In technological parlance, data is facts and statistics collected together for reference or analysis. Data can lead you to valuable insights upon Data Analysis. Almost every organization is employing data-driven insights to grow its business. But for any Data Analysis to give accurate results it is important to ensure that the data is migrated error-free and mapped in the right way. This is where Data Mapping comes into the picture.
In this blog, you will understand the importance of Data Mapping for Data Analysis and how data is migrated from the source to the destination.
What is Data Mapping?
Data Mapping is the process of matching fields from multiple datasets into a schema, or centralized database. To transfer, ingest, process, and manage data, data mapping is required. Data mapping’s ultimate purpose is to combine multiple data sets into a single one.
Different data sets with different ways of defining similar points can be joined in a way that makes them accurate and useable at the ultimate destination, which is known as data mapping.
Data mapping is a common activity in the business world. However, as the amount of data and the complexity of the systems that use it has grown, the data mapping process has become more involved, necessitating the use of automated and powerful technologies.
Example of Data Mapping
To help to understand what data mapping is and how it works, we are going to look at an example of multiple databases where data mapping is helpful.
We’ll look at several databases where the Data Mapping concept can be incorporated. In Data Mapping, when you merge databases into a single entry, you can query a single database to retrieve information on each. This is invaluable for businesses because it provides a comprehensive view of the company’s data assets.
Bringing databases together necessitates the creation of a map of the fields that clarifies and matches fields that should intersect. It specifies how to handle data from each input, what type it is, and what should happen if there are duplicates or other problems.
The data in this example is related to television shows. There are three databases available: TV Show, Actor, and TV Show Cast. Each of these databases contains fields that are both similar and distinct. Now you want to organize TV shows on the network, actors on the network, and actors within a show on the network. The data mapping between the three sources would look like this:
To summarise, Data Mapping is a set of instructions that enables the combination of multiple datasets or the integration of one dataset into another. This example is more direct, but the process can become extremely complicated depending on the following factors:
- The number of datasets being combined.
- The volume of data
- The number of schemas involved in the mapping process
- The frequency with which the data should be mapped
- The data hierarchy
What are the Data Formats?
Initially, the focus is on capturing the right data at the required level of detail, hence the format of the data takes a back seat.
The data is collected from a single source or diverse sources and this is the reason for data being in various formats. Consider some data stores location of public facilities in a city so it is inherently tabular and suited for the CSV format whereas nested data in the key-value format (for example, a categorized shopping list with quantities and prices) is best represented using JSON format.
Maintaining the right data format ensures easy and accurate Data Analysis.
What is the Importance of Data Mapping?
Data Mapping is the process of matching fields from one Dataset to another. It is the process of establishing relationships and ensuring interoperability amongst data in different formats.
Data fields from one or more sources are mapped to their related target fields in the destination. Data Mapping aims to make the data readily consumable by analytical and business processes down the line. It increases the quality and usefulness of your data. Another type of Data Mapping is called Schema Mapping, which entails Mapping source schema with destination schema.
Most of the time, when you extract useful facts (information) from a set of data, another set of data would add more meaning to it or correct your facts further. You need to map this data from different sources correctly to use it as a whole, to infer deeper insights and real meaning.
One misstep in Data Mapping can ripple throughout your fact-finding process, leading to replicated errors, and ultimately to inaccurate analysis. To eradicate this compounding effect, you must refine and map different sets of data correctly, such that they work in unison and give you the correct picture.
The volume of data that is generated these days is ever-increasing, so you need automated tools to make Data Mapping feasible on larger Datasets. These tools can handle your pre-processing needs and give better results than human intervention, which can be error-prone and subjective at times.
For processes like data integration or data migration, the quality of Data Mapping will determine the quality of the resultant data to be analyzed for insights. The pre-processing (automation, synchronization, extraction, data management) can be outsourced to the ETL tools.
The result of Data Mapping should be integrated data that is discernible, queryable, and actionable. It should lead you to valuable insights that lead to positive action, enabling competitive advantage.
Data mapping is required when integrating data into a workflow or a data warehouse. In many cases, the data that is being integrated is not the same as the data that is being kept in the warehouse (or elsewhere in the workflow).
The primary mapping procedure for a data warehouse is to identify the incoming data, assign it a name, and match it to the warehouse schema. Looking for locations where the datasets overlap and setting the rules that will govern the mapping process will be part of the process. Which database, for example, should be chosen if both databases provide comparable information?
Data transformation is all about taking data in a specific format and converting it into a different format or structure. This step can be a crucial stage to prepare information that is ready to ingest into a warehouse or integrate into an application.
Data mapping is vital in this process as it is used to define the connections between data and helps to determine the relationship between datasets.
How to do Data Mapping Effectively?
Let’s read about some of the steps that you can follow before, during, and after initiating the Data Mapping process.
- Define the Data fields for mapping includes understanding the tables, fields, and format
- Accurately mapping the fields in the data source to the fields at the destination.
- If you need to perform any transformation on data, it is a good practice to define it before using it.
- Testing the small chunk of data from the data source and checking if all the hairs are correctly mapped.
What are the Data Mapping Techniques?
The different Data Mapping Techniques are as follows:
1) Manual Data Mapping
This is the initial approach to developing a data mapping tool for your company. This necessitates the creation of linkages between the source data and the final database by developers. This could be a good solution for one-time data injections or custom data types.
However, due to the size of most datasets and the pace with which they must evolve in today’s data ecosystem, a manual approach may fail to handle complex mapping procedures. Businesses will need to switch to an automated solution in these situations.
2) Fully Automated Mapping
Businesses can use fully automated data mapping solutions to smoothly upload new data and match it to their existing schemas. Most programs display this process in a graphical user interface (GUI) so that users can see and understand the steps that data passes through, as well as map fields at each level.
Some allow customers to input data from thousands of different sources, and the mapping process allows them to bring data to their databases and solutions in an agnostic manner.
A completely automated system has the advantage of providing an interface that nontechnical staff may use to monitor and set up data mapping. Users may also monitor and observe how their data is being mapped, rapidly discover mistakes, and easily optimize the process.
Hevo Data, a No-code Data Pipeline helps to integrate data from 100+ sources to a data warehouse/destination of your choice to analyze it in your desired BI tool. Hevo is fully-managed and completely automates the process of not only Mapping data from your desired source but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code.
Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss. It provides a consistent & reliable solution to manage data in real-time and always has analysis-ready data in your desired destination. It allows you to focus on key business needs and perform insightful analysis using a BI tool of your choice.
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What are the Steps to Migrate Your Data for Analysis?
Data Mapping comes into the picture when you need to migrate data for your Data Analysis.
Steps to migrate your data:
Step 1: Identify Data
Identify the data that you need to map, and also identify data that may not be a part of the Mapping process. Clearly define the data relationships and their significance. Define any pre-processing that might be needed, and the frequency and priority of the Mapping process. You may want to map certain data first, and other data in later steps.
Ensure that there is no data loss and that data accuracy is maintained. Ensure that the semantics are in place. For example, each motorized vehicle in the world is assigned a unique identifying number, called a Vehicle Identification Number or VIN. The diagram below shows the semantic Mapping of VIN data – how codes may vary in the countries/values they depict.
The above example depicts the importance of knowing the semantics of your data and how they act as an indicator of facts. Finally, define and lay out your Mapping instructions and procedures.
Step 2: Perform Data Mapping
Identify the data flow. Map data from source to destination relevant formats. Maintain logs at the required granularity and keep a close eye on errors or bottlenecks.
Step 3: Transform Your Data
If required, a field should be transformed at the destination, to be able to store and use it efficiently later. For example, if your data is being collected from different time zones, you have to change it into a common Standard Time Format and then analyze it.
Let’s take another example, assume you are collecting sales data from different countries then you have to change it into a common currency to get accurate results.
Step 4: Test and Deploy
Testing can include visual, manual, or automated testing. Automated testing is a necessity owing to the sheer volume and diversity of data being processed these days. After being satisfied with the tests, one can deploy the data, i.e. migrate it to a datastore from where the analytical or business processes would consume it.
Step 5: Maintain and Update
As newer data and data sources are added, the Mapping process will need maintenance and updating. Maintaining and updating our foremost if you want to improve.
Providing a high-quality ETL solution can be a difficult task if you have a large volume of data. Hevo’s automated, No-code platform empowers you with everything you need to have for a smooth data replication experience.
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- Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.
- Schema Management: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming data and maps it to the destination schema.
- Minimal Learning: Hevo, with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.
- Hevo Is Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
- Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.
- Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
- Live Monitoring: Hevo allows you to monitor the data flow and check where your data is at a particular point in time.
What are the Advantages of Using Data Mapping Tools?
Apart from the obvious performance and accuracy improvements, Data Mapping tools provide some more benefits:
- Transparency and a unified view of the source and destination data help programmers, analysts, and architects to have a transparent bird’s eye view of the data at both ends resulting in fine-tuned analytical processes leading to better insights.
- These help you focus on Data Analysis instead of the rigorous processes such as Mapping, extracting, transforming, and loading data.
How to choose a Good Data Mapping Tool?
- Should support many data formats and diverse systems.
- Have the ability to handle complexities like foreign keys, aggregates, blobs, hierarchies, etc.
- Should support automation and scheduling.
- Maintain audit trail and logs to help find errors and refine the process.
- Should provide a visual interface depicting the Mapping.
- Should have data conversion, pre-processing, and validation facilities.
- Should be backed by a professional team.
To conclude, it’s important to ensure that Data Mapping is done right to facilitate insights in real-time and help you achieve your business or academic goals. Why spend your valuable time and resources on Data Mapping and Migration?
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