Sources
ERP, CRM, accounting, online shop, point of sale or planning. Each system remains the system of record for its own process; the platform only takes data from it.
Data platform
Want to build a data platform? Start with how it will be used.
To build a data platform is to collect data from your source systems centrally, model it and make it available for dashboards, analysis, AI and apps. Data Analytics B.V. builds the data warehouse and the data model; ENABLE is the usage layer in which your organisation uses the data securely.
Sources
Business CentralExact OnlineAFAS ProfitSalesforceExcel / CSV
Data layer
Data warehousePostgreSQL · per organisation+ Power BI model
ENABLE portal
Dashboards (Power BI)SQL Explorer + AICustom dashboardsAppsData API
One login · one access model · one logPeople
BoardFinanceBranchesCustomers
You build a data platform to bring data from separate systems, such as your ERP, CRM, online shop and planning, together in one place and make it usable for reporting, analysis and AI. A good data platform consists of layers: sources, a loading process, central storage, a data model and a usage layer.
Data Analytics B.V. builds the data warehouse and data model for organisations, and with ENABLE provides the usage layer: a secure portal with Power BI dashboards, a SQL Explorer with AI, apps and a Data API. That way the platform is not only technically sound, but also in daily use.
Many data platform projects stall because they start with the technology. We start with the question of what people need to be able to do with the data, and work back from there to the model and the sources.
Key points
Build a data platform at a glance
Loading
Data is loaded periodically from the sources into the warehouse, for example with Airbyte or via the source system's API. This happens outside the ENABLE portal.
Data warehouse
Each organisation has its own PostgreSQL database where the data comes together. ENABLE reads from it over an encrypted connection with read-only queries.
Data model
Tables and definitions that make the data understandable: customers, orders, revenue, margin. The model is the foundation for dashboards and for AI.
Usage layer: ENABLE
A portal in your own branding with embedded Power BI dashboards, SQL with AI, apps and access profiles per role.
Data API
Publish saved queries as REST endpoints with an API key, so other systems use the same data as your dashboards.
The layers of a modern data platform
A modern data platform is not a single product but a chain. Each layer has its own task, and the quality of the whole is determined by the weakest link. A polished dashboard on a messy model still produces questionable figures.
| Layer | What happens | Example |
|---|---|---|
| 1. Sources | Systems where the work happens | ERP, CRM, online shop, point-of-sale system |
| 2. Loading | Data is retrieved and stored centrally | Standard connector or custom integration via the API |
| 3. Storage | Central database or data lake | PostgreSQL data warehouse |
| 4. Model | Cleaning, linking and defining data | Fact and dimension tables, KPI definitions |
| 5. Use | People and systems use the data | Dashboards, SQL with AI, apps, Data API (ENABLE) |
Data warehouse, data lake or data lakehouse?
When you build a data platform, you come across three types of storage. They solve different problems. For reporting, KPIs and questions about business figures, a data warehouse is usually sufficient and the simplest to manage. A data lake or data lakehouse becomes interesting when you have large volumes of raw or unstructured data.
| Data warehouse | Data lake | Data lakehouse | |
|---|---|---|---|
| What it is | Database with cleaned, modelled tables | Storage of raw files in any format | Lake storage with warehouse features on top |
| Type of data | Mainly tables: orders, invoices, customers | Everything: tables, log files, documents, images | Structured and unstructured |
| Strong at | Reliable reporting and KPIs | Storing lots of raw data cost-effectively | Analytics and data science in one place |
| Watch out for | Less suited to unstructured data | Quickly becomes unmanageable without a model and governance | Requires more technology and management |
| Often suits | Organisations that mainly steer on business figures | Organisations with lots of raw or unstructured data | Organisations with their own data engineering team |
How to build a data platform: common pitfalls
Most problems when building a data platform are not technical. They arise because the goal is unclear or because nobody owns the figures.
- Starting with technology instead of with the questions the platform needs to answer.
- Loading all data just in case, without a model or an owner.
- No agreements on definitions, so the platform still produces different figures.
- No usage layer: the data is ready, but only an analyst can get to it.
- Arranging access and security only afterwards.
Enterprise or cloud data platform: what do you really need?
Terms such as enterprise data platform, cloud data platform and data lake platform sound big, but the question is always the same: what data do you have, who uses it and for what? For many organisations, a central data platform with a data warehouse, a good model and one portal is enough. Larger stacks with a data lake, streaming and notebooks suit organisations with lots of raw data and their own data team.
ENABLE itself runs in the cloud, at Vercel in the Frankfurt region. Where your data warehouse is located depends on your organisation; ENABLE connects to it over an encrypted connection with read-only queries.
The role of ENABLE in your data platform
ENABLE is the layer where the platform becomes visible to the organisation. Users open Power BI dashboards in a portal in your own branding, ask questions in plain language in the SQL Explorer and use apps that run on the same data. Queries in the SQL Explorer are read-only, with a timeout of 45 seconds.
ENABLE also takes care of the things that are often forgotten: access profiles, row-level security, two-factor authentication, an activity log and insight into which dashboards are used. With the Dashboard Builder, currently in pilot, users create their own dashboards on approved data sources.
In ENABLE
How Data Analytics builds your data platform
- 01
Sketch the end product
We start with the questions and dashboards you need, as a sketch or prototype, before anything is built.
- 02
Load the sources
Data from your source systems is loaded into your own PostgreSQL data warehouse. We determine the best loading route per source during the intake.
- 03
Model
We define tables, relationships and KPI definitions, so dashboards and AI calculate with the same definitions.
- 04
Set up the usage layer
In ENABLE we link dashboards, queries and apps to access profiles, in your own branding.
- 05
Use and adjust
Using usage statistics and the daily Power BI health check, Data Analytics monitors what is used and where attention is needed.
FAQ
Frequently asked questions
What is a data platform?
A data platform is the central infrastructure an organisation uses to collect, store, model and make available data from its systems. It usually consists of sources, a loading process, a data warehouse or data lake, a data model and a usage layer with dashboards, analytics and APIs.
What do you need to build a data platform?
You need access to your source systems, usually via an API or database, central storage such as a data warehouse, a data model with agreed definitions and a usage layer for dashboards and analysis. It is also important that someone in your organisation owns the key KPIs.
What is the difference between a data warehouse and a data lake?
A data warehouse contains cleaned, modelled tables and is designed for reliable reporting. A data lake stores raw data in any format, including documents and log files, and is more flexible but quickly becomes unmanageable without governance. For reporting on business figures, a data warehouse is usually the logical choice.
What is a data lakehouse?
A data lakehouse combines the cheap, flexible storage of a data lake with data warehouse features such as tables and SQL. It suits organisations with lots of varied data and their own data engineering team. For most reporting questions it is not necessary.
Is ENABLE a data platform?
ENABLE is the usage layer of a data platform: the portal in which you use dashboards, SQL with AI, apps and the Data API. The data warehouse and the loading of sources are also part of the platform, but happen outside the portal. Data Analytics B.V. builds those parts for you.
Further reading
- SolutionSingle source of truth: one version of the truthOne central source for your business data, so every department sees the same figures.Read more
- SolutionData integration: connect data sources for insightConnect data sources for reporting and AI, via one central data warehouse.Read more
- SolutionMicrosoft Fabric: what it is and when it fitsWhat Microsoft Fabric is, when it fits and how it relates to ENABLE.Read more
- ResourcesWhat is a data warehouse? Explained and comparedA data warehouse is a central database in which you bring together data from different source systems, such as your ERP, CRM and webshop, clean it up and organise it so that you can report on it and analyse it effectively. A source system is built to process transactions; a data warehouse is built for reading, comparing and keeping history. A data lake stores raw data in any format, and a data lakehouse combines the storage of a lake with the structure of a warehouse.Read more
- PlatformThe ENABLE platform: how it worksHow the ENABLE platform works: Power BI dashboards, your data warehouse, AI on your own data, custom dashboards, apps and a data API in one secure portal per organisation.Read more
- PlatformSQL Explorer with AI: ask your database questionsAsk your data warehouse in plain language. The AI writes the SQL, you stay in control.Read more
Share securely, live fast, no headache
In an online demo we show you the portal: dashboards per role, row-level security, plain-language questions and how we set it up and manage it for you.