Data Analytics & BI Dashboards: Decision Support Panels

Data everywhere, insight nowhere: that captures the analytics state of most mid-sized businesses in Turkey precisely. Sales figures live in the ERP, order data in the e-commerce panel, marketing spend in Google Ads, stock in yet another module, and the "real report" senior management actually looks at sits in an Excel file someone updates by hand every month. The month-end report takes three days, two teams calculate the same metric differently so the meeting turns into a "whose number is right" argument, and decisions are made late and on instinct rather than data. On the data side of digital transformation, the first thing we touch is exactly this: consolidating scattered sources into a data warehouse and moving to live KPI dashboards fed from a single source of truth, refreshed in real time or on a schedule: leaving Excel as a data-entry tool and bringing reporting into the system.

Our Approach

On a data analytics project we'd spend the first week focused on questions, not technology. Because the most expensive mistake is building a beautiful dashboard nobody actually looks at. We first ask "which 8 to 10 questions do you keep asking but struggle to answer?"; those questions turn into metric definitions, and metric definitions turn into the data model. The most common thing we run into at this stage is the same word (say "revenue" or "active customer") meaning different things in different departments. We'd nail that down in a metric glossary at the start of the project, because however beautiful the panel is, if the definitions underneath are inconsistent nobody will trust the number.

The second critical decision is a data warehouse for a single source of truth. The fix for data scattered across the ERP, e-commerce, CRM, ad platforms, and Excel is to consolidate it all in a central warehouse. Depending on volume and budget we recommend BigQuery, Snowflake, or PostgreSQL for more modest setups. To move raw data from the sources into the warehouse we build scheduled ELT jobs with Apache Airflow or Dagster; raw data is loaded as-is first (extract-load), and modelling happens inside the warehouse. We prefer this ELT approach over classic ETL because the transformation logic stays visible, versioned, and reversible: when a calculation error surfaces, we can see what changed and why.

The third layer is modelling and data quality with dbt. Rather than wiring raw data straight into a panel, we'd place a transformation layer in between with dbt. Each source can carry its own date format, customer code, and currency; in the dbt layer we normalise these into one consistent schema and define metrics in one place (for example, the definition of "net revenue" lives in a single SQL model, not across ten different panels). With dbt's testing and documentation features we continuously audit data quality: when a field that should never be empty goes empty, or row counts drop unexpectedly, an alert fires. That way we catch "the number in the report is wrong" surprises before the panel ever reaches a user.

The final layer is the dashboard and rollout strategy. For in-house analysis, self-service exploration, and a fast setup we recommend Metabase or Apache Superset; teams can write their own queries and licence cost stays low. Where the panel will be shown to a customer or dealer, needs branding, or must be embedded inside an application, we'd build a custom dashboard with Next.js. If operational metrics need to be real-time we build a streaming pipeline with Apache Kafka; otherwise a daily or hourly batch is enough and more economical. To keep panel load times fast we use DuckDB and a Redis cache for light queries, and wire in Sentry for error tracking.

Our Preferred Technology Stack

We typically reach for the following, adapted per project to your data volume, budget, and real-time needs.