Most organizations globally accept the fact that they should enable and encourage their decision-makers to base their decisions more on facts than on gut feel and experience. Deriving completely new insights and benefits from data is a key capability in digital transformation. However, the challenges and requirements have increased in recent years with the need for digitalization, speed, agility, big data and cloud computing.
These global developments and challenges drive a number of important trends related to the use of software and technologies for BI and data management, and also to the way BI is organized. BI practitioners have recently identified data discovery/visualization, self-service BI, predictive analytics, and the integration of BI and PM in one common platform among the most important trends in their work. Additionally, several data management trends like data quality management and the use of analytical databases are gaining in importance, providing the architectural and technical basis for the aforementioned BI trends.
Newer trends like self-service BI, data discovery/visualization and predictive analytics are increasing in importance for many companies due to constantly growing amounts of available data, both internally and externally, as well as improved software functionality to support self-service for business users. These trends have a major impact on integrated BI and PM and therefore also drive the demand for easy-to-use, visual, advanced software products covering BI and PM. Being one of the most important PM processes, many companies particularly lack the integration of BI with planning functionality.
To avoid time-consuming and error-prone data transfer processes between software systems, an integrated database for actuals and plan data represented in a consistent data model form the solid basis for integrated BI and PM software solutions. The centrally harmonized master data provides a single, common data basis for BI and particularly planning as well as other additional PM processes such as financial consolidation, strategy management and risk management.
Based on this, a consistent data model has to be established that flexibly supports the modeling of enterprise – as well as departmental – data views supplemented by flexible time horizons for short-term operational, mid-term tactical and long-term strategic considerations.