Data Platform or Analyst First?
Which fits your situation better?
Overview
Thinking about building dedicated data capabilities or scaling beyond basic reporting?
You might have systems that aren't reconciling, volumes of data that spreadsheets can't handle, stakeholders asking for different views of the same data, or pressure to move faster than manual analysis allows.
What do you do first - hire a dedicated data analyst, or build a data platform? Both can work. Which one works best for you?
Summary
Analyst-first has lower costs and faster time to first insights. You'll validate and clarify your data needs while building institutional knowledge. However, tech debt builds fast, metrics can be inconsistent, and you'll pay costs twice when reports are rebuilt in a data platform.
Data platform-first gives you an integrated, reliable foundation for reporting from the start, and faster long term growth. But the upfront costs are higher and ROI comes later.
Analyst First, Data Platform Later
Pros
Lower initial cash outlay - Analyst salary only. In London, approximately £4.3k per month.
Clarify platform requirements - A hands-on analyst will surface the must-have data sources, metrics and pain-points before any build, reducing the risk of re-work.
Cultural fit check - Evaluate whether an analytics mindset works in the team before committing to extra tooling.
Cons
Slower path to automation - Analysts could spend their first few months getting access, manually cleaning and integrating data, and writing SQL or spreadsheet formulas for reporting that gets thrown away later. This loses time that could have been spent delivering insights.
Risks
Frustration and churn - Good analysts might quit or lose motivation if they have to become stand-in data-engineers for long stretches.
Ad-hoc "spreadsheet engineering" - Analysts usually start answering specific questions, but as they generalise they'll reuse formulas. Sensible reuse can drift and snowball into an ad-hoc data platform. These can hold complex logic that becomes hard to extract, delaying and adding complexity when moving to a structured data warehouse.
Re-work later - Deferring external help saves cash now, but if the future platform has to replace spreadsheet engineering, you pay twice: first for the stop-gap solutions, then for the clean rebuild.
Data Platform First, Analyst Later
Pros
Fast time-to-insight - Automated ingestion and template starter dashboards get answers earlier. This foundation helps you clarify your questions and goals for an analyst, and make a more informed decision on who to hire.
Cleaner analyst on-boarding - New hire starts with tested models and documented metrics to work from, can reduce months of ramp up time to weeks.
Hiring magnet - Strong candidates know modern data platforms are better than working in spreadsheets. Good technology can attract better talent.
Scale Direction - Extra Pros
Fixed-scope, cancel-any-time - Scale Direction offers a hand-over at no extra charge, optional support and managed services on a monthly basis. You can end or move in-house whenever you're ready.
Built-in mentoring - External experts can work with your analyst, up-skilling them quickly on technical and strategic aspects of their work.
Cons
Higher up-front spend - You pay a setup fee of at least £15k, usually closer to £25k before proving ROI.
Potential mis-alignment - The platform is designed from today's understanding and industry standard templates. It might need revisions if an analyst brings new questions later.
Risks
Open-ended engagement - It's understandable to be concerned about an open-ended engagement. Milestones and clear exit criteria can mitigate this.
Scale Direction - Perceived Risks and Realities
Internal knowledge loss - Exposure to real-world data helps analysts build deep domain context. This isn't lost with Scale Direction, it's documented in the testing outcomes and fixes layer of the platform, and the data dictionary which details how reporting metrics are produced.
Flexibility on tech choices - Analysts might choose different reporting tools, so Scale Direction doesn't lock you in to anything. The reporting platform (Metabase) is easy for analysts to change, only costs $85 per month, and the monthly subscription can be cancelled any time.
Context Factors That Tilt the Scales
The right choice depends on your specific situation. Here are the key factors to consider:
| Factor | Analyst First | Platform First |
|---|---|---|
| Urgency of automated metrics | Can wait for gradual build-up | Board or partner reporting pain is acute |
| Cash runway vs. growth goals | Early stage, bootstrapped or cost sensitive | VC-backed teams racing for scale |
| Internal engineering bandwidth | Engineering team has capacity to build in-house | Engineering team is focused on core product |
Making the Right Choice
Both paths can lead to success, but the right choice depends on your specific circumstances, timeline, and resources.
If you're still unsure which approach fits your situation best, Scale Direction can help you evaluate your options and create a roadmap that works for your team and budget.
Let's jump on a call to discuss your specific needs and determine the best path forward for your data strategy.