KPI dashboard and management dashboard: which KPIs to track and why a dashboard alone is not enough

Short answer. A KPI dashboard, or management dashboard, puts the handful of figures you steer your organisation by on one screen: revenue and margin, cash, pipeline, utilisation and lead time. It works when three things are right: the figures are current, they come from connected systems with one definition per KPI, and someone discusses them in a fixed meeting. If one of those three fails, the dashboard becomes a picture nobody opens. A dashboard alone is then not enough. You also need a single source of truth underneath your figures, and a way to ask the questions nobody put in a chart in advance.

This guide is written for organisations of 20 to 250 employees that want to steer on their figures, not just look at them.

What is a KPI dashboard?

A KPI (key performance indicator) is a measurable figure that shows whether you are on track for a goal that matters. Not every figure is a KPI. The number of invoices sent is a metric. The average number of days customers take to pay is a KPI, because it says something directly about your cash position.

A KPI dashboard brings those indicators together visually, usually with a target or norm and a trend over time. A management dashboard is the version for the board and management team: few figures, across the whole organisation, meant to steer by. Operational dashboards show teams what is happening per day or per hour, such as the status of case files.

That makes a dashboard part of business intelligence: collecting, combining and visualising company data. The dashboard is the visible part. The real work sits underneath, in the data.

Which KPIs does a mid-sized organisation track?

The right set depends on your business model, but most organisations end up with a handful of KPIs per domain. These are the common ones. They are starting points, not benchmarks: what good looks like is something you decide from your own budget and history.

Finance

• Revenue against budget, per month and year to date.
• Gross margin, in total and per product, service or customer group.
• Liquidity and cash position, with a look ahead at the coming weeks.
• Outstanding receivables and debtor days (DSO): how long it takes on average before you are paid.
• Work in progress, for anyone who works on projects and invoices later.

Sales

• Pipeline value per stage, set against your revenue target.
• Conversion per stage, from lead to proposal to signed contract.
• Average deal value and sales cycle from first contact to signed contract.
• Revenue from new and existing customers, so you see where growth comes from.

Operations

• Utilisation or billability: the share of available hours that is productive or billable.
• Hours against budget, per project or assignment.
• Lead time and delivery reliability: how long an order, case or request takes and how often you hit the promised date.
• Quality, such as rework, complaints or customer satisfaction.
• Capacity and absence, because they determine what you can deliver in the coming months.

Two rules of thumb. Keep a management dashboard small: ten or so KPIs you actually discuss work better than forty you only look at. And combine backward-looking figures, such as revenue and margin, with forward-looking ones, such as pipeline and utilisation. The first show how things went, the second where they are heading.

Why dashboards often go unused

Almost every organisation has a dashboard somewhere: a Power BI report, an overview in Looker Studio, a spreadsheet full of pivot tables. Yet it is often not used to steer. That is rarely down to the charts. Three causes keep coming back.

1. The figures are not current. If someone puts a monthly export from the accounting system and the CRM into the dashboard, you are always looking at last month. An invoice that has been outstanding for two weeks only shows up when you open the dashboard, and by then you are too late. The dashboard becomes a rear-view mirror: it shows where you have been, not where you are going.

2. The data is spread across systems. Revenue sits in the accounting system, the pipeline in the CRM, hours in time tracking, planning and stock in the ERP. Each system has its own dashboard, and the overview that combines revenue, hours, margin and capacity either does not exist or is stitched together by hand. On top of that, "revenue" in the CRM often means something different from revenue in the accounts. As soon as two dashboards contradict each other, nobody trusts either of them. That is a data quality problem, not a visualisation problem.

3. Nobody asks the question the dashboard answers. A dashboard answers the questions someone had when it was built. The management meeting is about today's question: why is the margin on that one customer falling, which type of project keeps overrunning, what does that late payer mean for our cash position? If the answer is not in a chart, someone goes back to exporting. And a dashboard is passive: it waits for you to open it and does not flag on its own that something needs attention.

Having a dashboard built: what do you need?

Anyone having a dashboard built tends to start with the tool. Start instead with the decisions you want to make with it. The rest follows from there.

1. Decisions and owners. Which decisions do you make every week or month, who makes them and which figures do they need? A KPI without an owner and without a meeting where it comes up can be dropped.
2. One definition per KPI. Agree what counts as revenue, margin or a won deal, and which system is leading. This is the dullest work and the most important.
3. Connected sources. Pull data automatically from your accounting system, CRM, time tracking and ERP, not through manual exports.
4. A place where the data comes together. With one or two sources, a dashboard tool can run directly on the systems. With more sources, a data warehouse or a broader data platform starts to make sense.
5. The tool. Power BI, Looker Studio (formerly Google Data Studio) or a dashboard in your own software. The choice matters less than the steps before it.

A dashboard works best when the data already arrives structured. For Snelder Zijlstra Makelaars we built a secure tenant file portal in which tenants submit their details per profile. Because the intake itself is structured, one dashboard shows in real time which files are complete and what is still missing. The portal saves 30 to 60 minutes per file. At Burgstate, the market leader in business accommodation in Limburg, automated data visualisation and reporting from their own property software is one of the modules in an ongoing AI programme, so quarterly reports no longer have to be built by hand.

When do you need more than a dashboard?

A dashboard is good at fixed questions and trends over time. You need more when dashboards contradict each other, when figures are still exported to Excel to combine them, when the question from the meeting is never in a chart, or when you want to be alerted rather than having to look yourself. Then you need three building blocks.

A single source of truth. Every data point has one leading place and one definition, so every report and every answer shows the same figure. That is the data foundation under your figures. The concept itself is explained under single source of truth.

Connected systems. Data flows automatically between accounting, CRM, time tracking and ERP, without retyping and without a monthly export. Then the figures are current at the moment you need them. How that works with AFAS, Exact, Moneybird or your CRM is covered under system integration.

Asking questions in plain language. On top of that connected data you ask questions the way you would ask a financial controller: which customer delivers the highest margin this quarter if you combine hours and invoices? You get an answer with the figures and where they come from, even if nobody ever built that report. And the layer flags what needs attention, such as a project that is already over budget in time tracking while it looks on schedule in the planning. We call that layer an AI Operating System.

BI tools are moving the same way. With Copilot, Power BI has a chat feature for questions about your data. Microsoft itself says you should prepare the underlying semantic model first, otherwise you get generic or inaccurate answers, and that it requires paid Fabric or Premium capacity. The same applies there: a question feature is only as good as the data underneath. For Microsoft 365 Copilot alongside your own AI layer, see our piece on Microsoft 365 Copilot.

What this looks like in finance is shown by EVOKE, the finance platform we built together with Ficare. The starting point was explicitly not yet another dashboard. EVOKE connects to AFAS and builds a uniform data layer: one truth. It runs 15 core checks with automatic alerts, saving up to 40 hours a month of manual checks, and you ask questions in plain language about the balance sheet, profit and loss and cash flow. Every answer can be traced back to the individual booking. See also finance automation.

Your dashboard does not have to go. For periodic reporting and trends over time it stays useful. But if it is your only source of business insight, you miss half the story. The question is not whether you need a dashboard, but whether a dashboard is enough.

Frequently asked questions about KPI and management dashboards

What is the difference between a KPI dashboard and a management dashboard?

A KPI dashboard is any dashboard that sets performance indicators against a target, for a team, a department or the whole organisation. A management dashboard is the version for the board and management team: fewer figures, a broader overview and aimed at steering rather than day-to-day execution.

How many KPIs belong on a management dashboard?

As few as you need to steer your organisation. Ten or so that you discuss in a fixed meeting, spread across finance, sales and operations, work better than a screen full of charts. Give every KPI an owner, a definition and a norm.

Do you need a data warehouse for a KPI dashboard?

Not always. With one or two sources, a dashboard can run directly on the systems. If you combine more sources or want to keep history, a data warehouse is the logical next step.

Is Power BI enough?

For fixed reporting, often yes. What Power BI does not solve is data that is spread out, definitions that differ per system or systems that are not connected. That work sits underneath the tool, in the data foundation.

Where do you start?

With the decisions you want to make and the data they need. An AI audit goes deep into your processes, data and systems in 14 days and delivers a prioritised report of opportunities. Then you know whether a better dashboard will do or whether you first need to connect your systems.