For making you understand in the simplest form, data mapping helps databases to talk to each other. Data professionals link different attributes and values together between various data sources. To illustrate, imagine you have data about customers in two databases. Analysts do not want duplication and want the database to count their customers distinctively, so they map the two databases for counting a customer, supposing John Smith, to be counting as one customer. Data mapping also reduces severances and ensures a more accurate analysis, so everyone is confident of the results generated through the process of data mapping. In this guide, we will learn what is needed for data mapping and how can we use it to discover useful insights from data faster.
Value of data mapping in Business Intelligence
Mapping is a very crucial step in the processing of data preparation in data analytics and business intelligence. If the data is combined uncharted, analysts will not know if the data sources are redundant. Unmapped data might even result in misrepresented analytics results, but all this havoc can be avoided with intentional governance. With data maps, analysts can have greater control when they are combining data from different data sources and can be sure that the results will be the true representation of the efforts that were put in to solve problems and hurdles that are being faced by the organization.
Selecting the right data mapping procedure
First, consider the best data mapping technique which is the most relevant to your circumstances or needs and the total cost that will be borne by the organization in the purchase and implementation of that data analytical application within your organization. There is certain level of technical knowledge needed, even if these techniques do mostly the work for you.
There is a range of fully manual to 100% automated data mapping solutions, each having their own pros and cons.
Manual data mapping
- Benefits: Completely custom and flexible to your needs.
- Drawbacks: time-consuming, manual, tool-agnostic, dependent on coding, and resource intensive.
Manual data mapping requires immense expertise. It involves connecting data sources by using languages like C++, SQL, or Java. Data mappers use these coding languages to perform ETL (Extract, Transform, and Load) processes to move data between databases. Although data professionals are normally presented within the organization to perform these tasks, however, you can create data map with complete control.
Semi-Automated data mapping
- Benefits: It has the perfect balance of flexibility and effectiveness.
- Drawback: Required navigating into both manual and automated processes, resource intensive.
In the semi-automated data mapping, analyst use graphical representation of data links. It gets helpful in creating visual intensive schema maps for better understanding. For example, users match “StudentName” of one database with “Name” of another one by drawing lines, drag-and-drop function, or smart clustering functionality in software like tableau prep. On the other hand, there may be coding involved with the mapping processing, just like manual process as described above. Having an option to manually code for mapping data gives an extra option for the maps when you do not have required functionality available in the data mapping software.
Automated data mapping
- Benefits: not much technical knowledge required, fast, easy to scale, lesser barrier to entry, deployment flexibility.
- Drawbacks: Training should be executed on a specific software / tool, usually comes with a heavy price tag of the software being purchased.
Modern data mapping software are evolving to become fully automated. This means that not only technical. But also, non-technical professionals can complete data mapping processes without coding easily and swiftly. There are some mapping platforms which make using of NLP (Natural Language Processing) to match data fields and attributes and further help the data mappers in describing the contents in a data source.
What is required for a data mapping template?
It may be difficult for you visualizing how the process of data mapping looks like but following this simple outline will reveal how the attributes and other parts of schemas are matched.
- The name of the source data tables which will be joined or transformed together.
- The name of the target database where the data will live after transformation.
- The columns or attributes of the data tables that you are matching.
- Know the final format of the data after the transformation.
- What will cause the data transfer or integration of the databases.
- The time duration of running the data flows and how to troubleshoot if a failure occurs.
Managing data maps and its complexities
Data maps becomes overly complex real fast and that poses challenges. For example, maps must account for the metadata and schemas before transferring it to their destination. Data mapping software have their unique way of storing data and have different metadata. Data mappers must understand and combine them during the processes. Considering the data residing in your company will get larger every day, you need data management policies that facilitates the life cycle of ingestion, mapping, storing, and analysis.
Imagine you have a task of arranging all items in your house to their suitable places. When you are finished with your work, a guest enters your house and place all those items on different spots in your house. In the same way, data mapping procedures cannot be executed every other day. So, it is best to plan and execute the best possible ‘constant’ policies of data mapping in the organization.
How the mapping of data intersects with modeling, data prep, and data transformation
Firstly, data mapping process requires the data transformation to take place, such as setting a standard date format DD-MM-YYYY within all your tables. Data cleaning step is usually considered to be a project by itself, that is why it is kept separate from data mapping. Secondly, data mapping is a phase of data modeling. Data models are a framework of data maps. A data model is a representation of how a data flow within the system. Data professionals and business stakeholder work together to decide how the data model would look like. Once they decide on data models, data mapping begins. Analysts fill those frameworks with the cleaned and organized data generated. All these activities are a part of modern governance workflow.
The intersection between data mapping and data prep
Usually, data mapping involve data prep. Data professionals ensures that the data is ready and standardized according to their needs before they sew them together and maps them to their destination. Data prep does not need to be a boring manual process either as excellent software like Tableau prep can make the lives of data preppers easier by streamline this process at any coding level. Like data modeling tools, prep involves not only drag-and-drop features but also supports Python and R integration as well.