Business intelligence consulting for reliable management decisions

PostgreSQL is open source, ACID-compliant and strong for complex queries, transactions and large volumes. We recommend it when you need integrity and extensions (JSON, full-text search, PostGIS)—typical for web apps, ERP ties and reporting. Broad cloud support (AWS RDS, Azure, GCP) makes it a durable mid-market choice.

MySQL/MariaDB are widespread and straightforward to operate—ideal when you already run MySQL or need fast reads at moderate complexity (e.g. portals, CMS). MariaDB stays compatible with MySQL and often improves licensing. For heavy write loads we compare PostgreSQL or specialised engines.

MongoDB offers a flexible document model—suited to evolving schemas, prototypes or highly variable documents. We pair it with clear conventions when strict multi-document transactions matter, aligned with our database consulting.

Redis accelerates caching, sessions and real-time paths. We combine it with durable databases so speed and safety align.

(1) Identify and connect sources: We map ERP, CRM, spreadsheets and APIs—what exists where, at what quality, under which compliance rules. Gaps and duplicate maintenance surface early—often tied to data silos; fixing them improves every downstream metric.

(2) Build ETL: Extract, transform and load—cleansing, harmonising identifiers, currencies and dates into a central model. We document and automate ETL so your BI estate stays maintainable.

(3) Data warehouse or data lake: A single analytical backbone. Together we choose warehouse discipline, lake flexibility or hybrid patterns—e.g. lake for raw landing, warehouse for governed KPIs—built on our database solutions.

(4) Dashboards and reports: Using Metabase, Grafana, Power BI or tailored stacks we deliver live dashboards and standard packs. KPIs stay traceably defined—see BI tools & dashboards.

(5) Train departments for self-service BI: Where appropriate we empower teams with governed access. More: API and integration development, breaking down data silos, artificial intelligence services.

A manufacturer with five sites held data across ERP (orders and stock), MES (machine data) and Excel for ad-hoc analyses. Leadership lacked one KPI view and spent hours consolidating weekly; definitions diverged between plants—typical for manufacturing networks with grown IT landscapes.

Groenewold IT Solutions unified sources via ETL into a PostgreSQL-based warehouse, defined a shared metric model and shipped a real-time Metabase dashboard. Leadership now sees OEE, scrap and delivery performance across sites; production can filter by machine, shift or order with alerts on threshold breaches—supported by monitoring.

Monthly reporting time dropped by roughly 60 % while consistency improved through shared calculations and fewer manual errors—a pattern we repeat in data analytics projects.

Without trustworthy master data, BI stalls: duplicate customers, variant article numbers, inconsistent supplier labels. We often start database development that encodes cleansing rules, deduplication and golden-record logic before the first chart ships.

The result is measurable: identical definitions everywhere, comparable revenue across systems, reliable filters in self-service tools—see data quality for BI.

In practice this means fuzzy vs exact matching, source priorities (ERP before CRM before spreadsheets), pipeline validation (mandatory fields, value ranges, referential integrity) and approval workflows for fixes.

Workshops align terms like "active customer" or "open order" so shared Business Intelligence models replace competing spreadsheet definitions.

Overnight batches fall short when manufacturing, logistics or service need minute-level freshness. With change data capture or event-driven pipelines we stream operational changes into a data warehouse or streaming layer without hammering sources with full scans—supporting alarms, operational cockpits and same-day forecasts.

Latency, cost and fault tolerance must balance: not every KPI needs sub-second updates; critical signals often do. We dimension architecture with you and add monitoring (lag, failure rates, upstream schema drift).

Integration development connects heterogeneous systems—from REST and events to controlled batch exports when legacy exposes no modern API—so data analytics supports daily operations, not only hindsight. For Microsoft Power BI self-service dashboards, see our data analytics services.