BI, analytics or DSS discussion guide: 3-part answer plan

The three prompts are sequenced to test one competence — matching decision-support technology to decision type — and the first prompt's trap is answering before defining. It says so directly: define the business problem before providing a solution, at your own organization with funding assumed. The definition you write determines the answer: a one-off, model-heavy allocation question points to a decision support system, which is built for interactive what-if analysis of a specific semi-structured decision; a recurring need for monitoring, dashboards and standardized reporting points to business intelligence; and a problem whose value lies in prediction or optimization — forecasting demand, scoring risk — points to BI analytics. The strongest answers name the problem's structure (how repetitive, how structured, what data exists) and let the choice fall out of those attributes, rather than describing three technologies and picking one by preference.

The second prompt is a straight application of the planning hierarchy the textbook builds on: three decisions each in strategic planning, management control (the brief's own parenthetical: tactical planning), and operational planning and control, drawn from an organization you can actually observe. The levels are distinguished by horizon, scope and reversibility, and the graded skill is placing decisions correctly — opening a new facility or entering a new service line is strategic; budgeting across units, staffing plans and vendor selection sit in management control; scheduling, inventory reorders and daily quality checks are operational. Nine decisions from one named organization, correctly leveled, shows the framework understood; nine generic business decisions with the labels rotated does not. Pick the organization once and use it for both the first and second prompts, which makes the post coherent and halves the scene-setting.

The intuition prompt is not an invitation to romanticize gut feeling — it asks why intuition is still important in a course about replacing it with data, and the defensible answer is about where models stop: problems with no historical data, novel situations outside the training distribution, value trade-offs no dashboard encodes, and the expert pattern recognition that experienced decision-makers deploy faster than any query cycle. The decision-making literature gives this real support — expert intuition as compiled experience, reliable precisely in domains with fast feedback. A cleaning note: this record circulated with the buyer's hiring preamble (deadline, textbook citation, a warning to bidders) ahead of the actual prompt; it is excluded from the brief above, though it usefully identifies the course text as Sharda's managerial BI book, whose vocabulary — DSS, BI, analytics tiers, Anthony's triangle — is the vocabulary your answer should use.