Yves Synold

Jean-Pascal Vendeville

Each new human resource information system (HRIS) project launched confirms the growing role of data in organizations. Being “data-centric” has become a strategic priority in all major projects and is a fundamental part of the structuring and planning of HR transformation.

Today, HR data is considered a true asset of the HR function and a key component of performance.

The newfound status of data in the HR space can be attributed to the relentless quest for performance, the trials (and errors) of HRIS projects, and, ultimately, the rise of specialized tools dedicated to HR data management (e.g., Core HR, HR Analytics, Data Visualization tools).

What have we learned? Efficient data management is often the critical factor that determines either the failure or success of HRIS projects in the long term.


7 priorities for HR Data Management

Decipher and interpret the key elements of past performance.

Anticipate and optimize future performance.

Apply predictive analytics to HR data for strategic planning.

Facilitate evidence-based decision-making.

Respect regulatory frameworks.

Protect sensitive information from breaches or leaks.

Commit to an ethical approach.


How does being data-centric feed into being performance-centric?

HR transformation is based on a central goal of radically improving service quality and, therefore, overall performance. During the planning phases of HRIS projects we’ve worked on with our clients, this search to improve performance often translates into a few primary objectives that link directly to the management of HR data.

To master the data, you must accurately set, measure, and anticipate KPIs.

This focus on data-centricity allows businesses to objectively evaluate the value of new HRIS projects across verticals such as cost reduction, employee productivity, value-added knowledge from analytics, or even support of the organization’s larger business goals.


Key processes around deploying a data-centric HRIS project


Data structuring in a top-performing HRIS

According to function, the types of HR data can be divided into two subsets. The first is “core data,” which is necessary for deploying the main HR processes within the HRIS (e.g., administrative management, recruitment, training, evaluation). The second covers the data required to personalize user experiences, conduct forward-looking thinking, and create the conditions for better performance. These two subsets often call for different infrastructure and data management solutions within an HRIS.

To take care of the former, most organizations establish a Core HR function dedicated to data management. Unfortunately, whether they use third-party software for administrative management, or develop their own data repositories, many of these options are severely limited in terms of their capabilities.

Core HR software implementation has advantages in improving the structure and quality of HR data. It nevertheless introduces some complexity, mainly when used with existing infrastructure. For example, standardization and integration are needed to allow the new Core HR functions and legacy administrative or payroll management systems to communicate meaningfully with each other.

Beyond the core functions of HR, the expectations of HR data management increasingly concern the ability to anticipate, personalize, and create the conditions for sound decision-making.

Evidence-based management allows organizations to make informed decisions based on data. Using inductive analysis to process large volumes of diversified data – both structured and unstructured – provides insights for planning and further action.


Regarding HRIS planning and structuring, the response to the organization’s needs is mainly reflected in the implementation of architectures articulated around “data lakes” specific to the HR scope or company-wide. We’ve observed two main approaches:

The first consists of combining a data lake, an ETL-type tool (to extract, transform, and load data), and a data visualization tool. This architecture has proven its effectiveness but has limitations: it doesn’t operate in real-time and requires heavy maintenance.
The second is based on the implementation of Lambda- and Kappa-type architectures with data streaming platforms that allow the dissemination of large volumes of data in real-time. However, the setup is complex and requires heavy initial configuration and strong computational capabilities.

The two approaches serve different needs and should be chosen according to the organization’s end business goals. Measuring HR performance itself is nuanced and has several dimensions. At a minimum, it concerns the base aspect of regulatory compliance and the improvement of operational efficiency on the one hand and the digitalization of talent management processes coupled with an adequate capacity for data analysis/visualization on the other.

Even if each of these dimensions corresponds to different infrastructure patterns, the central role of data remains an essential factor in success.


Realizing the true value and ambitions of HR

Becoming data-centric is a new challenge for HR and IT teams. But it also represents an exceptional opportunity to create more value. Many companies have understood this without knowing how to carry out this transformation, which affects cultural, operational, and IS dimensions.

When faced with these large-scale projects, Wavestone recommends adopting a progressive, educational, and pragmatic approach.

There remains much to be done, including the embedding and education of HR and IT teams on the contribution of data, the exploration of technological levers, and experimentation within the framework of a dynamic and realistic trajectory which will allow you to initiate the necessary switch to being fully data-centric.

Want more details on building and implementing a customized HRIS project that optimizes data management to improve your HR processes?



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