Direct answer
Mapping technical investments like testing, caching, and refactoring to business KPIs means picking indicators that plausibly move BECAUSE of the investment, not just indicators that happen to be tracked already, and being honest that attribution here is inherently harder than in a controlled experiment.
Structured elaboration
Six KPIs, spanning leading and lagging: (1) deployment frequency (leading, reflects testing investment); (2) change failure rate (leading, reflects testing and refactoring); (3) page or API latency (leading, reflects caching); (4) customer-reported incident volume (lagging, should improve as the leading indicators improve); (5) engineering time spent on unplanned work or firefighting (lagging, a direct cost signal that should decrease); (6) a customer-facing outcome metric like conversion or retention, only weakly and indirectly tied to these investments, included specifically to show the full causal chain even where the link is thin.
Data sources: engineering telemetry for deployment and failure metrics, monitoring systems for latency, support or incident systems for customer-reported issues, and time-tracking or sprint data for unplanned-work hours.
Validation plan for attribution: rather than claiming precise causal attribution, which is genuinely hard here, track the leading indicators tightly around the time of each specific investment (did change failure rate drop noticeably after the testing investment specifically), and treat the lagging, more distant metrics as directional context rather than proof.
Worked example
After a caching investment, page latency (leading) drops from 800ms to 300ms within two weeks, cleanly attributable given the timing. Customer-reported "site feels slow" complaints (lagging) decline more gradually over the following quarter, plausibly connected but confounded by other concurrent changes, so the dashboard presents the latency drop as confirmed impact and the complaint decline as suggestive, not confirmed, correlation.
Trade-offs and pitfalls
The main risk is overclaiming attribution on the lagging, business-facing metrics, which are almost always influenced by many concurrent factors beyond the specific technical investment being tracked; the dashboard should visually distinguish confirmed leading-indicator impact from merely suggestive lagging-indicator correlation, rather than presenting both with the same confidence.
