Data transformation is a long, slow-moving journey requiring continuous investment in data capabilities. In this sense, CDOs must consistently demonstrate data’s value to stakeholders to maintain support and momentum. In this post, we share some successful strategies for doing that.

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Separate strategic and operational objectives 

Distinguish strategic business objectives from operational ones while ensuring transparency on the data’s contribution to each one.

Balance efforts across key value drivers: revenue growth, cost reduction, and risk mitigation

CDOs should invest in use cases across the three key value drivers while aligning with priorities set by the organisation and the senior leadership. However, experience shows that cost-reduction opportunities often outweigh revenue-generation possibilities.

Use logic models to calculate impact 

Focus on the impact of data, not just quantitative value. Use logic models (input > activity > output > impact) to do so.

Tell ‘performance stories’ with data

Use storytelling to convey both the qualitative and quantitative impact of data on the organisation’s performance, especially to articulate how data has improved decision-making. Incorporate metrics such as Net Promoter Score and user satisfaction scores. Use target setting and benchmarking to measure the progress of slowly-evolving KPIs over time.

Collaborate with function owners 

Craft stories with function owners on the productivity impact of available versus unavailable data capabilities. Feature the use of centralised data capabilities to support decision-making on strategic planning to highlight how data directly affects the speed and accuracy of the work, and improves forecasting in terms of productivity and new insights.

Enhance agility to increase revenue 

Increased agility enabled by data foundations leads to increased revenue. Collaborate with business partners to identify and showcase examples of better and faster data-enabled innovation.

Create a balanced portfolio of use cases

Having a balanced portfolio of use cases to cater to different stakeholders and spread risk is critical to demonstrating the holistic value of data. Include quick wins, near-term projects, long-term projects, and a small number of bold projects.