People Data as a Product | Deloitte US has been saved
Authored by Eric Lesser, Eric Bokelberg and Devon Johnson
People analytics and the quest for data
Imagine embarking on your weekly grocery shopping in a colossal warehouse filled with countless ingredients from various suppliers. Each item has a label, but the details are scant, leaving you unsure about the source, quality or potential issues when combined with other ingredients. Now imagine having to assemble these into nutritious meals that require significant effort, starting from scratch each time.
This scenario mirrors the challenges faced by workforce analytics professionals today.
In the quest for actionable insights, they grapple with disparate data-sets, each with limited documentation on definitions, lineage or quality. The process is cumbersome—often involving manual data cleansing and complex reconciliations for a single query, resulting in insights that hardly justify the effort.
In our work with clients, we often see organizations focused on people analytics struggle to effectively manage and use data from different sources. This can make it hard for them to make optimal business decisions. Our recent research involving over 400 companies shows that only half of the most mature people analytics organizations believe they do well in combining data from various human resources (HR) systems, and this number drops to just 5% for the least mature.1
A practical solution to these problems is to organize and access HR data through data products. These are premade subsets of data, organized, cataloged and stored in a way that makes them easy to find, understand and use throughout the organization. Data products help analysts better grasp what data is available, where it comes from, how it can be used and how to put it together to gain insights effectively. Moreover, they are actively managed as a distinct “package” that starts with the acquisition of data elements from source systems all the way through their assembly, marketing, downstream consumer use and ongoing maintenance.
Traditionally, HR departments that wanted to manage their employee data effectively built large data warehouses. These warehouses were designed to handle all sorts of data needs and connect different systems. However, these projects were expensive and often couldn't keep up with changing business requirements or updates in data sources. While they managed to provide basic HR data for companywide reports, adding new data or answering new questions required much effort to modify the data structure and update the data integration processes.
Data products make managing people data more flexible. Think of them as prepackaged meals at a supermarket with main courses, sides and desserts are ready to go. Data products gather information from various sources to meet everyday business needs. They are organized into specific categories and can be combined in different ways to answer different analytical questions. When a new business question arises, the needed data is often already available in a data product, making it easy to gather the correct information. If there's a need for new data, the clear-cut structure of data products makes it easier to integrate this new information.
Key areas for managing data products
To effectively implement data products, HR and information technology (IT) departments should consider revising their data management approaches by applying principles of product management across the data life cycle. This requires clear guidelines across several key areas:
- Ownership: Assign a specific individual to manage each data product from start to finish, including requirements gathering, development and maintenance. This manager will work closely with system owners, data engineers and end users to ensure the product meets its goals and complies with all specifications.
- Reuse: Design data products to serve multiple purposes. For example, a data product containing information on recent job candidates could be used to analyze candidate experience survey responses and evaluate different recruitment strategies.
- Quality: Set explicit standards for how data products should be used and maintained to guarantee data reliability and address quality issues proactively.
- Cataloging: Enable users to search for and access data products independently, similar to shopping online. This approach can reduce the need to directly contact data owners and streamlines data retrieval in accordance with privacy and governance standards.
- Security: Implement security protocols that restrict data access based on user roles and specific data fields. Provide the data product owner’s contact details for cases requiring special access. Include guidelines to help prevent the combination of data products when regulatory or ethical constraints apply.
Data at work: A global case study
A global professional services firm revamped its HR processes to enhance decision-making through advanced data analytics. To achieve its vision, the company developed a new cloud-based people data strategy—centered around data use cases, a centralized data hub, and the associated data products—to enable more timely and effective analytic insights. They also established new processes, roles and responsibilities to support managing the data products across HR and IT and ensure robust data governance. Within nine months, these data products provided reliable insights, which HR partners quickly embraced. The project's success led to the development of additional data use cases, further expanding the hub's capabilities.
Three steps to get started with data products
Redesigning a people data ecosystem with data products may seem overwhelming, but it allows organizations to address issues gradually, focusing on one use case at a time.
Key steps include:
- Educate HR and IT teams about data products: It's crucial for both business and technical teams to grasp the significance of managing data as a product. Understanding the value, ownership, processes and technologies involved will help them leverage these products to accelerate analysis and gain valuable insights.
- Focus on impactful use cases: Prioritize developing data products that respond to frequently asked questions and can provide immediate benefits. Instead of creating numerous data products, concentrate on those that effectively address pressing business needs.
- Establish a strong governance model: Coordination between HR and IT—along with input from risk, legal and business representatives—is essential. This collaborative approach will help determine which data products to develop, how to manage them and how to ensure their ongoing effectiveness.
When people data is distributed across various source systems, data products simplify the process of data management, much like how prepackaged meals simplify cooking. They streamline data collection, improve data accuracy and make data readily available to decision-makers—enhancing the decision-making process.
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Endnotes:
1Deloitte, 2023 High impact people analytics research, June 2023.
