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Why a modern data warehouse should be a compulsory part of the ERP architecture

Bilde av Alf Inge Johansen
by Alf Inge Johansen

02. Mar 2022, 5 minutes reading time

Why a modern data warehouse should be a compulsory part of the ERP architecture

Most companies aim to utilize their available data to achieve business goals. Here is how a dedicated data warehouse can become a complete and qualified data source for your organization – and how it enhances the accuracy and insight of your analytics.

The foundation of any successful reporting or analytics initiative depends on two factors: a single source of truth that exists in a unified source format. All operational software, including ERP systems, struggle to satisfy either aspect of that criteria.

Multiple sources – clearer insight

It is well known that data first turns into holistic information once it has been compiled from multiple sources. This, however, presents multiple challenges: demand for high technical proficiency, knowledge of the data sources, handling of changes, data governance, documentation, and perhaps most importantly: time and money. As such, many companies are reluctant to start what they worry might become huge and complicated IT projects.

The main purpose of a modern ERP system is to streamline the business, increase the quality of decision data and generate both improved and previously unavailable business insights. But the ERP system does not store all relevant data. Most companies implement third-party industry solutions and are locked to legacy systems that will be used in conjunction with the ERP system.

Additionally, a modern data architecture imposes many external data sources provided by partners, vendors, or other typically open data sources. As more and more data sources come into account, it is a huge task to administer all the integrations. Also, modern ERP platforms tend to be split into several applications – often from several vendors.

Building a dedicated data warehouse removes the question of whether your data sets are complete. You can automatically extract, transfer, and load data from source systems into star schemas with a unified format optimized for business users to leverage. Thus, the data is formatted around the business process rather than the limitations of the tool.

This way, you can run multifaceted reports, share data through APIs or conduct advanced analytics when you need it – without anchoring yourself to any specific technology.


Single source of truth – where do we start

If you want to establish a single source of truth, there are many strategies you can employ. Data warehouse implementations need guidance and buy-in at the corporate level. This starts with a well-defined enterprise data strategy. To create this strategy, you need to ask questions such as

  • What are your primary business objectives?
  • What are your key performance indicators?
  • Which source systems contribute to those goals?
  • Which source systems are we currently using across the enterprise?

The answers to these – and other – questions from decision-makers and end-users, will paint a picture of the current state. Without them, you’re hunting for sources in the dark.

 

Creating lasting data warehouse value

Depending on the complexity, ambitions, and the organization's maturity level, there are different ways to actually build the data warehouse. Traditionally, this has been a manual, time-consuming and costly operation – requiring expert consultants. But in the later years, things have changed. As more data sources and increasingly complex data structures have increased the need for automation, automation is precisely what we have gotten: Data Warehouse Automation (DWA) has changed the rules of the game.
As the figure below shows, the "manual" and custom-built approaches introduce many obstacles.

Data automation maturity
Using a DWA tool, however, removes many of these obstacles and lets you concentrate on the business needs rather than technology and data governance. These tools automate the very construction of a data warehouse – and in turn, significantly reduce the cost of maintenance and operations.


Want to know how? Check out "Why ETL Tools are not enough for Agile Data Warehouse development and operation."
Many of the different ERP vendors extract data directly from the ERP system’s database or API. This will work with a limited number of data sources but will struggle as more data sources are needed to establish a comprehensive data warehouse for the whole organization. The article "What is the point of a data warehouse if Power BI has ETL Capabilities?" discusses this issue.


If you are in the process of replacing or upgrading your ERP system, it is a good idea to start planning for an enterprise data warehouse. The planning process will effectively support the data migration and cleansing process – and solve issues of historical data.


As soon as it is up and running, the data warehouse will act as the key success factor we have been chasing all along: the single source of truth.

Alf Inge Johansen

Alf Inge Johansen

Senior Sales Executive Alf Inge has more than 30 years of experience in the IT industry. Alf Inge is known for his good mood and commitment to everything he does. He has both technical and mercantile experience with the use of IT in a number of industries. In BI Builders, he tries to convince customers that they should automate as much work as possible and make better use of data in reporting and analysis.

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