The ability to use data for the benefit of the business is crucial, even vital. It is critical to a transformation process that will enable a company to successfully position itself and assert itself in a highly dynamic, data-driven world. A basic prerequisite for exercising such an ability is properly functioning data management.
By data management we mean all conceptual, methodical, technical and organizational measures that serve to efficiently manage and profitably use data. These include disciplines such as data governance, data architecture, data quality, master data management, data warehousing and business intelligence as well as data security and a few more (see DAMA Framework at dama.org).
The image of data management – formerly a hidden process in the dark basements of the IT department – has changed dramatically in recent years. The professional and technical relevance of data management is constantly increasing, especially when it comes to making trustworthy and correct data usable for BI and analytics. As a result, the relevance of corporate strategy is increasing.
In this first study, we focus on data management for BI and data warehousing. However, the scope of data management is far greater and covers other data environments in the company in addition to BI. This includes operational systems, explorative environments for advanced analytics as well as a holistic view of company data with regard to strategic tasks (e.g., digitalization). Each of these areas has its own specifics regarding data types, technologies, methods, architectures as well as required skills and personnel resources.
Data management tasks for BI and data warehousing focus on data integration, data preparation and data modeling as well as data storage and provision for reporting, analysis and planning. Today’s requirements for classic BI architectures and technologies are to be able to keep pace with ever more dynamic requirements and growing complexity in the area of data processing and systems. Often there is no way around a fundamental modernization.
In this article you will find out:
which are the best data management software tools as rated by users (Data Management User Review Matrix) and
the most important lessons learned from surveying almost 700 respondents about data management software usage and selection.