Data Consulting for Series B Companies: The First 90 Days

The Reality of Data at Series B

You've raised Series B. Revenue is accelerating. Your engineering team is hiring. And suddenly, the spreadsheets and single-table SQL queries that got you here aren't cutting it anymore. Your CFO needs real-time unit economics. Your product team is drowning in ad-hoc requests. Your sales team wants dashboards they can actually read. And you're sitting in a board meeting realizing that nobody in the room has a shared, trustworthy source of truth for the metrics that matter.

This is the moment when most Series B founders call a data consultant.

But here's the problem: most data consulting engagements meander. They start with "discovery," drag into "architecture," and by month four, you've spent six figures and have a Looker instance that nobody uses. Your team is frustrated. Your metrics are still fragmented. And the consultant is asking for another three-month extension.

It doesn't have to be this way.

This playbook is built on a simple principle: a data consulting engagement at Series B should deliver concrete, measurable outcomes in the first 90 days. Not a roadmap. Not a data strategy document gathering dust on Slack. Real dashboards. Real self-serve BI. Real time back from your team. And a foundation that scales with you to Series C and beyond.

We've done this with dozens of Series B and C founders. Here's what works.

Why the First 90 Days Matter

The first quarter of a data consulting engagement is critical—not because it's when you'll solve everything, but because it's when you establish momentum, trust, and the operational rhythms that determine whether the engagement succeeds or stalls.

Think of it like a Series A fundraise. You don't expect to close in week one. But by week twelve, you need momentum: warm intros, a solid deck, a clear ask, and proof that investors should take you seriously. In data consulting, the same principle applies. By day 90, you need:

Visible dashboards that your leadership team actually uses daily. Not prototypes. Not "coming soon." Real dashboards powering real decisions.

Operational self-serve for at least one team (usually product or sales). This means your team can ask their own questions without waiting for an analyst.

A clear data stack that your engineering team understands and can maintain. No black boxes. No consultant-dependent systems.

Documented processes for how new metrics get defined, how dashboards get built, and how the team escalates questions that require deeper analysis.

Executive alignment on what success looks like in months 4–12. This is crucial. Without it, you'll spend the next three months debating priorities while your consultant waits for direction.

According to research on first 90 days in leadership transitions, the same principles that apply to new executives—momentum, visibility, early wins, and stakeholder alignment—directly translate to consulting engagements. The difference is that in a consulting context, you're not just building credibility; you're building systems that need to outlive the consultant.

The Three Phases of a 90-Day Engagement

A well-structured data consulting engagement breaks into three distinct phases, each with specific deliverables and outcomes.

Phase 1: Foundation (Days 1–30)

The first month is about understanding the business, the data, and the gaps.

This is not a theoretical discovery phase. It's hands-on, pragmatic, and focused on identifying the specific metrics and dashboards that will move the needle for your business.

What happens in week one:

Your consultant meets with the founder, CFO, VP Product, VP Sales, and VP Engineering. Not in a big room. One-on-one conversations, 30 minutes each. The questions are simple:

• What are the three metrics you check every morning?
• What decisions do you make weekly that depend on data?
• What metric do you wish you had but don't?
• What's your biggest data pain point right now?

These conversations surface the real priorities. Not the aspirational ones. Not the "it would be nice to have" ones. The I need this to run the business ones.

In parallel, your consultant audits the data stack. Where does data live? Postgres? Segment? Mixpanel? Amplitude? How is it currently being accessed? Are there existing dashboards? How old are they? Who owns the data warehouse (if you have one)?

This audit typically surfaces three categories of problems:

1. Data quality issues (bad schemas, inconsistent naming, missing tracking)
2. Access problems (data is siloed; only one person can query it)
3. Tooling gaps (you have a data warehouse but no BI layer; or you have Tableau but nobody knows how to use it)

By the end of week two, your consultant should present a one-page summary: the business priorities, the current state, and the gaps. This becomes your north star for the next 60 days.

Weeks 2–4: Quick Wins

While the team is still in discovery mode, your consultant should be building. The goal is to ship one or two dashboards that answer questions the team is already asking, but manually.

These are not fancy dashboards. They're functional. They might show:

• Daily revenue and churn (if you're B2B SaaS)
• Acquisition cost and LTV by channel (if you're growth-focused)
• Feature adoption and retention by cohort (if you're product-led)
• Pipeline and conversion rates by stage (if you're sales-driven)

The point is that these dashboards should immediately reduce manual work. Your CFO stops pulling reports. Your VP Product stops running the same SQL query every Monday. Your sales leader stops asking for weekly pipeline updates.

This is the moment when the consultant goes from being an external voice to being part of the team. Because suddenly, they're delivering value that the team feels every day.

At the end of month one, you should have:

• 2–3 operational dashboards live and in use
• A clear understanding of your data stack and its gaps
• Buy-in from leadership that this is the right direction
• A documented list of the top 10–15 metrics that matter to your business

Phase 2: Scale (Days 31–60)

Month two is about operationalizing the foundation and expanding self-serve.

Now that you have momentum, the consultant's focus shifts from building dashboards to building systems for building dashboards. This is where the engagement either scales or stalls.

Building the self-serve layer:

If you're using D23 for managed Apache Superset hosting, this is where you enable your team to explore data without writing SQL. If you're using Looker or Tableau, this is where you set up governed self-serve with row-level security and semantic layers.

The key is that your team should be able to ask questions like:

• "Show me cohort retention by signup month."
• "What's our churn rate by customer segment?"
• "Which features are used by high-LTV customers?"

...and get answers in minutes, not days.

This requires three things:

1. A clean data model. Your consultant should define the core dimensions (customer, product, time) and facts (revenue, usage, churn) that your team queries against. This is where schema design matters. Bad schemas kill self-serve BI.

2. Documentation. For every metric, there should be a one-sentence definition. "Churn" means what, exactly? Customers who didn't generate revenue in the last 30 days? Who cancelled their subscription? Who didn't log in? This seems obvious until you realize that three people on your team have three different definitions.

3. Governance. Who can create new dashboards? Who can edit shared dashboards? How do you prevent someone from accidentally breaking a dashboard that the board relies on? This is especially important if you're embedding analytics into your product (more on that below).

Expanding to the next team:

In month one, you probably built dashboards for leadership. In month two, you should enable self-serve for one additional team—usually product or sales.

For product teams, this might mean:

• Feature usage dashboards
• Retention and engagement by feature
• User flow funnels
• A/B test results tracking

For sales teams:

• Pipeline by stage and rep
• Conversion rates and cycle time
• Forecast accuracy
• Deal size and velocity by segment

The consultant should spend time with this team, understand their workflows, and build dashboards that fit into how they already work. The goal is not to force a new process; it's to make data accessible within the existing process.

Data infrastructure decisions:

By day 60, you should have clarity on your data warehouse strategy. Do you need one? If you're still small (sub-$5M ARR), you might not. But if you're scaling, you probably do.

According to McKinsey research on data-driven enterprises, companies that scale effectively to $100M+ revenue establish a modern data stack early. This typically means:

• A cloud data warehouse (Snowflake, BigQuery, Redshift)
• A reverse ETL tool (Segment, Hightouch) to push data back to your product and marketing tools
• A BI layer (Superset, Looker, Tableau) for exploration and dashboards
• Possibly a data catalog or metadata layer for governance

Your consultant should help you make these decisions based on your specific needs, not based on what's trendy. A Series B company with $10M ARR and 20 employees has different needs than a Series B company with $2M ARR and 40 employees.

At the end of month two, you should have:

• Self-serve BI live for at least one team
• Clear definitions for your top 10 metrics
• A decision on your data warehouse (build, buy, or defer)
• 5–7 dashboards in regular use
• A data roadmap for months 4–12

Phase 3: Foundation for Scale (Days 61–90)

The final month is about ensuring that the systems you've built will survive and scale without the consultant.

This is the hardest part of a consulting engagement, because it requires discipline. It's tempting to keep building dashboards. But the real value is in building sustainable systems.

Handoff to your team:

By day 90, your engineer or analyst should be able to:

• Build a new dashboard without the consultant
• Define a new metric and add it to the data model
• Troubleshoot a broken query
• Add a new data source to the warehouse

This means your consultant should spend the last month teaching, not building. Pair programming. Code reviews. Documentation. Recorded walkthroughs.

If you're using D23's managed Superset platform, this handoff is easier because the infrastructure is managed. Your team doesn't need to worry about provisioning, scaling, or security. They can focus on the analytics.

Embedding analytics (if relevant):

If you're a B2B SaaS company, you might want to embed analytics into your product. This is where text-to-SQL and AI-powered analytics become relevant.

With tools like Superset's API and embedding capabilities, you can give customers access to their own data without building custom analytics pages. They can explore their own dashboards, run their own queries, and get insights without leaving your product.

This is a major competitive differentiator. If Looker or Tableau requires your customer to log into a separate tool, but your product gives them analytics within the product experience, you win.

Your consultant should help you scope this. Is it core to your product? Or is it a nice-to-have? If it's core, you'll want to invest in it in months 4–6. If it's a nice-to-have, you can defer it to Series C.

AI and automation (text-to-SQL, MCP):

This is where modern data consulting diverges from the playbooks of five years ago.

Text-to-SQL—the ability to ask a question in plain English and get a SQL query—is no longer sci-fi. It's real, and it's starting to work. If you're using Superset or similar tools, you can enable your team to ask questions like "What's our churn rate by customer size?" and get an answer without knowing SQL.

MCP (Model Context Protocol) servers for analytics are even more interesting. An MCP server lets you connect your analytics platform to Claude, ChatGPT, or other LLMs. This means your team can ask questions in Slack, in email, or in their IDE, and get answers without opening a dashboard.

This is not hype. This is where data analytics is headed. And if your consultant is worth their salt, they should be setting you up for this in month three, not month twelve.

At the end of month three, you should have:

• A team member who can maintain and extend the BI platform
• Clear documentation of how to build new dashboards, define new metrics, and troubleshoot issues
• A decision on whether to embed analytics in your product (and a rough roadmap if yes)
• Early experiments with text-to-SQL or MCP if relevant to your product
• A clear roadmap for months 4–12 (more dashboards, new data sources, deeper self-serve, etc.)

What Success Looks Like

After 90 days, you should see measurable changes in how your team operates.

Time savings: Your CFO should spend 2–3 hours less per week on financial reporting. Your VP Product should spend 1–2 hours less on ad-hoc analysis. Your sales leader should have pipeline visibility without manual updates.

Add that up: 5–10 hours per week of freed-up time. At Series B, that's worth $50K–$100K per year in productivity.

Decision velocity: The time from "I need to know X" to "here's the answer" should drop from days to minutes. This compounds. Over a year, that's hundreds of faster decisions.

Fewer data silos: Before, data lived in Segment, Mixpanel, your data warehouse, and three Google Sheets. Now, there's one source of truth. This reduces confusion and arguments about what the "real" numbers are.

Team confidence: Your team should feel like they can explore data without fear of breaking something or asking a stupid question. This is cultural, but it's measurable. Ask your team: "Do you feel like you have the data you need to do your job?" The answer should shift from "not really" to "yes."

Consultant independence: This is the most important metric. By day 90, your team should feel like they can run the BI platform without the consultant. The consultant should be a resource, not a crutch.

Common Pitfalls to Avoid

We've seen enough Series B data consulting engagements to know what goes wrong. Here's how to avoid it.

Pitfall 1: Scope Creep

Your consultant comes in and says, "While we're at it, let's rebuild your entire data warehouse." Or: "We should really integrate with Salesforce, HubSpot, and Intercom first."

These are all valuable. But they're not month-one work. They're month-six work.

For the first 90 days, the scope should be ruthlessly focused: pick the three dashboards that will have the most immediate impact, and build those. Everything else goes on the backlog.

If your consultant is pushing for a bigger scope, push back. A good consultant will respect this. A bad one will keep expanding the scope until the engagement is unfocused and the deadline slips.

Pitfall 2: No Owner

Your consultant builds dashboards, then leaves. Nobody on your team knows how to maintain them. The first time something breaks, you have to call the consultant back.

From day one, there should be one person on your team who is the "data owner." This might be an analyst, an engineer, or even a smart ops person. The consultant should be training this person, pair-programming with them, and handing off ownership gradually.

By day 90, this person should be able to troubleshoot issues and build new dashboards with minimal help.

Pitfall 3: Dashboards Nobody Uses

You end up with beautiful dashboards that nobody looks at. This usually happens because the consultant built what they thought was important, not what the team actually needs.

The fix: involve the team from day one. Show them drafts. Ask for feedback. Iterate quickly. The best dashboard is the one people actually use, even if it's not as pretty as the one nobody uses.

Pitfall 4: Deferring the Hard Conversations

Your team disagrees on what "churn" means. Your CFO and your VP Product have different definitions of "active user." Your sales team and your product team have different definitions of "customer segment."

These conversations are uncomfortable. But they're necessary. And they should happen in month one, not month six.

A good consultant will surface these disagreements and help you resolve them. A bad one will just build dashboards and let the disagreements fester.

Pitfall 5: Picking the Wrong Tools

You've heard of Looker, so you buy Looker. Or you've heard that Tableau is the industry standard, so you buy Tableau. Or you try to build everything in-house with Metabase.

Each tool has tradeoffs. Looker is powerful but expensive and requires technical expertise. Tableau is flexible but also complex. Metabase is simple but limited. Superset (especially managed Superset with D23) is modern, open-source, and API-first, which makes it great for embedded analytics and AI integration.

Your consultant should help you pick based on your specific needs, not based on brand recognition or hype.

The Role of AI and Modern BI Tools

At Series B, you're at an inflection point. The BI tools you use now will shape your data culture for the next three years.

Five years ago, the choice was between Tableau, Looker, or Power BI. Today, there are better options.

According to Gartner's research on data strategies for mid-stage enterprises, companies that adopt modern, API-first BI platforms early have a significant advantage. They can:

• Embed analytics into their product (competitive advantage)
• Integrate with AI/LLM tools for text-to-SQL and natural language querying
• Scale without massive infrastructure costs
• Maintain data governance and security as they grow

This is where tools like D23's managed Superset platform come in. You get production-grade BI without the platform overhead. Your team gets self-serve analytics. Your product gets embedded analytics. And your consultant can set you up for text-to-SQL and AI-powered insights from day one.

The key question: does your BI tool have a modern API? Can you embed it in your product? Can you integrate it with LLMs? If the answer to any of these is no, you're picking a tool that will limit you in 18 months.

Building the Data Culture

Here's what most consultants miss: the 90-day engagement is not about dashboards. It's about building a data culture.

A data culture means:

• Decisions are made with data, not opinions
• Everyone has access to the data they need
• There's a shared understanding of what the key metrics are
• New ideas are tested with data before being rolled out
• Data literacy is valued and trained

You can't build this in 90 days. But you can lay the foundation.

Your consultant should:

1. Run a metrics workshop in month one. Get the whole leadership team in a room and define the top 10 metrics that matter to your business. Write them down. Make them official. Refer back to them constantly.

2. Set up a weekly metrics review. Every Monday morning (or whatever cadence works for you), your leadership team reviews the key metrics. Are we on track? Where are we off? What do we need to investigate?

3. Create a data dictionary. For every metric, write down: the definition, how it's calculated, who owns it, and when it was last updated. Put it in a shared doc. Update it quarterly.

4. Train your team. Your consultant should run a workshop on basic SQL, or on how to use your BI tool, or on how to interpret metrics. The goal is not to make everyone a data scientist. It's to make everyone comfortable asking questions and interpreting answers.

5. Celebrate wins. When a team makes a decision based on data and it works out, celebrate it. When someone uses a dashboard to catch a problem early, celebrate it. Culture is built through repetition and recognition.

The Consultant-Client Relationship

A good data consulting engagement is a partnership, not a vendor relationship.

Your consultant should:

• Ask hard questions about your business, not just your data
• Push back when you're asking for the wrong thing
• Be transparent about timelines and constraints
• Teach, not just build
• Be responsive and communicative

You should:

• Be clear about your priorities and constraints
• Make decisions quickly (don't let the consultant wait for approval)
• Involve your team in the process
• Give honest feedback
• Be realistic about what 90 days can deliver

According to research on Series B priorities from Andreessen Horowitz, the companies that scale most effectively are the ones that build strong partnerships with their advisors and consultants. They involve them in strategic decisions, not just tactical ones.

For a data consultant, this means:

• Invite them to board meetings (or at least a debrief after)
• Ask them about data strategy, not just dashboards
• Get their input on hiring decisions for data roles
• Involve them in product decisions that depend on data

What Happens After Day 90?

Your 90-day engagement ends. Now what?

Ideally, you have two paths forward:

Path 1: Ongoing retainer. Your consultant stays involved at a reduced level—maybe 4–8 hours per week. They help with new dashboards, mentor your team, and stay available for questions. This is great if you want to keep momentum and don't yet have a full-time data hire.

Path 2: Handoff to your team. Your team is ready to own the BI platform. The consultant steps back and is available for emergencies or for bigger projects (like embedding analytics in your product, or building a data warehouse). This is great if you've hired a data analyst or engineer who can take over.

Most Series B companies do a mix: the consultant steps back to retainer mode, and you hire a data analyst or engineer to own the day-to-day work.

By Series C, you should have a full data team. But at Series B, the consultant can be part of that team.

According to Y Combinator's guidance on Series B milestones, one of the key decisions at Series B is whether to hire in-house or use consultants. The answer depends on your burn rate, your hiring velocity, and your data needs. A consultant can fill the gap while you're hiring.

Real-World Example: A Series B SaaS Company

Let's walk through a real engagement.

Company: A B2B SaaS platform with $3M ARR, 30 employees, and Series B funding just closed.

Day 1–7: Discovery

The consultant talks to the founder, CFO, VP Product, VP Sales, and VP Engineering. Key findings:

• The CFO is manually pulling data from Stripe, Segment, and a custom database every month. This takes 8 hours.
• The VP Product wants to understand which features drive retention, but the data is spread across Mixpanel and custom logs.
• The VP Sales wants pipeline visibility, but the team is using Salesforce and a Google Sheet.
• The engineering team has no data infrastructure. Everything is ad-hoc queries.

Day 8–30: Quick Wins

The consultant builds three dashboards:

1. Financial dashboard (for the CFO): Monthly revenue, churn, NRR, and cash runway. Pulls from Stripe and Segment.
2. Product dashboard (for the VP Product): Feature adoption, retention by cohort, and usage trends. Pulls from Mixpanel.
3. Sales dashboard (for the VP Sales): Pipeline by stage, conversion rates, and forecast vs. actual. Pulls from Salesforce.

Each dashboard takes 1–2 days to build. The consultant uses D23's managed Superset platform because it has great Salesforce integration and makes it easy to embed dashboards later.

By day 30, the CFO is no longer manually pulling data. The VP Product has daily visibility into feature adoption. The VP Sales has a dashboard instead of a Google Sheet.

Day 31–60: Self-Serve

The consultant works with the VP Product to enable self-serve analytics. The team learns how to build their own dashboards and run their own queries. Key dashboards:

• Retention by feature
• Usage by customer segment
• Funnel analysis for the signup flow

The consultant also sets up a data warehouse (Snowflake) to consolidate data from Stripe, Segment, Mixpanel, and Salesforce. This is the foundation for everything else.

Day 61–90: Handoff

The consultant trains the VP Product's analyst on how to maintain and extend the BI platform. They pair program on building new dashboards. They document everything.

By day 90:

• The CFO saves 8 hours per month
• The VP Product can answer her own questions without waiting for an analyst
• The VP Sales has real-time pipeline visibility
• The company has a data warehouse and a BI platform
• The VP Product's analyst can build new dashboards and maintain the platform

Cost and ROI:

The consultant costs $30K (90 days at $1K/day). The company saves 8 hours per month from the CFO, plus countless hours from the VP Product and VP Sales. At $150/hour loaded cost, that's $1,200/month in savings, or $14,400 per year. The ROI is positive in year one, and the benefits compound in year two.

Plus: the company now has a data infrastructure that will support them through Series C. That's worth way more than $30K.

Choosing the Right Consultant

Not all data consultants are created equal.

When you're evaluating consultants, ask:

1. Do they have Series B experience? A consultant who's worked with 10+ Series B companies will understand your constraints and priorities. Someone who's only worked with enterprises will over-engineer everything.

2. Can they build, not just architect? You need someone who can write SQL, build dashboards, and ship code. Not someone who just talks about strategy.

3. Do they understand your tech stack? If you're using Postgres and Segment, do they know how to work with those? If you're considering Superset, have they deployed it before?

4. Will they teach your team? This is the most important one. A consultant who leaves you dependent on them is a bad consultant. A consultant who teaches your team and hands off ownership is a good one.

5. Do they ask hard questions? A good consultant will push back on your priorities. "Are you sure that's the metric you should be tracking?" "Have you thought about how you'll maintain this?" "Is this the right tool for your use case?"

6. Can they work with your team? Some consultants are brilliant but difficult to work with. Some are average but great at collaboration. For a 90-day engagement, collaboration matters more than brilliance.

If you're using a managed Superset platform like D23, you get the added benefit of working with a team that understands the platform deeply. The consultant and the platform team can collaborate on your behalf.

Final Thoughts: 90 Days Is Just the Beginning

A 90-day data consulting engagement is not the end. It's the beginning.

You're not trying to solve all your data problems in 90 days. You're trying to establish momentum, build a foundation, and prove that investing in data infrastructure is worth it.

If you do this right, by day 90 you'll have:

• Dashboards that your team uses every day
• A team member who can maintain and extend your BI platform
• A data warehouse that consolidates your data sources
• A clear roadmap for the next 12 months
• A data culture where decisions are made with data, not opinions

From there, you can scale. Add more dashboards. Integrate more data sources. Embed analytics in your product. Build AI-powered features. Hire a full data team.

But it all starts with a focused, outcomes-driven 90-day engagement.

According to research on scaling to $100 million, the companies that build strong data foundations early—at Series B, not Series D—are the ones that scale most efficiently. They make better decisions, faster. They understand their unit economics. They can run experiments and measure results.

Data is not a nice-to-have at Series B. It's a competitive advantage. And the first 90 days of a data consulting engagement is where you build that advantage.

Start now. Pick your consultant. Define your priorities. Ship dashboards. Teach your team. And by day 90, you'll have a foundation that scales with you all the way to Series C, D, and beyond.