Master data has a critical role to play for your organization when guaranteeing organizational visibility, operational efficiency, and product functional safety. 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:
In Part 1 of this series, we discussed the critical role that master data has in engineering and functional safety, examining how important it is to have control of your organization’s datasets, especially when considering the ever-present phenomenon of autonomous, hybrid, and electric vehicles. Also covered briefly, was the process of master data management (MDM) and how it can be beneficial for engineers and their work during product development. In part 2, we dissected the technical architecture masterof master data systems and found the trade-offs and constraints that come with building them. Now, in part 3, we hope to provide a promising path for organizations to utilize when implementing master data so that they can enable everything they want to achieve from the perspectives of engineering/manufacturing, workflow, and analytics.
As conveyed in parts 1 and 2 of our blog series, master data should be looked at as your golden ticket. It’s all of the information pertaining to business activities, which influences internal decision-making, revenue, and organizational growth. Prioritizing master data is not only an opportunity to streamline and leverage your data, but it is also an opportunity to improve organizational efficiency. The best way to maintain the data organized in your single source of truth (SSOT) is by implementing a master data management (MDM) plan that best reflects your overall needs and goals. There are different ways to get started with this process, and each route should ensure that the most accurate data components are available to the right people at the right time, in real-time.
Getting started with master data primarily starts with a sufficient amount of research beforehand. You have to know where your organization is in terms of the overall business, and where you want it to be. Then, part of this research includes exposing the right people to all of this unstructured data—there will be a lot of collaboration between different departments. Getting started with master data could involve data analysts, system developers, and IT specialists, amongst other employees within your organization. Gathering data from different regions inside the business will create a sense of urgency, encouraging you to solidify what data is essential for growth and success compared to what data is inconsequential. During this research phase, you may ask questions like:
After your organization has analyzed the particular needs and goals you want to achieve, the next step would be choosing an MDM solution style, ultimately leading to which software/application tool you will use. The four commonly used styles are: registry, consolidation, coexistence, and centralized; each has its own capabilities and benefits. Again, your choice of implementation style will be based on what seems the most rewarding for your organization. These proactive steps are necessary in order to guarantee a successful MDM strategy—this way, proper preparation prevents poor performance.
For the past decade, the value of master data has evolved into a pillar that affects your organization on all cylinders. More importantly, it provides internal visibility and traceability, allowing organizations to better understand what their data means and how it can help them achieve their goals.
Within engineering, one pivotal process that is heavily dependent on data is product life cycle development—motor vehicles are the best example to exemplify this. The entire life of a vehicle starts at a concept level. The requirements established for these products have to adhere to many different engineering requirements and safety regulations, which affect how vehicles are designed. Once OEMs receive the raw materials, they can keep track of all the items needed for production, ensuring nothing is missing or faulty.
When thinking about the growth of autonomous, electric, and hybrid vehicles—along with our current conventional vehicles—data is critical in ensuring that these products operate within their intended functionality once OEMs distribute them out into the market for people to buy and use. The technology in these vehicles communicates information that shows products’ operational performance when people use them—that continuous feedback loop is important. Though this somewhat extends into the conversation of data analytics, it is important to discuss because of how directly an organization’s master data influences how that data is analyzed altogether.
Aside from the more technical considerations, your master data simultaneously impacts any business your organization does with other companies. To use the aforementioned example, there is important data involved with product life cycle development. Once there is a cohort of vehicles created by an OEM, they then have to disperse their products to car dealerships. The master data records involved in this delivery process can include information such as:
It is clear that master data adds significant value to an organization from a business standpoint and an operational standpoint. The master data system your organization develops and the MDM solution that you implement influence how you execute production, which influences overall level of success.
Though it may seem like a rhetorical remark, the primary benefit that master data provides is organizational success. It is important to emphasize this because this core data asset is still something organizations struggle to prioritize. Consolidating your most valuable data and facilitating it all in one location can lead to efficient strategizing, which positively influences coordination across different teams. Since all of the information in your data systems would be in its most accurate state, you can accelerate workflow productivity while mitigating the occurrence of process errors. With everything aligning in sync, there is an increased chance that organizational performance will thrive—amplifying the organization’s profits and revenue. Again, this can be an advantage because competing organizations may not have their master data prioritized.
In our webinar panel, we take time to shed light on a master data implementation we did for a customer. They reached out to us because they needed someone to build an MDM solution for them. They had their data scattered across several regions, which made it difficult to access. To reference the potential trade-offs mentioned in the previous blog in this series, the performance aspect of their internal systems wasn’t that good. Our work with them was a year-long solution implementation process that involved gathering all the data, understanding where it came from, and versioning any changes, which ultimately allowed them to access all of their data whenever they needed to. The versioning aspect was especially important because the customer was able to see older versions of specific data coming out of several different systems. Even now, the customer is utilizing the solution we implemented for them. It is now the core service that allows them to access and update their master data so that it complies with their goals and needs.
There is great power in your organization’s datasets, but that power has to be identified, refined, and maintained. One key precaution to take is assuring that your organization has control of your data; by not having proper agency of your data, you can create challenges for your organization’s overall performance. In other words, you want to know what pieces of data are most important to your organization and have an easy way to access them all. It is common for different organizations to find themselves in the dilemma of not centralizing their data, but it is not something they should delay for long. There are particular challenges that organizations face frequently, which include:
Overall, managing and maintaining master data can be a very tedious, time-consuming process that takes frequent collaboration and, depending on the size of an organization, that may make it difficult to achieve. Sometimes organizations may have the time to implement an MDM solution, but not the resources to fund it, so they choose a cheaper option that may not fulfill the entirety of their goals and needs. In some cases, organizations may have the resources to fund an MDM solution but just not the time. Like with the work we did for that former customer, they knew that their data was scattered throughout their internal systems and needed assistance in consolidating and streamlining it all. These are some of the many considerations involved with implementing an MDM solution.
By prioritizing your master data and implementing a proper MDM solution, you are taking a step towards organizational growth and success. Due to the increased necessity of data centralization, the transportation industry is evolving. Data influences OEMs and organizations from a business standpoint and engineering/manufacturing standpoint because the operational efficiency of their business influences the products and systems being engineered and distributed out into the market. As the data ecosystem continues to transcend innovative technology and products in the automotive world to come, it will become much more of a requirement (than it already is) to optimize your data and the processes involved. Data is one of the most diverse organizational assets that add significant value to this industry, something that generates safer products on the roads we use every day.
In Part 3 of this series, we have covered how your organization can get started with centralizing and managing your datasets. In describing the value of master data, we also identified the potential challenges that may arise, along with any promising benefits. Overall, we have highlighted key aspects of master data and how it can affect your organization, along with the importance it holds within the transportation industry altogether. That concludes this blog series on master data—for any parties interested in the solutions LHP has to offer pertaining to Analytics & IoT, learn more here.
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