Erste Schritte

The Automotive Industry: Driving the Future of AI

Automobile data analytics isn’t just about self-driving cars; data science and machine learning technologies can help keep auto organizations competitive by improving everything from research to design manufacturing to marketing processes.

Data science, machine learning, and — ultimately — AI can improve efficiencies in every stage of automotive production, enabling organizations to cut costs, better serve customers, and perhaps most importantly develop new, innovative products.

High-Value Use Cases 

Research and Development: In the future, AI will play a massive role in R&D productivity, preventing expensive R&D projects doomed for failure from being fully realized. This translates to automotive companies saving both time and money, both of which can be focused on projects with more potential as well as other machine learning and AI initiatives outside R&D.

Manufacturing: When it comes to manufacturing, AI-based systems enable automakers to create and manage schedules more effectively, provide improved safety testing, and identify defects in produced components before going into vehicles. Thanks to predictive maintenance, manufacturing can become more efficient and less costly.

Schau jetzt

Supply Chain: Supply chain data analytics in the automotive industry aren’t new, but what AI can bring is the introduction of new and innovative data sources that help support prudent shipping decisions and minimize risk. With machine learning-driven systems, it is also possible to analyze huge data sets to rank suppliers according to on-time in-full delivery performance, their credit scoring, and evaluations which enable manufacturers to gain greater control over their supply chains, including logistics and management.

Marketing and Finance: In addition to benefiting large swaths of the core business, data science and data analytics in the automotive industry can also be used in lines of business — including marketing and finance — to introduce efficiencies and automation that impact the bottom line.

Democratizing Automated Forecasting at Mercedes-Benz Schau jetzt

Dataiku for the Automotive Industry

Dataiku is the platform democratizing access to data and enabling enterprises to build their own path to AI. Hundreds of companies use Dataiku daily to build, deploy, and monitor predictive data flows. For automotive organizations, Dataiku brings:

  • Secure processing for data and creating machine learning models, with or without coding. User permissions are only useful when they’re enforceable, and Dataiku supports organization best practices surrounding secure data usage and storage. Dataiku supports analysis by non-technical users, cutting out inefficiencies and potential compliance concerns in traditional systems that require data teams to facilitate all access to data insights.
  • Productionalizable models that drive value. Unless machine learning models can be leveraged on a regular basis with real data, their insights are a curiosity at best. Dataiku provides a seamless environment for the entire data pipeline, from data cleaning to production.
  • Stable machine and deep learning technologies. By taking advantage of popular open source libraries and toolkits, Dataiku’s machine and deep learning resources provide robust and dependable insights.
  • A central platform that enables data culture and collaboration to flourish. Data initiatives cannot create value unless teams throughout an organization can leverage them to decrease pain points and improve data-driven decision making. Since non-technical and technical users alike are able to manipulate and visualize data, Dataiku engenders greater trust in data projects and increased collaboration.

Predictive Maintenance in Transportation and Manufacturing

Predictive maintenance models can empower organizations to get ahead of system failures to tackle infrastructure repairs on their own terms. With Dataiku, predictive maintenance is even easier.

Read more

Go Further

GE Aviation: From Data Silos to Self-Service

GE Aviation's self-service system allows them to use real-time data at scale to make better and faster decisions throughout the organization.

Mehr Erfahren

AI for Advanced Supply Chain Optimization

Automation and demand forecasting are at the forefront of supply chain improvements that leverage AI, but organizational culture change is critical to drive true optimization.

Mehr Erfahren

Bringing AI to Marketing

The evolution of AI, machine learning, and data science have been an increasingly integral part of the transformation of many industries, and marketing is no exception.

Mehr Erfahren

AI in Transportation and Logistics

Today, AI is not a luxury but a necessity for organizations in transportation and logistics industry to gain and keep their position on the market.

Mehr Erfahren