DDBA 8580 Week 8 HR Analytics Note Example

Course	DDBA 8580 The New HR: The Savvy Strategic Partner
Week	Week 8
Paper type	Analytics note
Length	About 760 words, 5 pages
Format	APA 7 student paper
School	Walden University
Program	Doctor of Business Administration (DBA)
Updated	October 2026

Free sample paper for DDBA 8580 Week 8

What HR's Own Records Can Bear: An Analytics Note Grading Six Data Holdings for Description, Association, Prediction and Cause, and Taking a Side in the Debate Over Whether HR Analytics Is a Maturing Discipline or a Promise the Function Cannot Yet Keep

Student Name

Walden University

DDBA 8580: The New HR: The Savvy Strategic Partner

Instructor Name

Month Day, Year

What this page is doingThe title names the grading scheme and the debate the grades are used to settle.

The Question

Before HR can supply evidence to the business, it must know what its own records can show. This note inventories the data holdings of a composite health system's HR function, about 14,000 employees across four hospitals and 60 clinics, and grades each for four kinds of claim: description, what is happening; association, what tends to go together; prediction, what is likely to happen next; and cause, what produces what.

What this page is doingThe note turns the seminar's skepticism on HR's own data, grading what each holding can support.

The Grades

[after]

No holding supports causal claims on its own. Each lacks a comparison between people who did and did not receive something, assigned in a way unrelated to the outcome.

Table 1

HR Data Holdings Graded by Kind of Claim

Holding	Description	Association	Prediction	Cause \|
HR information system	Strong: complete headcount, roles, tenure	Moderate: fields are standardized but sparse	Moderate: tenure and role predict exits in aggregate	Weak: no comparison groups \|
Payroll	Strong: exact hours and pay	Moderate: overtime links to units, not to people's reasons	Moderate: overtime spikes precede some exits	Weak: pay changes are not assigned at random \|
Applicant tracking system	Moderate: recent years only	Moderate: source of hire against early tenure	Weak: few performance links	Weak: selection decisions confound outcomes \|
Engagement survey	Moderate: 62 percent response	Moderate: unit scores against unit turnover	Weak: annual, anonymous, not linked to individuals	Weak: self-report, cross-sectional \|
Exit interviews	Weak: 30 percent completion, self-selected	Weak: reasons given are socially acceptable ones	Weak: only leavers are asked	Weak: no comparison with stayers \|
Learning records	Strong: completions and dates	Weak: completion is required, so little variation	Weak	Weak: no comparison groups \|

Association Is Not Cause

The engagement survey illustrates the risk. Units with higher engagement scores had lower turnover last year. A report might conclude that raising engagement would reduce turnover. But both may reflect a third factor, such as a good unit manager, or turnover may lower engagement among those who remain. The records support the association; they do not support the causal reading. Exit interviews are weaker still: the 30 percent who complete them are not typical leavers, and departing employees often give reasons that avoid conflict, such as better pay, rather than the reasons that drove them, such as a manager.

What this page is doingThe note spends its largest section on the distinction most often blurred in HR reports.

The Debate

Rasmussen and Ulrich (2015) argued that HR analytics can avoid becoming a management fad if it starts from business problems rather than from available data, and offered examples of analytics informing decisions. Angrave et al. (2016) argued that HR is poorly placed to meet the analytics challenge, lacking the analytical skills and the data structures needed, and that vendors' tools often produce descriptive dashboards rather than insight. Marler and Boudreau (2017) reviewed the evidence and found relatively few peer-reviewed studies demonstrating that HR analytics improves organizational outcomes.

Tested against this inventory, the skeptics' account fits better. The function's holdings support description well and association moderately, but nothing in them supports the causal claims that would make analytics strategically valuable. Rasmussen and Ulrich's advice remains sound, but following it here would require new data collection, such as staggered rollouts that create comparison groups, not more analysis of existing records.

What the Function Could Do

Link survey responses to units and time so changes can be tracked. Design the next practice rollout so some units start later. Ask a sample of stayers the exit interview questions, providing a comparison.

An Objection From the Optimists

An advocate of HR analytics might reply that prediction, not cause, is what managers need: if tenure, overtime and role predict who will leave, HR can intervene with those employees without knowing why they leave. That is partly right, and the table grades prediction as moderate for the HR system and payroll. But interventions chosen on prediction alone can misfire. If overtime predicts exits because overworked units lose people, the remedy is staffing; if it predicts exits because employees about to leave stop declining extra shifts, staffing changes would do nothing. Choosing the right intervention requires some causal understanding, which the records cannot supply without designed comparisons.

What the Function Should Not Claim

Until it has designed comparisons, HR should present engagement and turnover links as associations and avoid language such as drivers or impacts in reports to executives.

Limits

Grades reflect this system's records; other functions may hold better data.

References

Angrave, D., Charlwood, A., Kirkpatrick, I., Lawrence, M., & Stuart, M. (2016). HR and analytics: Why HR is set to fail the big data challenge. Human Resource Management Journal, 26(1), 1-11. https://doi.org/10.1111/1748-8583.12090

Marler, J. H., & Boudreau, J. W. (2017). An evidence-based review of HR analytics. International Journal of Human Resource Management, 28(1), 3-26. https://doi.org/10.1080/09585192.2016.1244699

Rasmussen, T., & Ulrich, D. (2015). Learning from practice: How HR analytics avoids being a management fad. Organizational Dynamics, 44(3), 236-242. https://doi.org/10.1016/j.orgdyn.2015.05.008

The Walden DDBA 8580 Week 8 instructions

Week 8 asks you to inventory your HR function's data holdings and grade what each can support. List the holdings and build a table rating each for description, association, prediction and cause, with a specific reason in every cell. Explain the difference between association and cause with an example from your own data. Place your grades in the debate over whether HR analytics is a maturing discipline or an unkept promise, testing both sides against your inventory. Answer the optimists' strongest reply, recommend what the function should collect or design next and state what it should stop claiming. Four to six pages with one grading table is typical.

Inside the DDBA 8580 Week 8 example

The sample's table gives reasons such as complete headcount fields, overtime linked to units rather than to motives, recent-year applicant data, anonymous annual surveys and self-selected exit interviews. The association section explains that higher engagement and lower turnover may share a cause, such as a good manager, or run in reverse. The debate section reports the argument that analytics should start from business problems, the warning that HR lacks the skills and data and the review finding little outcome evidence. An objection that prediction is enough is answered with an overtime example. Recommendations include staggered rollouts and asking stayers the exit questions. A closing section tells the function to describe engagement and turnover links as associations, not drivers.

Where the marks sit in the DDBA 8580 Week 8 rubric

The grades earn credit only when their reasons are specific. A table rating surveys as moderate for prediction without saying why is an opinion in grid form, and graders mark it as such. The distinction between association and cause carries a large share, since reading a correlation in HR records as evidence of effect is the mistake the whole note exists to prevent. Literature engagement is judged on whether the optimistic and skeptical accounts are actually tested against the inventory rather than summarized. Practical recommendations for new data collection show the note can change practice. Recommendations that change what data are collected, rather than how existing data are analyzed, show understanding of the problem.

Common DDBA 8580 Week 8 mistakes, and how to avoid them

Ask your HRIS team for a list of every system that holds people data, then note what each records, how complete it is and how far back it goes. For each kind of claim, ask whether the data include a comparison and whether people chose their own treatment. Write a short reason in each cell. Pick one association your organization reports and list two other explanations for it. Read one optimistic and one skeptical article on HR analytics and decide which your inventory supports. Recommend one design change. Be fair to the optimists; their best argument concerns prediction, and it deserves an answer. Keep the table readable with one short reason per cell.

Get a DDBA 8580 Week 8 example written to your instructions

Everything needed fits in two attachments, the Week 8 prompt and whatever standard it is graded against, plus a note on any dataset your section provides. A finished note with the grading table, the tested dispute and the claims to retire is returned in 24 to 48 hours, and a first one costs nothing. Holdings are described by type only.

More DDBA 8580 papers and DBA samples

DDBA 8580 Week 8 questions, answered

Where can I find a free DDBA 8580 Week 8 sample paper?

The data holdings grading table and analytics note are free to read in full here.

Which holdings does the DDBA 8580 Week 8 note grade?

The HR information system, payroll, applicant tracking, engagement survey, exit interviews and learning records.

Can any holding support causal claims?

No; none includes comparison groups assigned independently of the outcome.

Why are exit interviews weak?

Only 30 percent complete them, and leavers often give socially acceptable reasons.

Which side of the debate does the note take?

The skeptics', since the records support description and association but not cause.