Data quality in HRIS systems can be defined in a number of ways and will have a significant impact on how Identity and Access Management (IAM) solutions work in the organization and how data quality is managed across the IT environment. From HR or business operations perspective, data is often evaluated based on whether it is “Fit for Purpose”. This guideline may allow a wide variation in certain types of data that may be fit for HR’s purposes but does not support the needs of rule-based IT systems such as IAM. In this post we look at these two approaches to managing Identity data, the businesses “Fit for Purpose” model and IAM/IT’s “Rule-Based” model, which represent different perspectives on what data is important, how to measure quality, and the challenge of aligning business requirements with IT requirements.
Getting to Data Alignment/ Data Detente
The creation and maintenance of user data in the HRIS system have traditionally been driven by the need to manage employee requirements such as payroll, benefits, and official communications. Additionally, HRIS systems provide centralized reporting, finance/accounting functions, and support compliance with government regulations. All of these factors drive what data is important to HR, which is focused on management of employee transactions and compliance requirements. Creating and maintaining these worker records is no easy task; however, government compliance requirements drive the diligent management of HR data. Because the worker data is actively maintained, HRIS systems are very important in providing user identity data to IAM systems. The danger inherent in this relationship is that the business has a different definition of data quality — where the organization’s data quality efforts are focused on finance and compliance, while IAM systems are more interested in the constantly changing organizational data that drives user access to systems and applications.
The open question is, can the HRIS system serve both the HR function and the IT/Identity Management requirements for access management and security purposes? This represents a significant challenge for the Human Resources Department as it reflects a dramatic shift in perspective on data quality. Now their operational scope expands to include providing trusted data to downstream systems. From an organizational and governance perspective, new connections to business units are required to understand the businesses specific data needs. The end game is a re-alignment of the HR function to provide more value to the business and have the HRIS become the single book of record for employee data.
For IT and the business, data quality is often the root cause of their Identity Management problems. Missing fields, inconsistent entries (e.g. Main Street vs Main St.), latency, and other challenges result in lost productivity, security issues, and IT audit/compliance problems. These issues are costly for the business and Identity Management provides a platform to manage the worker lifecycle efficiently and effectively. That said, organizational culture and politics also play a significant role in how these groups work together to manage the entire employee lifecycle across HR and IT (and the broader organization). There is no “one-size-fits-all” solution that will meet everyone’s needs; however, HR has an opportunity to support more of the employee lifecycle beyond the traditional HR domain.
Clearly, expanding the definition of data quality presents new challenges to HR and the business that they will need to overcome. These challenges also represent an opportunity to drive automation across more of the organization to improve productivity, reduce risk, and drive down operating costs. Although these initiatives can provide significant business value, it is usually poor data quality that undermines the long-term success and sustainability of IAM implementations. In the end, executive leadership must get behind these programs to break down organizational silos, re-align business processes and data quality initiatives, and establish effective governance that defines appropriate data standards for the enterprise to be successful.
We discuss this topic in-depth in our on-demand webinar Identity Management: Overcoming Data Quality Challenges.
Many organizations experience the hidden costs of low-quality user data, which makes automating routine Identity Management and provisioning tasks difficult and drives unnecessary operational and support costs. Learn how to overcome common data quality problems in our webinar “Overcoming Data Quality Challenges In Your IAM Implementation”
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