What are typical reasons why leaders from other teams may have doubts or mistrust towards the data and analytics function, and what has been your approach to overcome lack of trust in the data and analytics function?
I agree with all points you make Neil. One seemingly small additional point I would add is shifting data quality left.
Having working with both transactional system and data engineering teams I think that data literacy with effective ways to engage app teams early in the data lifecycle is also crucial. So often the data and analytics teams are responsible for data quality with little or no skin in the game by the app teams. This leads to lower quality data and lower trust by customers. The more data quality is shifted left with real controls and automation the better.
I think you hit the nail squarely on the head, and don't want to duplicate. But as an additional thought: we sometimes see distrust based on local/unit definitions (for attributes like employee, or student) differing from central definitions due to specific business needs for that unit. Having a data catalog which supports segmentation of key terms between 'the whole business definition' and 'your local business definition' can help clarify the presentation and meaning of data for end-users.
It could also mean that leaders have easy access to information at a high- level (e.g. dashboards with some business context for correct interpretation and use, year over year comparisons etc.) which they can dig into if further deep dive is needed.
During this period of doubt, data double-check is your best ally to ease them…
What I've seen work best to remedy the situation is to work with the other leaders in true partnership, prioritize on the asks, and to foster trust and credibility. This is not a once and done, fostering the relationship happens over time. It is not easy, as traditionally many stakeholders think of this as business vs. IT, or business vs D&A team.
In the context of a prioritized specific project , it's an intentional effort to establish clear expectations with the right leaders, to communicate this consistently and (almost) at nauseam with all the relevant stakeholders of the project at hand, and to hold each other accountable - but do this in the context of partnership and working together towards common goals. It's not an 'us vs. them', it's a let's work together to figure out what the real issue is; how we will measure progress; what success looks like/what it doesn't look like, before we even get into the more technical conversations of what is the best-fit architecture and solution to deliver for it. It takes persisting on this clarity and collaborating with clear roles, accountability, and ways of working & communicating across the cross-functional teams to build the trust and accept/work towards delivering credible results as one team. It is a win (or miss) for all.
In short, to build that trust it takes intentional focus on building the relationships, clarity of priorities/roles/expectations, and communicating/delivering holding each other accountable.
This means having a deep understanding of the relevant stakeholders business priorities and the metrics they rely on to track status / progress towards meeting their roles; then catering to these even if you disagree.
By all means still include other metrics and narrative you believe is a more valuable and reliable perspective and seek to influence those stakeholders to the extent you believe it will lead to a better outcome, but at least initially, as you are winning over the trust of a stakeholder, feed them the data and insight they already know the want and need first. Then they’ll be more receptive to angles they may have not considered/ deprioritised.
To build trust, try to ensure rigorous data quality controls and promote transparency by clearly documenting data lineage and the aggregation methodologies being used for visualizations and analytics. Communication can be improved by highlighting some of the data insights to make them more understandable and relevant, and clearer on how they support the mission and decision-making. Also, always make sure data initiatives are aligned with business objectives, clarify data (business and technical) owners, and decision-making authority for the relevant domain, and build strong relationships with internal teams to increase trust and further enable better communication.