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Automakers Are Getting Their Data Houses In Order To Drive Digital Transformation
True to their engineering heritage, automakers are embracing cloud computing, storage, and supplementary technologies to enable data-driven decision making and machine learning and analytics platforms to frame and fuel their transformation strategies.
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Automotive companies are always striving to be novel and distinctive in the marketplace. One thing they have in common is a deep tradition of creativity, innovation, and engineering excellence, and a firm belief that success is achieved and sustained through long-term thinking that continually seeks to reimagine and deliver exceptional customer experiences.
This long-range mindset is driving a new business imperative in the industry - disruptive digital transformation to increase agility and spark innovation, while continually improving operational efficiencies. True to their engineering heritage, automakers are embracing cloud computing, storage, and supplementary technologies to enable data-driven decision-making and machine learning and analytics platforms to frame and fuel their transformation strategies.
A foundational tenet in this shift is that building the tools for transformation requires having your data house in order – which inevitably means migrating data from multiple, often disparate legacy IT systems and databases to a centralized repository, or “data lake,” which can store structured and unstructured data at any scale.
Using data-heavy processes to make decisions is not a new idea in the automotive industry. Most often, however, the data exists in siloes – typically organized by functional areas such as marketing, engineering, and manufacturing. Each area in the company brings its own data-based insights to the table and decisions are made based on a complex combination of disparate data sets.
Data lakes liberate valuable information assets from siloed systems, allowing diverse datasets to coexist “as is.” This allows different types of holistic analysis and modeling from dashboards and visualizations to big data processing, real-time analytics, and machine learning to be done more quickly. The results are new and deeper insights that guide better decisions.
For example, Volkswagen is using data lakes to pinpoint operational trends, improve forecasting, and streamline operations by identifying gaps in production and waste. Toyota is leveraging data lakes to collect data from connected vehicles and apply it towards vehicle design and development, new contextual services such as carshare, rideshare, full-service lease, and new corporate and consumer services such as proactive vehicle maintenance notifications. The BMW Group is using data lakes to leverage information from across the company globally to make data-driven decisions that guide vehicle and technology development, manufacturing, sales, and service.
Data lakes are a foundational point of digital innovation in the automotive sector and often have an exponential multiplier value, helping solve some of the industry’s biggest challenges. However, the scale and complexity of organizing disparate data for global engineering-driven firms are monumental. As such, automakers are deploying data lakes in the cloud because it provides performance, reliability, availability, a diverse set of analytic engines, and massive economies of scale. Cloud advantages for data lakes include better security, faster time to deployment, better availability, more frequent feature/functionality updates, more elasticity, more geographic coverage, and costs linked to actual utilization.
For example, BMW created a global cloud data hub that employs AWS machine learning capabilities and tools to make global data accessible across regions. With AWS, BMW employees are able to process, interrogate and enrich development-, production-, sales- and vehicle performance data around the world.
Data lakes are opening a host of new possibilities for automakers. New data modeling approaches empower companies to apply advanced analytics and machine learning over new sources such as machine, manufacturing, and logistics data. While machine learning projects can be initiated using siloed data, data lakes give automakers a more expansive view of their overarching data landscape to fully understand what data is available, how can it be made easily accessible and what should be collected now to meet future needs. This broad visibility fits well with the automaker’s long-range mindset, helping them build product roadmaps that deliver a long-term, durable value. A strong example of the advantages data lakes can deliver is Toyota’s cloud-based, real-time “Toyota Connected Data Lake,” which captures and stores the billions of messages generated daily by in-vehicle telemetry systems. The Data Lake presents multiple opportunities to improve customer safety and enhance the vehicle ownership experience, including early detection and resolution of vehicle issues before the customer is affected, and ongoing vehicle health checks that can create optimal maintenance plans for worry-free driving.
By taking an intentional approach to treating data as an asset and dedicating human and technology resources to ensure data integrity and quality, automakers are able to develop and apply machine learning throughout the organization to realize internal transformation. The payoff is automated processes that simplify infrastructure management, accelerate the adoption of intuitive and accessible technologies, and create more meaningful and personalized customer experiences.
Disclaimer: The views expressed in the article above are those of the authors' and do not necessarily represent or reflect the views of this publishing house. Unless otherwise noted, the author is writing in his/her personal capacity. They are not intended and should not be thought to represent official ideas, attitudes, or policies of any agency or institution.