We turn scattered spreadsheets, ERP and CRM data into one source of truth and dashboards people actually trust.
In most companies the management meeting opens with three versions of the same number. Sales quotes the CRM, finance quotes the accounting system, operations quotes its own spreadsheet, and hours go into reconciling them. Our reporting and BI dashboard work brings those sources together in one data warehouse, puts KPI definitions in writing and builds dashboards where every manager finds the answer to their own question in seconds.
Across twenty-plus years leading media, sales and P&L, we sat on the other side of these dashboards, in the chair where decisions get made. So we start from the question, not the chart: which decision should this screen speed up? Reporting automation retires the hand-built weekly decks, data-quality checks watch the pipelines, and we keep owning the accuracy of the numbers long after launch.
- 01Finance and operations teams that hand-build a report for every weekly management meeting.
- 02CEOs and boards who want sales, stock and margin on one screen.
- 03Retailers and distributors whose ERP, CRM and e-commerce figures never quite agree.
- 04Groups operating across several branches, dealers or countries that need a consolidated view.
- 05Marketing leads who want to match ad spend against real sales and margin.
- 06Companies that licensed a BI tool but never managed to fill it with data they trust.
What is a BI dashboard, and how is it different from a report?
A report is a snapshot of one moment: last month’s sales, the quarter-end balance sheet. A dashboard is a live view that keeps updating; it shows the pulse of the business through the day, sometimes by the hour, so a manager spots a deviation without waiting for month end. Business intelligence is the whole discipline behind those screens: collecting, cleaning, modelling and presenting data.
In a well-run dashboard project the screen comes last. First you need to know where each figure comes from, how it’s defined and who owns it. “Revenue” can mean order value including VAT to the sales team and net invoiced amount to finance. Until those definitions are written down, every chart just starts a new argument in the meeting. To us, a dashboard is the visible form of numbers the company has agreed on; without a single source of truth it’s only a more colourful spreadsheet.
How a data warehouse and reporting automation work together
A data warehouse is a central database where data from different systems is reshaped and brought together for analysis. Operational systems such as ERP, accounting software or a CRM are built to record transactions; heavy analytical queries slow them down, and they rarely keep history consistently. So we build pipelines that pull data on a schedule from systems such as Logo, Mikro, Netsis and SAP Business One, from ad accounts and from shared spreadsheets. Typically that means a PostgreSQL-based warehouse, transformation jobs written in Python and scheduled tasks.
Reporting automation sits on top of that foundation. Instead of someone downloading from three systems and stitching it together every Monday morning, the report builds itself and reaches the right people by email or as a dashboard link. When a threshold is crossed, such as stock hitting a critical level or collections slipping in one region, an alert goes out. Behind every pipeline, tests check row counts, empty fields and unexpected spikes, so missing data gets caught at the source rather than discovered on the dashboard.
How to choose the right KPIs to track
The dashboard we see most often tries to fit forty charts on one screen. A screen that shows everything highlights nothing. For KPI tracking we start with goals: is this year about growth, margin or cash flow? For each goal we pick one outcome metric and one or two leading indicators that warn you early. Days sales outstanding, for instance, is an outcome; the spread of receivables coming due is what predicts it.
Metrics are then split by level. The board sees a handful of strategic figures and their trends, a regional manager compares their own branches, the operations team watches the flow within the day. The same KPI uses the same formula on every dashboard; only the level of detail changes. Anyone who clicks a number can drill down to the orders, invoices or campaigns behind it. That ability is what builds trust: a manager who can see where a figure comes from stops questioning it and starts using it.
Timelines, tool choices and the mistakes to avoid
With a well-defined scope, the first dashboard is usually live on real data within a few weeks; later dashboards come faster because they reuse the same data model. For the visual layer there are two routes: a widely used BI tool such as Power BI, Looker Studio or Metabase, or a custom dashboard built with Next.js and React and embedded in your product or internal portal. We choose based on the number of users, whether customers will see dashboards too, and licence cost.
The common mistakes are predictable: charting data before cleaning it, leaving KPIs without an owner, letting every department build its own dashboard with its own definitions, and nobody watching the pipelines after launch. The signs of a good result are just as clear: management meetings discuss decisions instead of disputing numbers, hand-built reports disappear, and the dashboard gets opened every morning without anyone being reminded.
- 01KPI dictionary and metric definitions
- 02Data warehouse and data model
- 03Automated data pipelines (ETL/ELT)
- 04Role-based management dashboards
- 05Scheduled reports and threshold alerts
- 06Data-quality checks and documentation
We own the numbers
We don’t hand over a dashboard and disappear. We monitor the pipelines, and when a field changes in a source system or data arrives incomplete, we usually notice and fix it before you do. Trust comes from numbers being right every single day.
Built from the decision-maker’s chair
Twenty-plus years of P&L responsibility taught us what a manager actually looks at on a Monday morning. We design dashboards around the decision on the table, not around how many chart types we can fit on a screen.
We know the source systems
We understand how systems such as Logo, Mikro, Netsis, Nebim and Paraşüt store data, along with e-Fatura records and ad platforms’ reporting APIs. That noticeably shortens the work of extracting and matching data.
Tool-agnostic by design
We don’t resell BI licences. Depending on the need, we set up a widely used BI tool or build custom dashboards embedded in your application, as we do in our own products. Either way, the data model and the data stay yours.
Questions and KPI workshop
We sit with managers to map which decisions rely on which numbers, then record every KPI’s formula, owner and refresh frequency in a single dictionary.
Data inventory and model
We list every source, from ERP, accounting and CRM to ad accounts and spreadsheets, and build a warehouse model with shared definitions for customers, products and dates.
Pipelines and dashboards
We build pipelines that pull data automatically, reconcile figures line by line against source systems and shape the first dashboards step by step with the people who will use them.
Launch, training and upkeep
We set access permissions, get teams comfortable reading the dashboards, and keep everything current as needs grow and source systems change.
We don’t quote packaged prices for reporting projects; every proposal is shaped by scope. What drives it is how many source systems need connecting and how easy they are to access, how clean the data is, how many years of history to load, the number of dashboards and users, how often data must refresh, and whether you choose an off-the-shelf BI tool or a custom dashboard. After a short discovery we send a written proposal showing the build and ongoing monitoring and upkeep as separate items.
Let’s talk about this
A dashboard is a screen that shows a business’s critical metrics in one place and keeps them current. It pulls figures such as sales, stock, collections and marketing spend from different systems and makes them comparable. It lets managers spot a problem without waiting for the month-end report and decide quickly where to focus their attention.
Business intelligence is the umbrella term for collecting, cleaning, modelling and presenting company data so decision-makers can use it. The dashboard is its visible face; behind it sit data pipelines, a warehouse and shared metric definitions. That invisible infrastructure determines how accurate the result is just as much as the screen itself does.
For internal users and analysis that changes often, tools like Power BI or Looker Studio can be quick and economical. When dashboards will be shown to customers inside your own product, when user numbers would inflate licence costs, or when you need custom interaction, an embedded dashboard built with Next.js makes more sense. We make that call together during discovery.
Not for a simple dashboard fed by a single source. But if you’re combining several systems, comparing historical periods or want to keep load off your operational systems, a separate warehouse is almost always needed. At small scale it can be as lean as a managed PostgreSQL database with a handful of well-designed tables.
We connect through read-only database access, the services these systems expose, or scheduled export files. Data is pulled into the warehouse at set intervals, and account, stock and invoice tables are matched to shared definitions. We never write back to the source system, so your accounting workflow stays exactly as it is today.
It depends on the need. Daily refreshes are usually enough for board dashboards; sales and operations screens can update hourly or every few minutes. Faster refreshes cost more in infrastructure, so for each dashboard we set the frequency by how quickly decisions based on it are really made, not by what looks impressive.
Hand-built reports take time and introduce errors; one row slipping during copy-paste can lead to a bad decision. Automated reports follow the same logic every time, arrive on schedule, and free the team to interpret numbers instead of collecting them. Threshold alerts flag problems as they happen, without waiting for report day.
We connect to source systems with read-only permissions and move data over encrypted connections. Dashboards use role-based access, so a regional manager sees only their region while finance sees cost detail. Fields containing personal data are masked or kept out of the dashboard entirely, in line with KVKK and GDPR requirements.
Yes. Source systems get updated, new products and branches appear, KPI definitions change. Under monthly upkeep we monitor pipelines, respond to data-quality alerts and add new reporting needs to the existing data model, so the dashboard keeps pace with the business instead of freezing in the state it was launched.
I usually reply within 24 hours.