Building BI: A Startup's Roadmap for Onboarding Enterprise Analytics

After a certain point, every growing startup needs to build a Business Intelligence (BI) infrastructure. The challenge: without in-house expertise, it's difficult to know when to invest, how much to invest, and where to start.

A structured roadmap during this "analytics onboarding" phase allows businesses to scale their BI or analytics team in a way that delivers positive ROI at each step. Below is that roadmap — a progression through five building blocks, each one creating the foundation for the next.

1. Data

Data is, of course, the key ingredient in analytics. It is also the most difficult and time-consuming part of any analysis — and exponentially harder at the enterprise level, where you must consistently connect and align data across many disparate systems.

The payoff, however, is substantial. Well-managed data saves thousands of hours in future labor costs per year. The goals of this first step are to:

1. Connect all valuable data sources into a single database

2. Calculate dimensions, segments, and metrics in a uniform way across the business

3. Ensure data and calculations are accurate

4. Automate the ETL process and provide seamlessly updated data for reporting and analysis

Tip: Skipping this foundation is the single most common mistake growing startups make. Every week spent on a poorly structured data layer compounds into months of wasted analyst time down the road. Start here, and start properly.

2. Reporting

Every decision-maker across a business — no matter how large or small the decision — can benefit from being better informed. This is where reporting comes in.

Building a reporting infrastructure is an iterative process: business operations provide feedback, and business analysts make the necessary adjustments. Tools like Tableau, Domo, and Looker allow users to build automatically-updated reports once connected to a data warehouse. Due to their ease of use and visual appeal, these SaaS tools are rapidly replacing Excel-based reporting. Even non-technical employees can build valuable reports on these platforms.

Regardless of the end user or the tool, all quality reports share certain qualities:

1. Metrics and segments are clearly defined

2. Contains a high-level or executive summary with the metrics most relevant to the end user

3. Combines charts and tables that allow users to uncover the data story

4. Automatically updates through a data visualization tool

5. Includes guides or constraints that prevent users from pulling incorrect data or drawing wrong conclusions

Tip: By the time a B2C company reaches 100+ employees, it's worth hiring a dedicated analyst. This person works closely with back-end developers or IT to own data and reporting. Prior to that hire, developers with business acumen — or strategists with technical skills — can fill the gap.

3. Exploratory Analysis

Reporting provides a foundation for operations to ask meaningful business questions. When those questions can't be answered through a standard report, you move to exploratory analysis.

The goals here are to:

1. Answer key business questions that are currently unknown, in the simplest way possible

2. Develop concrete findings and business recommendations that can be tested in the future

3. Document results in a way that can be shared with decision-makers and referenced throughout the life of the business

Bespoke analyses are naturally more time-consuming than standard reporting. Companies moving into this phase should consider hiring a small analytics team. Teams gain efficiencies of scale by specializing, building on prior analyses, and sharing templates (code, notebooks, Docker images).

One of the most important — and commonly overlooked — steps is maintaining a living archive of findings and recommendations that can be easily accessed and shared across the business over time.

4. Testing

A testing framework goes hand-in-hand with exploratory analysis. Here is how to build an effective cycle of strategic testing:

1. Business stakeholders ask questions based on hypotheses and their existing knowledge of the business

2. Analysts explore these questions — through A/B testing or correlative analysis — ultimately producing insights

3. Operations and analysts work together to develop strategic recommendations

4. Operations implement the recommendations (or MVP versions of them) and use A/B testing to measure incremental uplift from each change

5. Based on test results, operations and management determine a final course of action

This cycle of continuous testing and strategic improvement is the linchpin of Eric Ries' The Lean Startup. Originally designed for startups, it has since been adopted effectively by Fortune 500 companies.

Tip: The most common failure in analytics-driven testing isn't bad statistics — it's moving to the testing phase before having clean data and clear reporting. Stages 1 and 2 are prerequisites.

5. Advanced Analytics

After the analytics team has used reporting and exploratory analysis to develop a thorough understanding of their data, they can layer in advanced analytics — the application of statistics to achieve better business outcomes.

A few examples of statistical methods and their business applications:

The Underlying Principles

While the five stages above form the core of enterprise analytics for startups, three principles are equally important throughout:

• Start with the data. Make it clean, accurate, and accessible before building anything on top of it.

• Use prior insights and unanswered questions to guide next steps. Let curiosity — not technology — drive the roadmap.

• Build iteratively. Use feedback loops to continuously improve both reporting and analysis.

The journey from zero analytics to a fully functioning BI capability doesn't happen overnight — but it does happen in a predictable sequence. Following this roadmap will help you avoid costly missteps and ensure that each investment in analytics builds meaningfully on the last.

The Data Strategist helps startups and scaleups become data-driven. Get a data scientist on-demand, or advice on building your analytical data stack.