Automotive manufacturers have become data-driven. They use data to improve their production capabilities, enhance customer experiences, streamline the supply-chain process, manage their inventory and logistics more efficiently, and schedule planned downtimes better. With an increasing demand for connected and self-driven cars, automotive manufacturers must focus on data analytics initiatives.

Manufacturers have been gathering data through the car’s embedded sensors, cameras, and sensors to improve experience and safety. However, due to the increase in data volume, legacy systems and platforms are unable to manage or process them efficiently. With HADOOP playing a significant role in storing, sorting, and analyzing data, manufacturers are looking for better big data platforms.

There are plenty of vendors to choose from. However, Cloudera and Amazon Web Services (AWS) are the two leading platforms.

Cloudera is one of the supported distributions of HADOOP. It merged with Hortonworks, another big data management vendor, in 2019. Cloudera’s capability is not limited to gathering and storing data. It allows companies to perform in-depth data processing, analyze data, and track and secure it across all environments.

AWS was founded with the intention to provide cloud computing to manufacturers. However, they quickly realized that every application needs a combination of storage, compute, and database solutions. Thus, they provide all these solutions to help manufacturers build data-driven, differentiated products quickly and at low costs.

Cloudera And AWS’ Role in Analytics

To make better business decisions, manufacturers use different types of analytics such as:

• Diagnostic: a technique where data is examined to ascertain why a certain incident occurred and to find a way to tackle it
• Predictive: a technique where current and historical data are examined to make future predictions accurately
• Prescriptive: provides recommendations to help manufacturers find the best course of action
• Cognitive: simulates human intelligence to learn from unstructured data and use it to teach machines to self-learn and make decisions.

Let’s delve into how these two platforms support different types of data analytics.

Diagnostic

Diagnostic analytics can help manufacturers to diagnose the root cause of a problem to improve operational efficiency and reduce unplanned downtimes. It is especially important for automotive manufacturers to reduce defects and improve the output capacity by 2-4% each year. Manufacturers can look at the historical downtime events to identify and analyze the recurring issues to resolve them.

Both AWS and Cloudera provide diagnostic analytics support to automotive manufacturers. AWS’ Connected Vehicle Solution helps automotive manufacturers to diagnose trouble in remote vehicles and monitor their health to find ways to improve driver’s safety in the future.

Cloudera collects telematics, and sensor data feeds from connected vehicles on engine performance, vehicle speed, coolant temperature, etc., to automatically detect problems in advance and solve them.

Predictive

Predictive analytics is used by automotive manufacturers to predict the demand for vehicles based on geographic and demographic parameters, predict the downtime in production processes, and enable automakers to take proactive measures to ensure vehicle safety. Predictive analytics allows manufacturers to solve business problems and find new opportunities to improve business outcomes.

Cloudera offers an array of solutions that enables manufacturers to deploy predictive analytics. It helps manufacturers take a proactive approach to maintenance rather than a reactive approach. This helps manufacturers to reduce customers’ maintenance costs.

AWS offers an advanced set of industrial Internet of Things (IoT), artificial intelligence (AI), and machine learning (ML) integrations that help manufacturers to gather real-time insights on connected vehicles and improve the experience of drivers. Take BMW, for instance. They used AWS’ predictive analytics to collect real-time data from sensors in vehicles, data warehouses, operational systems, etc. Using such insights, they provide customers with real-time information on speed, temperature, engine status, battery levels, etc.

Prescriptive

Prescriptive analytics enables automotive manufacturers to find solutions to solve a problem. They include analytic techniques such as simulation, complex event processing, recommendation engines, heuristics, and ML to reduce human intervention, accelerate the decision-making process, and reduce the error potential.

Cloudera’s Dataflow technology helps companies to leverage real-time data insights to predict outcomes and use a data-driven approach to solve the problems.

Although AWS doesn’t offer a specific prescriptive analytics solution, it provides AWS Prescriptive Guidance to manufacturers to help them leverage the full potential of AWS to improve business outcomes and optimize cloud adoption.

Cognitive

According to IBM’s report on the auto industry, 60% of respondents said that cognitive technology would be a disruptive force in their industry. Cognitive analytics combines AI and data analytics practice to optimize the production line, and ensure that the safety instructions are well-understood even if they are in a foreign language to mitigate future risks. Manufacturers can also offer personalized services to customers using cognitive analytics. Most importantly, it helps manufacturers tap the potential of a huge volume of unstructured data to improve the driver’s experience.

Both AWS and Cloudera offer advanced cognitive analytics that allows manufacturers to build innovative solutions to improve driver’s experience and operational efficiency.

How to choose between Cloudera and AWS?

Both AWS and Cloudera provide advanced analytics support to automotive manufacturers. However, there are also other factors to consider while choosing the right platform for a data analytics initiative. The biggest benefit of Cloudera is that it is designed for a hybrid cloud approach. The workload can run on private or public cloud environments too. AWS expects all the workload to run on its cloud environment, so there is a potential for vendor lock-in. Manufacturers must determine their cloud strategy before choosing AWS or Cloudera. If they plan to use a multi-cloud strategy, Cloudera could be a perfect solution than AWS.

When it comes to costs, AWS follows the pay-per-use pricing model, while Cloudera has a multi-tier pricing model. AWS is the best option for manufacturers when it comes to upfront costs. However, they must know to optimize its usage to avoid additional charges.

When it comes to implementation and security, the choice depends on several factors. Cloudera provides more professional guidance, while AWS is apt for manufacturers with access to expertise. We recommend taking an expert’s advice to make the right decision.

Data analytics initiative requires an upfront investment, an overhaul of business processes and systems, and upskilling of the existing workforce. Hence, it’s important to weigh both the platforms’ pros and cons before deciding.