How to Build Master Data Systems: Architecture, Trade-offs, and Constraints

How to Build Master Data Systems?  Architecture, Trade-offs, and Constraints

Master data plays a critical role in ensuring organizational visibility, operational efficiency, and product functional safety. As data becomes increasingly complex, the need for master data in the automotive industry will continue to grow. 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:

• Part 1 is entitled “The Critical Role of Master Data in Engineering and Functional Safety.” It outlines the overall impact that master data has on the transportation industry, emphasizing its direct correlation with engineering and functional safety.
• Part 2 is entitled “How to Build Master Data Systems? Architecture, Trade-offs, and Constraints.” It focuses on the technical architecture of master data systems while presenting three scenarios that illustrate the trade-offs involved and any lingering constraints.
• Part 3 is entitled “Getting Started with Master Data.” It describes the importance of master data to an organization and what implementing a master data solution can look like for several types of organizations.

Why Master Data Equates to Achieving Data Consolidation?

In Part 1 of this series, we discussed the critical role of master data in engineering and functional safety, examining the importance of controlling your organization’s datasets, especially given the ever-present phenomenon of autonomous, hybrid, and electric vehicles. Also briefly covered was the Master Data Management process and how it can benefit engineers during product development. In this blog, we will dissect the technical architecture of these master data systems, revealing the trade-offs and constraints that come with building them. This overall process should achieve the organizational goal of consolidating information across different regions into a single place. By having a single location for all this information, organizations can leverage it to make better business decisions.

Again, master data should be considered an organization’s unstructured, foundational data component, and Master Data Management is the plan or methodology that centralizes and administers that data. By optimizing all this information, you create opportunities for enhanced data storage, increased organizational agility and efficiency, higher business profitability, and reduced risk, among other things. Master data solutions involve accessing your data, streamlining it, and creating systems to manage it properly; this can be time-consuming but still worth all the effort. So, how do you build systems for your master data? First, it is important to define these systems and examine their technical architecture to gain a clear understanding of what they consist of and what they can offer.

What are Master Data Systems?

Through an MDM solution best structured for your organization, you can build a dashboard full of your organization’s different silos of information. That dashboard is your master data system, consolidating all your data into a single source of truth (SSOT). Again, each organization will have silos full of information, including business-related products, accounts, policies, and financials, among many other things. You could further divide these silos. The level of specificity in these silos is initially determined by the characteristics of the information itself and by your organization's preferred way to categorize everything. So, what are Master Data Systems?  Having a master data system means having a structured, centralized repository for every aspect of an organization’s most essential data. That way, organizations can maintain their MDM solution and maximize their business activities, ensuring overall success and longevity.

What do Master Data Systems Consist of?  Outlining the Technical Architecture

In building, whether it's stacking a deck of cards or constructing a statue, everything starts with a framework. The same applies to considering the technical architecture of master data systems as well. Though there is no one distinct way that a master data system’s architecture has to look, there are several framework models you can employ, for example:

• First, there is the registry architecture. This type of framework involves a system with limited access, disallowing significant edits to the master data. This architecture is cost-efficient and helps eliminate duplicate and redundant information.
• There is the repository architecture structure, which may also be referred to as the enterprise, centralized, or transactional architecture. In this framework, an organization can use application software to store all its master data in a single location. Though it depends on the organization, this is often the most widely used architecture because it offers higher accuracy and reliability while mitigating the risk of delays.
• Then there is the hybrid architecture structure. As its name suggests, this architecture combines the aforementioned frameworks. Here, the application software you have chosen can work collaboratively with the system itself. The only downside to this option is that it isn’t very cost-efficient.

For LHP, we view these master data systems as event-driven. In other words, everything depends on the master data's situation. The overall framework you chose is paired with master data and engineering data to create these processes, workflows, single-source dashboards, and predictive analyses. That way, the main master data system becomes the central hub of all that information.

How to Build Master Data Systems?  Master Data Management Considerations

This master data methodology can be extensive, so organizations should consider several factors before committing to an MDM plan. The data deep within your organization can have a snowball effect on internal processes, ultimately affecting your business activities. Integrating your architecture and establishing your database can be complex and costly. Therefore, your organization has to take the time to analyze overall goals and then delegate the route that is most rewarding for your hub of information. While figuring out how you want to approach a management solution that best fits you, it is key to determine what data you plan to manage and why.

Examining the Potential Trade-offs

Within the scope of data analytics and computer science, LHP has found trade-offs among performance, complexity/cost, and referential integrity when building master data systems. Performance relates to system efficiency and the time required to perform a given task. For complexity/cost, that simply reflects the amount of any expenses made. Referential integrity is a concept that helps maintain relationships between tables in a database, ensuring everything is valid and consistent. Again, these overlapping trade-offs can derive from three different scenarios organizations may find themselves in, which are:

1. This first scenario is a demand-pull. This can be considered the best scenario for referential integrity because you are getting all the information from a single source of truth (SSOT). If you or another user wants to see charts, whether through Power BI or a custom application, all the information is gathered in a single place and is always up to date. Though this approach is very cost-efficient, it is not that performance-efficient. When you want to see these different charts, you essentially have to gather information from everywhere;  it is not unified. The master data is all over the place, so you constantly have to scatter to find the information you want. Smaller organizations can manage this scenario because the amount of data they have to deal with is much smaller. Medium-sized or larger organizations wouldn’t work as well with this scenario.
2. The second scenario is a periodic, automatic pull using a MapReduce approach. In this scenario, data is filtered because you won’t need every bit of data all the time, but just the latest changes. This scenario is good in terms of cost and performance, but not so good for referential integrity. This is because, at any given time, the state of the master data may differ from that of the individual silos. After all, the synchronization hasn’t occurred yet.
3. The third scenario an organization can find itself in is the triggered push. In this approach, organizations could use their own applications to push information from their silos into the overall master data system. This route would be good for performance and referential integrity, but not very cost-efficient. The reason is that you need to update and upgrade your organizational systems so they are active whenever a new customer is added, a product is created, or a sale is made. All these aspects need to be changed so that information can be pushed to the master data system.

Addressing the Potential Constraints

Your organization will face potential constraints in this process, including data redundancy and inconsistency, organizational disruption, and procedural errors. After choosing the best architecture to structure your master data system, your MDM solution helps maintain data so that these concerns do not develop into issues that need to be fixed. Regarding the deeper levels of your master data system, LHP offers one specific piece of advice: beware of complexity. Time complexity is an aspect that examines the programming involved in your MDM database and can be extremely important because it measures the time required to run different algorithms. Depending on your MDM solution software, how you search your organizational data can look different. There are a few types of database structures that each depict the importance of time complexity within the MDM process and why this concept should be looked at as a potential concern:

1. A Non-indexed SQL database is one way your database can be structured. Within this non-indexed SQL database, there could be millions of rows of data. You could try to search for charts or a KPI, for example, and that search query will jump millions of times through every row to find the results you are searching for. This, obviously, can be a time-consuming step within your MDM process. These non-indexed databases have linear time complexity, meaning that if the database doubles in size, it will take twice as long to search all rows.
2. In addition, an Indexed SQL database is another way to structure your database. Think about that same example where there are millions of rows full of information for you to search through. An index mapping file is created in this database, so the search query is more of a binary search tree. This reflects logarithmic time complexity, meaning that the searches are divided in half at each step. Because of that, the time to search through that same number of rows is much more efficient. There is a substantial increase in search performance with this indexed SQL in your database.
3. Then, a NoSQL database is another option to consider, given your structure. A few examples of these types of databases are Azure Cosmos DB and MongoDB. These databases use hash indexes, which provide constant-time complexity.

These are all examples of the fundamental concepts and concerns that system developers collaborating on master data must be aware of when implementing an MDM solution. Addressing potential concerns and considering them as your organization manages and maintains your data is critical. The outcome of your execution depends on the steps and planning done before the implementation.

Summary:  Adding Value to Your Organizational Information

Master data is a significant asset that helps add value to your organization in various ways. You can refine your data management, sustain internal visibility of business activities, and increase operational productivity. You can take the sporadic data scattered across your organization and develop a master data system that prioritizes your more critical information. Some organizations may face master data issues consistently, increasing their risk of disruptions and other risks. This thriving era of data is only growing from here on out—you can either use your data as an asset or let poor maintenance hinder your success. In the automotive world, the value of data drives the development of safer, more innovative products and systems that help define the rapidly evolving landscape of modern transportation.

In Part 2 of this series, we have defined what master data systems are and what their technical architecture looks like, while identifying certain trade-offs and constraints that are often involved. These considerations of master data are important because they give you an opportunity to leverage your data and positively influence organizational workflow.

In Part 3 of this series, we will expand on how your organization can begin a master data solution tailored to your overall goals.

Interested in learning more about Master Data for your organization? Contact our team today!