Making the best decision on which technologies will help your organization make the best people decisions is a big task. But rest assured, you’re not alone. As AI-powered people analytics continues to evolve and generate interest, HR and talent leaders are deep in evaluating the must-haves, nice-to-haves, and the art of what’s possible— both for today and future state. Here’s what’s top of mind:
1. What does ‘harmonized data’ really mean?
Data harmonization is the process of bringing together different formats, fields, and structures of data across various systems. It yields one cohesive, trustworthy dataset — a single source of truth that multiple business functions can rely on.
Successful data harmonization eliminates redundancies, reduces manual effort, and ensures compliance with global data protection laws. It provides a unified foundation that enhances decision-making, improves operational efficiency, and enables advanced analytics and artificial intelligence.
The process of data harmonization usually involves four steps:
- Data identification: Pinpointing the relevant data across various source systems.
- Data profiling: Assessing the quality and characteristics of the identified data.
- Data cleansing: Correcting inaccuracies and inconsistencies in the data.
- Data standardization: Reformatting data into a single, agreed-upon schema and set of rules.
2. If my company is already using business intelligence (BI) tools for HR and cross-LOB data analysis, what value would we get from adopting HCM-based tools?
To fairly compare business intelligence tools against next-generation people analytics tools, it’s important to examine the underlying capabilities. Here are a few considerations to keep in mind:
- Data access and semantics: People analytics tools provided by your human capital management (HCM) vendor preserve the rich HR-specific semantics that define and contextualize your metrics. This means you’re not just getting a headcount figure — you’re getting the right headcount figure calculated by the definitions your team already uses. With standard BI tools, that context can be lost when data is extracted and remodeled externally.
- Security and compliance: Keeping your people data within your HCM technology vendor’s existing security framework avoids unnecessary risk. This can be a decisive factor for organizations subject to strict compliance requirements.
- User experience and adoption: Managers and HR teams work with HCM tools every day. With native analytics, insights are surfaced right where these teams work, cutting down clicks, reducing friction, and driving adoption.
- From insight to action: Modern people analytics tools go beyond simply displaying information. They can recommend actions and trigger workflows inside the HCM tools instantly, achieving a level of end-to-end integration that BI platforms can’t natively replicate.
Moreover, if your organization wants to move toward predictive, AI-driven decision-making, an HCM ecosystem better positions you to provide insights without complex integrations. With the technology, you can evolve from analytics to automation faster.
3. How quickly can we access data insights?
A data cloud aims to significantly reduce “time to insights,” or the duration it takes to turn raw data into action items. This efficiency comes from eliminating data silos to provide real-time access to harmonized, high-quality data.
When you implement a business data cloud, your organization gets access to a unified platform connecting all of your company’s data. The cloud can be used with artificial intelligence (AI) and analytics to generate actionable insights quickly.
Some data clouds, like SAP Business Data Cloud, offer pre-built intelligent applications with ready-to-use analytics. These apps automatically turn HR and business data into workforce insights, saving you the time of building your own analytical models and dashboards from scratch.
4. If we’re using one data cloud platform, how do we integrate third-party data?
As you evaluate data platforms, it’s important to select one that supports third-party integration with an open ecosystem. Some elements to look for include:
- Open APIs: The platform should use common, open application programming interface (API) sets to allow for easy integration with different vendors. This enables you to combine multiple sources of data.
- Open standards: The platform should leverage open standards like Delta Sharing for data sharing and open resource discovery. This facilitates a more open and less restrictive data ecosystem.
- Combined structured and unstructured data: Structured data is organized in a predefined, tabular format — think databases and spreadsheets — and is used for queries and analysis. In contrast, unstructured data — like images, documents, and audio — is unorganized. This type of data can be transformed using AI and machine learning to extract value.
5. What is a knowledge graph, and what value does it provide?
A knowledge graph is a semantic network that connects real-world entities and illustrates the relationships between them. Think of it like a GPS. It understands the road network and the connections between different locations. You can ask it a question, and it can navigate the “roads” of information to find a relevant answer, rather than just giving you a list of addresses.
By moving beyond simple data points, knowledge graphs represent the deeper meaning and connections with your data. It acts as the foundation for AI, providing context that fuels more accurate, trustworthy and relevant results.
6. How is AI being used to enable better decision-making?
People analytics leverages AI in a number of ways to enhance decision-making, including:
- Powering intelligent applications: AI can be integrated into pre-built, intelligent applications that provide curated insights.
- Automating data analysis: AI can automate tasks, such as data preparation and analysis, leading to increased efficiency and improved data quality.
- Surfacing insights in the flow of work: AI copilot can automate workflows, extract key insights, and answer natural language questions about the business data.
- Enabling predictive and generative AI: AI can allow data scientists to use tools like SAP Data Bricks and Python notebooks to create models that predict outcomes like employee attrition.
- Facilitating advanced simulations: AI can be used to run complex enterprise simulations to forecast risks and analyze the impact of different business factors.
Discover the power of people analytics with People Intelligence
Here’s another frequently asked question: Who should we partner with to make the most of our HR data? If you’re looking for a people analytics partner, check out SAP’s People Intelligence.
This article is part of a series covering people analytics. Check out the first, second, third and fourth articles.