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Leadership analytics

How can leadership analytics support strategic choice or positioning?

AccessibleStrategicTeam2 min read

Contents

Leadership analytics seeks to uncover how good leadership is in your business.

Leadership analytics uses multiple forms of evidence to understand how leadership behaviour affects people, decisions and organisational outcomes. It replaces vague impressions with a governed inquiry while recognising that no metric can fully measure leadership quality.

When to use it

Assess leadership periodically and after meaningful transitions, reorganisations or changes in responsibility. New leaders may benefit from earlier developmental feedback, provided measures are not used as covert surveillance.

Useful questions include:

Which leadership behaviours help teams perform and remain healthy?

How do employees experience decision making, support and fairness?

Which behaviours matter in this context?

Who demonstrates leadership capability without holding a formal title?

Origins

Leadership analytics developed from industrial and organisational psychology, employee research, assessment centres, multi-rater feedback and people analytics. As digital HR data expanded, organisations began linking behavioural measures with workforce and business outcomes. The field has no single inventor; responsible practice combines measurement expertise, contextual validity and employee-data governance.

What it is

Why it matters

Leadership can affect clarity, safety, engagement, learning, retention and performance, but outcomes are also shaped by resources, market conditions, team composition and systems. Analytics should therefore examine plausible contribution rather than credit or blame one leader for every result.

How to use it

Define the decision first: development, succession, selection or programme evaluation. Create a behavioural model grounded in role requirements and organisational values, then test whether it predicts relevant outcomes without disadvantaging protected groups.

Use surveys, interviews, focus groups, observation and operational data. Protect anonymity through minimum reporting groups, access controls and clear purpose limits. Multi-rater feedback is usually developmental; converting it into hidden performance scoring can reduce candour and fairness.

Separate correlation from causation. Control for role, team conditions and prior performance where appropriate, and combine quantitative patterns with qualitative explanation. Give leaders actionable feedback, coaching and a route to challenge inaccurate data. Audit models for bias, privacy, gaming and unintended pressure on employees.

Practical example

Google’s Project Oxygen began with two questions:

What makes an effective manager?

Which behaviours are associated with managers struggling?

The work combined interviews, 360-degree feedback and outcome analysis. An early version identified eight behaviours:

Coaches effectively.

Empowers the team without micromanaging.

Shows interest in team members’ success and wellbeing.

Remains productive and results-oriented.

Communicates and listens well.

Supports career development.

Provides clear direction and strategy.

Brings useful technical capability.

It also identified three recurring difficulties:

A hard transition into the role.

Inconsistent performance-management and development practice.

Too little time spent managing and communicating.

Google used the evidence to focus manager development and repeat 360 feedback. The example illustrates contextual discovery, not a universal checklist; later versions of the work evolved as the organisation and evidence changed.

Top practical tip

Define observable behaviours for the specific leadership role, then triangulate confidential employee evidence with outcomes and context before acting.

Top pitfall

Do not build a leader score from opaque employee data or causal claims that the design cannot support. Protect anonymity, audit bias and preserve human review.