The term “data” can have an overall significance for our everyday wants and needs. Certain new-age products, like smartphones or smart cars, start with data. In the automotive world, data has a critical role to play in product development and safety altogether; this role will become more important as this industry moves towards distributing more highly sophisticated vehicles in the future. Having a secure baseline of master data is critical for automotive engineers because it allows them to make sense of the automotive world and the products they build within it. If you are an automotive organization looking to maintain growth in new technology and drive positive business outcomes, having a proper master data management solution will help provide real value in those areas. Also, the copious amounts of information within an organization’s master data can clearly define its core values, which can establish a clear path for longevity. Even in other industries—healthcare, manufacturing, aerospace—the centralization of data is a phenomenon that should be becoming normalized. Master data helps optimize organizational consistency, accuracy, and innovation, directly impacting a data-dependent world like automotive.
What is Master Data?
Master data can be considered valuable, complex information that provides context and visibility to business-related activities. This type of data reflects foundational pieces of information that do not frequently change over time, which allows for active data sharing opportunities in real-time. There are a few different domains that this data can be categorized, which usually depends on the organization and its overall needs. Within these domains, companies can establish areas of focus that surround specific subjects like customers, suppliers, products, and even employees.
To add some additional context for how to track this data in the automotive world, here’s an extensive example: let’s say an OEM builds and distributes their new inventory of vehicles to their customers—the car dealerships. They may be in the position to distribute these vehicles to the same dealership company but at different addresses. Eventually, their vehicles reach the dealership and get sold into the consumer market. In most cases, this data can be attributed to each of the master data domains an organization has established.
The suppliers who distribute the raw materials used to build the products, the locations of each dealership, any business partners that helped the product go-to-market, and any warranties and contracts involved are all informational data that should be tracked and utilized. Being able to access and understand this data can be critical in how any organization functions because without that visibility there is more exposure to risks. Master data provides an extended path of visibility that starts at a supplier level and trickles down to a consumer level. There are other types of data that influence organizational success, which all connect with master data on some level.
Types of Organizational Data
The types of data that organizations optimize can be categorized into different entities. If you were to search for the different types of organizational data in your favorite search engine, you would come across several results that pertain to Statistics, Research, Analysis, etc. Even if you specified by adding “business” or “organization” to the end of your search, you may find iterations of the same data types that have been named differently or merged with another type. Aside from master data, there are 5 other commonly used types of organizational data. To understand what master data is and its importance in automotive, it is important to describe each of them in detail.
- Unstructured data can be described as the most self-explanatory type of data—information that usually does not have a predefined schema or data model. This type of data is very qualitative and can be found in any form of text and/or image—emails, white papers, magazine articles, surveillance, audio, blogs, PDF files, etc. Traditionally, since unstructured data is not stored in a database format, it is usually in a greater abundance than structured data. Master data has more specific domains, so the information needs to be more structured than this type of data. That being said, proper master data management can help manage any unstructured information more efficiently.
- Transactional Data is information that comes directly from an organization’s transactions (sales, invoices, claims, etc.) A manufacturing company’s transactional data could involve information about all of its production activity. For other organizations, hiring and firing employees could be considered transactional data. Master data influences transactional data by adding business context to all of the transactional information. Unlike master data, transactional data often loses relevance over time because of its time dimension; since master data does not have a time dimension, the information involved usually does not change frequently. Transactional data is often a type of data that has a higher volume than other types. This type of information plays an important role in how an organization functions because keeping track of certain expenses can provide visibility of its financial standings.
- Reference Data can be looked at as a special subset under master data. This type of data is used to categorize information and relate it to other considerations outside of just the organization itself. Some examples of reference data are postal codes, transaction codes, language codes, currency, and units of measurement. Industries like healthcare, banking, travel, and government rely heavily on this information because it directly affects operational efficiency. Although both master data and reference data are two relatively static types, the latter rarely changes over time. Reference data still has to be managed properly, or organizations may face compliance risks, inaccurate analytics, and insufficient operations.
- Hierarchal Data is a type that captures the relationship between different pieces of information and is often structured in family tree models. This type is important because it can clearly define the relationships involved with the master data, which can influence the management process.
- Metadata, the last type of organizational data, is simply data about other data. Though seemingly confusing, it is often considered one of the more critical types of data to have. In essence, metadata helps answer any additional questions about pieces of data that cannot be answered strictly by looking at that data. It functions as a safety net that contains more valuable information about data, giving more insight into what it is.
What is Master Data Management?
Master Data Management (MDM) is an essential process that organizes and optimizes an organization’s master data, ensuring that the information surrounding their business is uniform. In other words, MDM often helps to ensure that an organization’s master data assets are accurate and consistent. Different organizations try to get their master data to a single source of truth (SSOT)—a quality that describes data facilitated to one origin—so that all of the information has one source point it can be referenced to. An SSOT is often achieved by the foundation that an MDM tool provides. There are different platforms and technology that organizations can use for their management, which can cover either one or multiple domains at once. MDM that covers multiple domains provides the opportunity to share reference data, which can be beneficial long-term.
In automotive, MDM plays a critical role in an organization’s growth. From an operational standpoint, the information relevant to product data keeps track of everything in the design and configuration stage, and into the production stage. As products evolve throughout their lifecycle development, engineers and product developers need visibility and traceability of every change made to products to verify they are operating within their intended functionality. From a business standpoint, organizations should maintain accurate customer data to track who they work with and how it affects them. In addition, this management process updates information throughout an organization’s system, which can accelerate workflow.
From both an operational and business standpoint, having a structured MDM that simultaneously covers the customer, supplier, and product data can mitigate risks. Without data visibility and coordination, there would be a snowball effect of irrevocable issues. Imagine a situation where an OEM distributes vehicles into the market, and they are faulty or violate multiple safety standards; a product recall is ordered. Accessing all of the information surrounding the customers, suppliers, and products in one place can accelerate how quickly this type of disruption is addressed and how efficiently those products are recalled.
Who is Involved in Master Data Management?
After an organizational leader establishes master data for their company and how it will affect the business they conduct, they can begin planning out their MDM solution. Since the process of MDM is intended to be continuous, several individuals within an organization can get themselves involved; some of which include:
- Project managers who develop and implement the MDM strategy
- IT administrators who collect, store, configure and maintain all the information within the management tool
- Supplier representatives who need access to self-service and modify certain information
- Engineers and product developers who need to track information about a product as it evolves over time
- Data analysts who gain an understanding of their organization’s needs by accessing and reporting the information within the MDM tool
MDM is a process that can be simple in concept but strenuous in execution. Proper updates need to be made as information expands to maintain accurate master data; this can develop for a long time. An organization would be investing a lot of time, money, and effort into their MDM solution to enrich their overall master data. Collaborating, coordinating, and working across siloes with several different types of employees can create more expenses, but the advantages of this process outweigh any potential costs.
Types of Master Data Management
The type of MDM solution an organization chooses to implement, along with the style in which they implement it, comes down to their specific overall needs. There are 4 different types of implementation styles—registry, consolidation, coexistence, and centralized—which have particular different qualities amongst each other.
- Registry, which is often faster to implement than others, helps identify duplicated data by cross-referencing information. C
- Consolidation implementation style, you are collecting master data from different places and facilitating it into an SSOT.
- Co-existence style often considered more expensive than the others, is similar to the consolidation style in that it also creates an SSOT for data. You can access the master data faster, so some organizations eventually go from consolidation solutions to coexistence solutions.
- Centralized implementation style can derive from either consolidation or coexistence implementation styles. This style can ensure that your master data is accurate in real-time and centralized for one or more domains.
What Are the Key Organizational Benefits of Master Data Management?
Inside the many different industries, you can find a success story or two illustrating the overall benefits of master data and why it is important in achieving organizational growth. In automotive, proper master data and MDM provides visibility on the customer, supplier, and product side, allowing consistent operational efficiency. MDM is a long, continual process—it takes time to collect, understand, and organize data and even more time to implement and maintain a specific solution. One key aspect of MDM to understand is that this process involves a lot of collaboration. This collaboration can be valuable to organizational workflow, mitigating risks, liabilities, and disruptions. Another key aspect is that there is not one ideal MDM tool that contains every management capability, so organizations can explore which solution to use that benefits them the most.
Master Data Influences Data Analytics
Talking about concepts like master data and data analytics, defining what each is and how they influence each other can get confusing. The process of master data often feeds into an organization’s data analytics process. Both are two separate things—some companies will even split up the two and operate them differently—but to have really valuable data analytics, it is wise to prioritize master data. Data impacts the operational aspects of automotive products by constantly collecting and analyzing the performance of vehicles on the road. Without the visibility or accessibility of master data and data analytics, it would be hard to verify that certain automotive vehicles are safe enough to be distributed into the market for people to purchase and use.
The complexity of technology is a growing trend that will continue to develop in the future. Electric vehicles and autonomous vehicles are becoming less and less science-fiction as we see these sophisticated machines glide across roads across the globe. These vehicles, parallel with the advancement of technology in this industry, will only grow moving forward, emphasizing the importance of prioritizing data centralization. Again, master data is core information embedded within an organization about its customers, suppliers, and products. Without constantly accessing and streamlining such data within automotive infrastructures, it would be hard to produce real growth.
Data—and the concepts surrounding it—will also continue to become more complex as time goes on. As the automotive industry develops more digitized vehicles, there will need to be more granularity for the engineers solving problems daily. As technological advancement increases, the need for master data will increase too. Organizations that prioritize their master data will positively affect other aspects; efficient master data and proper MDM influence data analytics and information slicing, affecting the process of IoT implementation, and functional safety, along with other considerations. Many concepts and processes start back with data because the industry is becoming more dependent on it. Master data impacts internal processes, which help achieve the overall goal of distributing safe, sophisticated vehicles on the road that operate within their intended functionality.