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I’m often asked by clients, “what is the top challenge you see holding back data teams?”

I love this question. The answer is suuuuper easy becuase it’s basically the same everywhere I go, and with every team I talk to.

👉🏼 Ad hoc data requests 👈🏼

Data requests are obviously a part of the role. In many cases they’re the reason the data team was establised in the first place. But it’s the ad hoc component that’s the real killer.

What do I mean by this?

Ad hoc data requests refer to unstructured and unexpected requests (or demands) for data, or reports, or dashboards etc. from outside the data team.

They can arrive in a variety of manners (email, text, slack, teams, whatsapp, scrunched up piece of paper, shouted across the office floor etc.), they’re invariably URGENT and they often take the form of what I call a “prescriptive solution”.

A presriptive solution is where the requestor (not a data expert) tells the data person (expert) the specific solution that they require. “I need a dashoard that shows xyz” or “can you put it in an excel file with abc headings for me?”

Of course, this is a some backwards BS right there. You wouldn’t go to a doctor to tell them what prescriptions you need! Particularly without discussing symptoms at all.

While most individual ad hoc requests appear small, their combined weight and volume is the number one challenge holding data leaders and their teams back from being a more influential and strategic contributor to the business.

And in an age where every organisation strives to be data driven, and emerging technologies live or die on the power of data, this is a challenge every single data leader will want to get a handle on!

Today, I’m going to show you how👇🏻

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I could probably talk all day about how allowing ad hoc requests to dicate the work your data team focuses on is a bad idea. But to save us all from that long lecture, I’ve listed below the key impacts this dynamic creates. This ain’t all of them, but these ones are bad enough… let’s dig in:

☢️ Permanent reactivity
Work becomes driven by whoever’s request appears most urgent in the moment rather than by any shared view of value or priority. Strategic initiatives the team would otherwise give their attention to are repeatedly delayed as teams are constantly distracted by the never-ending supply of ad hoc requests.

🚨 Everything is urgent
Typically, most requests received are labelled urgent. When that happens, the word loses all meaning. Stakeholders want their request done first of course, so they call it urgent to jump the queue. This makes it impossible for data teams to actually know where to focus their limited time and attention in a value-add way.

🫥 Steady decline in influence
A data team that is no more than a help desk doesn’t get a seat at the big people table. In the eyes of their peers, the data leader is a supporting act hired to answer the questions raised by the important people in the business. When this is the case, you can kiss good-bye to any influence you hoped to attain.

😶‍🌫️ Invisible pressure on the data leader
Instead of leading, they become the routing layer for the entire organisation. They spend their time negotiating priorities, shielding the team, and absorbing frustration, which leaves very little capacity for direction setting or relationship building.

🤺 Internal disengagement
Teams who are constantly interrupted struggle to see the point of deeper work. They don’t feel like they’re adding value so their motivation drops, and the role becomes about surviving the next round of layoffs that invariable hit “support” teams first.

None of this happens because the team lacks skill. It happens because of a lack of a controlled and systematic appraoch, that enables teams to focus on vlaue add work rather than endless random requests.

Fixing this problem does not mean shutting the door on requests or creating bureaucracy for its own sake. Ad hoc needs will always exist. The goal is to change how those needs enter the system, how you react to them and how badly they impact the team.

Here are some simple steps to help you get a handle on things:

1️⃣ Step 1: Create a single, front door for data work
Whether this lives in Jira, Google Sheets, Tally, or something similar is largely irrelevant. What matters is that every request enters through the same mechanism and is subject to the same questions. If folks want you guys to do some work, then they must submit it in the same manner every time (regardless of seniority).

Alongside being a way to receive data requests - it is also a data collection exercise (see bonus step on why this is key)

2️⃣ Step 2: Reframing requests away from solutions and towards problems
Requests need to take the form of “problems that need solving” rather than prescriptive solutions (described above). We must train our stakeholders to tell us what problem they are trying to solve is, rather than telling us precisely what they need from us. They are not the data expert. They may know what data we have/don’t have, how fresh it is, where it is stored etc. By desribing the problem that needs solving, we data experts can get to work discovering solutions, rather than just producing what they’ve asked for.

3️⃣ Step 3: Make trade-offs explicit
When ad hoc requests are visible alongside planned work, it becomes easier to show what will be delayed if something new is pulled forward. This protects strategic initiatives and creates healthier conversations with stakeholders. A systematic approach enables visibility and transparency for the team and the wider business.

4️⃣ Step 4: Use the process to rebuild relationships
As interruptions reduce, the data team has more space to engage earlier, ask better questions, and operate with confidence rather than defensiveness. Over time, this changes how stakeholders approach them in the first place and increases trust in the team’s knowledge and abilities.

⭐️ Bonus Step: Treate intake as a data collection exercise
Over time, the system shows where demand really comes from, which teams consume the most capacity, and which types of work dominate. This gives data leaders evidence they can use in leadership conversations about resourcing, autonomy, and scope. Not tracking this means you’re operating in the dark, which makes these crucial management conversations so much harder to have.

Handled well, a formalised intake process does more than create an orderly system. It gives data leaders the leverage they need to move from firefighting towards strategy, and from just being busy to actually being influential.

These are the first steps. Why not start taking them today?

Tristan Burns

Find me on LinkedIn or Book a 1:1 call