Carlos Anaya Ruiz
ES

Service · Mexico City

Dashboards and Business Analytics

A dashboard leadership opens on Monday and understands without a walkthrough. Wired to your data, not to last week's export.

Get a dashboard quote →See what you get →

Remote consulting from Mexico City.

Context

Why this matters

Your business already generates data: sales, user behaviour, operations metrics, campaign performance. The problem is almost never a shortage of data — it is that nobody agrees on which number to look at. Four people open four reports and walk into the meeting with four figures for the same question.

At Amazon I built a BI system in Power BI with a star schema and complex DAX queries, integrating sources through SQL and REST APIs. Before that, as a project manager at Master Loyalty Group, I stood up Power BI dashboards with DirectQuery to Azure DevOps and SQL Server. What both taught me is that the chart is not the hard part: the hard part is agreeing on the definition of each metric and wiring it to the right source so nobody has to refresh it by hand.

So I start from the decisions, not from the visualizations. We name the metrics decisions actually get made on, write down their formula, check where the data comes from and how often it refreshes — and only then do I design the screen. An honest dashboard shows few things, shows them well, and reads on a phone at eight on a Monday morning.

Scope

What I build

Interactive, data-driven dashboards for your platform

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  • Clear data visualization with interactive charts
  • Responsive design that works on any device
  • Real-time connection to your data sources (APIs, SQL, Firebase)

What changes for your team

  • One screen with the metrics your team actually decides on, instead of seven reports nobody opens.
  • The numbers come from the data source, not from a manual export someone pastes in every Monday.
  • Every metric has a written definition, so two departments stop reporting different figures for the same thing.
  • It reads well on a phone, which is where it actually gets checked.

How I work

How it works

Four stages, in this order. The first two are where a data project is won or lost.

  1. 01

    Define the questions

    Before choosing charts, we define which decisions the dashboard has to support and which metrics answer them, each with its formula written down.

  2. 02

    Source audit

    We check where the data comes from, how often it refreshes, and how trustworthy it is. This is where most of the surprises in a data project show up.

  3. 03

    Design and build

    Wireframes, visual hierarchy, and development with React/Next.js, with data loading arranged so the first view is fast and the rest arrives progressively.

  4. 04

    Handover and extension

    Cross-browser testing, documentation for every metric, and a modular structure so adding one new card does not mean rebuilding the dashboard.

Fit

Who this is for, and who it is not for

A good fit

  • Teams that already have data and lack a shared view to act on it.
  • SaaS platforms that need to offer an analytics panel to their own customers.
  • Operations that currently depend on a spreadsheet only one person knows how to update.

Not a good fit

  • Not for anyone who is not collecting the data yet. If the information does not exist or cannot be trusted, instrumentation comes first; visualizing early just produces wrong charts in better typography.
  • It does not replace a BI platform with organization-wide data modelling and governance. If that is what you need, a custom dashboard is the wrong tool.

What's included

What you get

  • Data source audit and requirements gathering
  • Dashboard UX/UI design with wireframes
  • Frontend development with React/Next.js and charting libraries
  • Integration with APIs, databases, and cloud services
  • Performance optimization and data loading
  • Responsive design and cross-browser testing

Stack

Sources and libraries

Chosen per project, not by default. A single-source dashboard does not need a BI platform behind it.

Visualization · Application

  • Recharts
  • D3.js
  • Chart.js
  • Plotly
  • React
  • Next.js
  • TypeScript
  • Node.js
  • Recharts
  • D3.js
  • Chart.js
  • Plotly
  • React
  • Next.js
  • TypeScript
  • Node.js
  • Recharts
  • D3.js
  • Chart.js
  • Plotly
  • React
  • Next.js
  • TypeScript
  • Node.js

Data sources · BI and modelling

  • PostgreSQL
  • MySQL
  • MongoDB
  • Firebase
  • SQL Server
  • GraphQL
  • REST APIs
  • Power BI
  • DAX
  • SQL
  • Pandas
  • ETL
  • PostgreSQL
  • MySQL
  • MongoDB
  • Firebase
  • SQL Server
  • GraphQL
  • REST APIs
  • Power BI
  • DAX
  • SQL
  • Pandas
  • ETL

Questions

Frequently asked questions

What visualization libraries do you use?

I work with Recharts, D3.js, Chart.js, and Plotly depending on project needs. For BI dashboards I also have experience with Power BI and DAX for complex queries.

Can the dashboard consume real-time data?

Yes. I implement WebSocket connections, Firestore real-time listeners, and optimized polling. The architecture is designed so data updates without page reloads.

Can I add new metrics later?

Absolutely. I design dashboards with modular architecture: adding new cards, charts, or sections is as simple as configuring a new component. The data structure is extensible by design.

Services

Related services

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the proof · file pending

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three channels · parallel

How you reach me

or write to carlos@carlosanayaweb.com · +52 55 4416 7974

Tell me which decisions you make every week.

We start by naming the five metrics decisions actually get made on.

Get a dashboard quote →Book a call →

I reply in under 24 hours, from Mexico City — or write straight to carlos@carlosanayaweb.com.