Setting the Bar: Data Mapping Standards and Best Practices in 2024
With over 2.5 quintillion bytes worth of data being generated daily, businesses are having to deal with increasingly larger data sets and more complex data. Data mapping is the key to staying calm in all the data chaos.
Get the data mapping process right and your business will be able to make informed decisions based on accurate insights. Get it wrong and you’ll have incomplete data analysis, which may result in poor decision making.
This is why it’s important to have data mapping standards to ensure consistency in the data mapping process.
This article will cover data mapping standards and best practices to implement in 2024, the technology and tools used in the mapping process and future data challenges and solutions.
Key Takeaways
- Data mapping standards help ensure data mapping quality, accuracy and consistency and help organize the data mapping process.
- Mapping best practices range from setting up a robust mapping strategy to implementing mapping tools and ensuring compliance with data regulations.
- The data mapping landscape is changing rapidly, and businesses should integrate advanced mapping tools to stay relevant.
Data Mapping Standards in 2024
Data mapping is the process of mapping the movement of data from one database to another. It helps simplify complex data movement and transformations and can help reduce data errors.
Data mapping also helps with data integration, data transformation and smooth data migration.
However, you’ll need to ensure consistency in the data mapping process to avoid mapping errors and duplicate or inconsistent data. This is where data mapping standards come in.
Data mapping standards can be defined as a set of standards and procedures that ensures data mapping is performed consistently and in line with business objectives. It also covers the standards for documenting the mapping process and risk-reduction procedures.
The Evolution of Data Mapping in Recent Years
A few decades ago, you could probably map the data flows of the average business on a piece of paper. Nowadays, even complex programs like Excel can’t accurately map out the data of small to medium-sized businesses.
This rapid growth is becoming a challenge for businesses, even with the rapid advancement in technology. More than 60% of businesses say that the rate at which data volume is growing makes data mapping and analysis difficult.
The rise in data privacy awareness is also affecting the way businesses do data mapping. Most large businesses have to deal with several regulations to ensure consumer data is safeguarded. These regulations are in place to ensure consumer data is protected and to reduce the risk of data breaches.
However, they also make the data mapping standards more stringent. While some regulations may only touch on mapping best practices, other’s require businesses to follow certain procedures to ensure risk-free data mapping. Data privacy regulations like the GDPR may also require businesses to have accurate, up-to-date data mapping procedures.
Even if you don’t fall under these regulations, you’ll benefit by following data mapping procedures and best practices. And, if you’ve already implemented mapping guidelines, you’ll need to update them regularly to stay ahead of data challenges in 2024.
The Significance of Best Practices in Data Mapping
It’s now clear that businesses need data mapping procedures to stay in line with regulatory challenges. But following data mapping best practices does more than just ensure corporate compliance.
Data mapping best practices ensure quality and consistency in the mapping processes. Think of them as a set of definitions on how to document data flows. Imagine how difficult it would be to communicate if every word had 10 different meanings!
The same applies to ensuring consistency in the data mapping processes.
The Role of Best Practices in Mitigating Data Risks
Following data mapping best practices also helps mitigate data risks. When the mapping process is consistent, you’re more likely to get an accurate picture of data flows, and identifying risk areas becomes easier.
Every best practice is associated with some form of risk mitigation. For example, having accurate mapping documentation allows compliance experts to do more accurate risk audits, and you can be assured that no data sets are overlooked.
Remember, most data compliance regulations place the responsibility of mitigating risk on businesses, and not implementing best practices can result in hefty fines.
Benefits of Adhering to Data Mapping Best Practices
Since data mapping is a crucial step in the data management process, it helps to know why implementing mapping best practices is so important. Here are three benefits of following standardized best practices in data mapping:
Enhanced Data Quality
Firstly, you get better quality data for analysis. The mapping process filters out irrelevant and duplicated data sources, which means you’re less likely to have skewed results.
Ask any accountant how crucial it is to ensure financial data isn’t duplicated in the records. The same applies to other business data.
Efficient Data Management
Data mapping best practices also help you manage data more efficiently. The average business gathers data from around 400 sources. Each source may be used multiple times, creating thousands or even millions of data flows.
Data mapping helps you make sense of massive data, and condense the data flows for easier management. And adopting a standardized approach and following best practices ensures quality is maintained during this process.
Regulatory Compliance and Reporting
You’re probably wondering, “Why follow standardized practices with data mapping when you can create your own procedure?”. After all, not all businesses deal with millions of data sets, right?
The answer has to do with remaining compliant. Even if you manage to make sense of duplicated and irregular data flows, you’re likely to miss out on some. And having accurate data maps is a requirement of the GDPR and other data privacy regulations.
The reason these laws require accurate data maps is to ensure consumer data is protected and that businesses follow an accountability framework for data management.
Key Data Mapping Standards
Now that the importance of data mapping standards is clear, you’re probably wondering which data mapping standards to follow. Well, it depends on which regulatory bodies your business falls under and where you’re getting consumer data from.
Global Data Mapping Standards
If you’re an e-commerce business or deal with data from global consumers, you’ll have to follow global data mapping best practices. Regulations like the GDPR apply globally, even if you’re not located in the EU.
For example, Article 30 of the GDPR requires businesses to have detailed data maps, which lays the foundation for compliance with other sections of the regulation. Understanding global mapping standards can be difficult, so it’s best to outsource this to data compliance service providers.
Industry-Specific Data Mapping Standards
Global standards may apply to all businesses, but you also need to consider industry-specific data mapping standards. The reason for this is that some industries deal with more sensitive data and therefore have stricter standards.
For example, HIPAA applies to businesses in the healthcare industry, and various cybersecurity regulations set standards for financial data processing.
Legal and Compliance Frameworks in Data Mapping
You’ll also have to consider legal and compliance frameworks when doing data mapping. Some laws may restrict businesses from accessing certain data, while others may require prior permission from consumers to process sensitive data.
Essential Best Practices for Data Mapping
Every regulation may have separate data mapping requirements, but most will cover common best practices. Here are some essential best practices to follow in the data mapping process:
Developing a Robust Data Mapping Strategy
Setting up a data management program takes time, and you’ll have to develop a data mapping strategy beforehand. Your data mapping strategy should cover the following:
- Goals and objectives of the mapping process.
- Understanding data sources and destinations.
- Aligning the strategy with business objectives.
- Guidelines for data mapping best practices.
Data Discovery and Inventory
Once you have a solid strategy for data mapping, it’s time to do data discovery. The data discovery process helps you analyze data sources, eliminate duplicate data and understand the type of data your business processes.
Think of it as keeping an inventory of business data.
Data Classification and Sensitivity Analysis
Now that you have an understanding of the data sources your business manages, you can easily classify the data. This helps with further analysis and ensures you don’t waste time working with irrelevant data.
A sensitivity analysis helps you identify the level of importance different data sets have.
Data Flow Mapping and Visualization
Once you’re done with a sensitivity analysis, you can start mapping out data flows in the business and represent it with visualization tools. These processes are integrated, and you’ll need data visualization tools to get an accurate picture of important data flows in the business.
Data Lifecycle Management and Governance
Understanding data flows is important, but you shouldn’t stop there. It’s also important to understand the data lifecycle in your business. How many times are certain data sets cycled through the business? Are you eliminating duplicate and irrelevant data each time data is recycled in your business?
And when does a certain data set become obsolete in the analysis process?
It’s also important to follow data governance best practices such as ensuring different departments follow the same data management principles, and that your data goals are aligned with compliance best practices.
Record Keeping and Documentation
Lastly, you’ll need to keep accurate records of data flows to avoid non-compliance with data privacy regulations. Some regulations require businesses to produce accurate data mapping documentation on demand.
While the data mapping documentation process is difficult, you can streamline it with the help of corporate compliance specialists.
Technology and Tools in Data Mapping
Managing data mapping manually is a recipe for a compliance disaster. To start with, it’s impossible to keep tabs of thousands of sources while maintaining a high level of accuracy. Even if you manage to do it, manual mapping is prone to human error and inconsistency.
Fortunately, you can easily integrate automated data mapping tools to simplify the process. The top data mapping tools like integrate.io leverages AI and machine learning for more accurate mapping.
Many tools are beginner friendly, and you don’t need to be a data specialist to understand the graphical data flows.
Challenges and Solutions
Even if you automate the data mapping process, maintaining a high level of accuracy is no walk in the park. You’ll have to deal with integration, security and regulatory challenges, amongst others, when doing data mapping.
Here are common challenges in the mapping process and how to overcome them:
- Staying compliant with changing regulations: With data compliance regulations being updated regularly, you’ll need to constantly evaluate the mapping process to remain compliant.
- Dealing with incomplete data flows: You’re bound to run into incomplete data flows during the mapping process. It could be due to human error, customers not filling out complete forms, or a range of reasons. Fortunately, AI integrations are making it easier to complete the mapping process with partial data.
- Managing outdated data records: Keeping data records updated is one of the biggest challenges of data mapping, especially when you may be dealing with thousands of databases. Fortunately, AI-integrated mapping tools can automate this process, ensuring your records are updated in real time.
The Future of Data Mapping Standards and Best Practices
Data mapping is continuously changing and so are the mapping standards. New trends such as open data sharing and adopting an ethical stance with regards to data management are changing the way businesses view the mapping process.
Improvements in mapping tools and the use of AI can help overcome many of these challenges. However, this requires an adaptive approach and businesses with more flexibility have better chances of staying ahead of the curve.
In the next decade, there will be more emphasis on governance and real-time data mapping to deal with the complexity and speed at which data is being generated and consumed.
Final Thoughts
Data mapping standards may be evolving, and businesses are prioritizing real-time mapping to accommodate the speed at which data is generated. This makes staying compliant with rapidly changing compliance regulations more difficult.
At Captain Compliance, we believe that nothing is impossible when it comes to streamlining your business’s data mapping. We help businesses with compliance audits, setting up data mapping tools and ensuring compliance with changing regulations.
Check out our compliance solutions today!
FAQs
What are Data Mapping Requirements?
Data mapping requirements include identifying data fields, following standardized naming conventions, implementing data transformation logic and ensuring consistency throughout the process.
Learn more about the data mapping process.
What is GDPR Data Mapping?
Data mapping under the GDPR requires businesses to create accurate maps of data sources and destinations, as well as data flows. Article 30 of the GDPR covers data mapping requirements in detail.
Explore expert GDPR compliance solutions.
Is Data Mapping a Legal Requirement?
Under certain regulations, data mapping is a legal requirement. For certain industries, it may not be a legal requirement, but it still lays the base for compliance with other legal requirements related to data management.
Learn more about the importance of data mapping documentation.
What Do Data Mapping Tools Do?
Data mapping tools help automate the data mapping process. They have built-in naming conventions and can present data flows in easy-to-understand visual charts.
