Robert Tischler
Senior Analyst Data & Analytics
Robert's focus is on research and advisory. He is also responsible for BARC's business in Austria.
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Data Preparation – Refining Raw Data into Value
This study clearly demonstrates the benefits, timeliness and relevance of data preparation for analytics.
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This study addresses the following key questions:
- The role of data pre-processing by IT and specialist departments (data preparation) in the analytical landscapes of companies.
- The current state and future plans of organizations in the field of data preparation.
- The measures taken by companies to achieve an appropriate and contemporary level of analytical flexibility without compromising data governance and data quality.
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The increasing digitalization of business processes is making it necessary for companies to enable as many users as possible to gain insights from data (democratization of analytics). Many companies today view data preparation as the key to increasing their ability to efficiently use data in a distributed manner to optimize business processes, or to enabling new, innovative business models in the first place.
The goal of data preparation is to support business analysts and data scientists by preparing different kinds of data for their analytical purposes. The preparation of data can take place either in business departments or be performed centrally by IT. Data preparation is a sub-domain of data integration that can be executed with dedicated tools or traditional tools for data integration like ETL tools, data virtualization or data warehouse automation.
This study clearly demonstrates the benefits, timeliness and relevance of data preparation for analytics. It shows how and by whom data preparation is being driven and how the balancing act between governance and flexibility can be achieved by specifying the requirements for data preparation governance. We also show how data preparation is used today, which challenges need to be overcome, and in which organizational framework this takes place.
The key findings from “Data Preparation – Refining Raw Data into Value” are condensed into seven “hot spots”, each designed to help organizations optimize their approach to – and achieve the maximum benefits from – data preparation.
Author(s)
Timm Grosser
Senior Analyst Data & Analytics
Timm Grosser is a Senior Analyst Data & Analytics with a focus on data management.
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