Data flow mapping is a technique used to identify the interaction points between all parties in an organization. It tracks the flow of data within the company, including the personal information of the customers, staff, and other third parties involved. In addition, it is essential to implement data flow mapping to comply with regulations like EU GDPR and PII.
Today, many organizations have moved to cloud services, increasing the need for data flow mapping even more. Hence, many organizations have automated their data flow mapping to reduce the required human input while increasing efficiency.
In this article, we will discuss why you should automate data flow mapping and guide you through the steps to give you a better understanding.
Why You Should Automate Data Flow Mapping?
Data flow mapping automation helps organizations migrate from a traditional manual approach to a modern automated framework. It enables an AI-powered PrivacyOps solution as the data flow can constantly gather information, find new data, and update the records automatically. Automating the data flow will help your governance, risk, and compliance tasks by your team's time to focus on using user data more responsibly.
Another important thing that we need to consider is the issues with GDPR data mapping. Based on research done by SecurityMetrics, only 9% of companies were highly prepared for GDPR compliance. On the other hand, over 70% felt that it was a medium-to-high priority for their business. However, most companies still follow the manual process when considering GDPR data mapping. Therefore, the procedures are delayed due to human resource issues and inefficient physical documentation.
How to Automate Data Flow Mapping?
Since you now understand how data flow mapping works and why we need it, let's see how we can implement it.
There are three main data mapping techniques:
- Manual data mapping - It involves connecting data sources and documenting the process using code.
- Schema mapping - Requires both coding knowledge and manual process. First, the connection between the data sources is made using data mapping software. Then employees with IT knowledge can review those connections and make necessary adjustments.
- Fully-Automated data mapping - This is a simple and efficient data mapping that doesn't use coding. This is appropriate for non-technical users.
First, we need to define the scope of the data to be moved and the purpose of the automation. Data harmonization is critical since the main aim is to transform different data sets so that they fit together. Also, it is essential to document every step to track which assets are used in each step for the automation.
Next, we need to match the sources to the destination fields. You need to conduct a test before transferring data into the actual system. In that test, you can identify the changes that are required. Then, you can make the necessary changes and transfer the data without any issues.
As the final step, the team should maintain and do the required updates and maintain the system regularly.
There are 3 types of data mapping software you can use for this process:
- Open-Source Tools —Provides services most suitable for small businesses with simple use cases at low cost.
- On-Premise Tools — Hosted on the company server and only accessible with company authorization methods.
- Cloud-based Tools — These tools are hosted in the cloud and accessed via a web browser. More automation functions are available.
Considering the scope and the data volume, you can decide what the best tool type is for you. For example, if your data flow is larger and the use cases are complex, it's better to use an on-premise tool or a cloud-based tool. Also, if you need to control multiple companies, the best solution is the cloud-based solution to save cost.
Each of these tools has unique features, and you need to consider them as well. For example, Polar Security is the First Automated Data Security & Compliance Platform that provides many advantages to the user.
Polar solves the problem of protecting managed, unmanaged, and shadow data. It helps companies comply with regulations by detecting and mitigating compliance violations before they become costly. Within the platform, you can identify cross-region sensitive data flows, misplaced PIIs, unencrypted credit cards, unauthorized collection of personal data, and more.
The main areas where Polar Security helps with are:
- Automated datastore inventory
- Audit data flow changes
- Prevent exposed data
- Prevent and act on ransomware
- Prevent compliance violations
The Polar Security solution is mainly aimed at security, compliance and data governance teams. Polar Security is a powerful tool for data mapping automation due to its ease of use, configuration, and maintenance.
What are the Key Challenges in Automating Data Flow Mapping?
A company can face critical challenges when moving into data flow automation. Identifying personal data is one of them. Sometimes the personal data is stored in papers, electronics, or audio files. First, you need to identify accurate and correct personal data. Then, you need to identify appropriate technical and organizational safeguards to implement the policies, procedures, and access level. These processes can be very complex, and it is necessary to have the required domain knowledge before starting.
Also, understanding legal and regulatory obligations is essential to determine the organization’s legal and regulatory responsibilities and the standards like GDPR, PCI DSS (Payment Card Industry Data Security Standard), and ISO 27001.