No, Your ERP Data Export to [Insert Data Storage] Is Not a Data Warehouse

For many businesses, building reporting solutions directly from operational systems is a practical and cost-effective approach. Without the resources to invest in a full-fledged data warehouse or other complex data architectures, companies often rely directly on their ERP systems to generate the reports needed for daily operations and strategic decisions. This method works reasonably well and for systems that are hosted on-premises, getting access to data is rather straightforward.

However, the shift toward cloud-based Software as a Service (SaaS) applications has complicated this approach. In a SaaS environment, direct access to the underlying databases is typically restricted or unavailable, making it challenging to extract the necessary data for reporting. To overcome this, many businesses opt to export their data to external storage solutions for reporting purposes. Vendors of ERP and other operational cloud systems often provide solutions for exporting data to some kind of cloud storage, such as a data lake.

While exporting data to storage is a viable solution for accessing and analyzing your data, it's important to clarify that this storage is not a data warehouse. Mislabeling it as such can lead to misunderstandings about its capabilities and may hinder your organization's ability to make informed decisions.

As many other times in life, it’s often practical to use a dedicated tool to get the job done. Many of you have probably heard the great analogy; If a hammer is the only tool you have, you treat everything as if it were a nail.

The same goes for a data warehouse. There's a great real-life analogy on Kimball group´s website that compares a data warehouse with a restaurant. To take raw food ingredients and cook them in a perfect meal served in a dining room with great service and atmosphere requires a lot of work. The same can be applied for data. You can serve raw data to anyone, but if they don’t know how to handle it properly, chances are they will have a poor experience using it.

The Misconception: Export Equals Data Warehouse

Many vendors offer straightforward solutions for exporting data from ERP systems to various storage platforms, such as a data lake. These tools are often marketed as quick fixes for organizations looking to perform analytics on their data. As a result, businesses may believe that by exporting their ERP data and connecting a reporting tool, they've effectively built a data warehouse. Unfortunately, this is also a common misconception among IT-professionals that are not experienced in analytical data architectures, sometimes leading to bad or simply wrong advice.

But here's the truth: A data dump is not a data warehouse.

What Is a Data Warehouse?

A data warehouse is a centralized repository designed to store integrated data, often from multiple sources, structured for query and analysis purposes. It supports business intelligence activities, providing a historical perspective of data that helps in decision-making. There's a major difference in how the data is physically stored and structured, i.e. modelled, between an operational system and a data warehouse. This comes from the fundamental difference in functional demands these systems need to solve. An operational system, like an ERP, focuses mostly on managing single records. Such as creating and managing new customer records or registering single orders and invoices. Data warehouses, or any kind of data architecture solution for that matter, often need to handle bulk data of millions or billions of records. This is one of the fundamental reasons data storage is completely different between the two.

Key Characteristics of a Data Warehouse:

• Subject-Oriented: Organized around key subjects or business areas.
• Integrated: Data is consistently formatted and standardized from various sources.
• Non-Volatile: Data is stable and does not change once entered.
• Time-Variant: Data is stored to represent historical snapshots over time.

Examples of a subject-oriented data warehouse:

A multinational retail company structures its data warehouse around key business subjects such as Customers, Products, Sales, and Inventory.

• Customers: Data includes customer demographics, buying behavior, and preferences.
• Products: Information on product categories, specifications, and pricing.
• Sales: Transactional data detailing what was sold, when, and through which channel.
• Inventory: Stock levels, reorder points, and supplier information.

By organizing data around these subjects, analysts can focus on specific areas like sales performance by region or customer purchasing patterns without interference from unrelated data.

Examples of an integrated data warehouse:

An organization collects data from multiple sources:

• CRM System: Customer contact information and interaction history.
• ERP System: Financial records and supply chain data.
• Web Analytics: Website traffic and user engagement metrics.
• Third-Party Data: Market trends and demographic information from external providers.

The purpose of an enterprise data warehouse is to Integrate this data into a unified enterprise data model that can be used and queried for analytical purposes. Giving one single version of the truth instead of needing to extract information from multiple sources.

Furthermore, a data warehouse job is to have:

• Consistent Formats: All dates are standardized to a single format (e.g., YYYY-MM-DD).
• Unified Coding Schemes: Product codes from different systems are mapped to a single coding system.
• Resolved Data Conflicts: If one system uses "USA" and another "United States," the data warehouse standardizes this to a single representation.

An integrated warehouse ensures that data from disparate sources can be analyzed cohesively.

Examples of an non-volatile data warehouse

Perhaps one of the most challenging aspects of doing reporting directly on top of operational systems is the aspect of volatile data. Typically, data in an operational system does not keep track of historical attributes, unless they are part of a transaction record. This can be things like customer types, product categories or geographical information. Once changed, you often loose the historical information from a reporting perspective.

• Once a financial transaction is recorded in the data warehouse, it is not updated or deleted, even if corrections are made in the operational system.
• A data warehouse keep track on all versions of data attributes and contain information about what was changed and when it was changed. Any adjustments are added as new records rather than altering existing ones.

This non-volatile nature ensures a stable and reliable dataset for analysis over time, essential for auditing, compliance, and long-term trend analysis.

Why Exported Data Falls Short

Lack of Integration and Transformation

Exported ERP data is often in its raw, transactional form. Without proper integration and transformation:

• Inconsistent Data Formats: Different systems may represent data differently.
• Duplicate Records: Merging data without deduplication leads to inaccuracies.
• Missing Context: Raw data lacks the business context needed for meaningful analysis.

Absence of Data Modeling

Data modeling is crucial for organizing data in a way that supports efficient querying and reporting.

• No Dimensional Modeling: Without star schemas, complex queries become inefficient, often resulting in poor performance or difficult to main.
• Enterprise data model: Having a proper enterprise data model, such as data vault, enables your organization to analyze data from a single version of truth.
• Poor Performance: Raw data structures are not optimized for analytical workloads.

Limited Historical Data

ERP exports may not capture historical changes effectively.

• Overwritten Data: Without a time-variant structure, historical values may be lost.
• Inadequate for Trend Analysis: Lacking historical snapshots hampers long-term analysis.

Conclusion

Exporting your ERP data to a storage solution is a step toward data utilization but falls significantly short of establishing a data warehouse. Without proper data modeling, integration, and warehousing practices, you're likely to encounter data quality issues, poor performance, and limited analytical capabilities.

Investing in a true data platform, such as a data warehouse, will be essential for unlocking the full potential of your data, leading to better insights and informed business decisions.

Don't settle for a data dump—build a foundation that supports your organization's analytical needs now and in the future.

Author's Note: If you're a business consultant or IT professional looking to deepen your understanding of data warehousing, consider reaching out to experts in the field. Building a robust data warehouse is a complex but rewarding journey that can significantly impact your organization's success.