Why Data Preparation Fails

As the digitalization of business processes progresses, it is becoming increasingly important for companies to enable as many employees as possible to gain relevant insights from data.

Many companies see data processing as the key to more efficient decentralized data usage, which enables optimized business processes and new, innovative business models.

The Business Application Research Center (Barc) has published the global study "Data Preparation - Refining Raw Data into Value". It confirms the high relevance of data preparation and looks at the use of corresponding tools and the associated benefits and challenges for companies.

In view of increasingly volatile and saturated markets, the efficient and agile processing of data is of crucial importance. Companies that exploit the potential of analytics can set themselves apart from their competitors.

The analytical landscapes are under pressure to provide data for explorative analyses. Competent specialists and a modern data preparation approach, which differs from traditional ETL in many respects, are required to meet the demand and requirements.

According to the study, data preparation initiatives fail primarily due to a lack of expertise and budget. The study participants criticize the fact that too few specialist departments currently have the prerequisites for efficient and largely independent data preparation.

They also consider employee training and coaching to be essential in order to implement sophisticated digitalization strategies and gain valuable insights from data. Management must provide special resources and budgets for this purpose.

"As with many other aspects of data management, data preparation is not something that can be done on the fly"

explains Timm Grosser, Senior Analyst at Barc and co-author of the study.

"It must be seen as an essential step that contributes to creating value from data - not a one-off project that can be outsourced and handled externally, but an ongoing effort that requires significant skills. Any company that takes data preparation seriously must strive to build and maintain the appropriate know-how and skills."