What does data-driven HR look like and how can technology help achieve it? In its simplest form, it implies that data gathered from current and prospective employees is used to obtain key insights into the organization.
These insights can be leveraged to make more effective HR decisions, design more efficient HR processes, and enhance the general well-being of the people in the company.
The datafication of HR
The primary trend behind a data-driven HR organization is the concept of datafication – the transformation of HR data into new forms of value. This approach allows HR to better understand their employees, job candidates, HR processes, and the industries they compete within.
Through intelligent technologies such as predictive analytics, the Internet of Things, machine learning, and artificial intelligence, organizations can leverage this HR data to discover meaningful patterns to not just answer questions as to why things happened, but, more importantly, what will or should happen in the form of advanced and predictive analytics.
For example, by analyzing candidate data and comparing their attributes to the current workforce, HR might be able to predict the quality and future success of our new employees. Predictive and advanced analytics examples like this can be employed across the HR spectrum:
- Recruitment: Taking the guesswork out of talent acquisition and identifying the best ways to attract suitable candidates who stay with the company for longer
- Employee engagement: Using sentiment analysis in emails and other communications to determine what employees are really thinking and feeling
- Talent retention: Extracting insights on employee turnover and identifying who might be about to leave the company and taking proactive measures to prevent it
- Learning: Creating evidence-based links between training and employee and company performance thereby identifying the skills employees should be acquiring
Data: internal vs. external, structured vs. unstructured
HR data can be classified as either internal or external. Internal data includes information that is owned by the organization and found on various platforms across the enterprise. External data could be publicly available on the Internet or privately held by another organization. Examples might include social media profiles, recruitment data from LinkedIn and job boards, economic data, and exit-interview data.
Data can also be categorized as either structured or unstructured (or semi-structured). Structured data implies the data can be neatly organized into rows and columns. Employees’ personal, organization, employment, time and absence, activity tracker, and similar data would be considered structured data. Unstructured data would be information that cannot be organized into a spreadsheet. Unstructured data could include social media posts, emails, survey data, photos, videos, and audio recordings.
For many HR questions, access to structured data alone is often not sufficient. For example, with internal structured data, you might be able to determine that the employee turnover rate is 15%, but without the unstructured external data contained within the employee exit interviews, you won’t know the why behind the turnover rate.
Indeed, structured data only accounts for about 20% of all the data in the world. The remainder is considered unstructured. Over time, the ability to analyze unstructured data will become more and more critical to the HR enterprise. Thankfully, with advances in storage and computing power, exploiting unstructured data has become a reality.
- “The Datafication of HR: Graduating from HR Metrics to People Analytics,” Visier.com, 2018.
- “Datafication in HR: What is Your HR’s Role in the Company? Operational or Strategic?” LinkedIn, 2017.
- Marr, B. (2018). Data-Driven HR: How to Use Analytics and Metrics to Drive Performance,” New York: Kogan Page.
- “The Importance of Universal Data,” SAP HANA Blog, 2018.
- “SAP Data Hub: Sharpening the Data Landscape View,” SAP, 2018.
- “Plan Collaboratively Across Your Whole Business,” SAP, 2018.
- “Deliver All Analytics for All Users Through a Single Product in the Cloud,” SAP, 2017.
- “Extend and Reuse Your EIM and HANA with SAP Data Hub,” SAP, 2018.
- “The Future of People Analytics: New Horizons of Intelligent HR Consumer Experience,” Digitalist Magazine, 2018.