Klarvo.

Dashboards for energy

High-frequency data runs slowly in off-the-shelf BI tools, and custom calculations hit the tools' limits.

▶ The solution

I build management dashboards from scratch with my own code instead of off-the-shelf BI tools. No limits on design, interaction, data sources or calculations; the dashboard is shaped exactly around what your business needs.

▶ Use case

A dashboard coded from scratch shows generation, consumption and equipment status as time series, with custom calculations and interactions.

▶ Data you need

Meter and equipment time-series data, maintenance records and target indicators.

The industry's data landscape

In energy, data comes from meters, field equipment, maintenance logs, billing and customer systems. Time-series data is abundant and often high-frequency. The real challenge is turning it into a form the people making decisions can use.

A custom management dashboard is a web application written from scratch around your data and your decisions. Charts, drill-downs, filters, custom calculations, role-based access, mobile views and embedding into your internal portal are designed to fit the need.

The math behind it

Correct rate = Σ numerator / Σ denominator ≠ average(numerator / denominator)

  • Example: store A makes 10 sales from 100 visitors, store B 45 from 900.
  • The average of the rates is 7.5%; the real conversion is 55 / 1,000 = 5.5%.
If a dashboard averages rates instead of dividing totals, it shows the wrong number. And if totals can't be broken down by segment, traps like Simpson's paradox go unnoticed [1]. I calculate every metric once, from one definition.

How I work

  1. Week 1

    Decisions and users.

    Who is the dashboard for, and for which decisions? Which metrics, at what level of detail, on which device? The screens are clear on paper before any building starts.

  2. Weeks 2–3

    Data connections and calculations.

    Sources are connected, metrics are calculated with one definition, and the refresh schedule is set up. This is where the numbers under the dashboard are made right.

  3. Weeks 3–5

    Design and build.

    The dashboard is built with real users in short cycles. Every cycle ends with a working version that is shaped by feedback.

  4. Final week

    Launch and handover.

    The dashboard goes live in the agreed environment and access is set up. How it works and how to update it is documented and handed to your team.

Full service details: Custom dashboards →

▶ Questions and answers

Dashboards for energy — what data do you need?

Meter and equipment time-series data, maintenance records and target indicators.

When is this the wrong choice?

Your current BI tool already meets your needs. There is no point replacing a setup that works. If the tool isn't the problem, a new dashboard won't fix it. Your team wants to keep changing the dashboard itself. If a team that doesn't code wants to add new charts every week, a BI tool's self-service features may fit better.

How does this work start?

With a short intro call. I listen to what you need and tell you plainly whether your data is enough for the job and what the first step should be. I reply to every message within 24 hours.

▶ Concepts

Linked to Wikipedia and Wikidata.

▶ Sources

  1. [1]Wikipedia, Simpson's paradox, 2026-10. en.wikipedia.org/wiki/Simpson%27s_paradox
  2. [2]Wikipedia, Performance indicator, 2026-10. en.wikipedia.org/wiki/Performance_indicator

▶ Intro call

Let's talk about your situation.

In a 30-minute call I listen to what you need and tell you plainly how I can help.

Book an intro call

▶ In other industries

▶ Other services in this industry