Dealing with large data flows is something many businesses struggle with. This leads to problems like data loss, risk of data leakages and duplicate data. To avoid these issues, businesses implement data mapping rules which help outline data flows.
Navigating Data Mapping Rules: A Comprehensive Guide
Data mapping rules refer to a set of procedures that help assess the type of data in an organization, how it’s moved and where it’s stored. Think of it as a set of procedures to guide your data audits.
This article will cover the basics of data mapping rules, why it’s important, and the tools and processes to implement them. It will also cover common challenges and pitfalls that businesses deal with when implementing data mapping.
So, let’s dive right into it!
Key Takeaways
- Data mapping rules help identify the type of data used in a business and how data flows are classified.
- Implementing data mapping rules helps maintain data quality and ensure regulatory compliance.
- It’s important to follow best practices for data mapping to avoid data loss, duplication and other pitfalls.
Understanding Data Mapping Rules
If your business handles large amounts of private consumer data, you’ll need to do data mapping to evaluate how data flows. Data mapping rules are the set of procedures you’ll need to implement when doing data mapping.
For example, when mapping the data flow of customer signup data, your data mapping rules may require you to maintain log access records, evaluate where data is transferred to third parties and analyze the data storage protocols.
This is helpful in creating reliable data maps, which can be used to devise data privacy audits. Having accurate data mapping is also a key part of regulations such as the GDPR, CCPA and other data privacy regulations.
The Role of Data Mapping Rules in Data Governance
Data mapping rules aren’t only there to ensure your business has reliable data audits for regulatory compliance. They also help improve your business’s data governance by ensuring data accuracy and facilitating data integration.
Let’s say you’re processing consumer account data for an e-commerce store. Data governance requires you to have no duplicate accounts, as this may affect customer purchasing records. However, if your data warehouse contains duplicated accounts, you’ll easily be able to identify and eliminate them through data mapping.
Now, take the same example and apply it to data integration. Let’s assume each customer account is linked to an email database managed by a third-party marketing company. With correct data mapping, you won’t have to deal with an inflated marketing budget due to duplicate accounts and inconsistent data.
This is just an example of how important data mapping is for establishing data governance rules in your business. So, what do data mapping rules focus on? Let’s explore their key components.
Key Components of Data Mapping Rules
The two key components of data mapping rules include identifying the data source and mapping methodologies and transformations.
It’s crucial that you know exactly which data sources are in play in your business for accurate risk analysis. This helps with:
- Data integration
- Data transformation
- Data transformation
These factors form the crux of how data is moved in your organization. Scrutinizing these techniques can help with risk audits and ensure regulatory and corporate compliance.
Data Mapping Basics
Now that it’s clear why data mapping is important and what the key components of data mapping rules are, let’s explore data mapping procedures in detail. While the process may vary across industries and organizations, here are three key data mapping processes that you’ll need to cover:
Identifying Data Sources
The first step of creating any data map is to determine the data sources used in data flows. Where does your data come from in each step of the process? Do you have to merge several databases into a single entry? Which databases are managed by third parties?
These questions help you understand the sources of data in your business, which can then be used for risk assessments. Once you have a list of sources, you can then scrutinize them for security and authenticity.
For example, a marketing form stored on your website’s database isn’t as much of a threat as a login portal database where consumers have access to features on your site.
Choosing the Right Mapping Methodologies
Another important data mapping rule involves choosing the right methodology to map out data. The purpose of implementing mapping methodologies is to help understand the relationship between data sets. If you fail to do so, you’re probably going to end up with lost, incorrect or duplicate data.
You can implement several data mapping methodologies depending on the type of data source and your desired outcome. These include:
- Data migration: Moving data from one source to another while permanently deleting it in the original database.
- Data integration: Scheduled and continuous transfer of data between two systems. For example, data saved in marketing forms is integrated in databases for planning marketing campaigns.
- Data Transformation: This involves deleting duplicate data, grouping similar data or enriching the data. For example, data collected by email marketing campaigns is then sent for statistical analysis, where consumers are grouped according to buying preferences and other traits.
You’ll likely encounter these mapping methodologies when doing data mapping. However, they aren’t mutually exclusive, and your business may implement all three simultaneously.
Understanding Transformation Rules
While data migration and data integration are pretty straightforward, data transformation can be difficult to track. This process distorts the data, and it’s where you get duplicated or lost data sets.
To prevent these issues, you’ll need to define transformation rules for consistency. Let’s say you have duplicated data from different databases. When doing data integration, you’ll have to transform data using aggregating rules.
You can also apply derivation, enriching, splitting and other techniques for data transformation. However, it’s important to keep track of these transformations.
The Role of Data Mapping Rules in Data Governance
As discussed above, data governance involves creating rules and procedures for handling business data. This is often in line with data privacy regulations such as the CCPA, GDPR, CPRA and other regulations.
Your business may also have internal data governance rules, especially if you outsource compliance. These rules are set by compliance specialists and define how data should be managed in your business.
Data mapping rules play an important role in robust data governance by:
Ensuring Data Quality
Data mapping rules help ensure that duplicate and unnecessary data is eliminated from your business’s data flows, thereby ensuring data quality. It helps identify errors in data flows and identify opportunities for enriching data.
Facilitating Data Integration
With the sheer volume of data that businesses have to handle nowadays, it’s almost impossible to get anything done without data integration. This is where data mapping rules play an important role.
They help map out data flows, making it easier for decision-makers to integrate different databases, which can then be used for data analytics and reporting.
Ensuring Regulatory Compliance
The most important impact data mapping rules have on data governance is that they ensure regulatory compliance. Most data compliance regulations require businesses to maintain a record of the flow of data in their systems.
Let’s take the example of the GDPR, which is the most widely implemented data privacy regulation. Under Article 30, the GDPR regulations require businesses to:
- Maintain a record of data processing activities.
- Maintain a record of the categories of data processing activities.
- Be able to provide these records on demand.
So, not only are data mapping rules important for internal data management policies, but they are also required for creating a compliance framework.
Data Mapping Tools and Technologies
If you’re running a small business that doesn’t collect consumer data on a large scale, you’re probably fine with creating manual data maps. In such cases, the process is often simple, and you may probably have business data mapped out in your head.
However, most medium to large-sized businesses nowadays deal with so much data that it’s impossible to map out manually. Also, the data flows in businesses have become so complex that you’ll need advanced tools and software for accurate data mapping.
Data mapping tools can:
- Maintain automatic records of thousands of data outputs.
- Automatically classify the data and group data flows.
- Identify duplicate and corrupted data and help with risk management.
- Help non-technical staff manage complex data flows.
The best part of using automated data mapping technology is that it automatically notifies you of any discrepancy in the data flow. This makes it a helpful tool when creating an accountability framework.
Much like choosing data protection compliance services, choosing a data mapping solution requires you to consider your business’s needs.
If you’re tech-savvy or an expert in data management, you can try open-source data mapping software like CloverETL or Pimcore. However, most businesses prefer to use cloud-based data mapping tools like Jitterbit or SnapLogic.
And if you’re really serious about preventing data leaks and your organization handles large amounts of sensitive consumer data, tools like Microsoft SQL and IBM InfoSphere are ideal.
Best Practices for Data Mapping Rules
While automated data mapping software can help simplify the data mapping process, you’ll still need to follow best practices for comprehensive data maps. These apply regardless of whether you have manual or automated data mapping systems.
Here are some best practices to follow when doing data mapping:
- Do proper data documentation: This involves maintaining data dictionaries and understanding the type of data that your business uses and collects. Data documentation also involves managing metadata and assigning correct identification tags.
- Follow standardization and naming conventions: Make sure data is collected and transferred in a standardized way across different sources. This involves taking care of things as simple as the capitalization of a consumer’s first name.
- Data lineage and traceability: another best practice for data mapping rules is to ensure data can easily be traced. This helps with data audits and can ensure transparency in data collection and processing.
These best practices make the data mapping process much easier and also ensure your business remains compliant with privacy regulations.
Data Mapping Processes and Workflows
Even with automated data mapping, you’ll have to establish a workflow to get accurate maps. This usually involves creating a strategy, executing, and maintaining the system.
Let’s explore each step in detail.
Create a Data Mapping Strategy
Data mapping isn’t as simple as getting software and waiting for your data to be categorized. Not all businesses have the same data flows, and you’ll have to map out data according to your business’s requirements.
This means understanding your business’s goals, knowing what type of data your business collects and creating a mapping strategy that ensures compliance with the regulations your business falls under.
Unfortunately, this can only be done with knowledge and expertise, not tools and software.
Execute the Strategy
The next step in your workflow is to execute the data mapping strategy. This involves choosing mapping software, integrating the software with your systems and maintaining quality checks throughout the process.
You’ll also need to do validation to ensure no data has been left out. This could involve a cross-check between data sources and destinations or scanning different databases for duplicate data.
Data Mapping Maintenance
Lastly, you’ll have to constantly update your data mapping according to changes in the digital landscape. For instance, AI technology is changing the way data is collected and processed, which warrants a change in mapping techniques.
Also, regulatory changes may require you to update your data mapping techniques accordingly. It’s a dynamic process, and you’ll need to update your data mapping software at the very least.
Data Mapping Challenges and Pitfalls
With the volume and complexity of data that businesses have to deal with nowadays, data mapping isn’t as straightforward as you may think.
Common challenges include dealing with large amounts of data and complex systems where several data sources need to be integrated simultaneously. The changing dynamic of data sources is also a major concern for businesses, and you’ll have to constantly update your mapping strategy to stay compliant.
To add to the challenge, penalties for inaccurate data mapping are becoming more severe. Even the slightest miscalculation could lead to data leaks, failed data audits and related problems.
Fortunately, the technology is evolving, and with the right data compliance solutions, you can stay ahead of these challenges.
Final Thoughts
Data mapping plays a crucial role in understanding the role and flow of data in your business. It helps with decision-making and identifying potential risks caused by inaccurate data handling. Having proper data maps is also a regulatory requirement, so it’s important that you get it right.
At Captain Compliance, we understand that changing trends can affect the way data is managed and collected. Our compliance services include helping your business implement an accurate data mapping strategy for compliance.
Get in touch today for integrated data compliance solutions!
FAQS
What are the Techniques of Data Mapping?
The two main data mapping techniques include manual and automated data mapping. Manual data mapping requires you to maintain physical logs and records, while automated data mapping is done using integrated software.
Learn more about data privacy compliance services.
What are the Responsibilities of Data Mapping?
The main responsibilities of data mapping is to facilitate data integration, transformation and migration. This ensures data quality, helps reduce data risks and is useful for data cleansing.
Learn more about the role of data clean rooms in data integration.
What Should a Data Map Include?
A data map should include a record of data sources, data flows and data processes in an organization. It should be implemented according to standardized naming conventions and be in line with transformation rules and your business’s governance program.
Explore data governance programs in detail.
Which Data Mapping Software is Best?
There’s no “best” data mapping software. Each has unique advantages and drawbacks. You can choose between open-source software, cloud-based software and system-integrated mapping tools.
