From Technical Expert to Trusted Data Leader – An Interview with James D. Matthews
- 2 days ago
- 7 min read
James Matthews built DataCareer.Coach around a pattern he saw repeatedly over 25 years spanning KPMG, banking and fintech: technical excellence alone rarely helps someone reach their full potential, get promoted, or ensure their work stays aligned with what the business actually needs.
In this interview, he explains why trust matters more than technical output, how AI is reshaping which skills carry value in data careers, and why the most technically skilled professionals may be the most exposed as the profession changes. He also draws on his own experience as an introvert to explain how presence and influence can be built without becoming someone else.
James D. Matthews, Founder & Mentor
What led you to build DataCareer.Coach around the skills technically strong data professionals often overlook?
I spent 25 years at the mid-point between business and technology, across Klynveld Peat Marwick Goerdeler (KPMG), banking and fintech. Over that time, I watched the same pattern play out again and again. The people producing the strongest technical work were rarely the ones being promoted. The people who moved up often produced fewer individual deliverables, but spent more time making sure what they'd already built was used to its fullest extent across the business. One piece of work, fully exploited across every team it could inform, often had more impact than a dozen piecemeal requests handled in isolation, each one answered and forgotten. That's usually a symptom of siloed responsibility, where everyone owns their own slice of work and nobody owns its broader potential. I mentored a direct report over three years who learned exactly this. He moved from underconfident and overlooked to a significant pay rise, part of it while I was still his manager and part of it after he'd moved on to a new one, proof the change had stuck rather than depending on me specifically. What changed wasn't technical skill. It was learning to make his work travel further than the request that created it.
After 25 years between business and technology, what have you seen consistently hold talented data professionals back from senior roles?
The pattern was consistent enough that I could predict it. Technically strong data professionals would produce excellent work, and then wonder why it wasn't registering with the people who mattered. What was missing almost always fell into the same categories. Commercial awareness, stakeholder influence, and presence. There's a related frustration I hear often. Data professionals wish non-technical stakeholders understood what they actually do, and conclude the stakeholders should learn more about the technology. There's a case for that, but it's a passive strategy. It asks someone else to change something you can't control. Learning the business yourself is the opposite. It's entirely within your control, and it pays off twice. It gets you recognised properly in the role you're already in, and it gives you the language to describe the value you've added when you're ready to apply for the next one. Nobody explicitly taught this. It sat outside what technical training ever covered.
What separates someone who delivers technically excellent work from someone the business genuinely trusts?
Technical excellence answers the question, "Is this correct?" Trust answers a different question, "Can I rely on this person to tell me what I need to know, even when it's inconvenient?" Good data professionals deliver what is asked. Excellent ones deliver what is needed, which sometimes means challenging the request itself, understanding the real business need well enough to propose and deliver something of greater value than what was originally requested. The professionals the business trusts lead with the business problem before the method, flag risk and uncertainty proactively rather than waiting to be asked, and take ownership of the outcome their work leads to, not just the accuracy of the output. They're comfortable saying, "Here's what I don't know yet," instead of hiding behind more detail. Technical excellence gets you into the room. Trust is what keeps you being invited back, and eventually gets you asked for an opinion before the analysis has even started.
As artificial intelligence (AI) makes technical execution easier, which human skills do you believe will become most valuable in data careers?
Technical execution is becoming commoditised, and really quickly, across the profession. AI tools are closing the gap between a strong analyst, data engineer or data scientist and an average one. Data engineers are seeing pipeline and infrastructure work automated. Data scientists are seeing model building become faster and more accessible through automated machine learning (AutoML) style tools. Natural language interfaces are rolling out across organisations too, giving everyone something close to an Alexa like ability to ask for the data they need directly, without waiting on a dedicated specialist. What doesn't get commoditised is judgment, knowing which question is worth answering, understanding what a stakeholder actually needs versus what they've literally asked for, and standing behind a recommendation when people push back. The analyst market isn't simply shrinking. It's splitting. Junior, narrowly specialised roles are being squeezed hardest, while a smaller number of senior people who can bridge engineering, modelling and business judgment are becoming more concentrated, and better paid. If you're early in your career, build those commercial and interpersonal skills much sooner than it used to be necessary. The pressure on junior roles means you no longer have the luxury of adding them later, once the technical foundation feels secure.
Why might the most technically skilled people actually be more vulnerable as AI reshapes the profession?
History has a clear precedent for this. Professional photographers used to build their reputation partly on darkroom mastery, chemical processing, precise timing, years of craft that shaped the final print. Digital photography and editing software made those effects trivial to replicate. Clients never cared about the darkroom. They cared about the photograph. The photographers who kept thriving were the ones whose real value had always been composition, storytelling and judgment, not the chemistry. The ones who'd built their entire identity around darkroom technique found the ground moving under them, because the specific skill they'd relied on stopped being the differentiator it once was. Data work is no different. Clients and stakeholders have never cared how a number was produced. They care whether it's right and what it means. As AI makes technical production easier, the professionals whose value was always judgment and communication will be fine. The ones whose value was purely technical execution are the most exposed.
When a stakeholder asks for a specific piece of analysis, how can a data professional uncover the business problem behind the request?
Most requests for analysis are really requests for a decision to be supported, and the two aren't always stated the same way. I encourage people to think a little like a child does, endlessly curious, comfortable asking "why" two or three times in a row until they reach the actual problem underneath the request. What decision will this inform? Who's making it? What happens if the answer isn't what they expect? Sometimes iteration is unavoidable. What's really needed only becomes clear once the first version is in front of someone and they react to it, and that's a normal part of good analytical work, not a failure. But asking more "why" questions upfront reduces how many times that loop needs repeating. Two or three of these questions before starting the work saves far more time than it costs, and it's usually the moment a stakeholder starts treating you as a partner rather than an order taker.
What can introverted professionals do to build leadership presence without feeling they need to become someone else?
The one thing they shouldn't try to be is someone else. To be seen as a leader, the first thing you need is to be comfortable leading yourself, fully comfortable with who you are and what you stand for. Many people try to build confidence from the outside in, acting a certain way, saying certain things they think leaders say. It works the other way round. Learn to know yourself properly first, which surprisingly few people ever do. Stay true to who you actually are and what you believe, and the resulting confidence isn't something you need to manufacture. It becomes inevitable, because no one else can be you as well as you can. Practically, that means going into meetings with a few points prepared rather than trying to speak spontaneously and often, and choosing one moment to contribute something considered rather than filling every silence.
How has your own experience with introversion shaped the way you think about influence and leadership?
I'm a natural introvert, and for a long time I assumed leadership meant becoming someone I wasn't, more outspoken, quicker to fill a room with opinion. It never felt sustainable, and I don't think it read as authentic to the people around me either. I've come to believe it would be unethical to ask an introvert to become an extrovert, and pretty much impossible even if it weren't. It's certainly unsustainable. People can't be truly happy unless they're being true to who they are, and as much as I want clients to reach their full potential, that can't come at the cost of their happiness. The two have to move together. What worked for me instead was building on preparation and depth rather than spontaneity, listening carefully and choosing my moments. That experience shapes almost everything about how I coach now. I help introverted clients build presence from what they already have, not from what they think they're missing.
If a technically strong data professional feels invisible in their organisation today, what is the first thing you would encourage them to change?
Carry your impact further than it naturally travels. The person who commissioned the work already knows what it enabled, so telling them again won't fix the invisibility. Impact usually stays trapped in that one relationship and never moves further up or across the organisation, which is often the real cause of feeling invisible. The first time I acted on this myself, I was surprised. I'd assumed the senior person I approached, someone outside my own management chain, was only giving me an hour out of politeness. Instead, we both got a great deal out of it. He learned more about other work I was involved in, and I learned more about what was actually causing him difficulty. Not every relationship like this will thrive. Some will feel forced and unnatural, and those are fine to let go of. But a few will be mutually beneficial, where both people clearly gain from the conversation, and those are the ones worth building. Do it naturally, staying properly true to yourself rather than compromising who you are to make it happen.
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