Topis around BigData

Use the ressource data

"Business Intelligence (BI) are IT-assisted procedures and processes to enrich data, with the aim to get knowledge from existing knowledge to support decisions." Business Intelligence is the closed loop between dispositive and operative data by the operationally usage of new findings - Turning Data into Action.

The approach goes back to in 1990 years by collecting data from different source and merged for multidimensional analysis (OLAP Online Analytical Processing) or to discover unknown relationships in the data (data mining). Iniitial concepts of Decision Support Systems has been developed since 1960. In the recent years, methods and procedures have been developed (Data Science). Individual or in combination used statistical methods, connectionistic methods, numerical methods, machine learning techniques, simulation methods, time series analysis or visualization procedures are used for different use-cases such as fraud detection, self-localization of machines, handwriting recognition, targeting, predicting share development or early production errors. The term Business Intelligence is increasingly being replaced by a focus on the analysis by the term Business Analytics. Only the knowledge of analytical methods, procedures and tools generated from the data does not add value:

  • Meaningful analyses require a team of experts on different technical areas (e.g. production, procurement, product characteristics, logistics), programming (e.g. scripting, SQL), mathematics
  • Simple but secure access to data and elimination of data silos (Data Governance)
  • Adequate consideration of data protection requirements
  • Use of structured and unstructured (source) data from the enterprise, social networks, partner companies and other sources
  • Data quality and traceability of previous data transformations when no raw data is available
  • Data need adequate preprocessing to employ selected methods
  • Procedures and methods have to be parametrized, adapted or to programmed for each question
  • The origin of the procedures and methods is taken from different disciplines and sources
  • Continuous examination of uses analytical models whether findings and predictions remain valid, because conditions are not stable
  • Confidence in predictions by models and plausible explanation of the procedures and methods toward business departments and management
  • Clear and transparent operational integration of analytical models (framework to run the models)
  • Clear and unambiguous separation of productive and non-productive analytical models
  • Rapid availability of analysis functions and freedom in the choice of report content with a rising number of (departmental) users and applications (self-service BI)
  • Analytical tools and Business Intelligence architecture as an integral part of an IT strategy

Our Offerings:

We support companies in the introduction, adaptation and implementation of Business Intelligence

Procedure examples:

  • Risk analysis of manufacturing process, analysis and evaluation sensor / measurement data creation (semi) automatic models for rapid fault detection and problem solving
  • Analysis of existing OLAP cubes / Reports, transferring OLAP cubes into central data warehouse storage, adaptation OLAP reports for changed data source, testing, and deployment of reports
  • Assessment of new requirements (e.g. Self-Service BI), testing existing reporting strategy, review existing reports and reporting infrastructure, design future reporting architecture and mixed-architecture during transformation
  • Transformation of a Reporting Infrastructure
  • Analysis existing reporting and analysis solutions, elimination of isolated solutions and develop comprehensive solutions
  • Design and construction of a framework for running analytic models for varying challenges: Near-real-time, high data complexity, high volume of data, accuracy of calculation and complexity of the model
  • Analysis of cross-departmental analysis requirements, data collection service / product portfolio analysis, construction Data Science Competence Center or Shared Service Center Data Science
  • Professionalization of business intelligence IT processes (Service Strategy, Service Design, Service Transition and Service Operation) to increase agility to satisfy (User) requirements
  • Construction / expansion of an organizational structure and infrastructure with data-driven business models
  • Creation of a demand-driven business intelligence architecture that allows a flexible exchange of analytical services

How can we help you? Depending on the situation in the form of Consulting, interim management, executive coaching and training.

Project examples:

  • Consulting: Implementation of a campaign management integrated into a data warehouse
  • Consulting: Software selection process for reporting components using a Proof of Concept
  • Consulting: Development and implementation of a BI security concept and business layer in the data warehouse, so that business users can automatically generate reports
  • Consulting: Setting up decentralized but homogeneously Business Intelligence services taking into account existing organizational structures
  • Interim management: Project management for the transformation of a Business Intelligence frontend