From Tech to Trust: The Human Side of Building Data Culture

Data and technology alone will not create a data-driven organisation. Research indicates that the biggest obstacles to creating data-based businesses are not technical; they are cultural. Cultivating a data-driven culture does not happen overnight. That is why it is important to cultivate a data-driven mindset within your organisation. Creating an organisation culture where the quality mindset can flourish, supported by technology advancements.

Embedding data into the identity of the organisation is accompanied by influencing mindsets, attitudes, and habits. In a data-driven culture, people ask the hard questions and challenge ideas. They come together with a shared mission to improve the organisation and themselves based on data. The key lies in treating data as an asset, not as a byproduct.

Five pillars of fostering a data-centric culture for enhanced efficiency and effectiveness

To successfully cultivate a data-centric culture, organizations must focus on key foundational elements that support data integration at every level.

1. Trust

• People build high-trust relationships with data.
• Teams encourage data access and transparency.
• Data governance instills confidence in data.
• Organisations set clear expectations for responsible data use.

2. Talent

• Organisations prioritise data skills in recruiting, developing retaining talent.
• Job descriptions clearly outline data skills for all roles.
• Teams tailor enablement programs to all roles and levels.

3. Sharing

• People actively share best practices across the organisations.
• Teams share data cross-functionally to support business objectives,
• Leaders create time and space for people to participate in communities.

4. Mindset

• People encourage experimentation and innovation.
• Organisations focus on outcomes, rather than vanity metrics.
• People feel comfortable challenging ideas with data.

5. Commitment

• Executives do not just sponsor data-driven behaviour; they model it.
• The organisational structure reflects the value of data.
• Analytics goals influence data collection and processes.

Starting your journey toward a data-driven culture by considering the following questions:

• What data capabilities do employees have?
• Are there gaps in data skills across different levels?
• Is leadership advocating to put data front and center in business decision-making?
• Do we have a broader internal community that is or will commit to getting people excited about data and its potential impact on the org? If not, how can we develop it?
• How sophisticated is our data management approach, and what resources might improve or help the organisation scale with confidence?
• What is our data strategy as an organisation, and if it’s undefined or ill-defined, what problems exist where data might help?
• Is there an understanding of what data exists, and do people trust it?
• Are we following analytics best practices? If not, what organisational standards should be instituted to ensure consistent practices are followed?
• What processes, if any, do we need to refine to ensure there’s sound data governance?

Actively reflecting on these critical questions will pave the path towards creating a data culture. We hope the following ideas can inspire you in building a data culture:

• Data-driven cultures start at the top.
• Choose data metrics with care.
• Make sure to fix basic data-access issues quickly.
• Integrating the right talent for your data culture.
• Culture catalysts, people who can bridge different worlds.
• Communicate the business value of data.
• Quantify uncertainty.
• Make proofs of concept simple and robust.
• Offer specialised training just in time.
• Use analytics to help out employees, not just your sales department.
• Get in the habit of explaining analytical choices.
• Data culture is decision culture; the fundamental objective is to make better decisions.

Developing a data-driven culture at your organisation takes time and effort. Ongoing monitoring, evaluation, and maintenance are equally important to verify that analytics performance supports business needs through change and maturation.

Remember, it is not a ‘one-and-done’ process but a ‘continuous’ one.