Basically, strategies are necessary above all to sustainably anchor guard rails and processes in the company. Based on typical challenges encountered in practice, an appropriately accentuated business analytics strategy can help solve them, align with business goals, and help achieve them. These challenges can be subsumed under the following three overarching themes and symptoms:

System diversity and need for consolidation

• • Unclear and poor data quality
  • Unclear data origin
  • Undefined analytical processes

Tactical approach

• • Orientation to day-to-day business
  • Alignment with the department
  • Orientation to IT strategy

Organizational challenges

• • Unclear or lack of organization for BA projects and applications.
  • Acceptance problems
  • Unclear responsibility

We support you in the development of operative business intelligence reporting via dashboards up to “data story telling”. A process model, which can be divided into five phases, has proven its worth. After an interdisciplinary collection or conception of initial use cases including functional/non-functional requirements, these are prioritized and rolled out within the framework of agile project concepts. We then develop a technical solution concept – from the identification of all necessary data sources, the creation of a data model, the detailed conception of ETL routes to aggregations or precalculations in (virtualized) reporting layers. We then implement the data integration concept and design the planned dashboard for you. Here, we pay particular attention to visually appealing designs and follow standards such as the IBCS. Finally, all components are tested, optimized, documented and approved.

Agile methods have proven to be best practice approaches. Nowadays, it is more important than ever to be able to adapt quickly to changing conditions and to be able to act. Important application areas are thus quickly productive, creating additional room for maneuver for optimizations.

Complementing the use cases, an accompanying concept ensures automation, systematics and collaboration. This includes, for example, a concrete definition of the data sources and the data model, the key figures and dimensions as well as the logics or the information design. Genuine value creation from data can only succeed if working with data becomes a natural part of daily work processes. One example is automated alerts, which proactively inform about changes in relevant company key figures. These help to provide important insights and thus to be able to react to events in a timely manner.

Using Advanced Analytics (AA), we offer a collection of different tools to predict future events based on structured or unstructured data. In general, AA goes beyond traditional business intelligence approaches and focuses on using predictive models, statistical methods, machine learning and artificial intelligence to analyze data.