Tools · People analytics
Analytics Maturity
Where your analytics practice actually stands — and the one constraint holding it there.
The method
Analytical-maturity staging (Davenport–Harris five stages across the DELTA dimensions)
A new head of people analytics inherits three dashboards, one data engineer, and a mandate to be more like the companies in the case studies. The board is ready to approve a platform purchase. Whether that is the right next dollar depends on where the practice actually stands — and on which constraint is binding.
Davenport and Harris's Competing on Analytics established the staging: five stages from analytically impaired to analytical competitor, with the finding that separates it from vendor maturity theater — the difference between stages is less about technology than about leadership commitment, an enterprise-wide approach, a distinctive strategic focus, and scarce analytical talent. The companies at stage five are not the ones with the most tools; they are the ones where analytics is the strategy.
The sequel, Analytics at Work, written with Robert Morison, turned the staging into a usable diagnostic: DELTA — accessible high-quality Data, an Enterprise orientation, analytical Leadership, strategic Targets, and Analysts. The five advance together or not at all, which is the diagnostic's whole point: staging exists to find the dimension holding the others back. The authors call their framework a compass rather than a rigid map, and that modesty is load-bearing — the stage number is a conversation starter; the binding constraint is the finding. Cindi Howson's Successful Business Intelligence corroborates from the BI side with survey data: across 634 practitioners, what separated moderate from wild success was executive support, business-IT partnership, culture, and relevance. Organizational factors, rarely the toolset.
Read together, the three books converge on an uncomfortable pattern for anyone holding a purchase order: the binding constraint is usually person-shaped or governance-shaped, and a platform will not touch it.
Describe the practice and the service stages each DELTA dimension from your evidence alone — honest not-described flags instead of invented maturity — then names the binding constraint and a three-to-five-move roadmap aimed at exactly that. The classic first-call diagnostic, without the engagement letter.
The books behind this tool
- Competing on Analytics: The New Science of Winning — Thomas H. Davenport & Jeanne G. Harris
- Analytics at Work: Smarter Decisions, Better Results — Thomas H. Davenport, Jeanne G. Harris & Robert Morison
- Successful Business Intelligence — Cindi Howson
How it works
Davenport five-stage staging across the DELTA dimensions (Data · Enterprise orientation · Leadership · Targets · Analysts), grounded in the business-intelligence corpus. Per-dimension placement carries evidence-from-input only (honest not-described flags — never invents maturity); the overall verdict names the binding-constraint dimension; closes with a 3–5-move next-stage roadmap targeting that constraint. The classic first-call diagnostic artifact.
You bring
{ practice, cluster? }
You get
{ practice_summary, dimensions[5] (stage · evidence · gaps), overall (stage · binding_constraint), roadmap[], grounded_in, provenance }
Use it for
- →First consulting call: prospect describes their shop → staged diagnostic + the roadmap conversation
- →Budget case: the binding constraint names what the next analytics dollar should buy
- →Annual re-run: stage movement is the program's progress measure
See it work
example outputA 200-person specialty retailer: one finance analyst building monthly Excel reports from POS exports, store managers deciding on gut feel, three siloed systems and no warehouse, a CEO who wants to "get into AI" but no budget, no sponsor, and an abandoned intern-built dashboard.
Analytics maturity diagnostic — specialty retailer (200 FTE)
Overall verdict: Stage 1 — Analytically Impaired · Binding constraint: Leadership
Four of five DELTA dimensions sit at analytically impaired: siloed non-integrated data, no enterprise coordination, buzzword-only leadership with no funding, and no defined targets. A lone Excel analyst gives a thin sliver of localized activity, but store managers still decide on gut feel and the one prior BI effort collapsed. The weakest well-evidenced dimensions anchor the org firmly in the impaired stage.
DELTA dimension placements
| Dimension | Stage | Key evidence from the input |
|---|---|---|
| Data | 1 · Analytically Impaired | No warehouse; POS, QuickBooks, and payroll SaaS don't talk to each other; reporting depends on manual POS exports |
| Enterprise orientation | 1 · Analytically Impaired | Analytics confined to one analyst in finance; no governance, standards, or strategy alignment |
| Leadership | 1 · Analytically Impaired | CEO's "get into AI" remark; no budget line; no executive sponsor beyond the comment |
| Targets | 1 · Analytically Impaired | No stated business target for what analytics should improve; no prioritized use case |
| Analysts | 2 · Localized Analytics | One analyst producing monthly Excel reports — real but fragile; skills stop at Excel; the intern departure killed the last BI effort |
Grounding: 28 canonical constructs from the business-intelligence corpus (25 books, incl. Competing on Analytics, Analytics at Work, Successful Business Intelligence) — e.g. Executive Sponsorship & Leadership Commitment, Governance, Strategy & Enterprise Orientation, Fact-Based Decision Making, Data Architecture, Storage & Integration.
Next-stage roadmap (advance the binding constraint first)
- [Leadership] Convert the CEO's "get into AI" remark into a funded mandate — name an executive sponsor and create an analytics budget line so effort survives staff turnover unlike the abandoned intern dashboard.
- [Targets] Have leadership pick one concrete, high-value decision to improve first — e.g., data-driven staffing or merchandising at the store level — and set a measurable target for it.
- [Data] Integrate the three siloed systems (POS, QuickBooks, payroll SaaS) into a shared, cleaned data store so reporting no longer depends on manual POS exports.
- [Analysts] Give store managers a trusted, maintained dashboard tied to the chosen target and train them to use it, replacing gut-feel merchandising and staffing calls.
- [Enterprise] Establish minimal governance — ownership of the integrated data, refresh process, and metric definitions — so the practice is not one departure away from collapse. The intern episode showed the org's fatal key-person fragility.
Staged on the Davenport five-stage model (Analytically Impaired → Localized Analytics → Analytical Aspirations → Analytical Companies → Analytical Competitors), assessed across the DELTA dimensions. Dimensions the input doesn't describe are honestly marked "not described" rather than guessed.
Run it on your data
Call it on your own inputs — over the API, or hand it to your AI agent via MCP. Discovery is open; running it is metered.