thirds of employers were taking some kind of action to increase merit pay differentiation, with 40% reducing or eliminating increases for lesser performers. This suggests that spreading pay raises across the board, like peanut butter on toast, has become an outdated practice.
In many cases (beyond cost of living adjustments) this is a step in the right direction. Rewarding mediocre performance can lead to mediocre results, which can also impact employee morale. As the former Chief Talent Officer for Netflix wrote in this HBR piece, when it comes to employee engagement, excellent colleagues “trump everything else.”
When a top performer leaves the company, the indirect costs in the form of lost client relationships or stifled product innovation can be staggering. Consulting firm Bain & Company has estimated that, across all job types, “the best performers are roughly four times as productive as average performers.” According to HR thought leader Dr. John Sullivan, at a high-performing technology organization, “a single top-performing programmer would produce an astounding $48 million per year in added value each and every year.”
It’s not surprising, then, that many organizations are adopting pay-for-performance cultures. But some experts have argued that pay differentiation should be the exception and not the rule: it can be difficult to determine the impacts of differentiation with sufficient precision, reliability, and accuracy. Furthermore, if retention is the goal, there may be other factors beyond compensation driving up resignations of top performers in critical roles.
The good news is that–with workforce analytics technology that yields granular insights from multiple systems–compensation teams now have the capacity to determine the optimal amount for increases based on hard data.
Follow these steps to model different merit increase configurations and make the right decision for your company:
Step 1: Determine How Role-Specific Performance Impacts Business Results
It is important to define how performance in different job types contributes to business performance. This will help determine what exactly constitutes a reasonable differential.
For example, the Bain & Company research suggests that, for roles involving repetitive, transactional tasks, top performers are typically two or three times as productive as lesser performers. The differential is likely to be a factor of six or more in highly specialized or creative work.
Another fundamental task is to determine what, exactly, is a critical role. To do this, ask these questions:
What is the cost of mistakes in this role?
How difficult is it to replace someone in this role?
How closely is this role tied to the success of our business strategy?
This does not mean you should rush out and, say, give an increase that is six times greater to all those people in a creative, critical role. It may not be sustainable for your business from a budget or employee morale perspective, and may not even be necessary. This is where segmentation and scenario modeling will be helpful.
Step 2: Objectively Assess Performance
Once you have defined who is in the business-critical roles, it is time to find those individuals who are really moving the needle in terms of performance. To objectively determine who is a top performer, look beyond performance reviews. Humans use heuristics, quick and dirty rules of thumb, to make snap judgments about other people. A salesperson’s recent fumble, for example, can overshadow a history of consistently strong performance.
To make a valid assessment of performance, gather data from multiple sources. Performance ratings need to be combined with other data like potential rating, tenure, and recognition awards, to get a full, unbiased view of how the person is performing. Job-specific data, such as units produced or number of sales deals made, can also be considered.
Step 3. Determine Whether Merit Increases Will Work
Now you need to answer a big question that many compensation teams forget to ask: whether merit increases will actually drive retention.
While compensation is consistently a retention factor, there are many other reasons why people quit, from job security and poor onboarding to workplace stress and location. One of our customers, Micron, found that a tweak in job descriptions helped address an issue with resignations due to job fit. So it pays to investigate the problem first before designing a solution.
Predicting flight risk can involve some pretty sophisticated technology and techniques, but simply put, it’s about building a profile of people who left in the past, which can then be used to identify similar characteristics in existing employees.
Advanced “in-memory” applications make it easier to run tailored algorithms to help identify flight risk. This has been proven by data scientists to be up to 17 times more accurate than other methods.
Step 4. Model Out the Increases
Once you have confirmed that salary increases will help retain a certain group of high performers, model out different increase scenarios to determine how they will impact your organization. The goal is to retain the highest number of people at the lowest cost. Start with three scenarios:
Scenario 1: In this baseline scenario, everyone gets the same amount — a 3% increase.
Scenario 2: Here, the differential is greater — an increase in 5% for top performers and 2% for everyone else.
Scenario 3: In this scenario, only critical roles within the top talent segment would be given a 5% increase while everyone else would receive 2%. Alternatively, a 5% increase could be assigned to all critical roles, regardless of performance.
As you review each scenario, consider other factors such as time-to-fill. A bigger differential may be required if the roles are critical and the talent required to fill them is scarce. If the battle for these people in the market is less fierce, you can likely get away with a smaller differential. You can go back and keep refining the scenarios until you have developed the best plan.
Merit Pay Differentiation: Finally a Reality
Merit pay differentiation used to be a good idea in theory. Now, with advances in analytics technology, it is also a good idea in practice. With a holistic approach to your people and business data, gathered instantly from multiple systems, you can gain the insights you need to reward performance and keep star players.
Leadership development can produce some surprising behavior.
I encountered this phenomenon several years ago when I was VP of Learning for a growing SaaS company. We had built a leadership development program for our Millennial population with the goal of reducing turnover costs. The idea was that we could increase company loyalty by increasing promotion rates.
After the first three cohorts went through the program, we dug into the data, expecting to see just that: promotions driving retention. But this wasn’t actually happening. Program participants were staying with the company longer, but they weren’t necessarily moving into more senior roles more often. Something else was preventing them from becoming a flight risk.
So we dug further, and found that a certain component of the leadership development program content (which was focused on techniques for influencing others) was triggering a specific behavior. Participants were moving more within the company into roles where they could have more of an impact within the organization, but not necessarily up. This was driving retention.
With this new insight, we changed the learning content to strengthen the “influencing others” component. This way, we could avoid spending money on learning content that was not bringing returns for the business.
This not only satisfied the retention need for our executives but also allowed us to market to, and support the development of, a broader audience. We also began to see some positive impacts on cross-functional collaboration in the employee engagement survey results.
Retention: One Way Leadership Development Delivers Business Impact
What this story illustrates is that, when there is a specific end in mind, L&D can refine the program to optimize spend and zone in on areas the business really cares about, while also producing some important side benefits.
Of course, not all leadership programs serve the same purpose: some are geared more towards meeting succession planning goals, while others are designed to ensure employees have the skills needed to deliver on a specific business vision.
But for many learning leaders who are looking to deliver a bigger business impact, driving retention should be a key priority. Learning investment is rising year over year, and naturally, L&D is experiencing increased scrutiny from the business. Executives are increasingly expecting learning leaders to deliver on areas that are keeping them up at night, and retention is at the top of the list.
Simply put, if you have a turnover problem, you have a profitability problem: When the direct costs of replacing an employee, interim reduction in labor costs, and costs of lost productivity are all taken into account, the total cost of voluntary turnover is $109,676 per exiting employee for an average US organization, according to Bersin by Deloitte research.
At the end of the day, making the link between leadership development and retention is one of several ways that learning leaders can demonstrate business value both in the measurement and design of learning programs.
Follow these steps to build a program that drives retention:
Step 1. Understand your business goals
Start by connecting with other areas of the business to understand how the organization makes and spends money. This will give you the context you need to establish precisely how turnover is impacting the bottom line.
Also consider what the broader business goals will be one, three, and even five years into the future. If there is an M&A deal on the horizon, for example, you will need to consider how the deal will impact retention, and whether learning programs can help.
Step 2. Apply dollars where they will have the most impact
Very high turnover for talent in critical roles poses the greatest business risk. Before designing any kind of retention program–whether it has a strong leadership development component or not–it is important to understand levels of turnover across tenure, roles, teams, and geographies.
With a granular view of where turnover is happening, you can then dig in to the why of the problem to gain an understanding of whether a learning program would be the right solution. A clustering analysis in the data will reveal what is common among people who are leaving, whether it’s long commute times or specific managers. If limited growth opportunities are indeed impacting retention, then the people within this population may be good candidates for leadership development investments.
This kind of analysis can’t easily be executed by querying disparate HR systems. A technology solution that identifies and ranks, in real time, all factors contributing to resignations, can help you design learning programs to reduce rates and retain high-performing or critical employees.
Step 3. Monitor trends to show causation
It can be difficult to determine whether your leadership development program is the main thing that is impacting retention. But when you reveal a trend, and not just point-in-time metrics, it is possible to show causation.
For example, a graph with a blue line that shows percentage of turnover in the overall population, and a red line that shows the turnover among the program participants, will help you can gain a clear picture of whether the program is working or not. You can then dig deeper into the data and refine the learning content to focus on what really matters for the business.
For many, getting this level of insight at the right time is just not feasible: it requires a mind-numbing amount of time to cobble the employee, learning, engagement, performance, financial, and business data together. With a technology solution that allows for dynamic interaction with the data, you can create charts on-the-fly that reveal key trends.
These trends can be used, not just to inform the program-building process, but to report on results to key stakeholders. Some say their leadership doesn’t have an appetite for this kind of data. But if you can clearly demonstrate how leadership development is impacting profitability, they will pay closer attention, making it easier to garner buy-in for future programs.
Learning: Moving from a Subjective to a Fact-Based Discipline
L&D is often characterized as a subjective discipline, particularly when it is focused on building soft skills through leadership development programs. But with a holistic approach to data, an organization can become free of confining assumptions, and learning measurement and program design can then become rooted in fact. This enables learning leaders to pursue what was once an impossible feat: demonstrating the true business impact of learning investments.
The profile of a successful Human Resources (HR) Data Scientist is a unique one, both within HR, and across the field of data science more broadly.
Entry into the discipline requires at the very least strong data science skills, knowledge of research design principles, the ability to clearly explain technical concepts to people with non-technical backgrounds, and, of course, an interest in studying people in the workplace.
Mid- and senior-level HR Data Scientists must cover all these areas. Moreover, they are expected to serve as consultants and advisors by presenting clear, actionable recommendations to their stakeholders. Figure 1 shows a typical cycle consulting framework for an HR Data Scientist.
There are no one-size-fits-all experiential or educational backgrounds indicative of the most effective, proficient HR Data Scientists. To the contrary, HR data science teams (commonly referred to as Workforce Analytics, People Analytics, or People Science teams) are often multidisciplinary functions by design.
From policy researchers to particle physicists, there appears to be no limit to the types of fields and backgrounds welcomed into the world of HR data science, assuming one has the foundational knowledge and skills mentioned above. Cognitively diverse teams typically outperform cognitively homogenous ones—and HR data science leaders know it.
There tends to be significant overlap with requirements of data scientists in other functions, such as demonstrating robust data science skills and coding abilities. However, additional challenges related to analyzing people data in the context of HR are often encountered. For example, HR Data Scientists must:
Actively listen in order to understand (and ask questions about) people problems to be solved
Display strong non-technical communication skills such that business leaders are able to fully understand the meaning and implications of results
Have a firm understanding of labor and employment law considerations
Be adaptable in the face of frequent context-switching (e.g. coding vs. emailing vs. meetings, or recruiting vs. diversity vs. retention)
These are core competencies required for the HR-Data-Scientist-as-consultant model. When studying diversity in organizations, for example, the ubiquity of this problem (particularly in the high-tech industry) presents unique data challenges—which are often pervasive throughout the employee lifecycle—such as small sample size issues.
Diversity program evaluations, such as determining the effectiveness of diverse slate talent acquisition initiatives, can be especially difficult to confront in practice, because it can take several months or even years to collect enough data such that statistical inferences can be drawn. Considerations of practical significance (as opposed to statistical significance), external data sources, and qualitative feedback are extremely important when the business needs an answer in days, rather than months or years.
Active listening sessions with recruiters, qualitative feedback assessments of candidate experiences, and proactive investigations of available outside data sources may offer critical evidence for or against the effectiveness of a diverse slate initiative.
A high-performing HR Data Scientist will apply the competencies mentioned above, toward fulfilling these core expectations and responsibilities:
Develop data science skills as new or improved techniques arise, and continue exploration of emerging methods and technologies in the analytics space
Demonstrating a healthy mix of soft skills and domain-specific knowledge, accompanied by a breadth of data science technical expertise, differentiates good HR data scientists from truly great ones.
The Difference Between the HR Data Scientist and I/O Psychologist
Notably, there is a large contingent of professionals from one social science discipline in particular—industrial/organizational (I/O) psychology—who are frequently drawn to HR data science.
As a branch of psychology centered on applying psychological principles to the workplace, I/O psychology practitioners focus on areas such as pre-employment testing and assessment, leadership development, talent acquisition, team effectiveness, total rewards, workplace safety, diversity, employee engagement, etc. The discipline as a whole has been ranked by U.S. News and World Report as a top science job in 2018.
While there is substantial overlap between HR data science and I/O psychology, the former tends to be heavily quantitative, whereas many aspects of the latter need not be. Both require subject matter expertise in the HR domain; however, an HR Data Scientist must exhibit quantitative skills, such as advanced statistical modeling, and technical skills related to data or software engineering on a near daily basis.
It may be somewhat confounding that a master’s degree or PhD is typically required to practice the discipline of I/O psychology. But this is not a requirement of an HR Data Scientist–a bachelor’s degree with a concentration in quantitative methods or relevant technical experience may be adequate for realizing full performance (which encompasses being able to fulfill the core responsibilities listed above).
HR Data Scientists Face Unique Data Issues and Projects
HR Data Scientists encounter a high frequency of data-related issues partially unique to the role. For example, the ability to apply so-called big data techniques to small data problems is a common necessity.
Even in large companies, HR datasets regularly pale in comparison to datasets from other areas and platforms (e.g., sales, manufacturing, etc.). Contemporary machine learning approaches may not work well (or may have to be adapted) in small sample scenarios; whereas, more traditional statistical or practical approaches may be better suited for these types of situations.
In addition, other areas may be more interested in prediction-heavy models. For example, predicting product defects prior to being shipped or forecasting a percent increase in sales in the next quarter. However, a vast number of HR data science problems are those which require elements of both prediction and inference.
Take, for example, one of the most common HR data science concepts of interest: regrettable turnover. It’s great to have the ability to predict if/when/which top performers are thinking about leaving your company, having the ability to infer why they might leave provides leaders with a fighting chance to intervene before it’s too late.
As someone who has studied regrettable turnover in different organizations, for different purposes, I have found that the core business problem or context can vary widely, and asking the right questions in order to understand the problem, is imperative. Are we concerned with high, or low, turnover? Do we care about identifying key drivers of attrition, or maximizing predictive power, or both? How will we intervene to prevent high-risk employees from leaving?
These are just a few examples of questions in which the answers will have important downstream implications for statistical modeling, interpretation, recommendations, and actions. These realities require careful thought around which methods or tools are best for a given HR-specific problem.
Hire the Right HR Data Scientist
The HR Data Scientist role is a fascinating study mainly because of the multi-dimensional nature of the job, requiring many different hats to be worn. The HR-Data-Scientist-as-consultant model (see Figure 1) serves as a useful framework for crafting specific roles and responsibilities appropriate for your organization. Please take advantage of this framework, as well as the sample job description that’s been provided as a template, to help you scale your organization’s own HR data science capabilities!
This may be an unusual way to start a career coaching conversation in most workplaces, but it’s an important question to ask employees from non-technical fields–and women in particular.
Increasingly, businesses of all types need more professionals–from ethical hackers to application security engineers–who can effectively ward off and respond to cyber attacks. According to a 2018 report released by ISACA (a non-profit professional association focused on information security), 50% of cybersecurity professionals say their enterprise is experiencing an increase in security attacks compared to a year ago.
But the demand for professionals with cybersecurity skills is continuing to outpace supply, putting many organizations at risk: A 2017 study led by Frost & Sullivan predicted that the global cybersecurity workforce gap will reach 1.8 million by 2022, a 20% increase over the forecast the firm made in 2015. According to ISACA, the “ratio of qualified applicants to open positions leaves much to be desired from the point of view of an enterprise trying to recruit the right security team members.”
Multiple long- and short-term solutions abound, from more educational partnerships to the recruitment of military veterans. Some experts have stressed that hiring managers need to shift the focus away from existing qualifications. In fact, 30% of people working in cybersecurity have come from non-IT or engineering fields, according to Frost & Sullivan, indicating that technical skills can be acquired on-the-job. There has also been a push to attract more women into what is typically a male-dominated profession.
But in spite of these efforts, the gap persists, and the gender needle has barely budged: 48% of women are in the US workforce, compared to just 14% in the US information security workforce, according to PWC. Evidently, we still have a long way to go before the trajectory of the cyber talent shortage shifts, leaving many HR departments scrambling for talent.
Tapping Non-Traditional Sources of Cybersecurity Talent
There is a silver lining, however, for those HR leaders who see a glut of open cybersecurity positions on the horizon. While employers will likely need to compete on the market for some positions, there may be some untapped sources of cybersecurity talent within the organization. The trick is to find them.
Cybersecurity is multidisciplinary, requiring knowledge of technology, human dynamics, finance, risk, law, and regulations–but attracting people to the field is difficult. The good news is that if a few outliers have already taken unconventional paths to cybersecurity, they will have left a data trail in their wake. This information can be used to find more internal candidates and support them as they acquire new skills.
Encouraging more people to pursue cybersecurity by highlighting growth opportunities during one-on-one conversations with managers may be more effective than a general internal hiring campaign.
If you are an HR leader who needs to ward off a cybersecurity talent shortage, ask these questions of your workforce data:
1. What types of transitions have other employees made in the past to get into these roles?
The path to cybersecurity can be non-linear and full of course corrections. Shelley Westman, for example, who is now a Partner at Ernst & Young in Cybersecurity, started her career as a lawyer, then left the field, and went to work at IBM in a number of different roles ranging from procurement to product management before she arrived at a role in hardware security.
Looking at the many twists and turns that women and people from outside of technical fields experienced in the journey to a career in cybersecurity can help you broaden your internal candidate pool.
For example, let’s say that you know you are going to need at least three more information security analysts in the long term. Start by looking at who is currently in these roles: Are any of them female? Did any come from backgrounds outside of IT and engineering?
If you see any commonalities, such as female business analysts eventually getting into computer forensics, you can then encourage line managers to broach the idea of a cybersecurity career with people in these roles during career coaching conversations. The goal, of course, is not to push people into these roles, but to let them know in a career coaching context where there are opportunities for growth in the organization.
2. How long does it take to transition from a non-technical role?
“Ideally, a major in computer science provides the foundation…But if you’re driven and passionate about cybersecurity, you can come from any background,” states a cybersecurity consultant in this Bureau of Labor Statistics career outlook interview.
Indeed, technical skills can be learned, but take time to acquire. To get an accurate understanding of how long it typically takes for someone from a non-technical background to move into a particular cybersecurity position, determine what the average time is for someone to get into that role. If there are multiple steps involved, this can be calculated by adding up the average time in each step. The managers and employees can either use this information to reevaluate goals or gain a realistic understanding of how much work is involved.
3. Who are the best mentors?
Transitioning to cybersecurity can be intimidating. The movement history of your workforce can help you identify other people who have previously gone through a similar job transition and who also come from the same professional background as your internal candidates. It can also help you connect female employees with other women. These people can act as valuable mentors as people acquire new skills and face new challenges.
A manager may resist having his valuable team member mentored by individuals from other departments. It’s important to remind the manager that this can help him earn a reputation as a talent catalyst and that he can grow his own career as a result. In fact, recent research released by Gartner identified that those managers who improve employee performance the most are “connector” managers who “guide their direct reports to people and resources beyond their own sphere and expose employees to the best opportunities to acquire experience, skills, and capabilities at the time they are needed.”
Defending Against Cyber Attacks With an Arsenal of HR Tactics
Armed with historical workforce data, HR leaders can help managers guide career conversations to attract potential internal candidates, better estimate timelines to get non-technical people the development they need, and uncover hidden gems of mentors who can guide people through tricky transitions. This is one weapon in an arsenal of tactics that HR can use to defend the organization against cyber threats.
When I think about women’s leadership, several big words come to mind–empowerment, leadership, parity–but I keep coming back to smaller words. In fact, it’s a string of two-letter words that, together, make a BIG impact: If it is to be, it is up to me.
These words have propelled me forward at every stage in my career.
My passion for advancing women in leadership was ignited by personal experiences within the financial services industry. I noticed that women were frequently subjected to various stereotypes and there were very few female role models to emulate.
For example, men were viewed as assertive–a good trait. In contrast, assertive women were viewed as being pushy and stubborn–bad traits. Or men were stern taskmasters, while women were “too difficult to work for” or that they missed the big picture and took things “too personally.” I was even told my emotions were a “liability” in the workplace and made me look weak. Can you believe that?!
If you want to be truly successful, you need to forget the old stereotypes and allow yourself to be vulnerable, especially at work. Give yourself permission to laugh, cry, get angry, and ask for help. Your vulnerability will make you more real to your supervisors, colleagues, employees, and help you connect with them at a deeper level.
My Journey to Gender Parity Advocacy
It was the biased thinking I–and many other women–endured that fueled my need to change the narrative. I needed to be a catalyst for change. I wanted to be a strong female role model.
In the male-dominated financial services industry, I knew one of the best ways to get noticed was to drive revenue, so I took a job as a wholesaler. I became the number one salesperson at the firm. This achievement helped me earn a coveted management role and ultimately positioned me to step up and become the President of Distribution for a large asset manager.
After five years of hard work, I’m proud to say that my business line was made up of 40% diverse individuals when I left–and we did not sacrifice quality one iota!
Nine years later, “If it is to be, it is up to me” motivated me again. In 2015, I joined a group of nearly 50 female business leaders, former CEOs, and academics to address the stalled advancement and pay inequity of women in corporate America.
What we discovered during our discussions was that the CEOs wanted action. They were not seeking to check another box. They wanted a plan with concrete steps to follow. Together, we defined the purpose of the Paradigm for Parity® coalition and a 5-point action plan that could be implemented and measured in organizations.
5 Actionable Steps to Gender Equity
The Paradigm for Parity® coalition’s mission is to achieve a new norm in corporate leadership, one where women and men have equal power, status, and opportunity.
We require a commitment from the very top–the CEO of each company wanting to join the coalition. Then we provide the roadmap–our five concrete steps–to close the gender gap and tools to support our committed companies.
The key to the plan’s success is to implement all five components simultaneously. As I lay out the steps you may realize that you are already doing some of these items. Fantastic! But we believe that the best results will only come from acting in all five areas at the same time in your organization.
So, let’s briefly discuss these 5 action steps in more detail:
1. Minimize or eliminate unconscious bias to improve intake
There are number of ways to accomplish this. First, ensure job descriptions use gender-neutral language. Consider hiring a consultant to help you write more gender-neutral postings; this may increase the number of qualified women applicants.
Second, institute blind resumes. The removal of names for resume screening is a powerful and proven tool for achieving more gender-neutral candidate pools. Additionally, avoid basing hiring decisions primarily on employee referrals. Instead, prioritize diverse candidate slates and decision-making panels.
Third, establish a Diversity Recruiting Officer. Effective diversity recruiting requires establishing relationships and identifying qualified candidates before a role becomes available. Designate an HR team to focus on diversity recruitment. This group can develop networks to identify and attract diverse talent, and standardize interview questions to be more gender-neutral.
2. Significantly increase the number of women in Senior Operating Roles
Gender parity isn’t just a good thing to do–it’s the smart thing to do. Why? You’ll improve your company’s bottom line. According to a study by the Peterson Institute For International Economics, there is a 15% increase in net revenue margin of profitable companies when there is a move from no female leaders to a 30% representation of female leaders.
Despite the indicators that gender diversity makes good business sense, there hasn’t been correlating progress to advance women in senior leadership roles. A study by Lean In & McKinsey shows that, from a pipeline perspective, the percentage of women significantly decreases as one moves up the ladder. Women included in the 2017 study represented 47% of entry-level corporate positions, but only 20% at the C-Suite level.
So, what can organizations do to advance women into senior roles? Take a top down and bottom up approach. The CEO needs to lead by example and diversify the senior team operating roles whenever possible. We believe the CEO should make full gender parity (50/50) his or her ultimate goal. A shorter-term goal could be 30% in five years for the Executive Management Group downward. Also, ensure that younger leaders are being moved across functions whenever possible.
3. Measure targets at every level and communicate progress and results regularly
Knowing where you stand is critical to knowing what progress will look like. The Paradigm for Parity® coalition helps our committed companies establish a baseline. From evaluating gender mix by role to analyzing how long someone has been in a certain role, we provide a list of ways to look at your data to identify potential obstacles for women.
Progress should be communicated regularly and throughout the company. Transparency is key to success and must come from the top. But hold leaders accountable for progress–not quotas. Instead, measure success across four critical areas: RECRUIT, RETAIN, ADVANCE, SPONSOR. Allow leaders to see how their areas compare to other parts of the company or the function they’re in, thereby creating friendly competition.
4. Base career progress on business results and performance, not on presence
Work can and should happen anywhere. To support career progress for both women and men, implement flexible working arrangements wherever possible. Consider offering employee benefits such as back-up care, paid parental leave, and child care reimbursement to ease the burden facing women and families.
5. Identify women of potential and give them sponsors, coaches, and mentors
Why? Leadership, like any skill, must be continually fine-tuned. A coach helps you achieve a specific personal or professional goal by providing training, guidance and encouragement and holds you accountable for the desired results. Mentors are someone you can trust, confide in, seek advice from, and someone that can help you learn new skills. Sponsors help you gain access to key leaders in your organization as well as networks of influence. They advocate on your behalf even when you’re not in the room.
The simplest way to distinguish between these three individuals are as follows: coaches speak to you, mentors speak with you, and sponsors speak for you.
To create a program like this, be clear on development initiative goals and match women with sponsors appropriately. For example, if the goal is to increase the number of women at senior levels, the sponsors need to be senior-level executives experienced with high potentials that have development gaps. These executives are also at the table when promotion decisions are being made.
Hold sponsors accountable for success starting at the most senior levels. Also, train leaders to understand how to sponsor versus mentor, and how to provide this support effectively.
Gender Parity Won’t Be Achieved in a Vacuum
Each of our committed companies is at a different stage on the journey to gender parity. To help our committed companies reach their goals, we have developed a toolkit. It includes a variety of suggestions and resources to help our coalition companies create a culture of inclusivity; from creating diversity objectives for managers to using slate-based promotion process or utilizing a “plus-one” tactic.
We view the toolkit as a living document, one we continually seek to enhance and augment through the exchange of ideas and best practices of the coalition.
Since officially launching Paradigm for Parity® coalition in December of 2016, our list of committed companies has grown from just 27 to now 83, nearly triple in less than two years. Included among the coalition are wonderful partners like Salesforce, Merck, Accenture, Walmart, Bank of America, and Ingersoll Rand. A key priority in their overall diversity efforts is to make gender parity by 2030.
Over the last few years as I’ve met HR executives, leaders and practitioners who want to learn more about people analytics, I have concluded that I nearly always get asked these three questions:
How can I improve my impact?
How can I create more value?
What should I focus on?
Clearly, the answers vary depending upon the situation, level of experience of the person, business challenges, and industry. However, when I reviewed all the work over the last few years in clients and organisations around the world, I realised that the answers can be summarised into nine dimensions which are grouped into three categories: foundational aspects, resources needed and value gained.
And so David Green and I created the Nine Dimensions for Excellence in People Analyticsmodel, which is now being used by many organisations to help gain impact in the discipline of People Analytics.
The foundational aspects of people management revolve around having the right elements in place up front to enable success in the future before the work becomes too complex.
Having the right structures in place to help with data standards, ethics, and privacy and the selection of projects and analytics work will ensure you have a greater chance of being successful. Start with the end in mind and set yourself up for success.
Starting with the business challenges that are most important and then managing work and projects once you know ‘why’ you are doing it will help you get more from analytics. This dimension focuses on what methodologies are most important to provide simplicity and avoid confusion. As an example, at a 2018 CIPD event in Northern Ireland, I was asked “What one thing would you recommend to do?” for a HR leader in pursuing analytics…my answer “Focus on your business challenges first!”
Understanding the people, functions, and groups that are most important and communicating regularly, appropriately, and with clarity will ensure greater impact and value. There are seven types of stakeholders that we recommend in this model to focus on.
The resources that are needed to develop solutions and deliver impact from people analytics are people (or skills), technology and data.
Sourcing, deploying and using technology for analytics is complicated by the fact there are literally thousands of vendors. Your technology needs will be across visualisation, business intelligence, statistics, machine learning, and AI amongst others. They will include both hardware, software and delivered on premises, ‘as a service’ or as a hybrid. The approach here is to recommend those categories of technology to focus on by understanding what your needs are, rather than focusing on ‘the next shiny thing’.
Knowing, using, integrating, managing and securing people and business data is essential if anything else is going to be done at all. This dimension is important to understand standards, security, data options and what you need to answer your most pressing challenges. It also covers the need to focus on internal and external data sources and how to analyse your situation and decide what additional data to gather to improve your analysis.
The value you derive from your people analytics activities will be determined by those with whom you interact.
Deploying analytics solutions with the ultimate benefit of the workforce is the most satisfying part of people analytics. This dimension of the model focuses on understanding your appetite and ability to focus analytics on those that benefit the most — the employees/workers and managers themselves — through personalisation, recommendation algorithms, the consumerisation of HR and democratisation of data to managers. As an example, Nielsen offered analytical value not just in financial terms, but by highlighting the benefits of career change to employees and sharing a video about what, how and why attrition analytics matters.
Delivering insights through effective people analytics will ensure your executives and leaders are informed with insights to make decisions. These insights can be about productivity, cost optimisation or revenue enhancement. Whatever they are this dimension ensures that the business will gain organisational benefit from people analytics in the future.
Deploying analytics is easier and more impactful when the culture of HR and the organisation overall is receptive to analytical insights. How to strive for a strong analytics culture is important and most often requested. “How can my team be more quantitative?” and “How can I train my HR business partners?” are two of the most frequent questions I hear.
Bring it All Together
Use this model as a roadmap for your success with people analytics. Start by assessing which dimensions your function currently has and what kind of impact this is having on your operations. If any elements are lacking or not having an impact, build them into your roadmap and strengthen them as you work your way through the foundation up to value. As you build up each dimension within your people analytics function, be sure to also periodically review the outcomes being produced by previous dimensions. To ensure true excellence, your people analytics function must always be producing value to your business.