Enterprises nowadays not only have a huge repository of data, but they keep collecting large volumes of data from a variety of sources for various analytics and business intelligence initiatives. Collecting large volumes of data and piling it up in data warehouses is no longer one of the biggest challenges facing organizations. In fact, what organizations struggle with is getting their databases to speak the same language so they can talk to each other. This is the first important step in the data integration process that, if not done right, can result in ineffective decision-making due to replicated errors and inaccuracies in data analysis. This is where data mapping comes in – but what is data mapping?
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What is data mapping and why is it important?
Data mapping can be defined as the process of matching data fields or elements from one or more sources to their corresponding data fields or elements in destination. In other words, data mapping allows us to create relationships between data models present in disparate sources.Many a times, organizations use what is known as a data mapping template to map data fields from source systems to target systems. There are various data mapping, data integration, and ETL tools that automatically map data fields from one source to another. This not only speeds up the process, but also results in massive savings in the long run, and greatly reduces the risk of errors.
There are certain data mapping best practices and techniques when it comes to mapping data fields from one system to another. Before we get to them, let us first see why data mapping is important. Data today comes from a variety of sources in different formats, and it is impossible draw insights if it is not homogenized. Data mapping enables two systems to communicateby ensuring that the data is in the required format at the destination.When data is mapped correctly, it can be integrated, transformed, migrated as well as loaded into data warehouses easily. It ensures that the data does not have inconsistencies and is accurate. This makes it accessible to the decision makers for confident decision-making.
Data mapping techniques
As far as data mapping techniques are concerned, there are mainly three types – manual, semiautomated, and fully automated.
- Manual data mapping
This is, as the name suggests, the manual method to map data. It requires professional coders from IT or other technical department to write code and map data sources to the target schema. While this is time-consuming and requires a lot of expertise, it offers full control and customization.
- Semiautomated data mapping
This data mapping strategy is also frequently called schema mapping. Once again it can be inferred from the name that it is a combination of both manual and automated data mapping strategies. What happens is that data mapping tools create links between the data sources which are then verified by IT or technical personnel. Any adjustments that are necessary can then be made.
- Fully automated data mapping
In contrast to manual and semiautomated data mapping, a fully automated data mapping requires no coding. Data mapping tools or other sophisticated data integration and ETL tools are fully capable of taking care of the data mapping process on their own. This normally happens in a code-free, drag-and-drop environment.
Data mapping best practices
Having gone through the data mapping techniques, let us now look at the 5 best practices to follow when mapping data:
- Identify and map data
Mapping data can be a challenge; however, it can be easily dealt with by identifying the data owner. These data owners are well-versed in their domain and bring a wealth of knowledge when it comes to giving meaning to the data. Clearly, it only makes sense when someone who has the knowledge on the history of data is involved in mapping it.
- Ensure data security
Data mapping tools, more specifically, data integration software these days are equipped with advanced security features that protect the data and data repositories from unauthorized access while providing certain level of access to users such as data analysts.
- Automate the process
Using automated data mapping, organizations can save resources in the long run as their data changes and evolves over time.
- Maintain periodically
For a data pipeline that continues to flow data seamlessly, it is crucial to maintain it from time to time, or when the need arises. This might mean updating or modifying existing data mappings; therefore, it is important to ensure that the mappings are as accurate as possible before the maintenance.
- Keep records of data sharing
Data maps are critical to keep a track of entities with whom the data is shared. It is necessary to document every aspect of data mapping and how it is used to ensure there are no disruptions when new people are involved in the process.
As with every other aspect of data integration, there are some best practices to take advantage of when mapping data. There are also different techniques that organizations can take advantage of depending on their use-case, budget, and specific requirements. While manual data mapping offers customization and full control at the expense of time and effort, and fully automated data mapping offers ease-of-use, speed, and code-agnostic environment, semiautomated data mapping strikes a balance between the two.
