Everything You Need To Know About Data Mapping

Are you running a business? Then, you would know the significance of data and its role in the success of your enterprise. Note that data used by different companies will give them an insight especially when it comes to making smart business decisions. Without any doubt, the extraction of data from sophisticated and humongous data can be a challenging task.

Do you know there are multiple data models that can help with such tasks? Note that you can develop a relationship between different data models by adopting robust and appropriate data mapping techniques. This is highly important for companies that often deal with myriads of data. Needless to mention, you can rely on data mapping techniques to enhance your business efficiency as well as customer relations.

Most importantly, this process will help you identify the upcoming business trends and work on them efficiently by allowing easy access to data. Of course, some entrepreneurs will hear the term data mapping for the first time. Hence, it’s significant for such people to understand the concept in detail. Below are some of the crucial and important facts about data mapping techniques that you might want to know.

Data mapping

Data mapping can be defined as ‘the process of mapping data fields from sources (system X) to their target fields in a storage repository or a data warehouse (system Y)’. You should note that data fields mentioned above can often times include phone numbers, monetary information, emails, etc.

As mentioned earlier, there are different types of data mapping techniques. You can choose which one is best for you by considering the ranking of data streams that you’re mapping as well as the dissimilarity between the source and the target structure. Furthermore, the process of mapping data can be accomplished using these three techniques; namely manual, schema, and automated data mapping.

Manual data mapping is all manual and is done using hand-coding methods. Even though it offers immense flexibility, it’s not a suitable choice for complex businesses. When it comes to schema type data mapping, two distinct yet semantically similar data objects are identified for building data mapping. This is a semi-automated method. In the case of automated data mapping, the data can be mapped successfully from a source to a target repository in a simple drag and drop way. This is a coding-free or automated method and comes with in-built transformations for converting data formats without any hassle.

The significance of data mapping

Data mapping is mainly used to extract necessary business information from a range of data collected from different sources. This is done by unifying the data and converting it into a suitable format that can be used for operational and analytical purposes. Then, an expert can convert the results into valuable business insights. There is a vital role in managing multiple processes and some of those options are listed below.

Data migration

Data migration can be defined as ‘the process of moving data from one database to another.’ In this process, the formation of mapping between the source and target is one of the most sophisticated and time-consuming processes. If there happens to be any inaccuracy or validity issues during this stage, this can result in the whole data migration process failing. Thankfully, several data migration software programs can be used to nail the task flawlessly.

Data warehousing

This is the process of using a data warehouse, which is built by integrating data from various sources that support decision making, analytical reporting, and structured queries. On a related note, data mapping plays a significant role in the process of data warehousing. In other words, data mapping can be used to create an appropriate data model as well as define the ways on how to structure data and store it inside a data warehouse.

Data integration

The companies tend to rely on source and target archives that feature the same type of data model to have successful integration of data. However, it’s extremely challenging and difficult to find the source and target data storehouses with the same data schemes. In this case, data mapping tools will be beneficial for your company. Note that these tools will let you tackle the dissimilarities and differences between the source and target data models and, thereby, let your organization gather necessary information from multiple data points.

Data transformation

The data that can be used to extract significant business insights will be present in multiple locations and an array of formats. Hence, it’s significant for enterprises to disintegrate the information silos at first to get insights. For this, employing data transformation methods is one of the best ways. The first step of data transformation is data mapping while it forms a suitable framework before loading the data to the target repository.