Modern enterprises collect a huge volume of data from a variety of sources and use the data through complex interactions across the organization. The organization can't analyze, transform, share, and derive valuable insights unless they have a common understanding of the data. Data Mapping is the process of establishing relationships between separate data models to bring a common understanding.
In this data-driven business environment, companies are collecting data from customers' mobile devices, websites, and vendors. The collected data is valuable only when we have the right system in place to handle the voluminous and complex data. Data mapping is used to integrate this complex data. A good data map is a necessary component of data management, data governance, data migration, and data integration. Increasingly data mapping has become the starting point for DataGovOps related work.
With new privacy laws (GDPR, CCPA) and increased customer demand for privacy, companies are required to understand what data they hold and how the data flow inside the organization. By mapping the data, they can comply with privacy laws and implement better privacy controls and protection. Data mapping has become a foundational work, using which organizations can understand what data they collect, process, share, and store.
For instance, GDPR (article 30 and 36) requires organizations to document their processing and conduct periodic data protection impact assessments (DPIA). Without a comprehensive data map, organizations can't comply with these requirements.
Interesting read: Ten Essential Attributes To Capture In GDPR / CCPA Data Mapping
Data mapping often involves people across the organization. Your approach largely depends on the cost and resources available to the project. Mapping can be implemented across the organizations simultaneously, or you could do one team or one microservice at a time. Companies must involve groups to conduct a high-level overview of their activities to proceed with a comprehensive plan.
Mapping can have varying degrees of risk. Identifying the data owner reduces the complexity; hence you must identify the data owners and stewards who represent different parts of the organizations. The particular employee will be responsible for the data within the organization. They bring a wealth of knowledge on the history of the data and context to the data.
Data mapping is a complex process. The tool used to map data has a significant implication on your outcome; hence companies should be careful in selecting the right tool for the job depending upon their existing Infrastructure, volume of data, and goals. With many solutions available in the market, from on-premises tools to open and cloud-based mapping tools, Companies should decide on a system that will help their data strategy. Before choosing the right data mapping tool, think about the following factors:
Data mapping tools must handle a variety of data sources. Some tools can handle different data types and sizes without comprising the accuracy while other solutions focus on very high accuracy on a specific type of data/data source. Companies must make sure the selected solution supports the diverse sources that they have in their organization. Plan for the future, identify the tool with a variety of data sources, and support new sources.
Automating parts of the data mapping process will save time when you update the map periodically, hence look for solutions that give options to automate without writing or changing the codes. The automation process should be simple, like a drag and drop method, to avoid complexity. Tools that offer process orchestration and scheduling features to automate mapping reduces the workforce and time.
Good data mapping tools allow users to track the impact of changes as maps are updated. It should also keep a trail of the time and data changes made to a particular data set. This record is beneficial for auditing and compliance purposes.
Data mapping tools should allow users to reuse maps, so you don't have to start from scratch each time. This feature saves time and resources for the organization.
Owing to growing privacy concerns and regulations, many advanced data mapping software applications allow users to identify personal data map flow within an organization.
The user interface is an essential factor for the data mapping tool. It should be simple to use for all the employees involved in the process. If you have many data stewards from business with less technical background, you have to pick a tool that caters to the audience.
Recently developed data mapping software solutions are equipped with various security features that enable users to secure your database while providing access to data through DPO and analysts. They also allow organizations to conduct a risk analysis of your data.
Growing privacy concerns and regulations such as GDPR, CCPA bring new responsibilities for companies in handling personal data. Advanced data mapping software applications allows organizations to identify and map personal data. You could use one of the tools to identify personal data within your company. In addition to the personal mapping data, you must ensure that the data is treated in compliance with privacy laws.
Suggested read: Role Of Data Tokenization In Data Security
Data inside an organization change and evolves; hence you have to update the map periodically, looking for options to automate parts of the process. Automating parts of the data mapping process will save time in the long run.
Most companies receive data from business partners, such as resellers and suppliers. Mapping and integrating data from third parties can be challenging due to differences in data naming. One partner might name the Customer field as 'Customer ID' while another partner might name it as 'Customer #.' Your data mapping solution and the process should address the challenge of naming conflicts.
Data maps are not a one-time deal. You may have to repeat the process periodically and involve new people to lead the process; hence you must document the process, the steps, findings, and decisions. Moreover, documenting avoid mismatch across the organization. For example, documenting the set of principles to classify data will help maintain a consistent approach across the company.
Data mapping is an integral step in various data management processes, including:
Data mapping is the first step in a range of data integration tasks, including data transformation between the source and destination. A data mapping tool connects the distinct applications and governs how the complex data is handled between them.
Data Migration is the process of selecting, preparing, extracting, and transforming data and permanently transferring it from one IT storage system to another. Using an efficient data mapping solution that can automate the process is vital in migrating data to the destination successfully.
Data Warehousing is the process of creating a connection between the source and target tables. Using a well-defined data mapping model, we can define how data will be structured and stored in the data warehouse.
Data mapping involves discovering, classifying, and understanding personal or sensitive data for privacy compliance. Companies need to identify all data sources for PII, discover what personal information resides in these sources, and analyze how the data flows to and from the sources. Additionally, organizations need to also determine the risk of PII for privacy compliance. This enables them to take remediation actions such as data masking, data tokenization, deletion, or strengthening of access controls.
With Protecto, discover PII and gain instant visibility to the type, sensitivity, and amount of personal data on your data store. Obtain an understanding of who has access to what PII data and how it is used. Remove unnecessary access to data to avoid data privacy risks. Reduce breach risks and privacy-related overhead costs by identifying and getting rid of stale personal data.
Data mapping plays a crucial role in understanding the flow of data within an organization by providing a comprehensive view of how data moves, transforms, and interacts across different systems and processes. It helps identify the origins of data, its journey through various stages, and its destinations, allowing stakeholders to grasp the data's lifecycle and dependencies. With data mapping, organizations can gain insights into data lineage, data integration points, and data relationships, which aids in making informed decisions, improving data quality, and ensuring data security.
During the data discovery process, data mapping involves several key steps to understand the flow of data within an organization.
Data mapping plays a significant role in data governance and compliance efforts by providing insights into data flow, integrity, and security. It aids in identifying and securing sensitive data, ensuring compliance with data privacy regulations.
Data mapping facilitates regulatory compliance by showcasing data handling practices and adherence to data protection laws. It assists in setting data retention policies and archiving strategies, ensuring proper data management aligned with compliance requirements. Overall, data mapping enhances data governance practices, instills accountability, and contributes to maintaining a robust and compliant data environment within the organization.