| Course | HRM-7620 Human Capital Management in Multicultural Organizations |
|---|---|
| Module | Module 5 |
| Paper type | Discussion forum: initial response and two comments |
| Length | About 525 words |
| Format | Discussion post with APA 7 citations |
| School | Thomas Edison State University |
| Program | MBA in Human Resource Management |
| Updated | October 2026 |
Free sample paper for HRM 7620 Module 5
Module 5 Discussion: Data and Systems for Inclusive Talent Management
You Cannot Fix What You Cannot See: Talent Infrastructure for Inclusion
When Hudson State's new inclusion council asked HR for pass-through rates, the share of applicants from each group who moved from application to interview to offer, we could not produce them. Our applicant tracking system records applications but not interview decisions, which hiring managers note in email. Pay decisions for staff are made by each division in spreadsheets, so we cannot easily compare pay for similar roles across the university. We had built processes for inclusion without the infrastructure to see whether they worked. That matters because inclusion depends on employees experiencing practices as fair; Nishii (2013) found that a climate for inclusion, built on fair employment practices, reduced conflict and turnover in diverse groups, and fairness cannot be demonstrated without data.
Chun and Evans (2014) describe talent infrastructure as the systems, policies and practices that support inclusive talent management across the employee life cycle. In my view it has three layers. The data layer captures each step of hiring, promotion, pay and departure in one system, with voluntary self-identification data stored securely and used only in aggregate. The policy layer sets the rules, such as mandatory posting, structured interviews and pay ranges for each role. The process layer defines who reviews the data, when and with what authority to act. Kalev et al. (2006) found that structures with clear responsibility produced larger gains than programs without it, which is why the process layer matters as much as the data.
Pay equity analysis shows why infrastructure matters. Without job architecture and consistent data, an employer cannot compare pay for similar work. With them, HR can run a regression of pay on legitimate factors, such as role, grade, experience and performance, and look for unexplained differences by group. The Diane B. Allen Equal Pay Act, New Jersey's broad pay equity statute, prohibits paying members of a protected class less for substantially similar work unless the difference is based on legitimate factors, so a regular review is both good practice and a compliance safeguard.
For Hudson State, I would recommend configuring the applicant tracking system to record each step, building a job architecture with pay ranges for staff roles, running a pay equity review every year under the direction of counsel and giving the inclusion council a semiannual dashboard.
Comment on Priya's Post
Priya, you raised a concern about privacy when collecting demographic data. I agree that this needs care. Self-identification should be voluntary, stored separately from managers' view of individual records and reported only in groups large enough to prevent identifying anyone. Explaining to employees why the data are collected and how they will be protected tends to increase response rates. Does your organization explain this when it asks?
Comment on Marcus's Post
Marcus, your point about succession planning is important, because succession lists are often built informally by senior leaders, which can quietly narrow who is considered. Requiring each division to nominate candidates against written criteria, and reviewing the full slate at the inclusion council, could open the process. Has your company tried reviewing who is on succession lists, and how they got there?
References
Chun, E., & Evans, A. (2014). The new talent acquisition frontier: Integrating HR and diversity strategy in the private and public sectors and higher education. Stylus Publishing.
Kalev, A., Dobbin, F., & Kelly, E. (2006). Best practices or best guesses? Assessing the efficacy of corporate affirmative action and diversity policies. American Sociological Review, 71(4), 589-617. https://doi.org/10.1177/000312240607100404
Nishii, L. H. (2013). The benefits of climate for inclusion for gender-diverse groups. Academy of Management Journal, 56(6), 1754-1774. https://doi.org/10.5465/amj.2009.0823
What the HRM 7620 Module 5 instructions ask for
The Module 5 forum asks about talent infrastructure, the systems, data, policies and processes that support inclusive talent management. Expect a prompt asking what infrastructure an organization needs, what data it should collect and how it should use them, or how systems such as applicant tracking, pay structures and succession planning affect inclusion. Answer from an organization you know, use the text and other sources, and reply to classmates with substance. Discussions carry 30 percent of the HRM-7620 grade together. Address privacy and legal requirements where data about employees are involved. Expect to discuss what data the organization needs, how they are protected and who uses them to make decisions.
How this HRM 7620 Module 5 example is built
The initial response opens with a specific failure: the organization could not produce the data its council requested. It defines talent infrastructure in three layers, data, policy and process, and shows how each applies. It uses pay equity analysis and New Jersey's equal pay law to show why infrastructure matters. It closes with four recommendations. The first comment adds privacy safeguards for demographic data; the second applies the idea of written criteria to succession planning. Each comment ends with a question for the classmate. The three layers give a simple structure that can be applied to any organization's systems. The pay equity example shows exactly what infrastructure makes possible.
HRM 7620 Module 5 rubric: what earns full marks
Forum marks here reward a strong first post, good use of the text and other sources, a concrete employer and a genuine exchange with peers. Posts that identify specific gaps in systems and data, and connect infrastructure to measurable inclusion outcomes, show depth. Accurate references to pay equity law and privacy practice demonstrate professional knowledge. Replies earn credit for adding safeguards or practical steps and inviting further discussion. Professional tone is expected throughout. Mentors also value posts that connect infrastructure to specific outcomes, such as pay equity or promotion fairness, and that recognize legal requirements. Replies that add safeguards or practical steps show engagement with classmates' ideas.
Common HRM 7620 Module 5 mistakes, and how to avoid them
Posts lose points when they describe infrastructure as technology alone, when they ignore privacy and legal limits on demographic data, or when they recommend dashboards without saying who acts on them. Name the data you need at each step. Set policies that make processes consistent. Say who reviews the data and with what authority. In replies, help classmates address privacy or fairness in their proposals. A free first sample of your own HRM-7620 Module 5 post is available on request. Name specific systems and data elements rather than calling for better data in general. Explain how privacy will be protected. Say who will act on the data and how often. Connect each recommendation to an outcome the organization cares about.
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HRM 7620 Module 5 questions, answered
Where can I find a free HRM 7620 Module 5 sample paper?
Here is the full infrastructure forum: a post on the data, policies and processes inclusion needs, with replies to two peers.
What is talent infrastructure?
The data systems, policies and processes that support talent management, from applicant tracking to pay structures and succession planning.
What is a pass-through rate?
The percentage of applicants at one hiring step who make it to the next, for instance from application to interview, usually compared group by group.
What does New Jersey's Diane B. Allen Equal Pay Act require?
Equal pay for substantially similar work across protected classes unless differences rest on legitimate factors such as seniority or merit.
How should demographic data be protected?
Through voluntary self-identification, secure storage separate from managers' views and reporting only in groups large enough to protect anonymity.