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Your First 30, 60, and 90 Days as a Data Leader - Community Roundtable
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Introduction
Your First 30, 60, and 90 Days as a Data Leader
The first, second, and third months of your career as a data leader can inspire change or slow change to a crawl, set you up for success or sow the seeds of inaction or failure. So what can you do to set the right tone, take the best actions, and build a platform for data leadership success? And how do you beat the meagre average tenure of a CDO, which still hovers at 2.5 years?
Data Leadership Collaborative gathered nine data leaders to discuss these issues just after this year’s Tableau Conference. The discussion was led by Lisa Ginther Huh, Business & IT Audience Marketing and Senior Manager, Product Marketing at Tableau.
Days 1-30: Assess, Listen, Familiarize
Data Leadership Collaborative (DLC) Let’s kick this off by asking everyone to think about that first quarter you were in your role. Talk about some of the high-level goals you defined during your first 30 days.
Adam Mico: The main thing I would look at is opportunities for quick wins. You really need a product under your belt as soon as possible. So identifying quick wins, talking with stakeholders, and looking for champions that will help support you is a great way to get started and lay the groundwork for what’s going to happen.
Heidi Lanford: I would be assessing who in the senior leadership or C-suite team is on board with a transformation, if that’s what you’ve been asked to lead. This would come before building out my quick wins. I don’t know if one has that complete picture across an organization when going through the interviewing process.
Colm O’Grada: I’d definitely add a plus-one to Adam’s point about quick wins, and I think that’s something to do in parallel to Heidi’s idea. I wouldn’t want to get into a role unless I knew who was on the C-suite behind me. If you’re not sure you have the support you need, you’re going to bounce out pretty quick.
Two more things that are important. One is looking at your teams and trying to get a sense of where the biggest pain points and gaps are, both from a people-process perspective, but also from a tech stack perspective. And then second, setting yourself up to get the right inputs as quickly as possible. Make sure you’re in the right meetings and discussions, make sure you’re getting the intel you need to know what you need to do after those 90 days. So that you’re part of those conversations, even if you need to have sharp elbows sometimes and wrestle yourself into them, and you’re doing that early so that you’re not getting to the end of the 90 days and not getting the input you need to be successful.
Kevin O’Callaghan: Plenty just said there that I would wholeheartedly agree with. I think part of it also is getting familiar with the culture around the organization of where people are when it comes to data and what they want to know and what is the driving goal behind that. Because that’s probably going to be a bit of a milestone in terms of not just the quick win, but how you make yourself relevant quite quickly. So I think it’s learning and very much getting involved with it.
I would say the other thing is, be a bit of a therapist as well for the organization. You’re coming in fresh, you have no hangups, you have no background to this. Let people purge their souls if it comes to it just to say, Yeah, the data’s great, or the data’s awful, or we struggle, because you’ll actually start to be able to piece together that picture. And I think being able to look at that and know the pain points, be able to look at the relationship building, will certainly start to help you when you’re putting out your plan in three months and six months and nine months. If you say, We expect to be here, you’ll know how it relates back to the overall org goals and your relevancy within the org.
Zak Geis: I always encourage people to lean into their experiences, lean into their perspective, what makes them different, what they can bring as far as new value to the organization. So definitely I would agree with the point about listening, therapy, being there, talking to folks, and just bringing in a fresh perspective. But as far as those actual goals, I don’t know that I expect anything from somebody in the first month that they’re at the firm. They’re going to get their feet wet and learn things, and then as time goes on, they’re going to provide their own benefit to the organization. But I think it takes time and I would be very fluid in those conversations with them.
Solomon Kahn: The first thing that I think about coming into a new organization are the relationships that I can build as quickly as possible with the senior people to understand what the real goals are. Heidi, I think you mentioned that you generally know 70% of what’s going to happen when you walk into a job, but that other 30% is really important. You don’t always talk to everybody before you join. So understanding who’s in the most pain is important, and those quick wins are probably more about removing pain quickly or changing things that are obviously not working quickly as opposed to some new big thing. So I always focus on that as step number zero.
Mark Palmer: No one has said anything I disagree with, but I have a slightly different perspective. For me it’s less to Adam’s point about quick wins than about slow productivity. There was a really good article recently about the benefits of slowing down. And I think when you come in new to an organization, your first hundred days are that opportunity to slow down and ask questions. I doubt that Adam would disagree with me on that, and I like the notion of quick wins, but at the same time I’ve always admired what Lou Gerstner did when he took over IBM. He said, The first six months I’m getting on the road, I’m talking to customers and you’re not going to see many decisions from me. I’m going to learn first. Zak, I think you said that most executives wouldn’t expect a whole lot from you in the first 30 days anyway, especially in a complex organization. So listening and asking more questions in your first hundred days to me is a good way to think about it.
Then in terms of knowing stuff upfront when you interview, I would argue you don’t know 70%. I’d argue that you know about 5% of what’s going to happen in a job during the interview process. That’s a demo version of a company until you really spend time with people and ask questions. One of my other favorite quotes is that you’re the smartest you’ll ever be in a company in your first 100 days and your last 10, meaning you have a fresh point of view in your first 100 days and in your last 10 you tell them what you really thought as you’re about to leave.
The mistake I see a lot of with my board positions is that people set numbers like KPIs, and I think OKRs (objectives and key results) are much more effective for business leaders to think about. If you haven’t read Measure What Matters, the preeminent book on OKRs, you should. I think there’s a huge difference between the two.
Zoher Karu: I would say the deliverables for the first 90 days are three things:
- Number one: know the team, what’s their work, what are the skills, what are their pain points?
- Number two is know the stakeholders. Who are your stakeholders? What are their goals? Because their goals are your goals. You’re not there to do something different. You’re there to help them. And related to that is what’s their temperature check? Are they a supporter, are they a detractor? So a stakeholder map would be another deliverable.
- Number three is know the business. There’s lots of things that data analytics can do, but what is actually going to have the biggest impact? Do you understand how the company makes money? That will help you focus your efforts and understanding who your customers are, how profitable are they, et cetera.
Felipe Henao: I think we’re probably all saying a little bit of the same thing, but one of the things I really enjoyed was OKRs. We transitioned from KPIs and metrics to more of an OKR stance. And bringing people into OKRs really helps them learn to listen because you’re not coming in and saying, Hey, these are the KPIs or these are the goals and the metrics that I’m marching towards. It’s more of, I’m going to review my OKRs every week and I’m going to have everybody chime in and speak up. So you start to build that rapport amongst the team, you start to do that knowledge transfer across everybody. So you’re not just depending on one person to do one thing.
The other thing for me is that when anybody that comes in relatively new, my comment to them is always challenge the status quo. There’s a reason that space exists and nobody was here yet. Don’t be afraid to challenge and push out of the box, right? One of the things that I’ve learned over the last 10 years with UI designs is that what was done yesterday doesn’t necessarily mean it’s the best thing today. We’re living in a constantly enhancing world and I think if you can come in and challenge the status quo and show a different way to do things, it really helps set you apart against the broader team.
Kevin O’Callaghan: A thought I had was that if you’re an aspiring data leader who’s getting into this and thinking, this is going to be my plan, it’s going to be foolproof, it’s going to be perfect, guess what? It’s not. It’s so important for others to hear that what their goal is at one month will probably be very different to two months or three months because the business is going to move. Change is constant and I think you have to allow yourself just that little bit of wiggle room in terms of those high-level goals that you can keep navigating and evolve them because they’re not one and done. They’re constantly going to change. And maybe it isn’t a one-month goal, maybe it’s a one and a half month goal or a two-month goal, but in terms of setting out that initial time, just give yourself that bit of a beat in terms of being able to craft and get to where those goals are. Because you will get there, ultimately.
Days 31-60: Strategize, Expand, Partner
DLC: These are great thoughts. Now if you imagine that you’ve gone through your listening tour as a new data leader, talk about developing your strategy, your data governance and use of AI, and matching business goals and imperatives to data capabilities.
Adam Mico: So developing on my first 30-day strategy of focusing on small wins and MVPs, obviously I’m listening to people and working strategically with my partners and stakeholders. However, when you do get those quick wins, you tend to get more support where you could help elevate the prospect of transformation. So ultimately, if you’re looking at being an AI-driven company or a modern focused companies with tech, you want to have data as a centerpiece of your organization – or organizations, depending on your business model.
I would be looking for more strategic partnerships, get some champions on different business lines to help support your initiatives and that will help other people follow and other teams follow. The second 30 days, I would be focusing on expanding those small wins and working with other teams and hopefully working with more cross-functional teams, too. A lot of times when you get into companies that have been around, you tend to have a lot of siloed teams that never work together. And part of data transformation is allowing people to have self-service, but having teams really understand their data in ways that they didn’t look at it before.
Mark Palmer: At the risk of repeating myself, I think it maps back to the objectives and the key results. Especially in big companies, data sharing is not as prevalent as it should be. One of my favorite sayings is that the best way to find the right answer to something is to post the wrong one on the internet. Everybody will go out there like crazy people and comment on it and fix it and edit it. One of the most productive tactics for OKRs is writing them down, sharing them liberally, and asking for people’s feedback on what you got wrong. If you articulate that in a way that’s faithful to what people have said and they bought in and helped you define that, it becomes much easier to get people’s buy-in to actually execute on it.
As to how you get that infused into rigor, practice, policy or governance frameworks, I had the head of Target’s data analytics group on my podcast, and he had a super simple practice, which is to put AI and data governance practices into their standard HR enablement process and quizzes. I haven’t seen a lot of companies do that.
Heidi Lanford: I’ve seen CISOs gain the buy-in they need by engaging the C-suite with a tabletop exercise – for a CDO, this could be a facilitated half-day exercise regarding AI and data transformation. It’s where you’re essentially role-playing a scenario that is relevant and specific to the organization. It’s obviously fictitious, similar to a made-up ransomware attack that CISOs use to garner understanding and buy-in, but it really starts to get into the detail on the role everyone has in the AI or data transformation. And it’s not just on the shoulders of the CDO or CDAO.
I would add that the CDO needs a little bit of time to understand things and the lay of the land, so I might do this when you hit that 90-day mark.
Felipe Henao: Heidi, we took a very similar approach. We’re in the midst of a project on AI that is coming up on the one-year mark, but essentially we got adoption from the top down, from our CEO and his direct report that we roll up to. Obviously AI is such a hot topic right now, but what we forget about is you can’t really have true AI if you don’t have clean or standardized data. And working for a financial institution, our risk appetite is not very large. However, we also understand that we don’t want to be reinventing the wheel, so how do we stay in the middle of the lane, not at the very end of the pack, not at the forefront?
We started with standardization, by creating that data library, that repository, those federated views. When you work for a company of the magnitude of Bank of America, you’ve got multiple mergers and acquisitions and data all over the place. And so you have to say, How do I take that data and bring it in? How do I standardize it so there’s no anomaly underneath the hood that says, If I ever go in and run a model on top of this data, I’m going to get two different results. I should get the same result time and time again. So we’ve been doing these monthly sessions where we have these sprint plans with each organization or each line of business and we’re looking at the data and we’re trying to figure out where is there bleeding from one area into another when it comes to a metric or data.
You don’t want to go out there and ask one line of business, Hey, what is this? And they say, Oh, it’s an apple. And then go to another line of business and they say, Oh, this is an orange. But they have the same definition. So to Heidi’s point, you’re having this broader exercise of bringing everybody in and standardizing, but not standardizing in a silo.
Days 61-90: Watch for Watch Outs
DLC: Let’s pivot a bit on this discussion and ask, What could be a mistake that you want to avoid as you settle into your second or third month?
Kevin O’Callaghan: I would say communicate, communicate, communicate. You’ve got to keep talking about it. If you don’t, and fall back into your safe zone of I’m going to get this done and get this done, you’ve lost the room. So if there was anything in terms of just make sure you don’t stop, it’s don’t stop communicating.
Colm O’Grada: One of the biggest mistakes you can make is trying to apply solutions that have worked in the past in a different environment, to a problem in your new role and environment – and just expecting them to work. They probably won't. It’s also not a great way to build relationships. So going back to that listening piece, trying to build that understanding is super critical and trying not to cookie cutter stuff that may have worked. Of course, use experience, use things that have worked well before, but don’t expect to be able to just go through the motions in a new role and repeat success. I think there’s a big hill to climb first before you’re able to bring that experience to use in a practical way.
Solomon Kahn: One mistake from earlier in my career when I didn’t have as much experience as a leader was not making some tough people decisions early on. I think that you obviously don’t come in in day one and make decisions, but I think pretty quickly you can understand if there are people on your team who are not going to be able to make it at the next stage that you’re trying to build towards. So not taking action on that has been a mistake.
Adam Mico: This kind of goes along with communication, but transparency, it’s important to gain and earn trust of people, and you need to be as transparent as reasonably possible in order to ensure that trust. So if you’re speaking with people, collaborating with people and stakeholders, you need to stand by what you mentioned and mention it clearly so they understand it and they have an opportunity to onboard with you. If you do things without people and you lack trust, you’ll never be able to earn it back.
Going Forward: Have a Vision, Have Confidence – and Have Fun
DLC: Establishing trust is a nice segue into this final question: what’s the best step you’ve taken to set yourself up for success after those first 90 days?
Kevin O’Callaghan: Have a vision. You’ve learned, you’ve been integrating, you’ve been talking to stakeholders, you’ve had some clear wins, you’ve kind of established yourself. Now have a vision that shows the direction of where you believe data and analytics is going to go within that organization. Talk to the leaders about it, engage them, make them part of that conversation. It’s going to be an ongoing conversation for a good while, but I think that’s your next step. Get your vision and start getting it out there.
Colm O’Grada: Two things to keep in mind. First would be trying to retain some sense of confidence, right? You’re being hired into this role, you’ve been given a mandate, don’t shy away from that. Stand up. And if you need to fight some battles, fight the battles. You’re not there to be passive. It’s a contact sport and sometimes you need to be a loud voice in the room. And then the second thing is even after 90 days, you’re still pretty new. Don’t expect it to feel easy, don’t expect it to feel comfortable. Even at 90 days it’s still going to be hard. You’re still pretty fresh. Accept that it’s not going to be easy at 90 or 180 days either. If you can have that mindset, you are more likely to be successful in the long term, too.
DLC: To close out, does anyone have a personal tip or piece of advice to a new data leader?
Solomon Kahn: I’ll just give the tip that I would say to myself, which is have fun with it. It’s a fun challenge. It’s not going to be easy, but that’s the point of what you’re trying to do with your career, right? Do challenging, interesting things. So have fun with it as much as you can.
DLC: Excellent. Let’s end it there. Thank you again for your time.