At this point, ADWEKO paves the way for an efficient, data-driven and targeted decision support with business analytics. For this purpose, various business intelligence tools form the link between descriptively processed visual information on the one hand and forecasts or simulations extracted from the data in the advanced analytics area on the other. In terms of a “best of breed” approach, ADWEKO focuses on Microsoft Power BI, Tableau and the SAP Analytics Cloud.

Starting with the selection of a suitable business intelligence software,we also support you in the functional and non-functional requirements analysis and associated data source analysis, through to data integration, information design, the creation of dashboards or forecasts, to approval and documentation. Business analytics thus contributes to a higher level of automation and targeted business management by allowing information to be scalably customized and consumed consistently. From monthly reporting to aggregated management dashboards, our best practice experience in implementation projects has shown that there are basically three overriding critical success factors:

Interdisciplinarity

Targeted value creation from data only succeeds when use cases and needs are the focus of consideration. In this respect, it is substantial that business analytics projects follow an interdisciplinary approach and involve stakeholders from different disciplines (e.g., mathematicians, business information scientists, risk managers, data engineers, BI developers, data scientists). ADWEKO supplies the appropriate consulting services for the individual application. This ensures from the outset that all parameters for a subsequent data-driven process and work culture have been taken into account. By example, this may involve IT infrastructure, data source, or statistical calculation model premises.

Procedure model

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 capable to act fast. After an interdisciplinary collection or conception of initial use cases including functional and non-functional requirements, these are prioritised and rolled out within the framework of agile project concepts. Important application areas are thus quickly productive, creating additional room 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 visual 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 quickly.

Organisation

In order to successfully establish a data-driven corporate culture with the help of business analytics, it is not only important to integrate future users from the very beginning, but also to communicate the overall process and identify goals: What kind of added value do we expect? Why is data-driven decision support important? Why do we expect deep data-driven value creation for our business? How do the tools help me personally in daily work processes? This change management process requires a person responsible for the business analytics/intelligence strategy who, together with the users, designs processes within the organisational and operational structure and establishes a data-driven mindset throughout the organisation. Does not the following example sound familiar?

• • • Business Area A already works with standardised key figures and uses modern business intelligence tools for evaluations.
    • Business Area B, on the other hand, is not yet as far advanced: Key performance indicators (KPIs) are evaluated using a spreadsheet or a database solution developed in-house.
    • Harmonised, uniform definitions of data models, processes and content are sought in vain.

In summary, successful business analytics projects are characterised by a strict orientation on the business objectives of the company, the business processes and the technical requirements of the users involve all interdisciplinary stakeholders. An actual state analysis in advance determines the business analytics maturity level, which serves as an orientation point and determines the next steps, methodology, models and tools as well as concepts. If you have any questions or need more information, please do not hesitate to contact us!

Jan Taraldsen

Manager

Marcel Selbmann

Managing Consultant