Wanted Analytics refers to a category of product and analytics tools that surface which features and dashboards are actively used, by whom, and how often, so data teams can prioritize investments and prove impact. This evergreen explainer clarifies the core mechanics of Wanted Analytics, common capabilities such as usage telemetry, adoption scoring, and retention signals, and how these differ from traditional BI alerts or row-level security. It outlines when and why organizations adopt these patterns, best practices for implementation, and realistic expectations for maturity and governance outcomes.
What Wanted Analytics Covers and Why It Emerged
Wanted Analytics describes systems that continuously measure engagement with analytics products, features, reports, and metrics, then translate that engagement into actionable signals. Unlike periodic surveys or one-off adoption studies, these mechanisms produce ongoing, comparative insight into what teams actually use and when. The approach emerged because many analytics orgs lacked visibility into how dashboards were used beyond login counts, making it difficult to prioritize roadmap work, retire unused views, or demonstrate value. By instrumenting usage at the event or interaction level and aggregating it into adoption indicators, Wanted Analytics helps organizations align analytics with real user needs and operational constraints.
Typical Goals and Problems It Addresses
- Identify high-value dashboards and features that drive decisions.
- Detect stale or redundant analyses for deprecation or archiving.
- Quantify the reach and depth of analytics adoption across teams.
- Surface friction points in navigation, definitions, or data freshness.
- Support governance by linking usage to ownership and SLAs.
Core Mechanisms Instrumentation and Event Design
At the technical base, Wanted Analytics relies on event instrumentation that captures interactions such as view, click, filter change, export, and scheduled delivery. These events are tagged with context like dashboard ID, user role, organization, and time, then aggregated into metrics such as unique users, session duration, and frequency. Well-designed instrumentation balances detail with privacy, avoiding noisy event floods while still preserving the paths that reveal meaningful workflows. Teams often start with coarse-grained pageview or interaction events and refine them into canonical actions as they learn what questions stakeholders want to answer.
Key Signals Tracked in Practice
| Signal | What It Indicates | Source Event Examples |
|---|---|---|
| Reach | Breadth of audience | Unique viewers, org segments |
| Frequency | Recency and repetition | Session count per time window |
| Depth | Exploration and interaction | Filter use, drill-throughs, export |
| Stickiness | Retention and habitual use | Return rate over days or weeks |
Adoption Frameworks Scoring and Classification
Many implementations translate raw signals into adoption scores or tiers, which provide a shorthand for prioritizing work. A common framework classifies assets into categories such as power users, regular users, occasional viewers, and at-risk, based on thresholds for frequency and reach. These classifications are not universal laws but configurable rules that an analytics team agrees on with stakeholders. They should be reviewed periodically because thresholds that made sense at launch may misclassify behavior after product changes. The scores are best used to guide conversations, not to penalize teams, and to surface anomalies rather than to produce rigid rankings.
Example Simplified Classification
| Tier | Reach Threshold | Frequency Threshold | Typical Actions |
|---|---|---|---|
| Power | High org reach | Regular sessions | Championing use, proposing enhancements |
| Regular | Multiple teams | Weekly or more | Routine reporting, validation |
| Occasional | Single or few users | Monthly or sparse | Ad hoc checks, low engagement |
| At Risk | Declining reach | Reduced frequency | Deprecation review, reeducation |
Implementation Patterns When and How to Start
Organizations typically begin Wanted Analytics when they feel pressure to prove the value of analytics or need to decide where to invest limited engineering time. A minimal viable approach can be as simple as enabling existing platform telemetry for dashboards and adding basic user and org tagging. From there, teams can incrementally add events for key interactions and refine scoring rules. It is common to pilot the approach with one product line or data domain, then expand once data quality and stakeholder trust are established. Clear documentation of event mappings, aggregation windows, and classification criteria helps maintain consistency as implementations scale.
Practical Rollout Steps
- Define the questions you need answered, such as which dashboards are unused.
- Audit current instrumentation and identify missing but low-effort events.
- Implement aggregation for core metrics like reach, frequency, and depth.
- Run classification rules in parallel with qualitative input before codifying tiers.
- Iterate thresholds and event definitions based on stakeholder feedback.
Integration With Governance and Product Decisions
Wanted Analytics is most effective when integrated into regular governance rituals rather than treated as a one-off dashboard. Data teams can use insights to recommend retirement of stale assets, guide onboarding by highlighting flagship reports, and align roadmap priorities with observed usage patterns. Because classifications can feel evaluative, it is important to pair quantitative signals with qualitative context, such as strategic initiatives or compliance constraints, to avoid misinterpreting low usage as low value. Transparency about how data influences decisions helps stakeholders see the system as a tool for collaboration rather than surveillance.
Limits, Privacy, and Common Misconceptions
Wanted Analytics does not automatically fix poor data quality, unclear semantics, or weak user research; it mainly improves the efficiency of how existing analytics are consumed. Event-level detail can raise privacy considerations, so implementations should adhere to organizational policies, anonymize where appropriate, and communicate practices to users. Another misconception is that low usage always indicates a reporting problem, when in some cases the underlying question or workflow simply does not require analytics. Context, such as regulatory requirements or one-off analyses, must inform how much weight to give usage signals.