Master data has a critical role in product functional safety, guaranteeing organizational visibility, and operational efficiency. As data becomes more complex in the future, the need for master data in automotive will continue to increase. This blog is the second of a 3-part series focused on the importance of master data, the technical architecture of master data systems, and the trade-offs and constraints that come with building these systems, ultimately proposing what a proper master data plan looks like, which can help organizations achieve growth and success. The content in each blog is centralized around the main topics from LHP’s DAS Master Data webinar panel that we held this past June:
One of the key measures of success within functional safety is the process of master data because not only does it allow for organizational consistency and accuracy, but it also prevents unnecessary risk from product or service use by prioritizing safety. Master data is one of many concepts that leads to the production of safer automotive mechanics, further leading to safer roads.
It is no surprise that the world as we know it is evolving into a more digitized atmosphere—for the past decade, new technologies have changed the perception of what seems to be mere imagination compared to what can be reality. Though it may not always be verbalized, data is at the cusp of each new invention somehow, in some way. Data has to be discovered and explored for many of life’s advancements to take place. In the transportation industry, the concept of master data plays a critical role in engineering and functional safety.
Currently, automotive vehicles are becoming more than just consumer products but rather safety products. This shift in definition changes aspects like liability and the importance and complexity of organizations’ datasets. Now, examine considerations like advanced driver-assistance systems (ADAS), autonomy, and functional safety: the quality standards that come out reflect the latest safety requirements, which becomes more essential to the deep engineering involved in product development rather than the after-the-build testing that some standards would require. Each is essential, but there is a higher urgency in earlier stages because of the different tools that software and hardware engineers utilize in the development process. In addition, standards affect the decision-making and training organizations can apply to achieve internal and external success. There are hundreds of work products and pieces of data necessary for various processes, emphasizing the importance that master data has in the automotive world.
Master data should be viewed as the referential and foundational data component within an organization. In engineering, this could reference an organization’s sensor data, sensor fusion, state of the system, telemetry data, and even environmental data. Though this slower-moving data is slightly different from faster-moving data counterparts like business data, master data is valuable information that provides both context and visibility for every aspect of an organization’s business activities. In addition, since this data includes information that rarely changes over time, it’s easy for organizations to perform data-sharing in real time. This level of visibility allows organizations the opportunity to clearly define their core values and goals so that they can plan for and successfully achieve them.
Within master data, there can often be well-defined silos (or systems) such as the Supplier master, Customer master, and Product master. Each silo can have designated teams—both business and IT—assigned to them that work in parallel to deliver the master data component to the business or engineering teams. That is why each silo is so important to the engineering involved. The Product master, though, is usually the more heavily focused category because the data is created, designed, simulated, tested, and then produced; once the data is finally serviced, the Product master is a great way to consolidate all the engineering data created. Again, these large amounts of data are essential for the work products, which play an important role in automotive product development.
Master data management (MDM) is a methodology that often involves organizing large business datasets, ensuring that an organization’s information can be referenced back to a single source point. Internal workflow can also improve due to updating an organization’s systems. Organizations should consider platforms that maintain multiple silos, including scalable tooling, and have several different features and capabilities to gain the most out of an MDM solution. Options within these requirements may be expensive, but you must prioritize a solution of some kind, best suited for your own needs.
For the engineering work involved, an MDM solution can prioritize all the product data for engineers to monitor from the design phase through the production phase. Engineers and product developers will often handle a substantial amount of work items, making it critical that they have visibility and traceability of the changes made as products develop through their entire lifecycle. These engineers benefit from MDM because they can make sense of automotive work products they are designing and building from the stable baseline of information aggregated to one single source point. Gradually, this process allows for steady growth in technology and creates positive business outcomes.
For organizations to develop products and systems that adhere to functional safety requirements, master data is key. By achieving that, you need visibility of every piece of data and a proactive master data management solution to follow. Functional safety could be described as the implementation of protective functions that prioritizes the safety of using a product or system. The ecosystem consists of six areas of focus: Tools and Equipment, Data Integration, Advanced Analytics and Insight, Safety Standards and Cybersecurity Regulations, AUTOSAR and MBD, and ALM. Pursued together, these areas allow organizations to properly design, develop, and manage safe products that operate within their intended functionality. To achieve functional safety, the application of standards, processes, and safety requirements is just as important. One key aspect of successfully achieving functional safety is optimizing product efficiency while mitigating unnecessary injuries or risks. Though this is how master data affects aspects of engineering now, it may serve an even more vital role in the years to come.
As the transportation industry strives towards developing more highly-sophisticated vehicles—that are heavily reliant on data—engineers will be required to solve the complex problems within functional safety growing daily. Without a doubt, the functional safety ecosystem attempts to advance the development of autonomous vehicles (AVs) so that it properly accommodates safety, standardization, automation, and advanced analytics. That’s where master data plays a critical role; absolute control of your datasets is important because it leads to the development of safe products. We have already entered a new era of autonomous, hybrid, and electric vehicles, which each display different levels of autonomy. According to SAE International, there are six different levels of autonomy that vehicles can have:
Luckily, the majority of vehicles with advanced technology currently on the road are only at Level 2 automation. Still, with OEMs striving towards adding electric vehicles (EVs) to their inventory rosters—and some companies that are already delivering EVs—it’s expected that vehicles will reach higher levels of autonomy sooner rather than later. The data incorporated in autonomous driving comes from different types of sensors—light detection and ranging (LiDAR), for example—that inputs data into complex software, allowing autonomous vehicles to react specifically to real-world scenarios. As both vehicles and ADAS elevate in levels of autonomy, the data needed to design, simulate, test, and produce will elevate in volume as well. Vehicles will need to generate a lot more gigabytes (or even terabytes) of data, making the production process much more complex. This will also increase the complexity of an organization’s overall Product master because there is more data to access, process, and develop to ensure quality AVs, hybrids, and EVs are being made.
Again, we are already in a new generation of sophisticated machines, and EVs are key players in this game. Conversations around the impact that these products can offer on the environment are growing rapidly, and OEMs are making their presence known within the EV market. The inevitable rise of EVs has been proposed to increase by 2030—with dilemmas like raw material shortages and the sporadic change of gas prices, that is becoming more of an explored possibility. What will this mean for aspects like functional safety and product development? The data surrounding an EV—from the design phase through complete production—has to be gathered, stored, understood, analyzed, and utilized to ensure safe and efficient production. In addition, once an EV is delivered into the market, constant feedback and informational data will need to be recorded so that organizations can monitor its performance and make improvements as necessary.
It would be helpful to emphasize the fact that master data often influences processes like data analytics. Once an organization prioritizes its master data and develops an MDM solution, analytics can be applied to those particular pieces of data. Constantly collecting and analyzing a work product’s data provides insight into its overall performance, which is how organizations verify that vehicles adhere to standards and safety requirements altogether.
It’s no secret how impactful data truly is for an organization and, when prioritized correctly, it can be its biggest asset. From one standpoint, master data offers internal visibility, which solidifies organizational goals and resources. Eliminating any insignificant data from your datasets allows the opportunity to optimize the tons of data that can add real value. From another standpoint, this industry creates products, systems, and services that directly affect the landscape of modern transportation, so data is critical for the engineering processes and procedures involved. To develop quality products and systems under adherence to safety standards, OEMs and organizations should achieve and consistently maintain functional safety; like many other key elements in this industry, that starts with data. As the future of transportation becomes more advanced and complex, a deeper dedication to data-driven operations is required.
In Part 1 of this series, we have examined the concept of master data and the different considerations in which it applies to engineering and functional safety, overall affecting organizational performance. In Part 2 of this series, we will dissect the technical architecture of master data systems to identify the key advantages and any possible constraints of building these systems.