March 12, 2020
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.
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 for data mapping 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-premise 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:
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.
Data inside an organization changes 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.
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