Figure 9.1 The Complexity-Impact Matrix for workforce analytics projects.
“You need to calculate the financial impact of Human Resources practices on workforce outcomes. That’s really what people are interested in, so pick your first project carefully.”
—Patrick Coolen
Manager of HR Analytics and Metrics, ABN AMRO
Successfully executed analytics projects are always important, particularly when establishing your credibility. The way your first project is chosen, how it is completed, and the results it delivers all send clear signals to your stakeholders about you and how the workforce analytics function will fare in the future. Because your first project should deliver business impact and be relatively easy to complete, we refer to it as a Quick Win project.
This chapter covers the factors to consider when choosing your first workforce analytics project, including the following topics:
Identifying Quick Win projects
Using a complexity-impact matrix to enable project assessment
Understanding what makes projects complex
Gauging the likely impact of a project
Eric van Duin leads the HR Information Systems and Analytics function at PostNL N.V.1 He shared the details of the project that first got him started:
“I was reading the newspaper one weekend and saw a story about how a Ph.D. researcher had investigated the relationship between the age of a manager and the engagement of the team. It got me thinking, so I started doing a similar analysis at PostNL. While insights like this would not be used for decision making on individual employees,2 they could be valuable for informing HR policies relating to training and communication awareness programs.”
Eric found that, at PostNL, the tenure of a manager strongly correlated to the engagement of the team. The shorter the tenure of the manager, the higher the team’s engagement. The study presented useful information that enabled Eric to help longer-tenured managers better engage with and manage their teams. The data also allowed the HR team to look at processes and policies for managers (for example, revising the training for managers with longer tenure).
“In this first project, I opened the eyes of HR leaders and more senior executives,” Eric says. “I shone a light on an important topic, engagement, with some hard evidence. It was the project that helped me get started and gave the function credibility.”
Since this project’s completion, Eric has had senior business leaders coming directly to his team wanting to understand the human factors that influence business outcomes, such as delivery quality. Clearly, Eric established credibility for his team. When you show that you can complete important projects and provide evidence, the business leaders will come directly to you.
Before you can prioritize projects, you need a list of projects to consider. Adopting a consultancy approach to identifying your projects can be effective. A consultancy approach includes interacting with prospective sponsors about projects you could complete on their behalf. The point to remember when talking to business leaders is that your projects should relate to your organization’s key performance indicators (KPIs) and the way people influence these indicators. While you’re doing this, to get inspired, you can review the examples in Chapter 6, “Case Studies,” and talk to peers in other organizations.
Then, using this chapter’s structured approach to thinking about complexity and impact, you can prioritize your list of projects to decide where to start. Importantly, closely following our prioritization process enables you to plan for most events that you will experience in delivering projects. Forecasting every eventuality is impossible, but careful planning helps you overcome most of the challenges you will likely encounter.
To help decide on your first project, a good approach is to plot the potential project opportunities on a two-by-two matrix according to the level of expected impact and the amount of complexity involved. Delivering a project of moderate-to-high impact makes the most sense; a project that does not have at least a moderate impact will likely go unnoticed. A project with low-to-medium complexity is also a good candidate; high-complexity projects take more time, and senior stakeholders might end up asking why your project is taking so long.
Figure 9.1 shows the Complexity-Impact Matrix3 that results from this exercise. Notice that it includes four types of projects: Quick Win, Big Bet, Trivial Endeavor, and Pet Project.
Readers of this book are likely approaching workforce analytics tasks from different perspectives. Some have a new role in a new function, others are new to the role but are joining an existing function, and still other readers are in the same role but with an expanded focus. Both experienced and inexperienced practitioners can encounter each scenario. For these reasons, it is important to consider these concepts relative to your level of personal experience and your function’s history. For example, one team’s Big Bet might be another team’s Quick Win. Similarly, a project that establishes an analytics functions’ reputation in one organization might be a standard project for a more experienced function.
Figure 9.1 The Complexity-Impact Matrix for workforce analytics projects.
A Quick Win is a project of low-to-medium complexity with moderate-to-high impact. In short, this project is one that you feel confident you will be able to deliver in a reasonable period of time and with tangible results. Projects that involve uncertainty about the team’s capability and projects that rely on dependencies over which you have little or no control are not Quick Wins; they generally involve too much complexity to ensure a successful result so early in the function’s existence. Projects that involve only a small or moderate impact (for example, to the efficiency of an HR metric) also do not constitute Quick Wins because the results are unlikely to get noticed beyond HR.
Senior managers at a public sector agency were interested in understanding the distribution and prevalence of sick leave across the organization. Marcus Champ,4 an analytics professional on the human resources team was tasked with reviewing the data and helping to develop an action plan to address the issue.
A review of the initial data highlighted a distinct spike in sick leave during a few weeks in the year. After some investigation, Marcus noticed that the leave usage coincided with an annual festival that attracted thousands of local and nonlocal visitors over a short time period in a confined area. The festival brought obvious risks of exposure to and spread of illnesses such as the common cold and flu. The agency already had a flu vaccination program in place, but it did not seem to be having much impact. Management could not identify a reason why.
Marcus noticed that the annual vaccinations were administered around the time of the festival. However, doctors indicated that the vaccinations required four to six weeks to take effect. This prompted a recommendation to reschedule the vaccination program to occur at least six weeks earlier the following year. Not only was such a move simple to administer, but it also came at no extra cost to the organization.
The following year, the outcome was significant: Absenteeism was reduced by 5 percent. Marcus explains, “Now, 5 percent might not sound like a high reduction on the face of things, but when you cost this out, it equates to over one million Australian dollars, and it cost the company nothing to change.”
Sometimes the least complex projects can have the most profound impact.
A Big Bet project is a high-complexity project that is expected to deliver high impact. Numerous factors can make projects complex. For example, you might still need to develop key internal or external relationships to execute the project. You might not have the data you need for your intended analyses, and even if you do, the data could be held in vendor systems or even governed by privacy considerations that prevent analysis. You might experience challenges from other functions that have overlapping responsibility for the area you are focusing on, and you might need to work with people in these areas at similar levels of seniority without having formal authority over them.
A Trivial Endeavor project has low-to-medium complexity and low-to-medium impact. If possible, avoid projects with lower expected impact in the early phases of an analytics function’s development. The critical objective of your first project is to deliver results that make a material difference to the business and build confidence in your ability to undertake more projects. Trivial Endeavors are unlikely to fulfill this requirement. Even though they are not as desirable as other project types, it is useful to know what kinds of projects fall into this category. For example, it can help to reframe your project so that it resembles a more desirable project type, such as a Quick Win.
The most common types of projects in the Trivial Endeavor quadrant are those that lead to decisions that management can make just as effectively without the use of advanced analytics. For instance, perhaps the company can benefit from cost savings or additional features by switching providers of an engagement survey or by shopping for a new supplier at the end of the term. These problems are generally manageable using standard administrative approaches.
A Pet Project is overly complex for the impact it delivers. These projects are more aligned with personal interests than what the business requires. Avoid Pet Projects: These projects (and Trivial Endeavors as well) won’t likely get you the positive recognition you require to build the support you need for your workforce analytics function. In fact, they could end up attracting negative attention. To make matters worse, projects in this quadrant are complex to execute because of factors such as the political environment, data requirements, skill gaps, data complexity, or project scale. You might find it difficult to imagine real-world workforce analytics projects that fall into this space, but they do arise.
Consider one anecdote that perfectly describes the idea of a low-impact, high-complexity Pet Project: A business analytics manager decided to crowd-source machine learning capabilities to address an analytics problem for the business. He offered a prize for its solution. A contractor who was crowd-sourced solved the problem, but by that time the business could not implement the solution because it had changed the way it operated. The analytics manager was not concerned, though; he was happy that the challenge had been won, even though it did not ultimately have any business impact. Nothing is wrong with being passionate about analytics in HR, but it should always be pursued with the goal of making an impact on business effectiveness or worker well-being.
The two important criteria discussed above when prioritizing projects are complexity and impact. In terms of complexity, consider five broad factors when rating potential projects as low or high:
• Politics
• Skills
• Data
• Technology
• Implementation
When it comes to impact, three broad factors play into rating projects as low or high:
• Return on investment (ROI)
• Timing
• Opportunity cost
Project complexity refers to the scope of the challenge you face in delivering the project. Complexity is a relative concept. An advanced workforce analytics function might consider a project low complexity, yet a nascent workforce analytics function that lacks access to more advanced technologies and resources might consider it high complexity. Therefore, your team must always evaluate the project’s complexity in terms of its own capabilities and the context in which you are operating. This section covers the five factors to consider when assessing the complexity of a project for your team.
“Make sure to evaluate the complexity of the problem before embarking on long and sophisticated approaches to solving problems.”
—Michael Bazigos
Managing Director, Global Head of Organizational
Analytics & Change Tracking, Accenture Strategy
• Political complexity. Gerald Ferris and Michele Kacmar developed an influential model of political perception at work in their 1992 Journal of Management paper. The model shows that the way workers perceive the political landscape in organizations is affected by both personal factors about themselves and organizational factors (such as how hierarchical and formalized the organization’s structure is and how much interaction colleagues have with each other). Perceptions of the political context then impact productivity at work. To succeed with workforce analytics, you need a finely tuned sense of political judgment to identify where you have backing for your ideas and where you don’t, and which of your ideas are worth pressing when you encounter resistance.
You will certainly encounter people who support your goals, and reserving your energy for only these people is tempting. But be sure to pay attention to areas of the business where you do not have support. Ensure that you have accurate answers to the following questions: Do senior executives agree that the project is worthwhile? And do they agree that your team is the best one to tackle the project? The less consensus you have, the more politically complex the project might be.
• Skill complexity. Analytics projects require a level of expertise in several areas. In particular, projects that require any of the Six Skills for Success (see Chapter 12, “Build the Analytics Team”) that your team lacks (or lacks in depth) are more complex. Ask yourself whether the people resources you have for the project are sufficient to deliver the services required for the project. A lack of technical or statistical skills means a higher degree of skill complexity. If you do not currently have the skills, you will need to hire in, develop, or partner to fill the gaps.
• Data complexity. The process of data collection and integration rarely proceeds uneventfully. Higher data complexity exists when creating the dataset required for analysis is difficult or impossible because of data access or characteristics. For instance, you might not be able to match cases across datasets if they lack a common unique identifying variable. For example, compensation data and turnover data will be difficult to link if compensation data are stored against employee identification numbers but turnover data are stored alphabetically according to surnames.
Another situation that leads to data complexity arises when privacy concerns prevent reanalysis of data that were originally collected for a different purpose. For example, linking selection data to individual development records might not be possible if employees were told that development data were to be used strictly for personal development. Nearly all data challenges can be overcome (see Chapter 10, “Know Your Data”), but often this requires specialist knowledge and expertise. When choosing your first project, consider your present ability to address the specific data challenges for the project.
• Technology complexity. Many straightforward projects do not require substantial investment in resources. Some projects can be undertaken with basic spreadsheet software. Still, it is good to remember that recent developments in data management technology using cloud-based computing make most of the technology and techniques available to organizations of all sizes for a reasonable cost.
Regardless of the technology required to deliver your first project, you need to clearly understand how well you are currently equipped to meet the demands of the project. Does the project require the use of specialized data management or data analysis technologies that are beyond the technologies you currently possess? For example, a project that requires integrating a data capture approach for streaming data or using special technologies for handling very large datasets is more complex than a project that requires analysis of a single snapshot of data.
• Implementation complexity. Organizational change theories make it clear that the best way to get a group of people to behave differently is to let the people who will be affected by any changes help you decide what to do. You often hear this captured in the wise maxim that change should be implemented “with people, not on people.” Change processes take hold over time as people consider and, hopefully, become accustomed to new ways of doing things. The more change that is required and the more people that are affected by the change, the greater the implementation complexity. Carefully consider how easy it would be to implement recommendations that result from your analyses.
The lower the level of change and the fewer people being asked to do things differently, the easier it will be to implement the recommendations. This is because these recommendations will require less training, communication, and stakeholder management to bring about change. If analytics leads to recommendations that require many people to behave in different ways, the project has a higher level of implementation complexity. A balance must be struck, of course: If change affects too few people, it will have lower impact.
You can use these five complexity factors to help classify your initial project. In the early days, pick a project that is low-to-moderate complexity so that you will be able to successfully complete it in a reasonable time frame, clearly establishing your value to the organization. Keep in mind that what a well-established, highly experienced analytics function considers complex might not be the same for a recently established function.
Impact is the level of benefit the business receives from undertaking the analytics and implementing the follow-up recommendations. When considering the expected impact of an analytics project, practitioners should have three issues in mind: return on investment, the timing of the project returns, and the opportunity costs of not undertaking other projects.
“Learn the logic of the business. What makes the business more successful? How do people contribute to achieving this success? This is how you identify projects that will make an impact.”
—Marcus Champ
Senior Manager, HR Analytics, Standard Chartered Bank
• Return on investment. The central aim of workforce analytics is to realize business efficiencies and take advantage of opportunities. Therefore, it is difficult to discuss the idea of impact in workforce analytics without some discussion of cost. At its simplest, the issue comes down to whether the expected cost of the project is less than the expected return to the business from completing the project. This concept might seem straightforward, but the cost decision is not quite so simple. Managers must consider the cost of the project relative to the returns it will deliver in relation to the next two factors (when precisely the benefits will be realized and the opportunity cost of not investing elsewhere).
• Timing. The project’s timing issues can often be addressed by asking yourself whether the project is focused on reducing costs or improving productivity. For the most part, cost reductions are quicker to realize than productivity gains. Therefore, focusing initial Quick Wins on cost reduction might make sense. The impact of a project, like its complexity, is relative to the business and the situation. In general, select a project that will have a short-term impact; otherwise, it cannot really be considered a Quick Win.
Although few people ever intentionally undertake an analytics project that will not have an impact, this situation does happen. For this reason, business executives must have clear insight into the project to make sure that the improvements the workforce analytics team is predicting are clearly tied to business expectations.
• Opportunity cost. When considering the impact of a project, it is important to realize that the evaluation must occur in the context of other possible workforce analytics projects—and also in the context of other possible business projects. Even highly appealing projects that are seemingly low complexity and high impact might be ranked behind other projects when all options are considered. For this reason, it is important to simultaneously consider several projects for impact, in case this process reveals that another project is even more attractive than one you are ready to initiate.
Selecting your first workforce analytics project can be a difficult challenge, but taking a systematic approach to considering both complexity and impact ensures that you make the most appropriate choices. In particular, remember the following guidelines:
• Spend enough time planning your project to address all the hurdles you expect to encounter, but be prepared for the unexpected hurdles that will invariably arise.
• Identify potential projects that relate to the organization’s key performance indicators.
• Classify your projects according to their complexity and their expected impact, and go for a Quick Win that is low-to-moderate complexity and moderate-to-high impact.
• When rating the complexity of the project, consider the following factors: politics, skills, data, technology, and ease of implementation.
• When rating the expected impact of your first project, remember that the project should deliver a sufficient return. The benefits should also be realized in the short to medium term and should offer a greater net return than investing in any other workforce analytics project.
1 PostNL is the premier provider of postal and parcel services in the Netherlands. Each day, PostNL delivers more than 1.1 million items to 200 countries. In addition PostNL operates the largest mail and parcel distribution network in the Benelux (Belgium, Netherlands, Luxembourg) region (www.postnl.com).
2 In some countries, including the Netherlands, using employee age in employment-related decision making is considered discriminatory.
3 The Complexity-Impact Matrix is a copyright of the authors of this book: Nigel Guenole, Jonathan Ferrar, and Sheri Feinzig.
4 At the time of discussion, Marcus Champ was a senior manager in HR Analytics at Standard Chartered Bank, based in Singapore.