Establishing people analytics as a common practice (part I)

There are many maturity models that guide organizations on how to build a successful people analytics practice. Personally, I appreciate the delta model of Davenport and Harris because it can be applied to various business analytical domains, such as risk, customer, and marketing analytics. Another favorite model of mine is the nine-dimension model of people analytics excellence by Ferrar and Green . Unlike the first model, this model is specifically focused on people analytics and, therefore, very useful for practitioners interested in cultivating people analytics as an organizational practice.

In this LinkedIn post, I want to present a model grounded on my experience as a ten-year people analytics leader and my recent Ph.D. research. In a way, this new model can be seen as a fusion between the delta model and the nine-dimension model. However, additional features of the model are the various types of necessary alignment (FIT) in and outside the organization and the specific role of people analytics leadership. Depending on the organization's context (e.g., market dynamics, product innovation, or geographical footprint) and analytical ambition (e.g., enterprise analytics, advanced and deployed analytics, or democratizing analytics), an organization can use the model to shape its ideal transformation path.

The model exists of six components. (1) people analytics fit with the strategy, internal organization, and its environment, and (2) stakeholder management. The other four components concern the necessary resources needed to perform people analytics. (3) structure & processes, (4) knowledge, skills, & abilities, and (5) data & technology, and (6) the specific role and responsibilities of people analytics leadership.

In this post, I share my thoughts and experience on the various types of people analytics FIT. Future posts will address the other components of the model, so stay tuned. The concept of FIT is not new. I won't elaborate in this post on the insightful and long history of academic work that has been done related to the concept of FIT. If you want to read more on this topic, and for many other reasons, I refer you to the book by Paauwe and Farndale (2017) titled "Strategy, HRM, and Performance."

"To be successful in people analytics, having strategic fit or alignment is not enough."

Strategic FIT

One of the most important drivers for a successful people analytics practice is alignment with your organization's strategic goals. This is not only my personal experience but also embraced by most of my peers and academia. Strategic alignment, or strategic FIT, is simply ensuring that your people analytics projects are serving the strategic opportunities and challenges of your organization. A good strategic FIT will create legitimacy and trust among important stakeholders leading to a broader acceptance of people analytics. Having business acumen, understanding the strategy of your organization or business lines, and business operating models within your organization is vital in creating a people analytics project portfolio aligned with the organization's goals. Business needs related to the strategy are not limited to financial performance, like products sold, market growth, or client satisfaction. For example, employee well-being or sustainability can (and should) also be part of the organization's strategy. But to be successful in people analytics, strategic fit is not enough. To establish people analytics as an accepted, routinized, and common practice in your organization, you also have to consider environmental, organizational, and internal FIT.

Internal FIT

A strong internal FIT ensures that the people analytics practice works closely together with other human resources practices such as recruitment, leadership development, or diversity & inclusion to increase organizational and employee performance and well-being. For example, building a sourcing model based on (unstructured) skill data that enables recruiters to search for internal employees improves the efficiency and quality of internal search and, ultimately, business performance. A second example is using analytical methods to create more gender-neutral vacancy texts to attract more women for certain positions. A final example is periodically assessing the characteristics of successful hires. The insights from this type of research may sharpen the recruitment profiles an organization is hiring for. In other words, blending people analytics with other human resources practices re-enforces the impact it jointly makes on the organization.

"Close alignment of people analytics with other HRM practices increases organizational performance and employee well-being."

Organizational FIT

The same reasoning as with internal FIT is true for organizational FIT. The more people analytics is aligned or compatible with organizational work or technology systems, the more impact it generates. Organizational systems can be, for example, client processes, business decision processes, employee career decisions, or risk control processes. For instance, supporting risk in detecting fraud by delivering automated alerts based on behavior or providing vacancy recommendations to support employees in finding new internal opportunities. But also examples without the use of technology may enforce organizational FIT. For instance, evaluating self-organizing units on their effectiveness provides relevant information to make the decision to implement this way of working throughout the rest of the organization. Accurately timing your people analytics delivery aimed at the right stakeholders will accelerate decision-making. In other words, people analytics can enhance decision quality and efficiency by integrating outcomes with business processes and technology.

?Environmental FIT

The faster and better a people analytics practice can align with its environment, the more legitimacy and trust it will have in and outside the organization. Additionally, it may result in a lower risk of potential internal or external negative press or reputation damage. For example, people analytics is bounded by data privacy and legislation. Some organizations have an ethical and privacy framework in place to justify the various projects they want to execute and test them against ethical and legal standards. Another example is public opinion related to equal pay. Society 'demands' your organization to be as diverse and inclusive as possible. Besides the organization's strategy, these types of external guidance and pressure should shape your people analytics portfolio.

I hope you appreciate the various perspectives on people analytics fit with the organization and its environment. Considering all perspectives will boost your efforts to establish people analytics as a common practice.

Thanks to Jaap Veldkamp for reviewing.?