What is a data platform?

At a glance

• Brings together data from different source systems in one place.
• Creates a unified, reliable data foundation for the entire organization.
• Supports analysis, reporting, and planning alike.
• Typical components include data integration, storage, data processing, and an access layer.
• Business intelligence solutions such as DeltaMaster build on data platforms and use them to deliver analysis and decision support.

Data platform: definition and classification

A data platform encompasses the technologies, processes, and rules a company uses to bring together data from different source systems, store and process it, and make it available for analysis. It is not a single piece of software, but an infrastructure that connects multiple components, including data integration, storage, and an access layer.

Within an organization, the data platform serves as an intermediary layer. It sits between operational source systems such as ERP or CRM and analysis tools for business intelligence, controlling, and planning, such as DeltaMaster. Without this intermediary layer, analytical tools would have to access many individual source systems directly, which can negatively affect performance, data quality, and maintainability.

What is a data platform needed for?

Companies need a data platform because, in practice, data is distributed across many different systems: ERP and CRM systems, Excel files, online stores, and machines each generate their own datasets. Without a shared platform, this data remains isolated, inconsistent, or simply difficult to find.

A data platform brings these sources together and processes the data so that controlling and business departments work with the same figures. This prevents discussions about which metric is the “right” one and accelerates decision-making because analyses no longer have to be laboriously compiled first.

The benefits are particularly clear in controlling: A well-structured data platform is the foundation for reliable reporting, planning, and analysis.

What are the components of a data platform?

A data platform typically consists of several layers that together map the flow of data from the source to the analysis.

• Data integration: Interfaces and ETL or ELT processes retrieve data from source systems such as ERP or CRM and transfer it to the platform.
• Storage: Data can be stored in a structured format, for example in a data warehouse, or in an unstructured format, for example in a data lake.
• Processing: Data quality rules, data cleansing, and data modeling ensure that metrics are calculated consistently.
• Access layer: Reporting and analysis tools such as DeltaMaster use this layer to access the processed data.
• Governance and security: Access controls and documentation define who is authorized to use which data and under what quality standards.

Data platform, data warehouse, and data lake: what is the difference?

The terms are often used interchangeably, but they refer to different things. A data platform is the overarching framework; a data warehouse and a data lake are possible components within that framework.

What types of data platforms are there?

Data platforms can be differentiated by where they are operated and by their technical architecture. This classification helps companies select the right solution for their needs.

What are examples of data platforms and their differences?

Various technologies have established themselves on the market that serve as data platforms. They differ above all in how storage and computing power are organized and how strongly they are designed for cloud operation.

• Snowflake: A cloud data platform that strictly separates storage and computing power. Companies pay separately for data volume and analysis performance, which makes costs plannable.
• Databricks: A platform that originally came from big data processing and is today positioned as a data lakehouse. It combines the flexibility of a data lake with functions for structured analyses.
• Microsoft Fabric: A cloud data platform from Microsoft that bundles data integration, data warehouse, data lake functions, and reporting in a shared environment.
• Google BigQuery: A cloud data warehouse from Google designed for very large data volumes and fast queries, without users having to manage their own servers.
• Amazon Redshift: A cloud data warehouse from Amazon Web Services that integrates into the rest of the AWS ecosystem and is often used in companies that already use other AWS services.
• SAP BW/4HANA: A data warehouse from the SAP environment that is closely integrated with SAP ERP systems and serves as the data basis for reporting in many existing SAP customers.

These technologies differ in operating model, scalability, and depth of integration into existing system landscapes.

Cloud data platform or on-premises: what suits your own company?

The choice between cloud, on-premises, and a hybrid data platform depends on factors such as data volume, available IT staff, and regulatory requirements.

Which solution suits my use case?

Which form of data platform makes sense depends on the specific requirements. For controlling and reporting with high demands on data quality, a data warehouse is often suitable as the core of the data platform. Companies with very large or highly unstructured data volumes often supplement this with a data lake or rely on a data lakehouse.

Companies with an existing SAP system landscape often first consider an SAP-oriented solution such as SAP BW/4HANA, while companies with a heterogeneous system landscape tend to favor vendor-independent cloud data platforms such as Snowflake or Microsoft Fabric.

In any case, it is crucial that the chosen data platform connects to the existing source systems and, where necessary, supplies business intelligence tools such as DeltaMaster with data in a high-performance manner. Bissantz advises companies on which data platform suits their existing systems and analysis goals.

What are common mistakes when implementing a data platform?

When implementing a data platform, recurring errors occur in practice.

• Lack of data quality: If data is taken over unchecked, contradictory KPIs arise. A fixed authority for data quality and consistent KPI definitions prevents this.
• Too many isolated solutions: If departmental data continues to be maintained separately, the benefit of the platform remains limited. A step-by-step connection of all relevant source systems prevents this.
• Lack of governance: Without clear access rules, security risks and inconsistent analyses arise. A governance concept from the start creates clarity here.
• Too technical a focus: If the platform is built purely from an IT perspective, the connection to controlling and the business departments is often missing. Involving the business departments early ensures practical usability.
• Underestimated migration: The transfer of existing reports and KPIs to a new data platform is often planned with too little time. A realistic migration plan with a test phase reduces this risk.
• Lack of a scaling strategy: If the data platform is designed only for current needs, it quickly reaches its limits as data volumes grow. A forward-looking architecture accounts for foreseeable growth from the start.

Application examples: Where are data platforms used?

Data platforms bundle distributed data and create the foundation for reporting, analysis, and planning in various areas. The following examples show typical fields of application.

Summary

A data platform brings together corporate data from different source systems in one place, creating the foundation for reliable reporting, analysis, and planning. Whether implemented as a data warehouse, data lake, data lakehouse, cloud solution, or on-premises infrastructure, the key is to establish a unified, validated data foundation. Bissantz consulting helps companies make effective use of data platforms and get the most out of their data.