Starting a new job as a data leader is hard.
First, you’re often inheriting a messy situation. The data infrastructure is held together by duct tape as the data team (if there even was one) is struggling to keep up with the pace of the business.
Chances are you’re also underresourced. You might even be starting as a team of one and have to fight for headcount and budget in an environment where CFOs are scrutinizing every incremental dollar.
Despite all of this, expectations are usually sky-high. Everyone hopes the new data leader can solve their most pressing problems — ideally, immediately. To top it off, you likely don’t have much time to prove yourself: The average tenure for Heads of Data is only 30 months. Time to impact is key.
Since it can be difficult to know where to even start, we put together this guide. If you’re starting a new job as Head of Data — whether you’ll manage a team or come in as an IC — it will give you a comprehensive framework for surviving your first 90 days, creating measurable value, and setting yourself up for long-term success.
A framework for success #
What do you need to be successful in a new data role? At a high level, four things:
A deep understanding of the status quo; this includes both the technical side as well as the business
Quick wins to show the value you can create
A robust long-term plan to set yourself up for success in the following 12-24 months
Trust and support from your business partners
Earning trust and support from your stakeholders depends on doing a good job on #1 - #3 (and takes time), so we’ll focus on those steps in this post.
The key points are the same whether you’re taking over an existing data team or if you’re the first proper data hire. However, we’ll get into specific considerations for each scenario and mark them accordingly:
💼 = sections relevant for those leading a data team
👩🏻💻 = sections relevant for ICs / teams of one
💼👩🏻💻 = sections relevant for both
While the specifics will depend on your organization, here’s roughly what your first 90 days should look like:
Now, let’s get into the specifics!
Phase 1: Analyzing the status quo #
Timeframe: Intensive deep dive day 1 to day 45; afterwards continued learning
This is the foundation for everything else; without understanding the status quo, you can't figure out where you can drive impact. While it can be tempting to immediately go into “doing mode”, that’s a mistake.
Don’t expect to be able to just copy / paste what you did in your previous roles:
Every company is different and requires bespoke solutions
You will not get a lot of supporters if you aren’t showing an open mind to how your new company operates
As a result, the focus of your first few weeks should be on listening, listening, and then… listening some more. This will help you understand three core things:
The business
The data stack
The data team
Let’s dive in.
💼👩🏻💻 Understanding the business #
Why is this important?
If you hope to make an impact with data, you need to deeply understand how the business works.
Without that context, you can’t be a strategic thought partner who helps your biz stakeholders identify opportunities and maximize impact. And more tactically, you can’t define metrics, design experiments, or run analyses that actually create business value.
Starting here also has some additional benefits:
When you dive into the data stack, you can evaluate it in the context of the needs of the business
To do this, you need to talk to stakeholders across the company, allowing you to start building relationships. You’ll need their trust and support if you want to get anything done later on
Since “understanding the business” can feel a bit daunting, here are the most important aspects you should focus on:
1. Understanding your company’s industry and business model
No two businesses are alike, especially if they operate in different industries or use different business models.
I learned this lesson the hard way when I moved into B2B SaaS after working on marketplaces and social media. Some fundamental insights and best practices were transferable, but many weren’t.
First, you need to understand the industry your company is operating in:
What types of companies operate in the industry and adjacent ones (e.g. in the automotive sector, there are suppliers, OEMs, transportation networks, etc.)?
What customer segments exist; are they growing or shrinking?
Who are the main competitors; how does your company compare?
To get an initial overview, you can read the quarterly reports of public competitors as well as industry reports from investment banks, VCs, or industry analysts.
I’d also recommend asking people in your company who the industry thought leaders are since this can be a great way to learn about up-and-comers who may not yet be on the radar of larger firms.
However, the real actionable insights come from going one level deeper. For example:
Follow competitors on LinkedIn to stay on top of their marketing and product announcements, as well as how their audience does (or doesn’t) engage
Review their websites, marketing materials, and support documentation to get an in-depth understanding of their functionality and positioning
Next, you need to understand your company’s business model. The best way I’ve found to do this is to map out the revenue equation and build a driver tree, which is simply the mathematical formula of how the business makes money.
For example, for a B2B SaaS business, it looks roughly like this:
Then, you can go deeper and break each component of the equation into its key drivers. E.g. if your company is Sales-led, you can break “New ARR” into a funnel from Leads to Won Deals.
This driver tree will give you a good idea of the metrics that matter, and where analytics projects can have the biggest impact on the top or bottom line of the business.
2. Understanding organizational structure and internal processes
Whether as a data team lead or an IC, you won’t get anything done without involving the rest of the business.
You’ll need to partner closely with your stakeholders to define priorities, plus you’ll need to get their buy-in in order for your work to get implemented and drive impact.
This means you need to know who does what. The org chart is a good place to start, but you’ll quickly find that you actually need to talk to people to understand the true dynamics.
How do Marketing and Sales work together? Where are the typical friction points?
Where do Strategic Finance and BizOps plug into the business?
You’ll also want to understand key processes in the company, such as:
How does quarterly and annual planning work?
How do you secure your headcount and tool budget?
How do decisions get made?
Understanding these things is not just a formality; it will give you a foundation to set yourself apart.
For example, quarterly planning is usually a massive source of friction between teams. So if you learn about the planning cycles early and run a smooth process, you’ll leave a lasting impression.
3. Understanding your business stakeholders’ priorities & pain points
Once you understand who your stakeholders are, it’s time to figure out what’s on their minds. If you want to establish yourself as somebody who creates value quickly, you need to prioritize the most pressing issues the business is facing.
Questions you should ask include:
What are their current priorities? What are their biggest pain points?
How are they working with the data team (if one existed in the past)? What’s going well and what isn’t?
You can (and should) also ask them directly what you / the data team could help them with; just take it with a grain of salt. To be successful as a data leader, you need to translate business problems into analytics solutions rather than taking and implementing analytics requests verbatim from stakeholders. When we’re providing customer support at Omni, we think of this as understanding the “why” behind the ask.
4. Company culture
While you should definitely use your advantage as a new hire to spot inefficiencies and things that don’t make sense, you’ll still need to adjust to the cultural norms and respect that the company has done (and will keep doing) things a certain way.
Here are a few key cultural factors you’ll want to understand:
Why this matters: If your new company moves fast and handles most things in a pragmatic fashion, you will likely face a lot of resistance if you want to implement a formal intake process for data requests that requires stakeholders to fill out lengthy JIRA tickets.
5. Metrics and data consumption
Most teams are held accountable to measurable goals, and you need to know the relevant metrics to understand what they care about.
Some key considerations:
⭐ Does the company have a Northstar Metric, and if so, what is it?
💼 What metrics are reported to the exec team?
🏛️ If the company is public, what’s reported to Wall Street?
🎯 What metrics are teams and ICs goaled on?
Similarly, you’ll want to understand how people consume this information:
Where do people go to see metrics (e.g. slide decks vs. dashboards)? What dashboards are being used the most, which ones not at all? Does everyone have access?
How do people learn about dashboards (e.g. is there a repository)?
💼👩🏻💻 Understanding the data stack #
Why is this important?
The choices your company made in its data stack determine what you can realistically do during your first couple of months and what friction you have to prepare for (e.g. missing or stale data, issues with consistency etc.).
Step 1: Mapping out the data stack and flow of data
Most companies, especially early-stage ones, don’t have proper documentation. To the degree they don’t exist, consider creating architecture diagrams and charts for how data flows through the various systems:
What tools are being used? Who is the main point of contact for each?
How is data being ingested, stored, and organized?
Do they have a data model? Where / how is data modeling taking place?
It may take you months to get a full understanding of the entire data stack and code base (if you ever get there).
So to maximize the usefulness of this exercise, work backwards from what you learned when talking to business stakeholders. For example, what data pipelines are powering their key reports? In what layer of the stack are their core metrics defined?
Creating the documentation yourself has a few benefits:
It will make your onboarding easier. You’ll be overloaded with information during your first weeks, and you will find yourself asking the same questions over and over again if you don’t create a summary
It gives you a tangible artifact to share with others early on. Having something valuable with your name on it circulate will give you instant visibility
You can get feedback and validate your understanding before you move forward. As a new hire, it’s easy to miss some of the nuances of the system that’s in place
You’ll later be able to refer back to this to show how much you improved things compared to when you took over
Step 2: Identifying gaps and opportunities
Even if there wasn’t a proper data team before you arrived, someone was in charge of data. Find out who that was and ask them what the biggest pain points with the current data stack are.
In addition, you’ll want to do a simple audit yourself:
Are any of the tools or architecture choices not a good fit for the needs of the business at this stage?
What are the contractual terms for each tool? You’ll want to understand 1) which ones are most expensive and 2) how quickly you can get out of the respective contracts if needed
Based on this, you can assign each tool in the stack a rating to determine next steps: Keep, Remove, Replace (not urgent) or Replace (urgent). Plus, you can flag missing tools as well so you can later make the business case for them.
💼 Getting to know the data team #
If you’re coming in to lead an existing team, getting to know each person, learning about the historical context, and figuring out what is/isn’t working should be a major focus during the first 45 days.
Here are some areas you might want to dig into:
What did the team ship in the last 12 months? What are the current priorities?
What does the team spend the most time on (this doesn’t always equal the highest priority)?
What are their biggest pain points and frustrations?
How does the team work with the business (how requests come in, how they triage and prioritize them, how they keep the biz in the loop etc.)?
What are the key processes and rituals (and what does the team think about them)?
How do they gather business context (where do they physically sit, what meetings do they attend etc.)?
Where does each team member’s expertise lie? Besides informing staffing later on, you can lean on subject matter experts to accelerate your onboarding
💼 Reflecting on what you learned #
After you talked to both business stakeholders as well as the data team, it’s a great time to reflect on what you heard:
Do the descriptions of how the teams work together match each other, or are perceptions wildly different?
Are the priorities aligned, or is the data team working on projects far removed from business goals?
If there are any glaring issues, pick the most critical one or two and include them in your immediate action plan (see below).
Phase 2: Getting quick wins under your belt #
Timeframe: ~ Day 30 to day 75
Now that you have a good understanding of how everything works, it’s time to realize some quick wins. This will give people a preview of the value you’ll be able to create for the business and help you build a reputation for getting things done.
Now, every onboarding guide will tell you to get some quick wins, but how do you actually find these opportunities?
Don’t worry, we got you. Below are a few relevant categories to help you focus.
💼👩🏻💻 Creating business impact and building trust #
There are dozens of things you can do to drive business impact, but here are 8 concrete ideas to get you started:
Quick win #1: Fix the biggest visibility gap: Pick the most glaring reporting gap from your conversations with business partners. The initial solution doesn’t have to be a polished dashboard with all bells and whistles; often, a quick MVP can get you 80% of the value with little effort
Quick win #2: Fix the most painful metrics misalignment: You don’t need to launch a fully-fledged semantic layer as a first step. Just pick the most frustrating inconsistent metric (e.g. Sales and Marketing define “Opportunities” differently) and work with biz stakeholders to standardize it. Over time, this can evolve into a broader effort to standardize all metrics
Quick win #3: Segment the data and highlight underperformers: Simply cutting data in a novel way can reveal fresh insights. For example, you might find that the decrease in revenue per user that the company has been concerned about is only driven by a particular user segment, giving the business concrete next steps
Quick win #4: Look for extreme distributions: People tend to assume normal distributions although in reality, that’s often not the case. E.g. a small group of whales often accounts for a large chunk of mobile game revenue, or a few customers cause the majority of support escalations. Highlighting a few of these cases will immediately lead to different decisions in the business
Quick win #5: Propose and run “low-hanging fruit” experiments: Often, simple but impactful things you did at your last jobs might not have even crossed the minds of people at your new company. Pick a few that seem transferable and run quick experiments to see if they drive similar results
Quick win #6: Cut / replace / downgrade a pricey tool: After you reviewed the data stack, it’s likely you found inefficiencies; pick one or two to address quickly. E.g. you might find that each department in the company is using a different BI tool, and some are paying for seats but really just using Excel. Consolidating these can help with metric alignment, costs, and even potentially increase data access because you can focus trainings and cross-departmental reporting. Additionally, this will show that you are a frugal operator and will make it easier to get future budget requests approved
Quick win #7: Address knowledge and documentation gaps: While you’re getting up to speed yourself, use what you learn to improve your stakeholders’ understanding of key topics. This will help to quickly establish you as someone who brings clarity
Quick win #8: Bring the team closer to the business: Especially if you’re running a centralized data team, you can always improve the flow of information and reduce friction between the data and business side. Try holding office hours, simplifying the intake process for requests, or simply seating subject matter experts closer to their business counterparts in the office
Remember: These are supposed to give a preview of your impact and help you build some initial goodwill. You don’t have to solve all of the organization’s problems at this point.
So pick a few that you can tackle with limited effort, and then shift your focus to building a robust long-term plan (see next section below).
💼 Boosting team morale #
If you’re coming in as a new Head of Data, you shouldn’t just focus on driving the top or bottom line. You can also make a significant impact by boosting the data team’s morale.
Based on your conversations with the team, you should customize your approach, but here are some tips addressing common pain points to get you started:
👀 Create visibility: Data teams often feel like they are not getting enough recognition. Address this by sending a weekly email update highlighting achievements, giving shoutouts in Slack channels, getting your team’s work presented in all-hands meetings, etc.
⏳ Fix the team’s biggest time waster: Whether it’s certain low-ROI meetings, labor-intensive weekly slide updates that end up in the appendix, or a bloated web of dashboards that has become impossible to maintain: Pick something and don’t be afraid to cut deeply
🎯 Clarify performance expectations and career pathing: Data teams are often among the newer teams in the company and tend to lack clear career ladders that show what is expected at each level and how to progress to the next. This is a perfect opportunity for a new joiner since you only need limited company-specific context. Note: We will cover how to create a strong data career ladder in a later post.
Now, you might be wondering, “How is this measurable impact? Team morale seems fuzzy compared to business impact.” But it actually doesn’t take much to make it measurable.
For example, you can send a weekly anonymous poll to your team asking them to answer a single question: “How likely are you to recommend working on this team to a friend or former colleague?”.
If you then use a scale from 0 (not likely) to 10 (very likely), you can apply the Net Promoter Score (NPS) calculation. Your NPS is the sum of people who rated 9 or 10 minus the sum of people who rated between 0 and 6.
Even if the initial number doesn’t mean much to you; if you do this every week over your first 90 days, you’ll get a good directional idea of whether your changes are having an impact.
Phase 3: Setting yourself up for long-term success #
Timeframe: ~ Day 60 to day 90
With some quick wins under your belt, you now need to translate that momentum into long-term impact.
💼👩🏻💻 Clarify the team’s mandate and structure #
The first thing you need to do is make sure the team has a crystal clear mandate.
If there is a clear structure in place, even if you don’t necessarily agree with everything, it’s usually not a good idea to do a reorg right after you join. However, in many cases, how the data team operates has organically evolved rather than being the result of intentional choices.
For example:
Everyone on your team has the title “Data Scientist”, but they spend most of their time building dashboards or fixing data pipelines
The team is supposed to be the “official” central data team, but in reality, it’s only really working with Finance & Customer Support. Other teams are doing their own analyses and reporting, leading to a patchwork landscape with inconsistent definitions
Or, there might not even have been a proper data team before you joined.
In cases like this, you need to clarify the team’s mandate and how it will operate; otherwise you can’t determine the resourcing you need and the projects you should prioritize.
Whatever mandate you land on, make sure that:
It reflects the needs of your stakeholders, and
You have leadership support for this path forward
💼 Build out the team #
If you’re taking over a data team, chances are it has been underresourced historically.
For example, when Gordon Wong came in to revamp HubSpot’s BI team, he estimated that the team was ~15% of the size it needed to be given its scope.
Based on the mandate you landed on, you should develop a rough estimate of the resourcing you’ll need:
How large does your team need to be at a bare minimum to fulfill the mandate?
What mix of roles and seniority do you need?
You might have to show impact before you get this approved (and you might only unlock additional headcount piece-by-piece), but the earlier you develop this view, the earlier you can start lobbying for your needs.
And once you have a rough idea of what you need, you should start hiring immediately.
You’ll likely drown in stakeholder requests, so it’s easy to want to punt on this. But the longer you wait, the more difficult your situation will become.
💼👩🏻💻 Create a roadmap #
Quick wins tend to be somewhat random low-hanging fruit, and you need to show that you can create a compelling plan for the team that maps to the company’s strategic priorities.
When you’re putting together your initial roadmap, the most important thing is to stay realistic. In most cases, the status quo that you’re inheriting is a mess, and you’re not going to untangle it within a few months.
There is a delicate balance, however. Even if some of the larger strategic projects will take multiple quarters to show an impact, you need to create measurable value for the business in the interim, or you might not get the chance to see them through.
As a result, the goal for your first roadmap should be to deliver enough business impact every quarter to “move to the next stage” (i.e. maintain leadership support and unlock more resourcing).
Remember: Nobody outside the data team knows or cares how much effort went into something. So prioritize projects with low effort and high business impact and/or visibility:
A few principles to keep in mind to set yourself up for success:
1. Focus on pragmatic solutions
You show your value by solving business problems, not by building fancy solutions. Overengineering just puts an unnecessary drain on your limited resources.
For example, instead of committing to building out an end-to-end data model as a source of truth (and risking becoming a blocker to the business), develop the data model incrementally as the need arises as part of other projects.
2. Set realistic expectations
Business stakeholders and leadership often see the arrival of a new data leader as an opportunity to voice their (unrealistic) wish list, and it’s up to you to push back.
This is especially important if you’re a team of one or resource-constrained. In order to be collaborative, you can present options as a “Give X, Get Y” (e.g. “with two more data scientists, we could build the lead scoring model”); that gives leadership a tangible trade-off.
Just make sure you’re taking into account that it would take several months to hire and onboard these additional resources.
3. Don’t rush into big data infrastructure projects
When you evaluate the current data infrastructure, you’ll likely find a number of issues that make your life harder. It can be tempting to try and fix them right away, but you won’t do yourself any favors.
The business impact of these projects is often not straightforward to articulate to non-technical audiences, and it’s easier to get buy-in for them once you have built up your credibility.
4. Make yourself essential
You should aim to both 1) integrate your team — or yourself if you’re a team of one — into key processes like forecasting, as well as 2) own discrete strategic projects (e.g. building predictive models):
If you are only doing strategic projects, you’re less essential. Projects are much easier to cut than core processes when priorities change
But if you are only supporting core processes, it’s hard to drive outsized impact. Nobody ever got promoted for keeping the lights on.
5. Leave room for ad-hoc tasks
You should reserve at least 30% - 40% of total bandwidth for ad-hoc requests.
This is one of the most common pitfalls for (new) data leaders: If you don’t account for ad-hoc needs of the business, your roadmap will fall apart before you even really start executing against it.
When you’re planning timelines for your projects, make sure you account for the seasonality of these requests; right before big meetings (e.g. board meetings) and during planning cycles, you’ll drown in “quick asks” from your stakeholders, so ideally these periods don’t overlap with critical phases of your strategic deliverables.
6. Get explicit buy-in
It doesn’t matter how good your roadmap is; if the business and leadership aren’t bought in, it’s pointless because it won’t drive impact.
Get your stakeholders involved throughout the process to ensure your roadmap is aligned with their needs. If you keep things close to your chest and then realize at the last minute that there are fundamental issues, you won’t have much time to address them.
If you partner closely with them, however, the final sign-off will just be a formality.
Quick recap: 90-day checklist for new data leaders & ICs #
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