About This Episode
In episode 5 of Data & AI Heads, host Ian Allison speaks with Alex Ingram, a business intelligence practitioner with 15 years of experience across finance, insurance, and retail. Alex makes a compelling case that the most valuable skill a BI or data professional can develop is not technical mastery alone, but the ability to build genuine, trust-based relationships with stakeholders. He explains how investing in those relationships allows practitioners to move from order-taking to proactively shaping the projects and initiatives that deliver the greatest organizational value.
Alex walks through his practical framework for project selection, covering how to sequence work for maximum ROI, how to assess whether stakeholders are ready to accept a given solution, and how to identify the people and initiatives most likely to gain traction. He also addresses the emotional and technical barriers to BI and AI adoption, drawing on a real example where fully automating 90 percent of someone's job led to a promotion rather than a job loss. The conversation is grounded in specific, actionable advice that data leaders at any career stage can apply immediately.
This episode is essential listening for data and analytics professionals who want to expand their influence beyond the dashboard, earn the latitude to choose meaningful projects, and ensure their technical work actually gets used by the business.
Key Takeaways
- Building trust with stakeholders starts with anticipating their needs before they ask, for example delivering month-to-date figures when only year-over-year was requested, because that proactive behavior earns credibility faster than simply meeting deadlines.
- Genuine relationship-building is not about self-promotion but about investing real care in understanding the concerns, pressures, and goals of the people making data requests, which also leads to better, more contextually informed questions.
- Alex frames emotional capital at work using Simon Sinek's insight that trust and affinity are built through millions of small moments, not a single impressive deliverable, so consistent, human engagement compounds over time.
- When selecting projects, Alex recommends listing all possibilities, eliminating those that do not sequence logically, and then evaluating each remaining option against its likely ROI and whether the intended audience is actually ready to accept and act on the output.
- A project's technical soundness is not sufficient for success; you must also assess whether stakeholders are emotionally ready for the change, because fear of automation or loss of familiar workflows is a real and legitimate barrier that requires patience and empathy.
- On the technical side of adoption, new BI tools must reduce friction rather than add it, for example pre-populating dashboards with a user's cost center hierarchy so they open to a ready view rather than requiring multiple configuration steps each session.
- Alex's experience shows that fully automating a large portion of someone's job does not necessarily threaten their employment; in one case the employee received a promotion, which is a useful data point for managing fear-based resistance to automation.
What We Cover
Frequently Asked Questions
How can a BI professional earn the ability to choose their own projects rather than just fulfilling requests?
According to Alex Ingram, it starts with building trust on smaller, routine requests by anticipating what stakeholders will need next, not just what they asked for. Over time, that demonstrated understanding of the business and its people gives leaders confidence to let you shape and select initiatives. The relationship you build becomes the foundation for that expanded autonomy.
What framework does Alex Ingram use to prioritize data projects?
Alex writes out all available project options, immediately eliminates those that are clearly out of sequence or misaligned, and then evaluates the remainder on two criteria: logical sequencing (does one project enable another?) and estimated ROI relative to the cost of the data function. He also adds a critical filter: whether the intended stakeholder audience is actually ready and willing to act on the output, because a technically excellent project that meets an unreceptive audience delivers no real value.
Why do employees and leaders resist new data tools or AI initiatives, and how should practitioners respond?
Alex explains that resistance is often emotional rather than purely technical. Entry-level employees may fear that automation eliminates their role, while leaders may fear losing the expertise that made them successful. He recommends assessing readiness honestly: if a stakeholder is not emotionally ready, it may be worth revisiting the initiative in a few months rather than forcing adoption. If the barrier is technical familiarity, focus on reducing friction in the new tool so the transition feels simpler than the old workflow.
Does automating a data or reporting job actually put people out of work?
Based on Alex Ingram's direct experience, it does not have to. He describes a situation where he automated approximately 90 percent of a person's job responsibilities, and that employee received a promotion as a result. While fear of job loss is understandable and should be acknowledged empathetically, it does not necessarily reflect the actual outcome of well-implemented automation.
How should a BI team approach driving adoption of a new dashboard or reporting application?
Alex recommends making the new experience measurably simpler than the legacy one. As a concrete example, rather than asking users to log in and manually configure report parameters every session, a Power BI implementation could use bookmarking and pre-populated filters based on each user's cost center, so the relevant dashboard loads automatically. The principle is that if you are asking someone to change their behavior, the new behavior must be more intuitive and require fewer steps than what they already know.
How does emotional intelligence help data professionals ask better questions of business stakeholders?
Alex Ingram argues that generic question frameworks from books or online resources are a poor substitute for genuine familiarity with a stakeholder's industry, role, and personal concerns. When you take time to understand a director of product or a director of finance as a person, you naturally develop more precise and contextually relevant questions. That knowledge comes from relationship-building, not from a script, and it also makes it easier to frame requests and findings in terms the stakeholder finds credible and actionable.
What role does forgiveness and trust play in a data professional's long-term credibility?
Alex notes that data work inevitably involves errors, and the stakeholder's response to those errors is shaped heavily by the relationship they have with the analyst. When stakeholders know someone personally and trust their integrity, a data mistake is more likely to be treated as an isolated slip rather than a reason to lose confidence entirely. Building that relational trust in advance is therefore a form of professional risk management that protects credibility over the long term.
About the Guest
Alex Ingram
Alex Ingram is a business intelligence practitioner with approximately 15 years of experience working across the finance, insurance, and retail industries. His background in economics and finance informs his approach to evaluating the ROI of data initiatives and translating technical outputs into tangible business value. Alex is recognized for his ability to build strong stakeholder relationships that earn him the trust and latitude to shape high-impact analytics projects, and he brings a people-first philosophy to every stage of the data workflow, from requirements gathering through adoption. Outside of his professional work, Alex is a member of the band Empty Atlas.
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