March 13, 2020

Many companies are increasingly beginning to understand the great importance of data, its visualization and use for decision-making. However, the first steps towards making these data-driven decisions and preparing the data for successful use, still cause many companies problems. After all, work in the area of data warehousing is often very time-consuming.

What is data warehousing used for?

Basically, data warehousing offers the possibility to: preserve data, create a single source of truth, increase the productivity to the end users, reduce dependency on key users and react flexibly to changing business requirements. The storage, maintenance and extension of all company data is a time-consuming matter.

In detail, this means that the Extract, Transform, Load (ETL) process combines data from several sources (in different formats) in a target database. During extraction, the relevant data is obtained from the various sources, transformed into the desired schema and format, and then loaded into the target database. When using a data warehouse, in which big data from several operational databases is merged and stored, this process can be very lengthy. This is a major disadvantage for the timeliness of the data, which is only achieved by regularity of the process. This information integration enables complete, centralized and consistent access to the data.

What are the advantages of data warehouse automation?

Automation creates the advantage that the ETL process does not have to be performed manually over and over again. Between 70 and 80 percent of the processes can be automated here. Data cleansing and transformation rules can be applied automatically. In this way, companies can achieve so-called real-time data warehousing without much effort. Thanks to automation, the time required can be reduced considerably (by about 60 to 70 percent).

Data warehouse automation is based on design patterns and processes that can be used to automate the planning, modeling and integration steps throughout the entire data warehouse life cycle. It provides an efficient alternative to traditional data warehouse design by reducing time-consuming tasks such as generating and deploying ETL code to a database server and giving end users greater control over the data. This gives managers time to focus on other tasks or exceptions, such as process development. In addition, there is a continuous quality in the ETL process, which results from the automatic input. Consistency and continuity are achieved, among other things, by using codes or names throughout.

With data warehouse automation tools, companies can then implement business intelligence projects within hours instead of months and at a fraction of the cost. In addition, a correctly set up data management is the basis for intelligent applications such as machine learning projects. This foundation should be stable in order to be successful.

In summary, with data warehouse automation:
  • You can accelerate your entire data pipeline,
  • Automate the capture, management, integration and streaming of your data,
  • Define data transaction paths in the shortest possible time,
  • Determine data flows fully automatically,
  • Set up data models in a targeted manner,
  • Simply transform data lakes into data warehouses.

The faster you can analyze your data, the earlier you discover important company-relevant information.

Closing Thoughts

The use of data warehousing facilitates the use of data for business reporting. However, the traditional approaches to capturing and managing huge amounts of data, as they exist in most companies today, through manual ETL coding, are no longer effective. In today’s competitive economy, business agility and time-to-market are critical. Data warehouse automation tools are suitable for such requirements, as they minimize the manual effort required to build and implement data warehouses and consolidate data for business reporting. Based on this, further data projects such as business intelligence applications and/or AI technologies can then be easily initiated.

How do you solve data warehouse processes in your company?


Jens Siebertz

Jens Siebertz ist Senior Vice President bei INFORM DataLab.