Are you prepared to modernize your people analytics function and transition your team from operational to strategic roles?

We recently hosted a webinar titled “Leveling Up Your Early Stage People Analytics Pipeline” with Spencer Sickle, Solutions Consulting Director at Crunchr. During the webinar, he examined how HR teams can set up their data pipelines, streamline workflows, and elevate their strategic roles.

Watch the recording today, or keep reading for the footnotes, which include technical tips on building data pipelines. We’ll even discuss why automating this process is more critical now, thanks to technology and increased demands on business leaders to do more with less.

Previously recorded in June, 2024

Data Harmony and Synchronization: Aligning People Analytics for Success

At the core of effective people analytics is data harmony. For organizations to extract meaningful, actionable insights, seamless synchronization between various data points is necessary. This synchronization ensures consistency, accuracy, and trust in the data.

The webinar outlined a practical guide for building effective data pipelines that harmonize and synchronize data across scattered systems. Here’s a breakdown of the five technical aspects we discussed:

1. Build the Foundation of a Data Pipeline

Data harmonization and synchronization are key to building a successful people analytics data pipeline. This involves consolidating disparate data sources into one unified system. Spencer explained that the first step is to identify all relevant HR data sources, which may include:

• Employee data from HRIS (Human Resource Information Systems)
• Payroll and compensation data
• Performance management tools
• Learning and development platforms
• Recruitment and onboarding software

It isn’t easy to ensure data consistency without clearly understanding where your data is stored and how it interacts. The pipeline must centralize this data into a unified platform to ensure seamless synchronization. Fragmented data sources can lead to inefficiencies, wasted time, and a general lack of trust in the final insights.

2. Choose the Right Technology and Infrastructure

The second step is to choose the right technology stack. Spencer stressed the importance of using a robust people analytics platform like Crunchr that integrates easily with existing HR systems. By simplifying the process of building an HR data pipeline, tools like Crunchr can provide:

• Automated data imports, pulling information from various HR systems in real-time.
• Data cleaning tools ensure that all data is standardized, reducing errors and inconsistencies.
• Cloud-based infrastructure allows for scalability as the organization grows and the data increases in volume.

Using cloud-based solutions, organizations can also avoid the hassle of on-premise hardware, ensuring that the system is accessible from anywhere and that data is backed up and secure.

3. Involve Key Stakeholders

Setting up a people analytics pipeline requires collaboration across multiple stakeholders. Spencer stressed the importance of involving both HR and IT teams early in the process. The key stakeholders typically include:

• HR leadership: They define the strategic goals of the people analytics initiative and determine which insights are most important for business decision-making.
• Data analysts or data scientists: These professionals help structure the data, ensure that it’s collected and stored correctly, and ensure that the tools used can generate meaningful reports.
• IT department: IT supports the technical setup, ensuring data from different HR systems flow smoothly into a unified pipeline.
• External vendors: Technology partners provide the backbone and ongoing support to maintain the pipeline, ensuring it stays updated as systems evolve.

By bringing together HR, IT, and data specialists, organizations can ensure that the data pipeline aligns with technical requirements and business goals.

4. Be Realistic about Timelines and Processes

The timeline to set up a people analytics pipeline will vary depending on the size of the organization and the complexity of its data sources. Spencer outlined a typical process:

• Initial Data Mapping (2-4 weeks): This is where HR and IT teams work together to identify and map all data sources. Understanding which data needs to be retrieved and how it will be utilized is crucial.
• Tool Integration and Data Consolidation (4-6 weeks): During this phase, the people analytics platform is integrated into the organization’s existing HR systems. Data from various platforms is consolidated and synchronized into a central platform.
• Data Cleaning and Standardization (2-3 weeks): Once data is consolidated, it undergoes a period of cleaning and harmonizing, ensuring that all data fields match up and that any duplicate or erroneous data is removed.
• Testing and Validation (1-2 weeks): Before full deployment, the pipeline is tested to ensure that it’s pulling data correctly, generating accurate reports, and providing actionable insights.

On average, Spencer suggested that a typical organization could expect to have a fully operational people analytics pipeline within 8-12 weeks. Larger organizations with more complex data sources might take longer.

5. Ongoing Maintenance and Monitoring

After the initial setup, ongoing maintenance is required to ensure the data pipeline functions optimally. However, the good news is that tools like Crunchr automate many of the most time-consuming tasks, including:

• Automatic data synchronization: Crunchr continually pulls data from various sources in real time, ensuring insights are based on the latest information.
• Data quality monitoring: Crunchr’s system includes built-in tools for detecting anomalies or inconsistencies in the data, alerting HR teams to any issues that might arise.
• Software updates and scaling: As organizations grow or their data needs change, Crunchr scales to accommodate increased data volumes or new integrations without requiring significant manual intervention.

With these features in place, ongoing maintenance is minimal. HR teams can focus on interpreting insights and driving strategy while the platform handles the technical side of data synchronization and harmonization.

The message at the end of this session was clear: by providing HR with timely, accurate insights, Crunchr enables HR to focus more on strategy and less on administrative tasks such as building and cleaning data, and data harmonization.

Ready to take your HR analytics to the next level? Discover how Crunchr can help you achieve data harmony and deliver insights with confidence.