Modern digital businesses generate huge amounts of data. The level of insight you can glean from that data depends largely on how you manage it. Good data management practices keep data quality high which enables you to conduct meaningful data analyses, make better decisions for your organization, and adhere to data standards and governance rules. Prioritizing how you manage your master data will save you time, effort, stress, and money in the long run. So let’s get into it.
Master data is the essential information you need to run your organization. It can include customers, accounts, products, suppliers, vendors, locations, and any non-transactional data your business needs to function. If, for example, a customer buys a particular product on a certain date, the customer and product info are classed as master data, whereas the date on which the customer bought it would be considered transactional data.
As master data is at the core of everything your organization does, it’s important to observe some master data management (MDM) best practices. It’s your duty to properly manage and protect your data, and a huge database is of little to no use unless it’s structured in a way that enables meaningful analysis of high-quality data. As your master data is the most business-critical information you have, getting master data management right from the start is a key factor to business success.
Understanding and managing data is all about storing it in the right context. There’s a lot of data that adds context to your business, but not all contextual data is master data. As we’ve already mentioned, master data refers to key factors that enable your business to run, like customer data, supplier locations, and product details. What you consider master data will depend on your specific business needs and goals, but the most common types of master data are:
It’s incredibly useful to have a single source of truth that can be used across your organization. All teams should have one access point where they can find relevant, business-critical information like customer IDs, product lists, and distributor info.
Master data management is important because it allows proper maintenance of this crucial data on a rolling basis. This means it’s easier to weed out inconsistencies or discrepancies which keeps your data quality high. Using this high-quality data, you can then make better data-driven decisions based on reliable figures, hypotheses, and extrapolations.
Master data management supports smart, smooth internal operations but how exactly does it do this?
Setting standards for your data is one of the most challenging aspects of master data management so it’s worth doing plenty of advance research and planning. One of the most fundamental MDM best practices is to set data standards that agree with other data types across your company. Different departments will have different needs, so the data standards you set should be adaptable while still maintaining the uniformity needed for standardization.
Data governance sets the internal rules for how you gather, store, use, and dispose of data. It also dictates who can access what, when, and why. If you work with big data, a data governance strategy is a non-negotiable necessity. Setting strong master data governance best practices and policies for your company allows you to have a clear overview of data use in your organization.
As we’ve covered in a previous article about data migration, data integration allows data to flow back and forth freely within your operations. This means you need data fields that can map easily to each other across different parts of the organization where naming conventions may differ. Data transfer from one application to another may throw errors and end up being anything but seamless, but MDM can help you anticipate potential errors through smart data integration policies.
Before you start, consider how to define your data integration policies and how to manage integrations between different applications. Using a tool that helps you automatically reformat data to meet the target data schema will help with this. You should also try to prevent errors and duplicates by setting up server callbacks to validate importer data against the target database.
Data stewardship is how you maintain the quality of your data and make sure your master data management system can work effectively. The majority of large companies will hire a data steward to specifically manage this task as bad data makes consolidation and integration difficult and creates problems for the long-term management of master data. For smaller organizations, it’s important to think about which roles should manage data stewardship, who can access, change, and create master data, and how to manage master data-related tasks.
When you’re setting up your master data management policies, it’s important to take all the different factors and best practices into account. Product master data management best practices will likely look slightly different to supplier master data management best practices, for example. The same goes when you’re setting up customer or vendor master data, or any other number of master data that may be necessary to your organization. Regardless of how flexible you need your systems to be, however, there are some hard and fast master data management best practices that should always be applied. The following will help you keep data quality high internally, and through exchanges with external stakeholders like customers or vendors. Let’s explore.
Master data management is a team effort. While certain roles and teams will oversee MDM operations, it’s crucial that all stakeholders understand how to maintain and benefit from your data. By educating all those who contribute to your databases, you’ll ensure widespread adherence to the policies you’ve set up in advance and divide the work among teams so that certain roles don’t get overloaded. This also makes sure you’re retaining knowledge within the organization and not at risk of losing it by limiting the information to one role or team. It’s important to remove barriers-to-entry here, so making data validations and cleaning processes for both internal and external stakeholders as easy and intuitive as possible is key. This means that every stakeholder can easily contribute to ensuring that imported or updated data aligns with data standards. Setting up smart customer onboarding systems and data ingestion processes will also make it easier for you to oversee data quality when you’re working with external stakeholders and spot inconsistencies before they become problematic.
Implementing automated processes that ensure data is validated and cleaned before it even enters your database will be a game changer for your master data management. Automations can turn frustrating, time-consuming tasks into quick checks. Good automations will also alert you to bad data or mismatched fields, which allows you to fix any issues before they become problems. In practice, this looks like setting up automations that validate and meet the target data schema, ensuring high data quality by checking cross-column dependencies, validating data with external APIs, and using import tools that provide immediate actionable feedback to the user so they can see, clean, and reformat data errors on the spot rather than having to restart the process.
Server callbacks allow you to validate data against your existing database. That means you’ll see errors, avoid duplicate entries, and be able to reformat data then and there before it enters your database. It’s important to use the best tools at your disposal and integrate them into a policy that adheres to your internal master data management best practices.
Since GDPR came into force in May 2018, companies have been required to ensure all data related to natural persons like customers or employees is properly managed. As much of the affected data could be considered master data, this means that GDPR changed how MDM functioned in many businesses. It became even more necessary than before to have an easy-to-manage, well-structured, and transparent data system. Any organization managing people-related master data needs to have a clear understanding of what’s needed for GDPR-compliance and in many cases it’s a good idea to hire a data protection officer.
AI can be the perfect complement to well thought-out master data management systems. AI-assisted data imports can automate data validation and cleaning to make sure that all transferred data complies with your pre-defined data standards, rules, and policies. This will again save you time that you might have to otherwise spend manually verifying data, matching fields, or correcting import errors. Once your schema and automations are in place, it’s good to work with a tool that remembers your set-up and applies the same logic every time a similar file is imported. This facilitates a smoother customer onboarding experience which is essential to reducing potential churn. This is also why your importer should be able to handle customer data edge cases without having to create custom import scripts. The easier the experience is on all sides of the import process, the better it is for everyone.
Managing your master data doesn’t have to be a daunting task if you have the right processes. It is however, a crucial one that requires thoughtful planning, best practice implementation, and smart support from well-chosen tools. By educating internal stakeholders, setting up simple processes for external stakeholders, and prioritizing GDPR-compliance, you’re already off to a good start. nuvo’s Data Importer allows you to automate and scale data transfers with external parties in a secure way. Data imports are only one part of a broader master data management system, but getting imports right can help ensure high quality data along the length and breadth of your processes.
Set yourself up for success and contact us today to find out how nuvo can support your master data management goals.
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