Digital analytics

In BearingPoint’s "Big Data in Automotive" study among 120 decision-makers surveyed from automobile manufacturers and suppliers , 94% of respondents attribute great importance to Big Data & Analytics. At the same time, only 7% of companies are fully using Big Data & Analytics. The scope of application covers all business areas.

Why Predictive Analytics? Why now?

Without Big Data Analytics, companies are blind and deaf, wandering out onto the web like a deer on a freeway.

- Geoffrey Moore, American organizational theorist, management consultant and author

The importance and size of data has increased significantly in recent years. One of the biggest challenges in the automotive industry is the intelligent evaluation and use of large amounts of data in the business environment and in vehicles. Advanced Analytics makes it possible to forecast future developments at an early stage and to identify meaningful patterns.

The focus is on processing and unifying data from different business divisions: only a holistic view shows the cross connections between business areas. That was reflected among the BearingPoint study participants, with three out of four of the opinion that current developments should not be missed and that Big Data & Analytics should be introduced.

Predictive analytics is about predicting the future, in contrast to conventional descriptive analysis which concentrates on the comprehensive analysis of historical data. Using state-of-the-art algorithms such as Machine Learning or Artificial Intelligence, complex patterns in huge amounts of data can be recognized in order to make statements about future events. Prescriptive analytics goes one step further, giving direct recommendations for action, such as real-time offering and pricing.

For example, our unique Quality Navigator enables our customers to increase quality along the entire product development process using artificial intelligence and machine learning:

In addition, Predictive Analytics typically faces the following challenges that require expertise in Big Data & Analytics:

• Data Engineering:
  • Creation of digital connectivity to industry 4.0 and smart devices.
  • Collecting, preparing and storing data in different formats and systems (Hadhoop, Kafka, Splunk).
• Data Science:
  • Analysis and definition of relevant data and information using modern tools and algorithms (R, Python, AI, Machine Learning, Deep Learning).
  • Definition of recommendations for action in cooperation with industry and process experts.
  • Target-group specific visualization in reporting tools (BOARD, Tableau, Qlik Sense, SAP BI).
• Data security
• Data quality and consistency

Why BearingPoint?

BearingPoint is your suitable partner in Predictive Analytics:

• We have successfully completed many predictive analytics projects in the automotive industry.
• We combine specific process know-how along the entire automotive value chain with the use of modern data science technology.
• We cover the entire service range from data acquisition and analysis to business management recommendations.
• With our BearingPoint Predictive Workbench, all modern data science tools are available.

Below you will find a selection of use cases in which our Predictive Analytics solutions have successfully identified and recovered potentials.