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Data is all you hear these days. From online sites to various corners around you in the office, data is the widely used term and is the technology that powers mega corporations today. With data, you have the power to gain insightful knowledge through data analysis.
Today, almost all businesses use data-driven insights to expand their operations. However, data must be correctly migrated and mapped for any data analysis to produce relevant results. This is where the concept of data mapping is helpful.
Data mapping is the process of combining fields from many datasets into a structured or central repository. Data mapping is necessary for the transport, input, processing, and management of data. The ultimate goal of data mapping is to merge various data sets into a single one.
The process of assigning or mapping a set of data to its destination, also known as the target, is known as data mapping. Data mapping aims to make your organization's data more organized, consistent, and available to your team or clients for improved usage.
Data mapping is widely standard in business as businesses regularly deal with large volumes of data. However, as data volume and system complexity have increased, the data mapping process has become more complex and calls for adopting automated technology for improved usage of data mapping.
Data comes from various sources, and each source has unique definitions for the same data points. For instance, a source system's state field might display New York as "New York," whereas a destination system would record it as "NY."
In essence, data mapping enables the precise and practical transfer of data from a source to a destination by bridging the gaps between two systems or data models, drastically improving the efficiency and effectiveness of the data at hand.
A data format is the structure of data residing within a database or a file system that gives meaning to the information. Since the emphasis is primarily on gathering relevant data, the data's format usually takes a backseat. Data comes in various formats because it is gathered from a single source or several sources.
Making the best use of data requires organizations to ensure the format of the data. The best format is one that is open and long-lasting. Here are examples of preferred format choices:
Data is an integral part of every organization. Organizations use data from their internal and external sources to derive business value. To do so, this data must be easy to process and analyze. Here are some key considerations to help achieve that objective:
For data to be integrated, the source and target data repositories must have the same schema. It is rare for any two schemas to be identical. This is where data mapping comes into play, bridging the gap between the schemas and allowing businesses to easily consolidate information from different data points.
To move data between databases, data managers must create maps between the source and destination. This can be a cumbersome task if done manually and bears the risk of being inaccurate. An automated data mapping solution addresses this challenge and enables the automatic migration of data.
Data can be stored in several locations and formats within an organization. Data mapping is essential to break this information into an easily analyzable form. Data mapping creates a framework of changes that must be made to data before it is loaded into the target database or data warehouse.
Data mapping is integral to Electronic Data Interchange file conversion by converting files into various formats, such as JSON, XML, and Excel. A data mapping tool can help extract data from different sources and utilize built-in transformations and functions to map data to EDI formats without writing a single line of code. This process helps streamline the B2B data exchange.
There are three types of data mapping techniques.
Automated data mapping needs specialized software to match new data to your current structure or database. These tools use machine learning to continuously enhance or monitor your data models. Data mapping automation has several benefits, including:
Semi-automated data mapping, commonly referred to as "schema mapping," is a method that combines the best aspects of both manual and fully automated data mapping where a team member manually reviews the system and makes any necessary modifications after the process has been laid out.
This is an intelligent approach when performing straightforward integrations, migrations, or transformations on tiny datasets, especially for teams working with tighter budgets.
Due to the enormous amount of data that modern businesses have access to, it is getting tougher to develop a solid data management strategy without automated tools. Instead, when the database isn't too extensive, manual data mapping is a decent option for a one-time activity.
Here’s the data mapping process:
Identifying which data needs to be transferred or restructured is the first step in the data mapping process. There isn't a universal recipe, which is unfortunate. Ensure that data accuracy is kept and that there is no data loss. Make sure the interpretations are correct.
Figure out the data flow. Map the relevant data formats from the source to the destination. Maintain logs with the necessary level of detail and pay particular attention to any problems or obstacles.
To store and use a field effectively afterward, it may be necessary to change it there. For instance, you must convert your data into a consistent Standard Time Format before analyzing it if it was collected from different time zones.
Visual, manual, and automated testing are all common testing types. Due to the enormous amount and diversity of data being processed nowadays, automated testing is no longer a luxury but a need. When the tests are complete and the user is convinced, the data can be deployed or moved to a datastore where analytical or business processes will use it.
The data mapping method will require upkeep and updating when more recent data and data sources are incorporated. Consequently, robust data mapping tools are required to keep up with the evolving needs and an increasing number of data sets coming your way.
Most data privacy laws encourage organizations to incorporate data mapping to comply with their requirements. These laws may not explicitly mention the need for data mapping, but some rules make it evident that using data mapping is the best way forward.
Under the CCPA, several requirements encourage organizations to conduct data mapping:
Under the GDPR, several requirements encourage organizations to incorporate data mapping. For example:
These are just a few examples of how data mapping helps organizations fulfill their legal requirements. Even though data mapping is not a statutory requirement, it is the best way to organize your stored data and make it easy to present to consumers upon request since data mapping ensures organizations know precisely where their customers' data is stored, what type of data is stored in the various data stores, how it is processed, the purposes of the processing of the personal information, and to which entities it is transferred.
Data mapping can help organizations gather all this information and maintain an accurate and complete record of personal information.
Three types of data mapping organizations leverage to improve the efficiency of this process. These include on-premises, cloud, and open-source.
These tools are found on the native computing infrastructure of the organization, eliminating the need for hand-coding any complex mapping and automating any repetitive tasks in the data mapping process.
These tools recruit the help of cloud-based services to perform their data mapping operations.
This can be a low-cost alternative to an on-premises solution. This type of data mapping is ideal for small businesses with minimal data and simplistic use cases.
Software tools for cloud-based data mapping are quick, adaptable, and scalable, and they are designed to tackle challenging data mapping requirements without breaking the bank. Although the features and capabilities of a data mapping tool depend on the company's requirements, there are several standard aspects that you should look for.
Most programs support common file types such as XML, JSON, EBCDIC, delimited text files, and Excel. In your industry, look for a solution that supports standard formats like SQL Server, Sybase, Oracle, DB2, or others.
Repeated processes can be automated using an easy-to-use cloud-based solution, significantly reducing time, boredom, and the possibility of human error.
Furthermore, the data mapping tool should:
Securiti works towards automating business processes such as data mapping and DSR fulfillment to give organizations an edge when complying with global privacy regulations.
Securiti’s Data Mapping Automation solution enables organizations to seamlessly migrate from their traditional data mapping approach to a fully automated approach, providing value at each step.
Request a Demo on how this solution can benefit you on your road toward compliance.
At Securiti, our mission is to enable enterprises to safely harness the incredible power of data and the cloud by controlling the complex security, privacy and compliance risks.
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