Data, Business Intelligence and Reporting: From scattered data to a single source of truth.
We consolidate data from your different systems into a single foundation and bring it into your decision-making through management dashboards and automated reports.
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Data, Business Intelligence and ReportingIllustrative interface, sample data
In short
A data warehouse and business intelligence setup brings data from separate systems such as ERP, CRM, e-commerce and accounting into one place, cleans it, and turns it into management dashboards and automated reports. CoreTechs builds the ETL/ELT pipelines that connect your sources, a data model with shared definitions, and dashboards in Power BI, Looker Studio, Grafana or a custom build. The result: every team looks at the same number with the same definition, and reports are no longer compiled by hand.
Scope
Data infrastructure
- Data warehouse and data lake setup
- ETL/ELT pipelines
- Data integration across systems
Business intelligence
- Management dashboards (Power BI, Looker Studio, Grafana or custom)
- KPI definition
- Automated report distribution
Data collection bots
- Price, product and market data from permitted sources
- Competitor and price tracking
Deliverables
- Data model
- Live dashboards
- Data dictionary
- User training
Who is it for?
- Companies where the same metric (revenue, margin, stock) is reported with different numbers by different departments
- Finance and reporting teams that spend days building monthly reports by merging spreadsheets
- Executives who want ERP, CRM, e-commerce and marketplace data on a single screen
- Organizations whose existing Power BI dashboards have become slow or show numbers nobody trusts
- Companies that want to get their data foundation in order before starting AI and forecasting projects
- Retail and e-commerce teams that want to collect competitor and pricing data regularly from permitted sources
How we work
Requirements and KPI definition
We identify which metrics management and departments need for which decisions, and write down how each KPI is calculated.
Data source inventory
We map which systems hold the data, in what format and how often it is updated, and report data quality issues and access methods.
Data model and data warehouse
We build ETL/ELT pipelines that move sources into the data warehouse and create the shared data model all teams will use. Transformations are written to be testable and documented.
Dashboards and reports
We design management dashboards and operational reports with their users, and set up automated report distribution and role-based access.
Validation and training
We reconcile dashboard figures against the source systems, deliver the data dictionary and train users.
Operation and expansion
We monitor pipelines for reliable runs, get alerted on failures, and add new sources and metrics step by step.
How long does it take?
The main factors are the number of source systems, how clean the data is, how clearly KPIs are defined and the number of dashboards. A single management dashboard on a few sources and a company-wide data warehouse need very different schedules, so we usually split the work into phases, starting with priority metrics. After a free 30-minute intro call we usually start discovery within a week, and confirm the exact timeline in the proposal that follows discovery.
Phases
- Intro callA free 30-minute call to discuss reporting pain points and priority metrics.
- DiscoveryKPI definitions, source inventory and a data quality assessment.
- Fixed proposalPhased scope, timeline and price, in writing.
- DevelopmentPipelines, data model and dashboards in two-week sprints, with a working dashboard demo at the end of each.
- Launch and handoverValidation, data dictionary, user training, and transfer of source code and intellectual property.
What drives the cost?
Business intelligence cost is driven less by the number of dashboards than by collecting and cleaning the data behind them. Phasing sources and metrics keeps cost manageable and delivers value early.
| Factor | Effect on cost |
|---|---|
| Number of source systems and access method | Sources with APIs or direct database access connect quickly; file-based transfers or legacy systems need extra development. |
| Data quality | Missing, duplicated or inconsistently coded data needs cleansing and matching rules, often the largest part of the project. |
| Data volume and refresh frequency | Daily-refreshed dashboards and near-real-time dashboards require different architecture and infrastructure costs. |
| Number of dashboards and reports | Each dashboard requires design, calculation and validation effort; a shared data model lowers the cost of new ones. |
| Tools and licensing | Tools such as Power BI need per-user licenses; Looker Studio, Grafana or custom dashboards offer a different balance of licensing and maintenance. |
| Deployment model and access control | Cloud versus on-premise warehousing, role-based access and masking of personal data affect infrastructure and development scope. |
Technology options
Data warehouse and databases
- PostgreSQL
- SQL Server
- BigQuery
- Cloud data warehouse services
Data pipelines (ETL/ELT)
- dbt
- Python
- Scheduled pipelines
- API and database connectors
BI and dashboards
- Power BI
- Looker Studio
- Grafana
- Custom dashboards (React, Next.js)
Data collection
- Python-based data collection bots
- Price and product data from permitted sources
Infrastructure
- AWS, Azure or Google Cloud
- On-premise servers
- Docker
Example solution scenario
One management dashboard for multichannel sales
Representative scenario, not a real client project.
- Profile
- A retailer selling through physical stores, its own e-commerce site and online marketplaces.
- Need
- Sales and stock data for each channel sat in separate systems. The weekly management report was built by manually merging files from different teams, and discussions about channel profitability stalled on conflicting numbers.
- Approach
- With management, we agreed on a single written definition for metrics such as revenue, gross margin, returns and stock turnover.
- We built nightly pipelines loading ERP, e-commerce platform and marketplace data into the data warehouse.
- A data model mapping product and channel codes made it possible to track the same product as one record across all channels.
- We created a summary dashboard for management and detailed dashboards for channel and category managers, with role-based access.
- We scheduled the weekly summary report for automatic email distribution.
- Technologies
- PostgreSQL
- dbt
- Python
- Power BI
- ERP and marketplace integrations
- Outcome
- The weekly report is no longer compiled by hand, and meetings now discuss decisions rather than whose numbers are right. The shared data model later became the foundation for a demand forecasting project.
Frequently asked questions
What is the difference between a data warehouse and business intelligence?
A data warehouse is the infrastructure where data from different systems is cleaned and stored in a common structure. Business intelligence is the layer that presents that data to decision-makers through dashboards, reports and metrics. Reliable BI usually needs a solid data warehouse underneath.
Can we just have Power BI dashboards built?
Yes. If your data already sits in an organized source, we can design and build Power BI dashboards directly. If sources are scattered, we recommend a lightweight data model first so the dashboards are accurate and fast.
Can you connect to our existing ERP and accounting systems?
Yes. We pull data from ERP, CRM, accounting and e-commerce systems through APIs, direct database access or scheduled file transfers. Connections are set up so they do not load the source systems, usually with read-only access.
Where is our data stored, and how is data protection handled?
The data warehouse can be built in your preferred cloud environment or on your own servers. Personal data is left out of the warehouse or masked if reporting does not need it, and dashboards use role-based access. A non-disclosure agreement is signed before the project starts.
How can we trust the numbers on the dashboards?
Every metric's calculation is written down in the data dictionary. Before launch we reconcile dashboard figures against the source systems and add automated quality checks to the pipelines. If a pipeline fails, the right person is notified.
What do cost and timeline depend on?
Key factors are the number of source systems, data quality, refresh frequency, number of dashboards and tool licensing. We usually start discovery within a week of the intro call and confirm a fixed timeline and price in writing after discovery. The work can be phased, starting with priority metrics.
Can our own team extend the dashboards after launch?
Yes. We hand over the data model, transformation code and data dictionary fully documented, and train your users and technical team. If you prefer, we can take on pipeline monitoring and new metrics under an SLA-backed maintenance package.
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