ETL processes: extract and combine data from silos

In today's data-driven business world, information is the life elixir of each company. But too often these valuable data are caught in isolated systems, so-called data silos.

These silos hinder teamwork, complicate sound decisions and prevent a holistic view of the company.

Here, ETL processes come into play, a proven method to resolve data silos and unlock the full potential of your data.

What are data silos and why are they a problem?

In today's data-driven business world, information is the life elixir of each company.

For ETL processes: extract and combine data from silos, see Data Analytics & Business Intelligence und Discover solutions on our website for rollout paths and planning.

Data silos are separate data collections that typically arise in different departments or business units of a company.

Each department – whether it is marketing, sales, human resources or production – often uses its own software and systems that do not communicate with each other.

The result is isolated “singles” of data that are only accessible to a limited user circle.

The disadvantages of data silos at a glance:

• Selective uniform view: Without a central data source it is impossible to obtain a complete picture of customers, processes and general business performance.
• Inefficient processes: Employees spend valuable time compiling and comparing data from different sources, which is susceptible to errors and time-consuming.
• Redundant and inconsistent data: The same information is often stored in several systems, resulting in deviations and outdated data.
• Research collaboration: departments work together instead of working together on a shared, trusted database.
• Stop Business Intelligence and Analytics: Review of data for obtaining business insights is made considerably more difficult by fragmenting the information.

The ETL process as a solution

ETL stands for Extract, Transform, Load and describes a three-stage process that merges data from different sources to consolidate it in a central target database, a so-called Data Warehouse.

This process is the key to resolving data silos and creating a uniform, reliable data base for the entire company.

The three phases of the ETL process:

1. Extract: In the first step, the relevant data is extracted from the different source systems (e.g. CRM, ERP, Excel tables, databases). This can be done at regular intervals or in real time.
2. Transform: The extracted raw data are often present in different formats and structures. In the transformation phase they are cleaned, validated, standard and brought into a uniform format. Business rules can also be applied and data can be enriched.
3. Load: The last step will be.

References and further reading

The following separate references complement the topics in this article:

• Bitkom – German digital industry association.
• German Federal Office for Information Security (BSI).
• European Commission – Digital strategy.
• MDN Web Docs (Mozilla)
• W3C – World Wide Web Consortium.

> "ERP programmes rarely fail on software selection; they fail on unclear process ownership."

— Björn Groenewold, Managing Director, Groenewold IT Solutions

Frequently Asked Questions (FAQ)

What is this article about: “ETL processes: extract and combine data from silos”?

Here we cover ETL processes. Extract and combine data from silos — focused on architecture, process, and business outcomes.

In short: In today's data-driven business world, information is the life elixir of each company.

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