How to generate insights and value – without boiling the ocean

Organisations frequently compartmentalise their insights generation and data analytics capabilities into specific functional areas, and silo their workforce capabilities. This can lead to significant disconnects, inconsistencies, missed opportunities, and underperformance.

While very substantial amounts of data are already collected by technology platforms and systems, extracting insights and value from that data still often relies upon ad-hoc analysis, and a lot of manual effort.

Customer journeys are now likely to require workflows that span multiple parts of an organisation. This means that the broader organisation needs to be capable of sustainably generating insights, and be capable of continuously transforming those insights into value and outcomes.

Consistent methodologies and data analysis capabilities are needed across the organisation. This is now critical to achieving world-class organisational performance. Technology is an enabler, but it is just a small part of the picture.

Very few organisations are currently architected to enable an end-to-end approach, and even fewer are able to rapidly deploy innovation and transformation initiatives that may be inspired by insights.

In particular, acting upon the insights generated may require new products or services, or significant adjustment to existing products or service delivery.

These changes may need to occur frequently, and could span business processes, workforce training, new key performance indicators, ways of working and culture, and workforce incentives.

This means organisational alignment will need to be regularly revisited, and adjustments made very rapidly.

Pragmatic steps you can take to improve organisational alignment

Strategic Alignment

• Strategy and operational plans are cascaded throughout the organisation.
• Ensure strategic priorities and operational plans have specific outcome measures.
• Outcome and operational measures are used to track progress

Governance and Reporting

• Ensure your governance and reporting framework is fit for purpose.
• Risk management and decision making is the focus of governance.
• Workforce and leadership effort to generate reporting is minimised. Preferably through the continuous automated collection of data, and self-service dashboards.

Operating Model

• Organisational design is aligned to demand and service delivery priorities.
• Evidence based decision making is integrated into core workflows and processes.
• Enable regular adjustments to resource allocation, based on evidence based insights, strategic priorities, and agreed measures of demand and supply.

Ways of Working

• Embed data gathering and analytics into BAU and core workflows.
• Encourage data enrichment and analysis, where allowed by privacy, security, and compliance considerations.

Workforce Capability

• Expand data analysis learning and development opportunities to the wider workforce.
• Identify ways to democratise data analysis, in line with privacy, security, and compliance considerations.

Culture

• Link data analysis and insights generation into workforce performance management.
• Highlight successes in reward and recognition programs.

Generating value without boiling the ocean

Evidence based decision making is the foundation stone that supports efforts to extract value from data analytics. This requires leaders and their teams to build a common level of understanding, mechanisms for communicating insights and evidence, and a decision making culture that is underpinned by data analysis.

These mechanisms involve more than technology, and are often formalised into a governance framework, with standardised policies and processes relating to reporting and data analysis.

As organisations scale, there will inevitably be specialisation, and different parts of the organisation will develop differing levels of workforce capability and resources. Compliance and regulatory requirements could potentially require a different approach to the collection and handling of data within some sections of the organisation.

In addition, strategic priorities and operational plans could potentially need temporary analytics and insights generation capabilities, which may not be easy to justify for the entire organisation, or for business as usual.

It is therefore critical for executive leadership to put in place governance systems that take into account the differing levels of maturity and capability across the organisation.

Organisational versus departmental alignment and maturity

Maturity models are a useful method for standardising the evaluation of capability which spans multiple measures and dimensions. They allow for a consistent and systematic analysis of current state, prior time periods, and desired future state.

Leadership teams should incorporate consideration of organisational maturity into strategic and operational planning, particularly for key capabilities such as risk management and evidence based decision making. Items that should be considered include the following:

• Maturity of the overall organisation
• Maturity of key departments
• Desired state versus current

Scaling up insights and analytics capability

Here are four actions leaders can take to improve the maturity of their organisation’s data analysis and insights generation, and enhance the extraction of value.

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