08 / Dashboard Development & Business Intelligence

Dashboard Development & Business Intelligenc

Decision-ready dashboards built on reliable data, not another layer of spreadsheet confusion.

Wireframe of an operations dashboard: headline figures, a trend over time, and a breakdown by segment. Layouts are illustrative — every build is shaped around your own data.

I build executive and operational dashboards that combine data from CRMs, finance tools, marketing platforms, databases, and custom APIs. The work starts with KPI definitions and source reconciliation, then delivers a maintainable data model, automated refreshes, role-aware views, and monitoring for stale or broken data.

Use Cases

Executive performance dashboards

One governed view of revenue, margin, pipeline, operations, and service health with definitions leaders can trust.

Sales and CRM reporting

Pipeline coverage, stage conversion, velocity, forecast changes, lead sources, and account activity without manual exports.

Operational control towers

Near-real-time monitoring for orders, queues, delivery, inventory, incidents, or service levels with actionable alerts.

Marketing and customer analytics

Join acquisition spend, campaign activity, product usage, retention, and customer value into one decision layer.

Common Questions

Which dashboard tool should we use?

It depends on your licenses, data volume, sharing model, and internal skills. I commonly work with Power BI, Looker Studio, Metabase, Grafana, and custom React dashboards.

Can you connect several systems?

Yes. I build the ingestion and transformation layer needed to reconcile CRM, finance, marketing, support, database, and spreadsheet data before it reaches the dashboard.

How do you prevent incorrect KPIs?

Definitions are agreed before visual design. I add source reconciliation, data-quality tests, freshness monitoring, and visible ownership for every critical metric.

What you get

→KPI and reporting requirements workshop
→Source-system audit and data-quality assessment
→Analytics data model and transformation pipeline
→Power BI, Looker Studio, Metabase, or custom dashboard build
→Role-based access, refresh monitoring, and alerts
→Documentation, training, and handover

Start a project

Related services

Data Pipelines Python Automation n8n Workflow Automation

How a project runs

The same sequence every time. It is deliberately front-loaded: most of the risk in an automation project sits in understanding the process, not in building it.

Map the process before writing anything

The first session is spent on how the work actually happens, which is almost never how the documented process says it happens. Who touches what, in which order, and where the time really goes. Most failed automation projects failed here rather than in the build, because they automated the described process instead of the real one.

Measure the cost of doing nothing

Hours per week, error rate, and what those hours would otherwise be worth. This is what decides whether a process is worth automating at all — and it is also the number you compare against afterwards, which is why it gets recorded before anything is built rather than estimated after.

Build the smallest useful version

One process, working end to end, in production, before anything else starts. A narrow system that people actually use beats a broad one that waits on a second phase, and the edge cases that matter only surface once real work runs through it.

Run it against reality

The first two weeks of live use produce more design corrections than any amount of planning. Failures get surfaced loudly, retried and logged, because silent failure is the most expensive property a workflow can have and the one noticed last.

Hand it over properly

Documentation, credentials, and a walkthrough with whoever will maintain it. A system only one person understands is a liability regardless of how well it runs, so handover is part of the work rather than an optional extra at the end.

Ways to work together

Three arrangements cover almost every engagement. Most start with the first or the second; the third only makes sense once something is live.

Fixed-scope project

One defined process, a fixed price and an agreed definition of done. The right fit when the problem is clear and bounded — an order flow to connect, a CRM to build, a reporting pack to automate. Most first engagements are this, because it lets both sides find out how the other works without a long commitment.

Assessment first

One to two weeks mapping processes and measuring where the hours actually go, ending in a ranked list with effort and payback estimates. Useful when there is a backlog of automation ideas and no agreement on which matters. The document stands on its own and is yours whether or not you build anything with me.

Ongoing retainer

A recurring block of time for maintenance, extension and new automations once systems are live. Integrations break when the systems either side of them change, and a retainer means that gets fixed before it becomes an outage rather than after.

How the working relationship is set up

Remote, with real overlap

Work is delivered remotely. Across Europe and the Nordics the working day is effectively identical; in the Gulf it starts three hours ahead, which still leaves your full morning covered. There is no local office in any market, and none is claimed anywhere on this site.

You own what gets built

Source code, infrastructure and data stay yours. Systems are deployed on infrastructure you control — your server, a European provider, or a VPS in your own account. There is no per-seat licence and no dependency on me continuing to be involved.

Self-hosting is a first-class option

Self-hosted n8n, self-hosted databases and locally run models are all supported and, in several of these markets, preferred. Where no data may reach a third-party API, that constraint shapes the architecture from the start rather than being retrofitted.

Direct contact, one person

You deal with the person building the system. There is no account manager relaying requirements, which is the main practical advantage an independent consultant has over an agency at this size — and the main reason scope stays honest.

Questions worth asking before you commit

What if we are not sure automation is the right answer?

Then the assessment is the right starting point, and it is designed to be able to conclude that you should not automate something. A process that is broken should be fixed before it is automated, and one that runs twice a month rarely earns the build. Receiving that answer in week one is far cheaper than discovering it after a project.

We have been burned by a failed automation project before.

That is common, and the cause is usually scoping or adoption rather than technology — a system built for the documented process rather than the real one, or one nobody was trained to maintain. Both are addressed by mapping the real process first and treating handover as part of the work.

How do we avoid depending on one person?

By owning everything: source code, infrastructure, credentials and documentation, with a walkthrough for whoever maintains it. The test is whether another developer could pick the system up from the repository and the documentation alone, and that is the standard handover is written to.

Is our data safe?

It stays where you need it to. Systems can run entirely inside your own infrastructure, including self-hosted models where no data may reach a third-party API. Where GDPR applies, data stays in the EU by default, with named access control and audit logging as standard rather than as an upgrade.

How quickly can something be running?

A first working version of a single process is typically weeks rather than months. Larger platforms are sequenced as modules so something is in production early and the rest builds on a foundation that already survives real use.

Sectors this is built for

AI Automation for Clinics and Healthcare AI Automation for Retail and E-commerce AI Automation for Logistics and Warehousing AI Automation for Marketing Agencies AI Automation for Hospitality and Tourism AI Automation for Finance and Insurance

Markets where this is delivered

Albania United Arab Emirates Germany the Netherlands Malta Sweden Denmark Norway

Get in touch

Tell me what needs automating

Describe the process that is costing you time and roughly how much. I reply to every enquiry personally, usually within one working day.

Email: inquiries@artenisalija.com
Response: Usually within one working day, Mon–Fri CET
Delivery: Remote across Europe, the Nordics and the Gulf