Option evaluation, strategic fit and analytics

Start with strategic fit

Strategic fit asks whether an option suits the organisation's direction and circumstances. A profitable opportunity can still divert attention from its purpose, weaken its customer promise or require capabilities it cannot develop in time.

Connect the proposal to the mission and intended advantage, the external opportunity, and internal delivery conditions. Include a realistic baseline: what happens if the organisation maintains its current approach? An option's benefits should represent an improvement over that baseline, not simply the revenue of the proposed activity.

Separate requirements from preferences. A requirement is a condition an option must satisfy; a preference helps compare acceptable options. An attractive score should not compensate for failure to meet a mandatory safety requirement or an absolute funding limit.

Concept sources: NASA — 6.8 Decision Analysis — Decision context, criteria, alternatives and uncertainty inform this application to strategic choices.

Use value and effort to screen options

A value/effort assessment is a quick comparison of expected benefit with the work required. Value can include profit, service improvement or progress towards a social purpose. Effort includes money, staff time, coordination and implementation difficulty. Define the time horizon and what “high” means before comparing proposals.

Concept sources: ASQ — Impact Effort Matrix — Impact/effort screening concept; this comparison table was prepared for these notes.

This is a screening aid, not an automatic approval rule. A small project can appear easy while consuming the same scarce specialist needed by a more valuable initiative.

Choose criteria that reflect the decision

Criteria translate strategy into specific comparisons. Include both financial outcomes and the conditions needed to deliver them. These groups organise useful questions; an organisation would select and define the criteria relevant to its particular decision.

Concept sources: ASQ — What is a Decision Matrix? and HM Treasury — Use of Multi-Criteria Decision Analysis in options appraisal of economic cases — Selection of relevant financial and non-financial criteria; this business application and grouping were prepared for these notes.

Capability, resources and capacity are different. A bookbinder may know how to restore damaged bindings, own suitable equipment, and still have no spare skilled hours. Funding another machine will not necessarily remove that bottleneck.

Avoid counting the same benefit repeatedly through overlapping criteria. Profit, ROI and IRR are related financial indicators, not three independent reasons that an option creates value. A high percentage return also does not, by itself, establish the greatest total value.

Build a weighted comparison

A weighted criteria evaluation makes priorities and judgements visible. Define a favourable rating scale for each criterion, assess options using evidence, multiply each rating by its weight, then add the results. Weights should total 100% when expressed as percentages.

Keep the direction consistent: a high rating must always mean a more desirable result. For a risk criterion, this means a high rating represents more manageable remaining risk, not greater danger. Record why each rating was awarded.

Concept sources: ASQ — What is a Decision Matrix? — Weighted scoring method; the options, criteria, ratings and calculations are an original worked application.

For A: (4 × 0.40) + (3 × 0.35) + (4 × 0.25) = 3.65. For B: (5 × 0.40) + (4 × 0.35) + (2 × 0.25) = 3.90.

B leads under these assumptions. The 0.25-point advantage does not prove it will succeed, nor mean it earns 0.25 more profit. It is a combined preference score whose meaning depends on the criteria and rating scales.

Test whether the ranking is robust

Sensitivity analysis changes uncertain ratings or defensible weights to see whether the preferred option changes. A close result deserves particular attention. Discuss disagreements rather than adjusting weights until a favoured project wins.

In the bookbinder example, suppose reliable delivery becomes more important. Readiness rises to 40%, customer benefit falls to 25%, and financial contribution stays at 35%. A now scores 3.65 and B 3.45. The ranking reverses. Managers should investigate the cover range's readiness gap and agree the real trade-off before committing resources.

Simple scoring is not the same as a full multi-criteria decision analysis, or MCDA. Formal approaches examine the value of performance differences and may use swing weighting, which considers both the size of an improvement and how much it matters. The simple example above is a discussion tool, not a claim of formal MCDA or a substitute for financial appraisal.

Concept sources: HM Treasury — Use of Multi-Criteria Decision Analysis in options appraisal of economic cases — Sensitivity, swing weighting and the distinction from simple scoring; the numerical sensitivity example was prepared for these notes.

Use business analytics to strengthen the evidence

Business analytics uses data and analytical methods to examine business questions. Option evaluation can combine internal transactions, service records and staff feedback with external customer research, industry information and economic data.

Concept sources: IBM — What is business analytics? and IBM — What is prescriptive analytics? — Analytics categories and constrained decision recommendations; this comparison table was prepared for these notes.

A model recommends actions within its assumptions; it does not determine the organisation's values. Check data coverage, changing conditions and model performance. A relationship between two variables is not automatically evidence that changing one will cause the other to change. Supplement analysis with operational knowledge and targeted trials where appropriate.

Understand the data behind the analysis

Data differs in structure, not simply in whether it is digital.

Concept sources: AWS — Data lake foundation — Supported data categories and raw/transformed storage; definitions and business examples were prepared for these notes.

A data lake can hold these different forms and retain raw source data alongside versions prepared for analysis. Keeping the original can support later questions that need details excluded from an earlier transformation. Preparation still matters: preserving raw data does not make it accurate or immediately usable.

The organisation needs a catalogue, clear ownership, appropriate access and documented transformations. Without these controls, a large collection can become difficult to interpret or trust. Retention should serve an identified purpose and applicable obligations, rather than an assumption that every item must be kept forever.

Concept sources: AWS — Modern data architecture and AWS — Data lake foundation — Governance across data stores, cataloguing, access and data lifecycle concepts.

Turn evaluation into a defensible recommendation

Concept sources: NASA — 6.8 Decision Analysis — Decision criteria, alternatives, uncertainty and reporting; this condensed flow was prepared for these notes.

The recommendation should explain the preferred option, conditions for proceeding, important uncertainties and why alternatives were less suitable. If the deciding assumption is weak, a focused pilot may be more useful than adding decimal places to the score.

Sources and further reading

• NASA — 6.8 Decision Analysis
• ASQ — Impact Effort Matrix
• ASQ — What is a Decision Matrix?
• HM Treasury — Use of Multi-Criteria Decision Analysis in options appraisal of economic cases
• IBM — What is business analytics?
• IBM — What is prescriptive analytics?
• AWS — Data lake foundation
• AWS — Modern data architecture