The Automotive Industry: Driving the Future of AI

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.

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Supply chain: Supply chain analytics 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 machine learning techniques can also be used in lines of business – including marketing and finance – to introduce efficiencies and automation that impact the bottom line.

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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, and could be potentially harmful if the data does not reflect the population that a drug will be used in. 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.