Foundations of HR Technology and Digital HR

Introduction

Human resource management has always depended on information. Managers need to know who works in the organization, what skills employees possess, how much they are paid, whether legal requirements are being met, and how workforce decisions affect organizational performance. Historically, this information was recorded in paper files and managed through administrative routines. Today, HR information may be stored in cloud platforms, exchanged through application programming interfaces (APIs), analyzed with artificial intelligence, and accessed through mobile devices.

The transformation from personnel administration to digital HR is not simply a change from paper to computers. It represents a change in the nature of HR work, the expectations placed on HR professionals, and the relationship between people, processes, data, and technology. HR professionals increasingly act as process designers, data stewards, technology users, change agents, and advisers on responsible use of employee information.

This chapter establishes the conceptual foundation for studying HR technology. It explains the historical evolution of HR systems, distinguishes related terms that are often used incorrectly, introduces essential terminology, and examines the opportunities and risks associated with digital HR.

Learning Objectives

By the end of this chapter, students should be able to:

1. Explain the evolution from personnel administration to modern digital HR.
2. Distinguish digitization, digitalization, automation, and digital transformation.
3. Describe the purposes and components of HRIS, HCM, HRMS, and cloud HR platforms.
4. Explain essential terms such as SaaS, API, interoperability, data governance, analytics, and artificial intelligence.
5. Analyze how HR technology affects employees, managers, HR professionals, and organizations.
6. Identify ethical, legal, security, and change-management issues in digital HR implementation.

1. From Personnel Administration to Strategic HR

Personnel administration

Personnel administration developed around the need to maintain accurate employment records and carry out routine employment transactions. Typical activities included recruitment paperwork, attendance recording, payroll preparation, benefits administration, filing, and compliance reporting. The function was often viewed as clerical and reactive: personnel staff responded to employment events after they occurred.

Paper-based administration created several limitations. Records could be duplicated, misplaced, or accessed by unauthorized individuals. Preparing reports required manual searching and calculation. A change in an employee's address, job, or pay often had to be entered in multiple files. These conditions increased the risk of error and made organization-wide workforce analysis difficult.

Personnel administration nevertheless established important principles that remain relevant: confidentiality, accuracy, consistency, legal compliance, and reliable documentation. Technology does not eliminate these responsibilities; it changes how they are performed.

The emergence of human resource management

From the late twentieth century, organizations increasingly recognized that employees and their capabilities could contribute to competitive advantage. HR expanded beyond record keeping to include workforce planning, employee development, performance management, organizational design, employee relations, and strategic advice.

This development did not make administration unimportant. Instead, it created a dual requirement. HR had to deliver efficient transactions while also supporting strategic decisions. HR technology became important because it could reduce repetitive work, integrate information, and produce evidence for decision-making.

Computerization and early HR information systems

Early computer applications were often designed for a single activity, such as payroll or time recording. Later, organizations created HR information systems (HRIS) that stored employee data and supported multiple processes. An HRIS can be understood as an organized combination of people, procedures, data, software, and hardware used to collect, maintain, process, and report HR information.

An HRIS typically contains a central employee record, sometimes called a system of record. It may store job information, organizational structure, compensation, attendance, qualifications, and employment history. Modules may support recruitment, onboarding, performance management, learning, benefits, payroll, and workforce reporting.

Early HRIS implementations often remained internally hosted. The organization purchased or licensed software, maintained servers, controlled upgrades, and relied on specialist technical staff. These systems improved consistency and reporting but could be expensive, difficult to modify, and disconnected from other organizational systems.

Enterprise systems and integrated HR

Enterprise resource planning (ERP) systems integrated finance, procurement, operations, and HR. Integration made it possible to connect employee data with cost centers, payroll accounting, budgeting, and organizational structures. For example, a job change approved in HR could update an employee's organizational assignment and support accurate financial reporting.

Integration also introduced new risks. An error in one system could be transmitted to other systems. Access permissions became more complex, and HR data could become visible to users who did not need it. Thus, integration creates value only when supported by sound governance and carefully designed controls.

Web-based and self-service HR

Web-based HR systems enabled employees and managers to perform tasks directly. Employees could update personal information, request leave, access payslips, enroll in benefits, complete learning, or respond to surveys. Managers could approve transactions, view team information, and initiate recruitment or performance processes.

Self-service can improve speed and ownership, but it also redistributes responsibility. Employees and managers must understand the processes they are performing, and HR must design accessible instructions, validation rules, escalation routes, and support services. Self-service is not automatically employee-friendly; poorly designed self-service can transfer administrative burdens to users without improving the underlying process.

Cloud-based HR and digital HR

Cloud-based HR applications are hosted by a technology provider and accessed through a network, usually the internet. The provider commonly manages infrastructure, system maintenance, security controls, and software updates, although the customer remains responsible for many configuration, access, data-quality, and governance decisions.

Digital HR is broader than cloud HR. It refers to the use of digital technologies, data, platforms, and redesigned processes to deliver HR services and improve workforce decisions. Digital HR may include cloud applications, mobile tools, analytics, collaboration platforms, digital learning, chatbots, robotic process automation, artificial intelligence, and employee experience technologies.

The evolution can therefore be represented as a progression:

Figure: A conceptual timeline of the evolution of HR technology.

2. Distinguishing Related Concepts

The terms used in digital HR are related but not interchangeable. Confusing them can lead to poor project planning and unrealistic claims about organizational change.

Digitization

Digitization is the conversion of information from an analogue form into a digital form. Scanning paper personnel files into electronic documents is digitization. The content may be easier to store, search, and transmit, but the underlying process may remain unchanged.

Digitization answers the question: How can existing information be represented digitally?

Digitalization

Digitalization is the use of digital technologies to improve an existing process. For example, replacing paper leave forms with an online workflow that routes requests to a manager and updates leave balances is digitalization. The process is redesigned to use digital capabilities, but the organization may still be pursuing the same basic service.

Digitalization answers the question: How can digital tools make an existing activity more efficient, accessible, or reliable?

Automation

Automation occurs when technology performs a task or sequence of tasks with limited human intervention. A payroll system calculating deductions according to configured rules is an example. Robotic process automation may transfer data between systems when specified conditions are met.

Automation is not the same as intelligence. A system may follow rules efficiently without understanding context. Automation can reproduce a flawed process at greater speed, so process analysis should precede automation.

Digital transformation

Digital transformation is an organization-wide change in strategy, operating model, capabilities, culture, and stakeholder experience enabled by digital technologies. It may involve redesigning how work is organized, how decisions are made, how employees interact with the organization, and how HR creates value.

For example, introducing an online recruitment form is not necessarily transformation. Creating an integrated talent ecosystem that uses skills data, internal mobility, learning recommendations, workforce planning, and manager decision support may form part of a broader transformation.

The concepts can be compared as follows:

These categories can overlap. A transformation program may include digitization, digitalization, and automation. However, none of the smaller activities automatically constitutes transformation.

3. Core HR Technology Systems

HRIS, HRMS, HCM, and people platforms

Terminology varies between vendors and organizations, but the following distinctions are useful.

• HRIS: A system for managing HR information and processes, especially employee records, reporting, and administration.
• HRMS: Human Resource Management System; often used as a broader term that includes HRIS functions and operational HR processes.
• HCM: Human Capital Management; commonly refers to a wider suite covering core HR, talent, workforce management, analytics, and strategic workforce activities.
• People platform: A contemporary term emphasizing an integrated digital environment for employee services, work experiences, skills, collaboration, and organizational processes.

These terms do not have universally fixed boundaries. One vendor's HRIS may offer functions another vendor labels HCM. Students should examine actual capabilities rather than rely on product labels.

System of record and system of engagement

A system of record stores authoritative data for a defined purpose. Core HR may be the system of record for employment status, job, position, and organizational assignment. A system of engagement supports interactions, communication, learning, collaboration, or employee services. Modern architectures often connect both. The distinction is important because a highly engaging interface should not replace controls over authoritative data.

Modules and processes

Common HR technology modules include:

• core HR and organizational management;
• recruitment and applicant tracking;
• onboarding;
• payroll and time management;
• benefits administration;
• performance and goal management;
• learning and development;
• succession and career management;
• workforce planning and analytics;
• employee listening and engagement;
• case management and HR service delivery.

A module is not valuable merely because it exists. Its value depends on process fit, data quality, user adoption, integration, security, and measurable outcomes.

Cloud service models

Cloud HR is frequently delivered as Software as a Service (SaaS). In SaaS, the provider makes an application available to customers over a network and manages much of the technical infrastructure. Customers generally configure the system, manage users and business rules, and pay according to a subscription or usage arrangement.

Other cloud service models include:

• Infrastructure as a Service (IaaS): virtualized computing, storage, and networking resources;
• Platform as a Service (PaaS): an environment for developing and deploying applications;
• SaaS: a complete application delivered as a service.

For most HR professionals, SaaS is the most directly relevant model. Cloud adoption may reduce the need for local infrastructure and provide more frequent updates, but it does not transfer all accountability to the vendor. Organizations must still assess privacy, security, lawful processing, data location, business continuity, service availability, and contractual responsibilities.

4. Essential Digital HR Terminology

Data, information, and insight

Data are recorded observations or representations, such as a job code, hire date, training completion, or survey response. Information is data organized and interpreted in context. Insight is a useful understanding that supports judgment or action. A list of turnover counts is data; a trend showing higher turnover among newly promoted employees is information; a well-supported conclusion about promotion support and retention is insight.

Database and data model

A database is an organized collection of data. A data model defines entities, attributes, relationships, and rules. For example, an employee may be linked to a position, department, manager, location, and compensation record. Poorly designed data models create duplication and inconsistent reporting.

Integration and interoperability

Integration is the connection of systems or processes so that data or actions can move between them. Interoperability is the ability of different systems to exchange and use information meaningfully. A payroll system may receive approved compensation changes from core HR through an API.

An API is a defined set of rules and methods that allows one software application to request or exchange data with another. APIs can support real-time or scheduled communication, but they require authentication, authorization, error handling, monitoring, and clear ownership.

Data quality

Data quality refers to the fitness of data for its intended purpose. Important dimensions include accuracy, completeness, consistency, timeliness, validity, uniqueness, and accessibility. A technically sophisticated dashboard cannot produce trustworthy conclusions from inaccurate or incomplete data.

Analytics

HR analytics uses data and analytical methods to describe, explain, predict, or support decisions about people and work.

• Descriptive analytics: What happened?
• Diagnostic analytics: Why did it happen?
• Predictive analytics: What might happen?
• Prescriptive analytics: What actions might be considered?

Analytics should support—not replace—professional judgment. Correlation does not establish causation, and a prediction about an individual may influence the very outcome it predicts.

Artificial intelligence and machine learning

Artificial intelligence (AI) refers broadly to systems performing tasks associated with human cognitive capabilities, such as classification, language processing, recommendation, or pattern recognition. Machine learning is a set of methods in which systems learn relationships from data rather than relying only on explicitly written rules.

In HR, AI may assist with candidate matching, employee-service chat, learning recommendations, skills inference, document classification, or workforce forecasting. Risks include biased training data, inadequate explainability, privacy intrusion, automation bias, security vulnerabilities, and inappropriate use of sensitive attributes. Human oversight must be meaningful rather than merely symbolic.

Robotic process automation

Robotic process automation (RPA) uses software agents to execute structured, repetitive, rule-based actions, often across applications. RPA can be useful when systems cannot easily be integrated. However, it may create fragile dependencies, especially when screen layouts or business rules change. A sustainable solution may be process redesign or API integration rather than an increasing number of software robots.

5. A Framework for Evaluating HR Technology

A useful evaluation framework considers six connected dimensions:

1. Purpose: What organizational or employee problem is being addressed?
2. Process: Is the existing process sound, or is technology merely accelerating waste?
3. People: Who will use, manage, approve, and be affected by the system?
4. Data: What data are needed, who owns them, and how will quality be maintained?
5. Technology: Can the solution integrate, scale, remain secure, and support accessibility?
6. Governance: Who is accountable for privacy, fairness, compliance, decisions, and outcomes?

This framework discourages technology-first thinking. A project should begin with a clearly defined problem and desired outcome. For example, an organization experiencing slow onboarding should identify where delays occur, whether information is duplicated, which approvals are necessary, and what employees experience. Only then should it select a digital solution.

A simple value logic can be expressed verbally as:

Technology value = improved outcomes − total costs and risks.

This is not a universal financial equation. It is a decision principle. Costs include licensing, implementation, training, integration, support, process redesign, and employee time. Risks include privacy breaches, discrimination, service failure, vendor dependence, and loss of trust. Benefits may include reduced processing time, improved compliance, better employee experience, and more informed decisions.

6. Applied Case: Northstar Retail's HR Transformation

The following is a fictional illustrative case designed for learning purposes.

Background

Northstar Retail operates 120 stores and employs approximately 8,000 people. Its HR information is distributed across spreadsheets, a payroll application, paper forms, and separate recruitment and learning tools. Store managers spend substantial time correcting employee records and emailing HR. Employees often cannot determine whether their leave requests or address changes have been processed.

Problem

The executive team proposes purchasing a large cloud HCM suite. Some leaders describe the proposal as a digital transformation. HR specialists are concerned that the organization has not defined data ownership, redesigned processes, or consulted employees. The employee association is concerned about monitoring and the use of algorithmic recommendations in recruitment.

Analysis

An initial assessment identifies several different problems:

• paper forms represent a digitization opportunity;
• duplicated approvals represent a digitalization and process-design opportunity;
• repetitive data entry may be suitable for automation;
• fragmented workforce information limits analytics;
• the broader employee-service model may require transformation.

The project team maps the employee life cycle and discovers that the same personal information is entered up to four times. It also finds that job titles and store codes are inconsistent across systems. These are data-governance and process problems, not merely software problems.

Approach and solution

Northstar adopts a staged approach:

1. Define a minimum authoritative employee record and data owners.
2. Standardize job, location, department, and employment-status codes.
3. Redesign onboarding and leave processes before configuring software.
4. Implement a cloud core HR system with employee and manager self-service.
5. Integrate payroll and recruitment through controlled interfaces.
6. Establish role-based access, audit logs, retention rules, and incident procedures.
7. Pilot the system in ten stores with different workforce profiles.
8. Train users and provide accessible alternatives for employees with limited digital access.
9. Introduce dashboards only after data quality has been tested.
10. Assess AI-enabled recruitment features separately through an impact and fairness review.

Outcome

After the pilot, Northstar reports fewer duplicate data entries, faster routine requests, and improved visibility of onboarding status. It postpones automated candidate ranking because the available historical data reflect inconsistent hiring practices. Instead, it uses structured selection criteria and requires human review. The project is described as a digital transformation only after changes to governance, processes, employee services, and decision-making are demonstrated—not merely because a cloud application was purchased.

Learning points

The case illustrates five principles. First, technology selection should follow problem definition. Second, data standards are foundational. Third, cloud adoption is not equivalent to transformation. Fourth, employee access and trust are part of system design. Fifth, responsible restraint—such as delaying an AI feature—can be evidence of digital maturity.

7. Benefits, Challenges, and Limitations

Digital HR can provide several benefits:

• faster and more consistent transactions;
• improved employee and manager access;
• reduced duplication and administrative effort;
• better workforce visibility;
• support for distributed and hybrid work;
• more personalized learning and services;
• evidence for workforce planning;
• improved auditability when controls are properly configured.

However, benefits are not automatic. Common challenges include poor data quality, weak process design, insufficient user adoption, excessive customization, integration failures, vendor lock-in, implementation fatigue, and unclear ownership. Organizations may also measure activity rather than value—for example, counting logins without examining whether employee problems were solved.

Digital systems can create new forms of exclusion. Employees may lack reliable internet access, confidence with technology, assistive tools, language support, or the ability to use a system privately. Accessible design and alternative service channels are therefore important components of responsible HR technology.

Technology can also intensify surveillance. Productivity monitoring, location data, keystroke analysis, sentiment tools, and behavioral analytics may affect dignity and psychological safety. The fact that information can be collected does not mean that it should be collected or used for every purpose.

8. Ethics, Governance, and Regulation

HR data are particularly sensitive because they concern people’s livelihoods, identities, health, behavior, relationships, and opportunities. Responsible digital HR requires governance throughout the data life cycle: collection, access, use, sharing, storage, retention, correction, and deletion.

Important governance principles include:

• Purpose limitation: collect and use data for defined legitimate purposes.
• Data minimization: avoid collecting data that are unnecessary.
• Transparency: explain what is collected and how decisions are made.
• Access control: give users only the access required for their role.
• Accuracy: provide methods to correct inaccurate data.
• Security: protect data against unauthorized access, alteration, loss, and disclosure.
• Accountability: assign responsibility for systems, decisions, and failures.
• Fairness: test for unjustified disparate effects and discriminatory outcomes.
• Human review: ensure consequential decisions can be examined and challenged.

Legal requirements differ across jurisdictions. They may address privacy, employment discrimination, records retention, electronic communications, accessibility, cross-border data transfers, and automated decision-making. HR professionals must work with legal, information-security, procurement, and employee-representative colleagues rather than treating compliance as a software setting.

Governance should also cover vendors. Contracts should address data ownership, subcontractors, security standards, breach notification, service continuity, audit rights, data return, deletion, and exit arrangements. A vendor's certification is useful evidence but does not remove the organization's accountability for its own use of the service.

9. Contemporary Developments and Professional Implications

Current digital HR developments include skills-based talent practices, employee experience platforms, people analytics, hybrid-work technology, digital learning ecosystems, generative AI assistants, and integrated service delivery. These developments create opportunities but also require critical evaluation.

Skills-based approaches may improve internal mobility by focusing on capabilities rather than job titles. Their success depends on valid skills definitions, employee participation, and avoiding false precision. Generative AI may draft communications, summarize policies, or support HR service agents, but outputs can be inaccurate, confidential information may be exposed, and users may overtrust fluent language. Human review and approved-use policies are essential.

The future HR professional therefore needs more than software familiarity. Employability requires:

• process mapping and improvement;
• data literacy and critical interpretation;
• understanding of privacy and security;
• vendor and project-management skills;
• communication with technical and nontechnical stakeholders;
• ethical reasoning;
• change leadership;
• empathy for employee experience.

A digitally capable HR professional asks not only, “Can the system do this?” but also, “Should it do this, for whom, under what conditions, and with what safeguards?”

Best-Practice Principles

Organizations beginning an HR technology initiative should:

1. Start with a clearly defined workforce or service problem.
2. Map and simplify processes before automating them.
3. Establish data ownership, standards, and quality controls.
4. Involve employees, managers, HR specialists, IT, security, legal advisers, and representatives early.
5. Use phased implementation and test with real users.
6. Design for accessibility, inclusion, and alternative service routes.
7. Apply privacy and security by design.
8. Measure outcomes, not only system activity.
9. Maintain human accountability for consequential decisions.
10. Review AI and analytics tools for validity, bias, explainability, and unintended effects.
11. Plan for system failure, vendor exit, and data portability.
12. Treat digital transformation as organizational change, not as a procurement event.

Chapter Summary

HR technology evolved from paper-based personnel administration through computerized payroll, HRIS, integrated enterprise systems, web self-service, and cloud platforms to broader digital HR ecosystems. Each stage improved particular capabilities while introducing new requirements for governance, skills, and accountability.

Digitization converts analogue information into digital form. Digitalization improves an existing process through digital tools. Automation enables technology to perform defined tasks with limited human intervention. Digital transformation is a deeper organization-wide change in strategy, operating model, capabilities, culture, and stakeholder experience enabled by digital technologies.

HRIS, HRMS, HCM, and people platforms are overlapping terms whose practical meaning depends on actual system capabilities. Cloud SaaS can improve scalability and access, but organizations remain responsible for data quality, privacy, security, compliance, and user adoption. APIs and interoperability connect systems, while analytics converts data into descriptive, diagnostic, predictive, or prescriptive support. AI and RPA can create value but require human oversight and careful risk assessment.

Successful digital HR begins with a real problem, sound process design, reliable data, stakeholder involvement, appropriate technology, and effective governance. The central professional principle is that technology should improve human and organizational outcomes without weakening fairness, dignity, transparency, or accountability.

Key Terms

Digitization: Conversion of analogue information into digital form.

Digitalization: Use of digital technology to improve an existing process.

Digital transformation: Organization-wide change enabled by digital technologies and redesigned capabilities.

Automation: Technology performing defined tasks with limited human intervention.

HRIS: System of people, processes, data, hardware, and software used to manage HR information.

HRMS: Human Resource Management System, often covering broad operational HR functions.

HCM: Human Capital Management, commonly referring to integrated HR and talent capabilities.

Cloud computing: Delivery of computing services through a network rather than exclusively through local infrastructure.

SaaS: Software as a Service; application software delivered and managed by a provider.

System of record: Authoritative system for maintaining defined data.

System of engagement: System supporting interaction, communication, collaboration, or service experience.

API: Application Programming Interface that enables controlled communication between software applications.

Interoperability: Ability of different systems to exchange and meaningfully use information.

Data governance: Policies, roles, standards, and controls governing data throughout its life cycle.

Data quality: Fitness of data for its intended purpose, including accuracy, completeness, and consistency.

HR analytics: Use of HR data and analytical methods to support understanding and decisions.

Artificial intelligence: Technology performing tasks associated with human cognitive capabilities.

Machine learning: Methods through which systems learn patterns from data.

Robotic process automation: Software agents carrying out structured, repetitive, rule-based tasks.

Employee self-service: Digital capability allowing employees to perform or initiate HR transactions themselves.

Review Questions

Short Answer Questions

1. What were two limitations of paper-based personnel administration?
2. Define digitization and provide one HR example.
3. What is the difference between a system of record and a system of engagement?
4. Name four dimensions of HR data quality.
5. Why is cloud HR not automatically equivalent to digital transformation?

Long Answer Questions

1. Explain the historical evolution from personnel administration to digital HR.
2. Compare HRIS, HRMS, HCM, and people platforms, noting why vendor terminology should be examined critically.
3. Explain how APIs, integration, and interoperability support HR service delivery.
4. Discuss the benefits and limitations of employee and manager self-service.
5. Analyze the governance responsibilities of an organization using a cloud-based HR platform.

Analytical / Application Questions

1. A university plans to scan all paper employee files and claims that it has completed digital transformation. Evaluate this claim using the distinctions among digitization, digitalization, automation, and digital transformation.
2. An organization wants to use AI to rank job applicants based on previous hiring data. Identify the potential benefits, risks, governance requirements, and conditions that should be met before deployment.
3. Design a high-level implementation approach for replacing spreadsheet-based leave administration with a digital HR process. Explain how you would address process design, data quality, user adoption, accessibility, privacy, and evaluation.

Multiple Choice Questions with Answers

Question 1

Which activity is the clearest example of digitization?

A. Scanning paper employee files into electronic documents

B. Redesigning onboarding around employee experience

C. Using AI to recommend learning resources

D. Automatically approving leave under defined rules

Correct Answer: A. Scanning paper employee files into electronic documents

Question 2

What best describes digital transformation in HR?

A. Purchasing a new payroll application

B. Converting paper forms into PDFs

C. Redesigning HR strategy, processes, capabilities, and experiences through digital technologies

D. Adding more computers to the HR department

Correct Answer: C. Redesigning HR strategy, processes, capabilities, and experiences through digital technologies

Question 3

What is the primary purpose of a system of record?

A. To provide social collaboration features

B. To maintain authoritative data for a defined purpose

C. To generate informal employee communications

D. To replace all human decision-making

Correct Answer: B. To maintain authoritative data for a defined purpose

Question 4

Which term describes software delivered and managed by a provider through a network?

A. SaaS

B. RPA

C. ERP only

D. Data minimization

Correct Answer: A. SaaS

Question 5

Which type of analytics asks, “What might happen?”

A. Descriptive analytics

B. Diagnostic analytics

C. Predictive analytics

D. Prescriptive analytics

Correct Answer: C. Predictive analytics

Question 6

What does an API enable?

A. The physical storage of paper files

B. Controlled communication between software applications

C. The elimination of all data governance

D. Automatic proof that data are accurate

Correct Answer: B. Controlled communication between software applications

Question 7

Why should an organization improve a process before automating it?

A. Automation is only possible with paper records

B. Automation can reproduce an inefficient or flawed process at greater speed

C. Automation always reduces privacy risks

D. Process improvement prevents system integration

Correct Answer: B. Automation can reproduce an inefficient or flawed process at greater speed

Question 8

Which is an important data-governance principle?

A. Collect every available employee detail

B. Give all managers unrestricted access

C. Use data only for defined legitimate purposes

D. Retain all data permanently

Correct Answer: C. Use data only for defined legitimate purposes

Question 9

Which statement about AI in HR is most accurate?

A. AI outputs are always objective because they are computer-generated

B. Historical data can contain biases that affect AI-supported decisions

C. Human review is unnecessary for consequential decisions

D. AI systems cannot process language

Correct Answer: B. Historical data can contain biases that affect AI-supported decisions

Question 10

What is a major responsibility of an organization using cloud HR software?

A. Ignoring vendor contracts because the provider owns all accountability

B. Managing access, data quality, privacy, and appropriate use

C. Preventing all system updates

D. Replacing every HR professional with software

Correct Answer: B. Managing access, data quality, privacy, and appropriate use