A version of this article first appeared on LinkedIn Pulse.
When it comes to people analytics, there are always more questions than answers. The newness of the field, the complexity of human behavior, and the ever-changing nature of organizations generate a huge list of potential questions. It can be easy for the aspirations and imagination of business leaders to overwhelm their people analytics teams with demands and unrealistic expectations.
The drive to deliver and show progress leads people analytics teams to overextend. However, the fastest way to lose the interest of key executive stakeholders is to miss their expectations. Every emerging or growing people analytics team needs a process for identifying which questions get answered and which don’t.
Over our 10+ years in the people analytics space, Visier has supported hundreds of teams to get established and scale their impact. From this, we have identified and built a rich knowledge base of what works and what doesn’t. The following decision model is the result of that work. It can help you quickly assess, justify, and explain the work that your people analytics team will and won’t do.
Phase 1: Determine project viability
It’s easy to generate interesting questions about how people are impacting the business. One of the most exciting aspects of people analytics is that there are more questions than answers. The opportunities to find new knowledge, new approaches, and to differentiate the business through people programs are enormous. Still, all organizations have finite resources, and there’s no way to allocate resources to every question that comes up.
This combination of factors leads to the need for a decision model that focuses on balance. The model needs to balance multiple components related to value, data, and resources. The goal of the model is to establish which projects or “products” are a go, and which aren’t. In doing so, it needs to cover the spectrum of possibilities, from the most basic of reports to the most compelling and complex options for new research.
The diagram below shows the components organizations should consider when making decisions about a people analytics project or product.
The three components are as follows:
a. Value: The goal of any people analytics activity should be to generate value for the business. It’s easy to lose sight of this when mired in all of the complex references and permutations of people data. In simple terms, organizations spend up to 80 cents of every expense dollar on their people. Actions or decisions which improve outcomes related to people will deliver value to the business.
The first thing to gauge for every people analytics project is how it will deliver value to the business. Is the linkage between action and value well established—like retention and cost-saving? Or is it vague and undefined—like the impact of network connections on overall revenue?
For example, risk of exit models are now very commonly used. This is because their value is clear and easy to link to a business outcome. Not only does retaining an employee save the costs of replacing them, but it’s also associated with an impact on revenue or risk. Visier’s own research shows that retention projects consistently provide a large ROI to the organization.
When it comes to gauging value there are two key questions to answer:
- Is there a clear, well-articulated, and commonly understood link between the PA work and the proposed business impact?
- This is a yes or no question. If you can’t articulate this link, you should not proceed until the link is clear to all.
2. What is the scale of the value that can be delivered?
b. Data: The people analytics revolution stems from the increased availability of, and ability to process people data. Every day, organizations record vast amounts of information about their people, past, present, and future. For a people analytics project to be viable, you need to back it up with data and that data needs to be properly structured to be useful.
A recent conversation with a group who were exploring how to analyze learning behavior revealed that they had not adopted an effective data capture mechanism. Whilst they had lots of data, there was nothing in the data that would uniquely identify a specific learning experience.
The same YouTube video or Harvard paper could be represented in any number of different record sets, and there was no way to break out the required lower level of detail. There was a record that indicated a person had experienced some learning. However, there was no way to determine what learning this person had experienced compared to anyone else.
As you can imagine, this was a source of frustration for this group. It was disappointing that the data they thought was available was not, due to how their transactional system was set up.
In this example, the lack of properly structured data made it impossible to answer any questions about the impact of learning consumption. It demonstrates the critical importance of data as the fuel that makes people analytics work possible.
More commonly the judgment in relation to data is about whether or not there is enough data, of sufficient quality, to provide a trustworthy answer. People analytics teams should be able to perform a data review, processing a data set to see if it’s sufficiently complete and well structured to support answers to relevant questions.
There’s rarely a need for perfect data, the focus needs to be on data that is fit for purpose. The people analytics team needs to be capable of evaluating if the available data is fit for purpose, and articulating this to their stakeholders.
It’s also crucial that the organization has an ethics standard for the people data they will and will not use. The data required to answer the business question must fit within this ethical standard.
To gauge the viability of the data related to the business question, ask yourself: Does the team have access to a properly structured data set, that aligns to our ethics policy, that will support effectively answering the business question?
Hopefully, the answer to the above question is yes. However, if the answer is no, that does not mean the question should be put aside. What becomes important is the cost or resources needed to generate the data.
c. Resources: Resources covers a wide range of inputs. You may have a high-value question, for which data is available. However, if you don’t have people with the time and skills, the right technology, or relevant stakeholder support, the project will not progress.
An example of resource misalignment comes from a presentation I saw a couple of years back. A team had built a risk of exit model. It had taken two PhDs six months to assemble the data, run the model, and generate the outcome. The CEO was excited by the result and could see the value. The CEO wanted the risk of exit information provided monthly.
The PA team couldn’t meet this expectation. Their methodology and infrastructure would not allow them to produce the result more frequently than once every three months. They had the data and the value was clear. What they lacked were the resources to deliver the consistency of insight that the business needed.
This pattern is all too common: an analytics team takes a project-focused approach and provides a one-off answer. The answer may be interesting, but the value to the business only comes once the analysis can consistently support the decisions of relevant stakeholders.
This is why it’s important to consider resources upfront. It’s also important to consider resource demands for the full lifecycle of the project to deliver the full value to all the business stakeholders.
The best way to gauge the resource demands related to the business question is to use the structure below:
NOTE: One outcome of the above model is to look at technology not as a one-off tool, but as an investment that supports a wide range of projects and expands the volume delivered by the PA team.
Phase 2: Allocate projects
Having outlined the components of the viability model the next phase is understanding how to put it into action. Project viability is a balance between the business value, data quality, and the resources you have to approach the project.
It would be possible to turn project viability into a complex study in its own right. We recommend, and practice, something that is more pragmatic, allowing for quicker decisions and only going into detail where the stakes are high.
The matrix below shows the projects which automatically get onto the list, those that never get on the list, and those that require a judgment call. In each of these cases, the team must have access to a properly structured data set, that will support effectively answering the business question and aligns to the organization’s data ethics policy.
High-value projects with a low resource demand are clear winners. Business questions with low value and high resource demand are non-starters. The projects that fall into that category require careful judgment or further investigation before they get onto the team’s worklog.
If the data set is not available, you have a decision to make. It becomes relevant to determine whether the potential business value from the project makes it worthwhile investing the resources to get the required data.
Finding the path to success with people analytics
The practice of people analytics has matured over the last five years. Today, proven approaches and processes are being established and followed. One of the core practices which determines the success or failure of an emerging people analytics group is the ability to select and deliver only the most relevant pieces of work.
Through balancing the components of business value, data availability, and resource capacity it becomes clear which projects to prioritize and which ones to drop. This model also helps to communicate why these decisions are being taken to a broad range of stakeholders.
If you’re ready to start getting buy-in for people analytics, be sure to check out the guide, Make Your Business Case for People Analytics.