12 Steps To Build an HR Data Strategy [+ Examples]
With the new technologies and data sources available today, HR professionals have the chance to move beyond traditional metrics like time-to-hire or turnover rates and begin measuring the real impact people have on business results. Are you ready to join the future?
A high-impact HR data strategy isn’t about collecting more numbers. With the right structure, tools, and habits in place, HR teams can translate day-to-day data into decisions that create real business impact.
At Credit Suisse, predictive analytics helped identify employees at high risk of leaving by analyzing patterns across engagement, performance, and compensation data. This gave managers a chance to intervene early, adjusting workloads, offering development, or addressing concerns. The strategy improved retention and saved the company an estimated $70 million annually in turnover-related costs.
That kind of impact is only possible when HR has the analytical capability to connect the dots. And that starts with embracing data, developing new skills, and seeing AI as a practical partner in the work, not a far-off concept.
Contents
Why you should have an HR data strategy
What to include in your HR data strategy
How to build an HR data strategy
HR data strategy examples from practice
Why you should have an HR data strategy
An HR data strategy is a structured approach to collecting, managing, analyzing, and using workforce data to drive business outcomes. It defines how data collected across the employee life cycle supports broader business priorities by setting clear priorities for what data to collect, how to interpret it, and how to embed those insights into decision-making.
A well-developed HR data strategy ensures consistency, accuracy, and governance. It also addresses the tools, systems, and talent needed to support data literacy and effective decision-making in HR.
Implementing a robust HR data strategy delivers numerous measurable benefits, including:
- Improved decision-making by providing HR leaders and business stakeholders with real-time, evidence-based insights rather than relying on instinct or anecdotal evidence.
- Better problem-solving by identifying patterns and root causes of issues like high turnover or low engagement.
- Increased business impact, positioning the HR function as a partner in driving productivity, profitability, and workforce agility.
- Stronger compliance with data privacy laws and ethical standards is critical in managing sensitive employee information.
- Improved workforce planning, better talent forecasting, and stronger alignment between people strategy and organizational performance.
What to include in your HR data strategy
A well-designed HR data strategy gives HR professionals the tools to turn workforce data into timely, focused insights. Think of it as your practical framework for using workforce data in ways that directly support organizational goals.
However, before you can build an HR data strategy, it is important to identify the different components that you will need to address across your organization. Let’s take a look.
Strategic objectives and business alignment
According to Gartner’s global survey of over 1,400 HR leaders, strategic alignment is a core driver of successful HR transformation, with the most effective teams leveraging data to support broader organizational priorities.
Begin by clearly articulating what the HR data strategy must achieve, whether the focus is on improving workforce planning, supporting DEI outcomes, optimizing recruitment, or enhancing employee engagement. This alignment ensures that data-driven insights are relevant, actionable, and prioritized according to business needs and that your strategy is rooted in broader organizational goals.
Data collection methods
Detail where your workforce data will come from and how it will be collected. This includes structured data from HRIS and payroll systems, ATS platforms, performance management tools, and learning systems, as well as unstructured data from employee surveys, engagement platforms, and feedback channels. A successful strategy captures data across the entire employee life cycle to provide a 360-degree view of the workforce.
Data governance and privacy
Define your governance framework and make sure that data is collected, stored, and used ethically and securely. A successful framework assigns data ownership across the HR function, establishes access controls, and develops policies that comply with local and international privacy regulations like GDPR.
Data quality and integrity protocols
High-quality data is the foundation of credible analysis, so implement processes for data validation, cleaning, deduplication, and enrichment. Without consistent data hygiene practices, even the most sophisticated analytics tools will yield misleading results, so schedule regular audits to assess the accuracy and completeness of your datasets.
Advanced analytics and data modelling
Outline how your organization will move beyond basic reporting into deeper analysis and achieve higher HR analytics maturity. This includes the use of descriptive analytics (what happened), diagnostic analytics (why it happened), predictive analytics (what might happen), and prescriptive analytics (what to do about it).
Invest in data science capabilities — internally or through external partners — to develop models that support forecasting, scenario planning, and decision simulation. It’s worth the effort and investment.
McKinsey’s research into people analytics found that organizations that embed analytics into talent processes outperform their peers across multiple dimensions, including talent acquisition efficiency, employee retention, and leadership development. In fact, McKinsey recommends using data to benchmark performance, uncover bias, and directly link talent strategy to business impact, all goals central to this element of your HR data strategy.
AI and intelligent automation
According to Deloitte’s recent Global Human Capital Trends research, high-performing organizations are more likely to use predictive tools for workforce planning and performance optimization, and they tend to achieve stronger financial results, including improved stock performance. Increasingly, AI’s role is growing beyond isolated use cases, becoming a key enabler of boundaryless HR, helping HR move from a siloed function to an integrated discipline embedded across the business.
AI and machine learning can support this transformation by powering scalable, real-time people analytics; informing workforce planning based on live skills data; and supporting cross-functional collaboration. Organizations can also use AI to screen candidates, identify attrition risks, analyze employee sentiment, and match skills to shifting roles.
However, deploying AI in the people function requires clear governance. It’s critical to be transparent about how algorithms are trained, monitored, and tested for bias, especially in areas involving people’s decisions. The shift to boundaryless HR starts with a new mindset, but it’s brought to life through the intentional use of AI, new metrics, and business-aligned people strategies.
Reporting and communication frameworks
Develop a consistent approach for delivering insights across the business, including real-time dashboards for operational use, as well as executive-level reports that track key HR metrics and their impact on business performance. Effective reporting should display data, tell a story and offer insight into what actions should be taken.
Technology infrastructure and tools
Specify the systems and platforms that will support your data strategy. Focus on your core HRIS, cloud-based analytics tools like Tableau or Power BI, data warehouses, and integration platforms that connect disparate data sources and prioritize tools that support scalability, real-time analytics, and ease of use for HR and business users.
Data literacy and capability building
Even the best tools are ineffective without people who can use them, so commit to upskilling HR teams in data interpretation, storytelling, and basic analytical methods. It’s also a good idea to partner with Learning and Development to roll out foundational and advanced training that equips HR professionals to work confidently with data and engage in evidence-based decision-making.
Ethical use of data and AI
Address the growing need for ethical standards in how data — and especially AI — is applied in HR. The entire HR function should be transparent about how data is used to make decisions, from hiring to performance evaluation. Establish checks to prevent misuse or bias and ensure that employees understand their rights in relation to how their data is collected, analyzed, and applied.
Scalability and future-readiness
Finally, as business models evolve and new technologies emerge, your HR data strategy should be able to accommodate additional data sources, new regulatory requirements, and the growing need for real-time insights. It’s therefore important to design your strategy to be flexible and future-proof, laying the foundation today that can support the strategic ambitions of tomorrow.
How to build an HR data strategy
Here’s how you can start building your HR data strategy step by step.
Step 1: Establish clear objectives aligned with business priorities
Begin with clarity. What are you trying to achieve with your HR data strategy? Whether the goal is to reduce turnover, improve workforce planning, or identify skills gaps, your objectives must directly connect to business challenges and opportunities.
Do this:
- Meet with executive leadership to understand top business priorities and where HR can provide support through data.
- Define three to five core HR objectives (e.g., “Improve leadership pipeline visibility,” “Predict and reduce voluntary attrition”).
- Use these objectives to guide which data you collect, which metrics matter most, and how success will be measured.
Step 2: Audit existing data and identify gaps
You can’t build a strategy without knowing what you’re working with. Most organizations already hold a wealth of people data, but it’s often fragmented, outdated, or underused. Auditing your existing HR data sources gives you a clear view of your current capabilities and uncovers opportunities for integration and improvement.
Do this:
- Map all current HR data sources (HRIS, ATS, payroll, engagement tools, exit interviews, etc.).
- Assess data types (structured vs. unstructured), quality, and accessibility.
- Identify gaps where key data is missing (e.g., skills inventory, training ROI, internal mobility data).
- Document duplication or inconsistencies across systems need to be addressed later.
Step 3: Build a governance and privacy framework
A sound HR data strategy requires strong foundations in data governance and compliance to ensure accuracy, clarity of ownership, and protection of employee privacy. Governance builds confidence in both your team and your stakeholders that the data being used is reliable and ethically managed.
Do this:
- Define data ownership: who is responsible for maintaining what data?
- Set standards for accuracy, storage, retention, and version control.
- Ensure compliance with data privacy regulations.
- Establish access levels so that only authorized personnel can access sensitive data.
Step 4: Ensure data quality and consistency
No matter how advanced your tools are, poor data quality will undermine everything because inconsistent or inaccurate data leads to bad decisions, reduced trust in HR, and wasted effort. Start with data hygiene as your foundation and make this an ongoing practice.
Do this:
- Create data validation and cleaning routines to remove duplicates, correct errors, and standardize fields.
- Build a data dictionary so key terms (e.g., “high performer”) are defined and used consistently across systems.
- Run monthly or quarterly audits to maintain data health.
Step 5: Integrate disparate data sources for a unified view
To uncover meaningful insights, you need a complete picture of the employee life cycle, not a disconnected set of spreadsheets. Integrating systems gives HR professionals access to richer analysis and supports predictive modelling that drives real value.
Do this:
- Choose an integration approach (e.g., API connections, data warehouses, or middleware tools).
- Prioritize integrating critical systems first (e.g., HRIS, performance, learning).
- Work with IT or an external partner to ensure scalability, security, and clean architecture.
Step 6: Upskill HR in analytics and data literacy
Building HR’s analytical capabilities is essential. A strong strategy means little if the team lacks confidence in using the data, so make sure your team has the training and knowledge to ask the right questions, interpret results accurately, and use insights in decision-making.
Do this:
- Assess current data literacy levels across your HR team.
- Provide training in data interpretation, storytelling, basic statistics, Excel, and business intelligence tools like Power BI.
- Encourage the use of data in team discussions, planning sessions, and decision-making processes.
- Nominate internal “data champions” to lead by example.
Step 7: Invest in the right technology, tools, and people
Technology is a critical enabler, but it’s only effective when paired with the skills to use it, so choose platforms that support your strategic objectives, and don’t neglect the human capability to extract real value from these tools.
Do this:
- Select tools based on the problems you’re solving, and don’t be swayed by features you won’t use.
- Invest in platforms that support dashboarding, analytics, visualization, and forecasting (e.g., Tableau, Power BI, Visier).
- Ensure you have support (internal or external) to configure and maintain the tools effectively.
- Allocate budget for training, ongoing support, and upgrades.
Step 8: Operate with ethics and transparency at the core
As HR increasingly uses AI and predictive analytics, ethical use of data becomes a business-critical issue. Employees must trust that their data is handled responsibly and that algorithms are not making unfair or biased decisions.
Do this:
- Establish clear ethical guidelines for how employee data and AI tools are used.
- Regularly test algorithms for bias or unintended consequences.
- Be transparent with employees about what data is collected and why.
- Ensure consent is obtained and data is anonymized when appropriate.
Step 9: Collaborate across departments for holistic insights
HR data doesn’t exist in a vacuum. Partnering with Finance, Operations, IT, and business units unlocks richer insights and ensures data is used in cross-functional planning, not just HR reporting.
Do this:
- Set up regular touchpoints with other departments to align on goals and share data.
- Identify common challenges (e.g., absenteeism, productivity, turnover costs) where joint analysis adds value.
- Use shared dashboards or reports that show HR metrics in business context (e.g., cost per hire linked to revenue growth).
Step 10: Turn insights into actionable strategies
The goal isn’t just insight, it’s impact. Your HR data strategy should culminate in better decisions and smarter action, which means translating data into stories and recommendations your stakeholders can understand and use.
Do this:
- Build dashboards that show trends and provide context and suggested actions.
- Present data with recommendations, not just charts.
- Use insights to inform real policy or program changes (e.g., revamped onboarding, internal mobility programs).
- Track what actions were taken and evaluate the results to build a feedback loop.
Step 11: Communicate progress and value to stakeholders
To maintain momentum and funding, your data strategy must be seen as valuable, so show how HR data has led to better decisions, saved money, or improved outcomes, and tailor this message for each audience.
Do this:
- Report on outcomes (e.g., reduced turnover, faster hiring, higher engagement) tied to data initiatives.
- Create stakeholder-specific reports or presentations (e.g., what matters to the CFO vs. the COO).
- Share quick wins and use them to build confidence in long-term goals.
Step 12: Review, evolve, and stay ahead
A data strategy is a living framework. As new business questions arise, systems develop, or your workforce changes, your strategy should adapt too, so treat this as a continuous improvement process.
Do this:
- Set quarterly or biannual reviews of your strategy and tools.
- Collect feedback from HR, IT, and business users.
- Stay updated on new technologies, AI tools, and compliance changes.
- Adjust your roadmap and retrain your team as needed.
HR data strategy examples from practice
Case study #1: Empowering managers with data at Shutterstock
Shutterstock, a global creative platform, launched a transformative journey to bridge the gap between employer and employee data. Under the leadership of Max Iacocca, Head of Global People Operations, the company identified two primary challenges: a lack of actionable insights from existing data and a top-down approach to employee engagement that limited managerial autonomy.
To address these issues, Shutterstock prioritized standardizing data definitions and reporting periods, ensuring consistency across departments. This initiative was strengthened by close collaboration between HR and finance teams, aligning workforce planning with cost allocation strategies.
Recognizing the important role of managers in driving engagement, Shutterstock shifted its culture to empower them with accessible data analytics tools. Managers overseeing teams of five or more were granted access to detailed engagement data, helping them to make informed decisions and create a more inclusive work environment.
A significant milestone in this transformation was the overhaul of the employee engagement survey process. Transitioning from a traditional, top-down model, Shutterstock implemented a more agile and inclusive approach, integrating engagement data with broader workforce metrics. This supported more nuanced insights into retention, collaboration, and autonomy, ultimately boosting organizational health.
The key takeaway: Through these strategic initiatives, Shutterstock successfully democratized data access, empowered its managers, and cultivated a high-performing, engaged workforce
Case study #2: Making people analytics operational at CBRE
CBRE, the world’s largest commercial real estate services firm, transformed its HR strategy by embedding people analytics into its decision-making processes. Led by Méline Van Slyke, Director of Human Resources at CBRE Limited (Canada), the HR team partnered with HireRoad to align analytics with business needs, particularly in recruitment and workforce planning.
Initially, CBRE lacked the tools to track its “Strategic Recruitment Initiative” effectively. By implementing tailored dashboards, they gained visibility into recruitment trends, supporting data-driven talent conversations. This shift helped the identification of gaps and informed decisions beyond intuition.
Recognizing the unique performance metrics in sales roles, CBRE collaborated with HireRoad to integrate revenue and commission data with demographic insights and gain a better understanding of sales performance, which was crucial for a company in constant recruitment mode.
The HR team tackled the challenge of comparing performance across different offices by developing side-by-side analytics for key metrics like support staff ratios and demographic breakdowns. This gave market leaders a clear view of how their teams stacked up and the data they needed to take targeted action.
The key takeaway: By aligning people analytics with specific business needs and making insights directly actionable for leaders, CBRE moved beyond static reporting to a more dynamic, operational use of HR data, supporting smarter decisions in recruitment, workforce planning, and team performance management.
To sum up
The value of HR data lies not in the numbers themselves, but in how people use them to inform decisions, build trust, and act with intention. From analyzing the impact of learning on promotion rates to equipping managers with real-time insights to support their teams, the message is clear: data becomes transformational when it is relevant, accessible, and aligned to outcomes people care about. HR professionals who treat data as both a cultural and operational priority are better positioned to drive measurable business value.
Of course, a well-designed HR data strategy alone isn’t enough. Effective execution depends on cross-functional collaboration, clear governance, continuous upskilling, and a strong commitment to quality and ethics.
The most successful HR teams do more than report on trends; they connect data to action, using it to shape policy, improve performance, and elevate the employee experience. As you plan your next steps, your priorities should be to invest in capability, build credibility with stakeholders, and ensure the systems you build today can flex to meet the needs of tomorrow.
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