We see a common mistake when companies hire their first analytics role, so here's a structured approach to getting it right.
The Common Misstep
Many businesses, when ready to invest in data, look for a 'full-stack' analytics hire. This is a person expected to do everything from setting up tracking to building dashboards for the board. This is a mistake.
No single person possesses all these skills at a senior level. If they claim they do, they are likely overestimating their abilities, or they are a unicorn.
Expecting one person to cover all bases dilutes their impact. They spend too much time switching contexts, and not enough time excelling at any one thing.
Phase 1: Data Engineering Foundation
Start here. Before you can analyze data, you need data. And before you need data, you need to collect it reliably. This is where an analytics engineer or data engineer comes in.
Their job is to build the pipelines, ensure data quality, and create a usable data warehouse. Without this, any analyst you hire later will spend 80% of their time on data wrangling rather than analysis.
- **What they do:** Design and build data pipelines, manage data infrastructure, ensure data quality and availability.
- **Why it's first:** Analysis is impossible without reliable, well-structured data. They lay the groundwork.
Phase 2: Analytics & Reporting
Once you have a solid data foundation, bring in your first analytics-focused role. This person will use the clean data your engineer provides to answer business questions.
They will build reports, create dashboards, and perform ad-hoc analysis. They translate data into insights your teams can act on.
- **What they do:** Build dashboards, generate reports, perform deep-dive analysis, communicate findings to stakeholders.
- **Why it's second:** They operationalize the data engineered in Phase 1, creating value from it.
Phase 3: Advanced Analytics & Science
Only after you have robust reporting and a culture of data-driven decision making should you consider advanced analytics or data science roles. These roles focus on predictive modeling, experimentation, and complex statistical analysis.
Without the foundational layers, their work would be speculative and difficult to integrate. They build on the work of the first two hires, not replace it.
We have seen companies skip steps. They almost always backtrack or fail to get value from their analytics investment. Build intentionally.
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