My Experience in Building a People Analytics Function


Three and half years ago I joined Vodafone to create the People Analytics function. My focus during this period has been proving the value that analytics brings to HR, building the analytics and dashboarding infrastructure, and scaling the team.

Below, I share my thoughts on how organisations can start their People Analytics journey in an incremental way and with minimal budget. This is not a solution that suits everybody – all organisations are different – and should be considered by HR teams who have the ambition to become data-driven, but, for strategic or financial reasons, are not able to make a large investment upfront (proving the value of a new team is hard, if the team does not exist yet).

This is based both on my current and past experiences and on discussions I had, through the years, with the many HR and People Analytics professionals I was lucky to meet and who I would like to thank. I hope you find this read interesting.


Organisations that are new to analytics perceive the benefits of data-driven decision making, but lack the experience on how to build and grow a team. In addition to that, a People Analytics team requires a complex set of skills that go from software development to advanced analytics, from HR to business partnering and consulting. Acquiring all these skills in a short period of time is difficult and requires a large investment, whose return is uncertain and far ahead in the future.

In this situation, an incremental approach will work better because the investment can be distributed over a longer period of time and can be made conditional to meeting agreed milestones:

  1. Early-stage People Analytics. The first step consists of setting up a small function whose sole focus is to identifying opportunities to prove value to the business. This can be achieved with a small team of 1-2 people: reach is very limited, but so is its financial and organisational footprint, which reduces pressure to deliver.
  2. Mature People Analytics. Once the value is proven, the team can grow and take on wider responsibilities across HR, including accelerating the transformation of HR into a truly strategic and data driven function. At this point People Analytics is a core function of HR and the analytics strategy is an integral part of the HR strategy.


An early-stage analytics team is small and agile and grows organically depending on business requirements. The small size ensures optimal return on investment because the team can focus on the 1-2 projects that have higher chances of success (easy wins). It also ensures easier integration with, and acceptance by, the rest of HR who may need time to adjust to a new way of working (data-driven).

The first 1-2 hires are very critical to success. A varied set of skills, competencies and pre-requisites are needed:

  • Technical skills. The team requires a range of technical skills to build and maintain a data infrastructure that will depend on the support they will receive from other technical teams e.g. IT or Engineering. Skillset may include IT skills (e.g. databases and data engineering), coding, machine learning, visualisation.
  • Business skills.  The team requires good communication skills to talk to non-technical stakeholders; they must have commercial acumen to identify opportunities that bring value to the business; and they should have experience delivering complex analytics projects.
  • Mandate from senior leaders. Introducing an evidence-based culture for managing and evaluating talent requires a shift in how HR professionals think and operate. There will be resistance to change and support from senior leaders will be key to drive adoption.
  • Stakeholder management. Senior leaders’ expectations will need managing during the first few months when business outcomes will be limited. This will be due to the time required to: set up the team, run the first project, implement the actions derived from the analysis, and wait for visible effects on the workforce.

The new team will spend a long time (up to 18 months) establishing itself as an analytics function. Ability to create value is critical in these initial phases and the team should focus on delivering actionable insights to the business by adopting an evidence-based analysis methodology, i.e.:

Figure 1 Evidence-based Analysis Methodology

(An earlier version of this model can be found in my earlier whitepaper)

  1. An analytics project is aligned to the business priorities and provides answers to a relevant business question. For example: individual/team productivity, sales effectiveness, organisational efficiency.
  2. The high-level business objectives are translated into a set of hypotheses that can be tested with data. Business leaders usually have opinions, derived from experience and intuition, on what a solution to a problem should look like. This is important domain knowledge that can be leveraged to define the working hypotheses and prioritise the work.
  3. Workforce data is analysed to extract insights (Facts) that prove or disprove the hypotheses and contribute to a better understanding of the business problems.
  4. Based on the evidence derived from the insights, the analyst works with HR and the business to formulate a set of actions – measurable initiatives that can be tracked over time.
  5. Tools are built to monitor implementation of the action plan and its impact over time. The business priorities may change as a result of these actions and this may lead to a new cycle of analysis.


While an early stage team should focus on data insights, a mature people analytics team has a wider scope that covers all areas in the data and analytics space such as reporting and dashboards, data insights, data-driven solutions, automation, data strategy & governance. This requires a larger team and a different structure, but it enables the creation of an integrated data & analytics strategy that can be leveraged to accelerate the digital transformation of the whole HR function.

Mature People Analytics


Reports and dashboards are very labour-intensive and should not be in scope for an early-stage team, which has limited resources and needs to prioritise high value projects over business-as-usual activities. A mature function, on the other hand, should drive the dashboarding initiative to address the growing data needs of HR.

Initially, reports tend to be limited to standard HR metrics (e.g. attrition, headcount, diversity); as the use of dashboards increases, requests become more elaborate and require integration with non-HR data sources (e.g. productivity measures such as costs and revenues). Implications for the people analytics team are substantial:

  1. Reporting and dashboards require a dedicated function to ensure they do not consume the resources dedicated to advanced analytics activities.
  2. Integration with the wider analytics ecosystem (e.g. BI, Big Data, IT, and Ops teams) is required to ensure access to business data, alignment on data definitions, sharing of infrastructure whereas possible.


Compared to the early stages, the need for data insights will grow and the demand will exceed the supply. This requires a framework of engagement that can ensure appropriate planning and allocation of resources to projects as well as management of key stakeholders.

Analytics initiatives should have a sponsor from the business to ensure relevance, clear objectives, measures of success, milestones. The analytics team should be responsible for the insights, while the sponsor should be ultimately accountable for driving the implementation of the action plan derived from the analysis and for meeting the agreed KPIs.


Maintaining tools and software solutions is usually out-of-scope for early-stage analytics teams, who focus on ad-hoc and self-contained projects. A mature team, on the other hand, may need to develop software solutions in-house and, occasionally, manage 3rd party tools. This requires:

  1. Software skills to develop proof-of-concepts and oversee implementation of 3rd party tools.
  2. Access to IT infrastructure dedicated to People Analytics. Ideally, this is provided by the organisation’s IT function to ensure alignment with the overall IT strategy.


Automation of manual processes and workflows, for many companies, is a key pillar of the digital transformation. Automation requires highly technical skills and detailed domain knowledge, both of which can be found in the analytics team; it is, on the other hand, a very resource intensive activity and a question remains on whether it should sit outside of it, and possibly even outside of HR.

In general, the automation strategy has a strong impact on the organisational structure of HR and on the tools that the team will adopt as a result of it. The People Analytics should have visibility and be involved enough to ensure alignment with the overall data strategy.


The analytics strategy sets the long-term vision for the team and provides a framework to assess the business challenges of the organisation against available data and resources and provides guidance on how opportunities are identified, prioritised, approved, executed, monitored:

  1. The head of the people analytics team should be part of the HR leadership team to have better visibility of the short and long -term priorities of the business and contribute shaping the data-driven strategy for HR.
  2. The team should partner with HR and the business to ensure the analytics projects are aligned to the business needs.
  3. They should interlock with Finance, Privacy and IT and with other technical teams to ensure alignment of the data & technology roadmap.
  4. They should be the innovation engine of HR. They identify synergies with other teams across the business, support collaborations and research opportunities and engage with external stakeholders, including universities and research centres.


Building a people analytics function is not easy and there are many reasons why things can take longer than expected to complete, and not all organisations are prepared to jump into what is, effectively, the unknown. A phased approach based on incremental investment may help getting started when resources are scarce, or more time is required to overcome the internal barriers.

In addition to that, organisations that have already started their journey into HR analytics may also benefit from this approach, for example, if they need to evolve a reporting team into a mature function: making growth conditional to delivering value creates a sense of urgency that helps the team focus on what really matters – which, in this case, is their ability to support not only HR but also the wider business.

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