Sarah (00:08)
Hey everyone, and welcome back to unDUBBED the podcast where we strip back the buzzwords and get real about what it takes to unlock value from data. I'm Sarah, co-founder of Dub Dub Data, and I'm here with my brilliant co-founder and partner in crime, Fiona Gordon. Today, we're getting into what we really wish leaders outside of data understood about the work that we do. Fi this one's close to our hearts, right?
Fi (00:37)
Absolutely. I mean, we've spent years, too many years in the thick of it building dashboards, wrangling messy pipelines, but also sitting in those meetings with execs, translating their business problems into potential technical solutions. Hopefully something useful, but yet the same problems keep showing up.
Sarah (00:59)
Exactly. It's not about blaming everyone. It's just that data teams and leaders often speak completely different languages. So today we're sharing what we wish leaders knew because when they do, everything flows better. The conversations, the expectations and the results.
Fi (01:18)
For sure, listeners out there, there's some things on here that might hit you hard when you're just like, totally, I wish that insert leader name here would do that or would remember that. But a problem shared is a problem halved. So if you found it useful, make sure that you share this podcast with your colleagues. Perhaps put it just on a Teams channel or Slack channel where non-data leaders might tune in and change their tack
Sarah (01:47)
Sounds perfect. Let's get into it.
So Fi, let's kick it off with you. What's one thing that you wish non-data laders knew about data teams?
Fi (01:59)
Well, Sarah, first up, data projects are not one and done. leaders often think, we just plan this right up front, we'll only need to do it once. And not quite the case. What they really need to know is even the most perfectly planned data projects will actually evolve. we all know that business priorities shift, new questions emerge.
What looks good on paper or in our heads can actually fall apart in practice. So agile iteration is not a flaw, it's actually a feature.
So why this matters for leaders is that if they expect it to be done once done and only once done, they're going to get frustrated when the project continues to need time, budget or tweaks. they'll be saying We've already paid for that once. Why do we need to keep going
And this could lead to disillusionment with the data teams and also poor decision-making on behalf of the data leaders down the track. One thing that I think about when I think of this one and done is I've worked on a number of large data initiatives with extensive discovery phases, but...
despite months of prep that we did, really thinking, we've got to prep it, we've got to make sure that we understand all of the different requirements across the business. The real value only really emerged after we launched. And that's because we realized the gaps, even though we'd spoken to a lot of people, because the audience was huge, and I'm talking over 400 people were using the dashboards, it just meant we could never gather all of the requirements. What I find is that people are
much better at reacting to something that they can actually see rather than imagining or even whiteboarding what they want from scratch. Waiting too long for that perfect brief delays the progress and ultimately it will miss the mark.
Bottom line, leaders expect change. Budget for iteration and launch earlier. Improve as you go along. Thoughts?
Sarah (04:08)
Yeah, completely
agree, Fi. And I think some of the best success I've seen in projects is where the start point has been completely different to the end point. And that's when everyone's been invested and it hasn't been about just replicating something. It's been actually developing the story and getting to the why and allowing that agile iteration change to come through.
Fi (04:31)
Great points there, Sarah, What would you like people to know about data teams?
Sarah (04:38)
Yes, I've been in many a situation where a non-data leader has gone, hey, this is just a simple chart dashboard. It can't take that long, right? Hmm. Well, great dashboards may feel really simple and that's what makes them super powerful, but clarity isn't accidental. It's the product of deep thinking, hard trade-offs,
and confident design decisions. I love Richard Branson's quote, complexity is your enemy. Any fool can make something look complicated. It's hard to make something simple. Fi, how many dashboards have you seen that just look like data vomit on a page?
Fi (05:27)
Too many and it's really hard when people are asking you for feedback on them as well. and to your point, it's not just limited to dashboards either, that complexity. You and I have been going through our own process at the moment of pitch decks and we're on our fourth iteration, I think I would call it. Okay, so we're on our fourth iteration of it. Fingers crossed we're ready to.
Sarah (05:46)
I've lost count.
Fi (05:53)
go live with it soon. where we've started and where we've ended up quite different. The complexity that we had in there initially really stripped it back, niched down. there's a lot of important points that I feel that aren't just about dashboarding, but also about any kind of work that we do, whether it's a PowerPoint presentation or even thinking about what a new process is for an operating model.
Sarah (06:21)
Agree, agree. just reiterating why this matters, there's a real craft to creating clarity in a dashboard, telling that story.
Non-data leaders can really undervalue the effort that is put behind in telling a great story with a great data visualization. And I think it's a really dangerous belief that simple looking dashboards, visualizations, pitch decks are really easy to put together.
I'd love for non-data leaders to take that one on board.
Fi (06:54)
I think even data leaders should take that on board too.
Sarah (06:56)
Yeah,
you're right.
So Fi, what's next on your list?
Fi (07:02)
Well, building on yours there, Sarah, I want to say that dashboards are not an end to end strategy. what non data leaders often think is if we can just get one big dashboard that covers everything, then we're so, yes, one dashboard to rule the world. That will mean that we're sorted. Uh-uh. β
Sarah (07:15)
One dashboard to rule them all.
No, no.
Fi (07:27)
What they really need to know is trying to make a dashboard that solves everyone's problems creates tools that are slow, bloated, and unusable. the strategy should guide the dashboard and not the other way around. And this is really important because if leaders treat their dashboards as a solve for all, they risk
overwhelming users with noise instead of insight. I once inherited a dashboard designed to serve sales, finance, ops, and I think even execs on one dashboard. And it ran like a dog. It had 40 filters, no one knew where to look. You can't imagine the number of data sources behind it and how we approached it.
Sarah (08:09)
And I'm thinking
of how many clicks for each person to get to what they actually wanted to see as well, right?
Fi (08:15)
Yeah, but it was like spinning, the donut of death. So what we did was we broke it up, created tiered dashboards that were specifically solving problems for different people at different levels and different departments. And guess what? It worked. Usage soared. Yeah. And people trusted the data again. Bottom line, the niche dashboards
Sarah (08:30)
success.
Fi (08:38)
are more effective than these catch-alls. Think about that strategy instead.
Sarah (08:45)
I really like that. Often non-data leaders think that they're cutting costs - I just want one dashboard, but that dashboard is actually so bloated and so big and, hard to build, hard to maintain that if they'd broken it down into maybe the five or so dashboards that you explained, it would have been a much easier build and a lot more agile, easier to move and really get into those personas.
Fi (09:08)
I'd love to say you're wrong, but you're right.
Sarah (09:13)
Here's one I'm pretty sure we're both familiar with, the invisible success of data teams. You know, when everything's running perfectly, do you see us get thanked for it? No, but when something goes wrong, who's the first person they jump on? The data team. I see it time and time again, and I think non-data leaders need to understand how much work and effort goes in
to that smooth sailing. And it would just be nice, I think, to get a little bit more thanks for that.
When everything's working, data teams are invisible. Dashboards refresh, pipelines run, and stakeholders get what they need.
that seamlessness is often the result of an immense unseen effort, testing, governance, performance tuning. It's all in there happening behind the scenes. And I think it would just be nice for non-data leaders every now and again to maybe recognize a little bit about that effort, even when everything's going perfectly. How do you feel, Fi?
Fi (10:17)
I have a couple of thoughts on that. First up, think that the managers of the data teams can actually help the non data leaders with that recognition. It's very difficult when things are invisible.
to think, should I be grateful for? Which is why when things go wrong, that's when the attention or the spotlight is put onto it. Data leaders, if they can support the non-data leaders by saying, there's really great work happening in this space to ensure you get this regular piece of work or product on time, would you mind jumping on our
teams call sometime or a channel and just doing a shout out for the team about how much it matters or the things that you're using it for to drive value, that would be really powerful in motivating the teams.
Sarah (11:14)
Yeah, really good point because I think if you don't know, you don't know. And sometimes it needs to be brought to the front.
Fi (11:18)
Yes. Yeah.
The other thing that came to mind as you were talking about it is
For data teams to be given some time to be able to measure the ROI or the impact of what they're doing, that means that they're not going to be able to build new products in the meanwhile because they're going to have to be doing some analysis on existing products. But here's the kicker. If they can do that return on investment analysis,
then they can give that to the non-data leaders to say the actions that you're taking from this report or this data product or whatever has been delivered is worth this amount to the business. Again that will give a good story that people will be able to share more broadly and then come back and have the celebrations as well in the more positive sense.
Sarah (12:24)
Yeah, really like that. sharing the knowledge, sharing the load, sharing the understanding.
Fi (12:31)
Mm.
Sarah (12:32)
Great.
Fi (12:34)
I want non-data leaders to know that training isn't a time waster or a time suck for the analysts or the analysts in their business to be spending time on. It's actually a retention strategy, which is an IP strategy. What leaders often think is we don't have the budget or the time for training. We need to focus on delivery.
But what they really need to know is that training makes people faster, better, and more likely to stay. And when teams grow their skills, they build their confidence, deliver high quality work, and feel valued as well.
Guess why this matters? Well, high turnover, low quality and slow delivery often have that root cause in under investment and capability. I don't know if you've read Reddit recently, but there's so much on there about how organizations aren't investing in their people, they're not paying me enough, everything else. One of the things that you can do that's a little cheaper than giving people pay rises is doing some training. And it's not a nice to have, it's actually a strategic asset.
I've worked with teams where just a few hours of focused up-skilling a month made a noticeable difference, not just in the output, but in the team morale. We had incredible, incredible team engagement. thing was that people were excited to apply what they'd learned. Plus they stuck around longer, saving leadership thousands in recruitment costs.
The bottom line there is, invest in your people, train teams, perform better, and stay longer.
Sarah (14:11)
Yeah, in this case as well, it doesn't take a lot to show you care. if you're helping your team, you're supporting them, you're training them, you're educating them, they feel that they're growing themselves as well as, spending time and effort in the organization. I know, Fi you've got some great statistics around, deploying training, big training programs like you have about that retention.
Fi (14:34)
That's right. So, an attrition rate of 7.8 % versus a benchmark of 35 % at the same time. Just incredible to know if you invest in people, they will actually appreciate it.
Sarah (14:47)
Yeah, really good. you know, working with some of our clients at the moment, specifically on training, I'm seeing how energized the teams are because they feel like they're being valued as they move within tools, they feel like people are taking their time and taking their patience and actually educating them and allowing them that time to do that as an organization as well as really showing a lot of value.
Fi (15:09)
Hmm, good stuff.
Definite food for thought. Okay, Sarah, what's up next?
Sarah (15:17)
When we're talking with non-data leaders and data experts, think something that we've got to take into account here that data experts are highly technical and often super introverted people. And they may approach the business very differently to what business people would, particularly if you're in the sales department. They're not kind of all guns blazing. They're very much
give me a problem and I'll go and solve it. They might not necessarily ask the right questions. Quite often, Fi, you and I specialize in that area of the between business and tech and really getting into the weeds on both sides to understand that. But often the middle person isn't there to do that. So it's understanding enough around what type of...
data person you're dealing with and understanding where they may not feel comfortable coming in and asking more information, getting more clarity or understanding the business as well.
Fi (16:25)
It's a good point, but I feel like there's more to that as well. What I was hearing was that the non data leader needs to understand that the data person may be introverted or maybe working in this kind of perspective. That's totally two way process.
The data person needs to understand that the non data leader is not used to speaking in the data space, not used to necessarily getting dragged down into the weeds and your lengthy email that you're sending with all of the details is not going to cut through. They're going to check out as soon as they open it or, after you keep babbling on for quite a while.
The eyes are going to glaze over, they're going to pick up their phone, they're going to be thinking about something different. There's definitely steps that need to be taken on both sides to ensure that that communication works well.
Sarah (17:22)
For sure. when I think technical, you've got the whole Jira ticket process then you've got the business, they're much more like in a Slack conversation, two very different styles going on and very different areas of translation needed between the two. And it's helping find that middle ground.
Fi (17:42)
Hmm. Thinking into the AI space, is this something that is going to be a role that's super important? Or do you think that AI could be that middle man, middle woman, technical to non-technical translator?
Sarah (17:57)
Great concept as long as it doesn't hallucinate.
Fi (18:00)
Truth.
Fi (18:05)
Hey everyone. our podcast actually got interrupted and unfortunately we couldn't kick it off again. It was just too late in the day, so I'm just popping in to say we're wrapping this podcast a little early and I wish that we got through more, but we hope that there were a few things that might help non data leaders learn more about the data teams.
If any of it hit close to home or if you thought, I've definitely done that, don't stress about it. We're not here to point fingers. We're here to build better bridges along the way. Because when data teams and leaders actually understand each other, everything flows better.
Thanks very much for tuning in and listening to unDUBBED and we look forward to catching you next time.
