The world of people analytics: An HR professional’s perspective
Table of contents
- What is people analytics about? What does this sphere include?
- What are the key metrics and KPIs in people analytics?
- What data sources are typically used in people analytics?
- What are the challenges and potential ethical concerns related to people analytics?
- What skills and expertise are required to work in people analytics?
- What’s the role of people analytics in the future?
Meet Madalina Banuta, people analytics senior manager at Schneider Electric. Madalina has a diverse and extensive background in various roles related to people management, analytics, and quality/business intelligence. Her experience at Schneider Electric focuses on people analytics and employee lifecycle management.
In this interview, we discuss various aspects of people analytics, including its definition, key performance indicators, data sources, data privacy measures, ethical concerns, required career skills, and the future role of people analytics in organizations. Madalina emphasized the evolving shift from retrospective analysis to predictive and prescriptive insights while highlighting the importance of data governance and privacy.
Q: What is people analytics about? What does this sphere include?
People analytics (or HR/workforce analytics) teams collect and leverage all employee-related data to optimize any decision-making related to the workforce of an organization. The sphere’s core would constitute the data you gather on your workforce.
The richness, completeness, and usability of the data you collect on your employees will determine, to a large extent, the quality of the output, whether it’s a plain report or predictive modeling (“garbage in, garbage out” principle).
The second layer to your sphere: fit-for-purpose tools and tech stack! You need tools that enable your data collection and analysis strategy, with options for user-led design, enhanced user responsiveness and interactivity, iron-clad cybersecurity, and continuous innovation.
The partnership with the business side is the third layer that completes your sphere! People analytics teams work closely with:
- Talent acquisition
- Performance management
- Learning and development
- Employee engagement.
And business process drivers within the HR ecosystem to ensure a consultative partnership on all data-enabled decision-making processes.
Q: Could you share some examples of KPIs and metrics you usually use in people analytics to help the business?
We have two big categories of KPIs when it comes to people analytics. The biggest and core of all activity in people analytics is the business-facing one, meaning the KPIs that are led by the business side and defined by the centers of excellence by the business stakeholders.
If I’m in talent acquisition, I will have KPIs related to the requisitions, the types of profiles I’m bringing to the company, the length of the recruitment process, and so forth.
In people analytics, what we do is that we provide the necessary data, the right visualizations, and the right tools for people to understand and consume these KPIs and support them in figuring out how I am versus target:
- How has our performance evolved compared to the previous month, quarter, and year?
- Are we progressing in the desired direction and at the appropriate pace?
This becomes particularly crucial when we have ambitious long-term targets spanning 3-5 years. We should continually assess whether our current trajectory will enable us to achieve these targets or if significant adjustments are required.
Within this sphere, we encompass all KPIs related to our workforce, including diversity and inclusion metrics, demographic splits by gender and age groups, seniority profiles, employee well-being, attrition rates, talent acquisition effectiveness.
Additionally, we have another set of KPIs that are inward-facing. Our people analytics team largely defines these to gauge whether we deliver the right tools, content, and analytics to our stakeholders. These KPIs focus on utilizing our tools, including adoption rates and the number of registered users. For instance, if we create a dashboard for our HR community, we want to ensure its usage and understand who is using it and why some individuals may not be utilizing it.
While these KPIs may be less directly relevant to the business side, they play a critical role internally within People Analytics, serving as benchmarks for evaluating our content delivery effectiveness to our audience.
Q: Could you please share some of the data sources you usually use? Are there only internal ones related to your company, or are there external ones?
We have two main categories of tools in our toolkit. First, we have foundational products. These are usually stable dashboards or self-service tools fed data from corporate-level tools. These corporate-level tools are essential for tracking employees throughout their journey, from the initial employment data entry to all the changes that occur during their employment. Most of these tools are integrated, ensuring the data remains consistent across different platforms. However, each HR domain has its specific requirements.
A recruiter’s input tools differ from those used by someone in learning and development. Our role in People Analytics is to gather data from these various HR profiles’ input tools and consolidate it into a comprehensive data lake. We make sense of this data on the output side through appropriate visualizations and filtering.
There are cases where we need to look beyond our internal data. It typically occurs when we benchmark or conduct market-level analyses to compare our team and company’s performance with industry standards.
Additionally, we explore large datasets and employ machine-learning techniques. Some of the algorithms and language processing models we use are more generic and not explicitly created for our data. We adapt them to our specific needs and apply them to our internal data. We know that many companies are increasingly exploring tools like OpenAI and ChatGPT. However, we approach these possibilities cautiously, as we are responsible for safeguarding our employees’ data.
Data governance, security, and privacy are always top priorities in our decision-making.
Our goal is to strike the right balance between protecting employee data and utilizing the best tools available. We have successfully replicated certain aspects of OpenAI using our internal tools while ensuring data privacy and security.
Q: Do you revise your security planning or contracts with your employees based on the quick digitalization of the workforce workplace and labor market?
There are two main aspects of our approach in terms of data privacy and security in response to the rapid digitalization of the workforce and labor market:
- Non-Disclosure Agreements (NDAs). Regardless of the extent of access, everyone with access to HR data must sign a non-disclosure agreement before gaining access. These NDAs are comprehensive and cover various scenarios. They outline strict guidelines on how data can be shared, with whom, and in what format. If someone needs to deviate from these restrictions, the NDAs provide instructions on who to contact and what approvals are required. It ensures that data governance is clear for all employees using HR data.
- Compliance-Related Courses. Anyone interacting with employee data in any capacity should complete mandatory compliance-related courses annually. These courses cover GDPR for European countries, global cybersecurity, data privacy, and security with specific HR use cases. This ongoing training ensures that all HR personnel are well-informed about what data they can share, with whom, and what constitutes private, restricted, or confidential data.
We also pay attention to data usage transparency. We provide employees with clear visibility into their data. A comprehensive disclaimer explains how the data will be utilized, analyzed, and processed whenever data is input into any company platform, such as surveys or profile updates.
In people analytics, we refer to the disclaimer before using any data. Any use of data that goes beyond what is mentioned in the disclaimer is not permitted, ensuring strict adherence to data usage guidelines communicated to employees.
Many surveys on employee engagement and satisfaction offer the option to contribute anonymously, allowing employees to provide feedback without disclosing personal data. While certain basic data points are legally required for all employees and are automatically captured, employees can decide what additional data they want to share. It includes creating profiles on internal recruitment platforms, linking external profiles like LinkedIn, or sharing skills assessment results.
Employees can share such data with their managers for collaborative development planning or keep it confidential for personal use. We respect employees’ preferences in this regard.
As for the analytics level, we only analyze data voluntarily shared by employees. We do not access or analyze data that employees have chosen to keep private. This approach balances leveraging employee data for business insights and respecting individual privacy and data security concerns.
Q: What are the challenges and potential ethical concerns related to people analytics?
- How accurate and complete is the data we’re basing our analysis on?
- Are we interpreting what we see correctly?
- Are we allowing our personal or historical data-perpetuated biases to influence our analysis, or how do we build our algorithms?
- Are we sharing the analysis only with the people who are legally and functionally warranted to be able to access it?
- Do our employees know and have given their consent on how their data is going to be used?
- Are we creating self-fulfilling prophecies that may negatively impact employee morale or job security?
These are all valid concerns we keep at the top of our minds whenever we set out on any analytical project, regardless of size and scope. We have strong data governance principles and mechanisms that considerably reduce the risks and potential fallbacks. We are also mindful of striking the right balance between safeguarding all these aspects vs. limiting our analytical capabilities beyond the point of bringing any value.
You can frequently use data aggregation to make sense of the data at a macro-level and be able to make valuable recommendations while at the same time protecting individual-level confidentiality of the data.
Q: What skills and expertise are required or would be preferable for the person who works in people analytics?
In our people analytics team, we benefit from having a relatively large team with specialized roles. Here’s a breakdown of the key roles and the skills required:
- Technical specialists. Our team is focused on testing, creating small prototypes, and exploring specific platforms and technologies on the cutting edge of predictive analytics and forecasting. These roles demand highly technical skills, including expertise in the platforms and tools they use.
- Data consultants. Another group closely interacts with the business side and serves as data consultants. They are responsible for guiding how data should be used and helping make sense of data in the context of business decisions, strategies, and organizational changes.
- Product owners. This group plays a dual role, combining technical expertise with soft skills like community management, data visualization, and storytelling. They balance understanding user needs and translating them into practical products, dashboards, and tools.
The common thread among these diverse roles is the ability to tell a story with data. It’s not just about presenting raw data points but making sense of the data within a business and evolutionary context. Concrete skills that prove valuable include:
- Strong presentation skills. The ability to effectively communicate data insights through presentations, whether in PowerPoint or other tools, is crucial. Presentation skills help convey the message and highlight key points for decision-makers.
- Technical skills. Depending on the role within the team, technical skills may vary. Some roles may require proficiency in Excel and Tableau, while others may demand expertise in Python, machine learning, and advanced analytics.
- Stakeholder interaction and empathy. Understanding the needs of the business and end-users is vital. It involves strong persona management—identifying who will use the product, their goals & challenges, and how data analysis can enhance efficiency.
- User-centric approach. Engaging with end-users through interviews and conversations to understand their challenges and needs. This empathy translates into effective data product design and continuous improvement.
Automation and advanced tools are taking over routine tasks. This shift allows professionals to focus on the role’s more imaginative and consultative aspects. Algorithms and automation handle tasks that could be more exciting and valuable to humans, leaving room for human professionals to envision possibilities, aid in scenario planning, and engage as partners in strategic conversations rather than mere executors of instructions. This shift emphasizes the unique value that human insight and expertise bring to people analytics.
Q: What’s the role of people analytics in the future?
We’ve recently rebranded our team from people analytics to people insights and data governance, and I think the changes tell what’s to come. Over the last few years, a lot of work has been put into measuring, collecting, and making sense of the data.
We are now in a place where we can cut through all the noise and see “what has happened.” However, we are moving more and more towards a point where we can tell you “What can happen” (predictive analytics) and “What should happen.”
Having a seat at the table, advising in a consultative manner the business, building the path from insights to added value and to action — all these elements are here to some extent and will constitute the logical developments for this function in the near to medium future.
As more and more data is consumed in many shapes and forms and for such varied purposes, data governance becomes even more integral to the endeavor’s success. Data literacy, security, and privacy are critical to safe, ethical, and fair data consumption. The shift we will see more and more here is that data governance will become “everyone’s business,” and all of us will share in the sense of responsibility and diligence we should have when inputting, analyzing, and using data for decision-making.