Dashboards

All company numbers on one screen: leads, sales, marketing and finances.

• individuallyScope
• Business systemsArea
• 6 hours – 2 daysLaunch

Who this is for

Owners and managers who want one clear picture.

Problem

Data is scattered across five systems. This most bothers owners and managers who need to see the whole, not five fragments.

What We Do

We assemble a dashboard with key metrics: leads, sales, marketing and finance.

What's included

Four stages, we start with metrics, not charts:

• Metrics
• Data sources
• Dashboard
• Automation

Timeline

Implementation takes 6 hours – 2 days. Stages are metrics, data sources, dashboard and automation.

Cost

It comes down to two things: how many sources the data has to be stitched from and how often it has to refresh. Once a day and live are two different constructions, not two variants of one.

What you get

Decisions based on numbers, not intuition.

How to calculate the cost of a single dashboard metric

Every metric on a dashboard costs more than screen space — it costs work on the data source and the attention of the person looking at it. It's worth calculating before you add the twentieth chart.

• Cost of implementing a metric = time to connect the data source + time to define exactly what counts and what doesn't
• Cost of maintaining a metric = response time to source changes (a new field, an API change, a tool's pricing change), spread across months
• Value of a metric = the number of decisions someone actually made by looking at it, over the last quarter

If the value comes out at zero — nobody made a decision by looking at that chart — the cost of maintaining it is pure loss, no matter how good it looks.

A report nobody opens is a cost, not a tool

Let's say it plainly, because it's the most common reason dashboards stop working: a chart nobody has looked at in three months isn't neutral — it costs screen space, update time, and the attention that could go to a metric someone actually looks at.

• Pick metrics that actually change a decision — not the ones that are 'nice to have,' but the ones that made someone act differently last month than they would have without them
• One metric per decision — if two charts lead to the same reaction, one of them is redundant
• If nobody has asked in three months why a particular chart dropped, nobody is looking at it — and it's worth removing before screen overload pushes out something that matters more

A dashboard showing ten things at once gets closed as a browser tab more often than one showing three things someone checks every day.

A dashboard, a spreadsheet, and off-the-shelf BI — what's the difference

Three routes to the same goal — seeing your company's numbers in one place — differ in how much work stays on your side.

• A spreadsheet with manually pasted data — zero tool cost, but someone has to copy numbers from five places every week, and a copying mistake doesn't stand out
• A self-configured, off-the-shelf BI tool — sources connect faster, but configuration still takes time, and the license is paid regardless of whether anyone looks at the result
• A dedicated dashboard — sources connected once, metric definitions agreed with you, but an upfront build cost and dependence on who maintains it after launch

The choice isn't final: companies starting with a spreadsheet often move to an off-the-shelf tool, and order a dedicated dashboard only once they know exactly which numbers they want to see every day.

When a dashboard doesn't pay off

Four situations where we advise against building a dashboard, before anyone pays for the first stage.

• The company hasn't yet decided which numbers matter to it — a dashboard built on guessed metrics will need rebuilding the moment someone starts using it seriously
• Source data is incomplete or wrong at the source — a dashboard will display the wrong data faster and more attractively, but won't fix where it comes from
• Nobody at the company regularly looks at numbers, and there's no plan to start — a tool with no audience gets opened once at a presentation and forgotten
• Data sources change more often than once a month — the dashboard will be in constant repair instead of giving a stable picture

What you need to have on your side

Building a dashboard starts with deciding what you actually want to see — not with the charts.

• Access to the data sources: ad accounts, CRM, sales spreadsheets, the accounting system — the places metrics need to be calculated from
• One person who decides exactly what each metric means (for example, what counts as a 'lead' or an 'active client') — without that definition, two people will count the same thing differently
• The refresh rhythm the numbers need — daily, weekly, or monthly — since that determines the maintenance cost
• A real list of the people who will actually open the dashboard, and the decisions they're meant to make based on it

How to check after a month whether the dashboard paid off

You calculate these measures from the dashboard itself and your own memory, without our involvement.

• The number of actual dashboard visits over the month, counting each person separately — a tool nobody opens doesn't work, regardless of data quality
• The number of decisions someone made citing a specific chart as the reason — if nobody can name a single one, the dashboard's value needs re-checking
• The time it used to take to manually collect the same numbers from five places, compared to today
• The number of discrepancies found between the dashboard and the source when checked — it should shrink, not grow

If after a month none of these numbers changed, the problem isn't the dashboard — it's the lack of a habit of looking at it, and it's worth naming that plainly before adding another chart.

What a dashboard does not do

A dashboard adds up totals, filters, and shows trends according to rules set at the start — that's arithmetic, not artificial intelligence, and the line between the two is worth knowing before anyone expects a chart to make a decision. A dashboard doesn't predict the future beyond simple extrapolation, if you specifically order that, doesn't explain WHY a number dropped — only that it dropped — and doesn't replace a strategy conversation, because it shows the consequences of decisions rather than making them for you.

The most common mistake when building a dashboard

The most expensive mistake is building the dashboard starting from the charts instead of the questions someone at the company is actually looking to answer. Clients order 'all the important numbers,' get a screen with twenty metrics, and a month later it turns out they regularly check three of them. The rest take up space, need maintenance, and bury those three important ones in the noise. That's why the Metrics stage in our scope of work comes before the Dashboard stage, not after: first we establish which questions need an answer, only then do we design the screen.

What you get

• KPI model and definitions of the displayed metrics
• Connection and normalisation of agreed data sources
• Dashboard with filters, refresh rules and interpretation notes

When the problem lies elsewhere

If your problem sounds different, neighbouring areas of the same system stand right next to it — AURA connects them to each other rather than selling them separately:

• Email integrations — Emails that send themselves at the right moment: greetings, offers, confirmations and newsletter — from one correctly configured sender.
• Telegram / WhatsApp Integrations — Requests, alerts and reports go straight to the messenger where the team sits all day anyway.
• Custom business tools — Custom applications and internal tools — from proposal generators to systems tailored to one process.
• Lead forms — Forms and quizzes that people want to fill out: several short steps with conditional logic instead of one long list of fields.