Companies often use various tools and methods like mapping to organise their data and records. Professionals like data analysts and specialists can use the process of data mapping to analyse the gathered information and transform it into a suitable format. Understanding how the mapping process works can help you fulfil your data management requirements. In this article, we define data mapping, explain its usage in the industry and share different types of mapping methods to structure the database.
What is data mapping?
Data mapping is a process of analysing multiple sets of data and finding similar data to transfer them to a single or centralised database. This process allows professionals to perform various actions such as managing the data, transferring it from one database to another or ingesting and processing the data. Companies often gather information in the form of facts and statistics and use them for reference and analysis. They use mapping to study distinct patterns and create a single set of data.
Usage of a data map in the industry
Companies use mapping techniques to study the key insights in data analytics and business intelligence. Unsorted or unchartered data may create confusion and make it difficult for data analysts to understand which information is redundant. The mapping process can help data analysts in formating, organising and moving data into the structured target fields. It can make your data management strategy more effective. You can use mapping of data to ensure the accurate transfer of data from its source to destination.
Here are a few other benefits of creating data maps:
- helps in data mediation and transferring data from its source to a specific destination
- helps in understanding the conversion rules of the business and applying them to the source data
- shows the changes and descriptions associated with the data
- assists data lineage analysis by identifying the relationship between them
- provides a description of the source and target data
- ensures that the data when moved to its destination is usable
Types of data mapping
Here are the different ways to map data that companies may use:
Data integration
The ongoing process of collecting and bringing all data into a centralised location or source is data integration. Professionals who execute the mapping process may use this method to move data from two different sets into a single stream. It is the process of moving data from one source to another. For example, the sales and marketing teams of an organisation collaborate with each other to combine their database of leads and their contact information. In this example, data analysts may take databases from both teams and use mapping to remove duplicate information. The final step in this process is choosing the right format for the new database. Data integration helps organisations make better decisions to improve customer experience and increase overall efficiency. Businesses gather relevant data from different sources and use data integration to structure it in a usable format.
Data migration
Data migration is a single-time process that businesses may use to transfer data from its source to a destination. This process can help you transfer data from outdated sources to new systems. You can use data migration for various purposes such as website consolidation and data centre relocation. Many businesses use this process to reduce redundant data, minimise data storage costs, improve collaboration and create a backup for future use. Data migration also allows you to transfer data from a local data source or centre to the cloud. The process of transferring data to the cloud may come into two categories which are online and offline migration. In online migration, data gets transferred through the internet or a WAN connection. The offline migration involves the transfer of data using a storage appliance that gets shipped from the source location to its destination. The first step in data migration is converting the source database format to make it compatible with the destination location.
Data transformation
The process of converting a specific data format into another is data transformation. The major goal of data transformation is to make the data clean, valid and ready to use. Many companies prefer cloud-based data storage that allows them to access and scale their storage resources in lesser time. They can use data profiling tools to discover and understand the data in its original or source format.
The next step is planning the transformation process of the data. The final step of data transformation may require reviewing the final set of data and ensuring the results are according to the expectations. A few examples of data transformation are reducing the duplicates, changing data formats, decreasing the size of data and adding new information to the data set.
Data warehousing
Many companies use secured storage called data warehouses to store their data or information. The process of storing the data in these storages is data warehousing. The main purpose of this process is to store all the important data in one location so that it is easily accessible for making organisational decisions.
Professionals like data analysts gather new data which gets automatically added to the data warehouse. The storage allows data analysts to run queries and analyse historical data to get insights. Data warehousing can be a useful tool for improving the operations and future performance of businesses.
For example, a company that deals in manufacturing gym equipment plans to start a new campaign to support the marketing of its specific product. They refer to the data warehouses to understand their current customers, retailers and dealers on their previous selling performances. The company gathers all the important information related to the product and focuses on devising the best possible strategy for marketing. Data warehouses can also give insight into the location of retailers and dealers. The company uses all this information to develop strategies best suited to advertise and market the product.
Effective steps to map data
Here are the steps you can use to map data:
- Define your data
The first step of the mapping process involves selecting the data you want to move. This data can be in the form of charts, graphs, groups or tables. After selecting the data, determine how you want to format the data. If you are using the data integration method, consider defining its frequency.
- Map the data
The next step is mapping data from its source to a destination. You can do this by matching data categories and additional features of the source to the new data set. By mapping data, you can move it to an accurate location. It can also help you reduce possible issues with the new location. Once you map the data, you can transform it according to your requirements.
- Transform the data
If you want to introduce any change in the data you store at the destination location then this step can be helpful. This step allows you to remove duplicates or irrelevant information from your data set. You can also change the form of data to fit its new formatting.
- Test the data
After creating a data map and executing transformations, you can test the data. Consider beginning with creating a set of test data similar to the data set you want to move. After moving the data from its source to a new destination, compare it to check accuracy. Make sure you move all the data to the destination.
- Move the data
Consider creating a backup for the source data before executing the system for moving it. If there are any challenges in the system, address them in this step. Record the mapping process and use it to move another data set. You can repeat these steps for each data integration you perform.
