Data Mapping is specifically used in Big Data Integration, to collect randomized Big data sets, and convert them into easy-to-use sorted and organized formats.
“Data is the new oil.” — Clive Humby
- Definition Data Mapping:
- What is Data Mapping:
- How does Data Mapping work?
- Data Mapping Steps:
- What is the purpose of data mapping?
- What is the use of data Mapping/ What are the Benefits of Data Mapping
- Data Mapping Techniques:
- Purpose of Data Mapping in Artificial Intelligence & Machine Learning:
- What will be the Future of Data Mapping?
Definition Data Mapping:
Data Mapping is a process used in Data warehousing (Collection of huge business data) by which different Data models are interlinked to each other by a specific method to characterize Big Data in a specific definition.
What is Data Mapping:
Specifically speaking Data mapping just provides instructions to multiple data sets to configure and align to a single system configuration. we can also say data mapping is the linking of data sets.
The main goal of data mapping is to mix multiple data set into single data set in a homogenized form.
Data Mapping is specifically used in managing Big Data by many big and small companies.
How does Data Mapping work?
Let’s consider you have to apply data mapping to a family (their food, income, clothes, daily expenditure, etc), so let’s consider the income of a single family and we have to link the data of the first family to another family let’s say it as family 2, in this the process of linking data of two families with the help of data mining, data warehousing is called as data mapping.
In simple words, we can say linking any type of specific (Related type of data) data is called data mapping, which can be done with the help of many factors like data mining (extraction of data) and data warehousing (storage of bigdata) and much more.
Data Mapping Steps:
1) Data Warehousing:
Data Warehousing is done with the help of Data Integration, data integration or data warehousing simply means, transferring or integrating the data that is in another form to a data warehouse where it should be in another required format.
It’s a little bit complex process to understand, but in data mapping, the primary data is studied and decided to transfer to the specified data warehouse.
Data warehousing simply means storing data into a specific schema.
2) Data Transformation:
Data Transformation simply means Transferring data from the previous form to another required form, It is considered one of the important tasks because it decides where the data is going to integrate or transferred.
3) Data Migration:
Data Migration simply means the source data is migrated to the new targeted data repository, it simply means migration of data to its required space.
What is the purpose of data mapping?
The main purpose of data mapping is to process a huge amount of randomized data and convert it into a usable form, that can be easy to handle and process further for analytical purposes.
It simply means to leverage business useful data that can be transformed into a suitable format for data analysis, statistics, and much more.
Here are some of the points that specifies what is the purpose of data mapping
- Through Data Mapping we can precisely link all the data related to specific niche and schema, so it will be easy to manage huge jumbled-up data.
- To convert a large amount of data into a systematic format that will be easy to access.
- Data mapping can increase work speed.
- High productivity can be obtained with the help of data mapping as it reduces the hassle of sorting important elements from big data.
- As data mapping organizes data into a usable format so there are fewer chances of error, so data mapping can help to decrease error.
What is the use of data Mapping/ What are the Benefits of Data Mapping
Here are some of the uses of data mapping or benefits of data mapping that can help to grow businesses
1) Well organized:
So the main job of data mapping is to keep data well organized, in data mapping we sort data elements into the organized form so then it would be easy to pick the required data packets.
2) Faster Decision Making:
Faster decision-making is very essential in any corporate world, data mapping helps users to locate the required data and thus increases decision-making ability, it’s easy because we have sorted the required data from a bunch of random data sets.
3) High Efficiency:
High efficiency is simply how efficient the system works, and for efficient working of the system we have to organize the data in such a manner that it would be easy to fetch and use it.
4) Improved customer relationships:
It’s a bit tricky thing about improve customer relationships by using data mapping, but it used in chatbots that are used in customer support.
How does that work is simply chatbots that are used in customer support has dedicated segments related to human emotions and search queries, these search queries information are sorted with the help of data mapping.
5) Decreased Error:
As we know data mapping is used to arrange data, indirectly arranged data leads to decreased error.
But how does data mapping decreases error? let’s take an example if I had data of 71 teams of football and I have to fetch the best players for attacking, so for that, I have to go through all 71 teams and their team players that which one is best.
Instead of that, by using data mapping we can sort out the best-attacking players from all 71 teams, now we have the sorted data, so we can easily pick the best players from the sorted data set, and thus it decreases the error of finding another type of players like defenders.
6) Time Saving:
Many companies data mapping, and believe me in the future many small-scale companies will too implement data mapping in their systems, it’s because using sorted and arranged data for a specific purpose can easily save tons of hours.
7) Increased Productivity:
It’s directly related to the decreased error, time-saving, high efficiency, and much more, in the end, we can say data mapping is amazingly beneficial for increasing productivity.
Data Mapping Techniques:
First of all, we have to consider what type of data mapping we have to implement generally, there are three types of data mapping techniques what we call as
Let’s discuss these data mapping techniques and how they work
- Manual Data Mapping
- Automated Data Mapping
- Semi-Automated Data Mapping
1) Manual Data Mapping:-
As the name suggests it’s manual and done with the help of individuals doing it manually, it is basically done by manually extracting, and processing the data with the help of programming in C++, Java, or MySQL.
In manual data mapping professionals are required for data warehousing, data migration and, data transformation.
The major cons of manual data mapping are that humans control it and there are high chances of error, and also huge time consumption, and as we are using professionals their salary is also a concern for investors.
2) Semi-Automated Data Mapping (Schema Mapping):-
Semi-Automatic data mapping consist of half work is to be done by human and the remaining is done by computers, it simply means it also requires coding knowledge and human effort but in a less technical way.
In this, the software itself compares and arranges the schema data, but it requires human commands and guidance for further processing.
In schema mapping, humans have to identify and command the software what kind of data is to be extracted and utilized and processed, after that the software handle’s the remaining tasks in languages like C++ & C#.
The major drawback is it requires a little bit of programming and maintaining knowledge.
3) Automated Data Mapping:-
Here comes the most advanced type of data mapping, automatic data mapping uses specialized tools to sort your random big data, There are many tools that help to automate your task of mapping your data in formats like a google sheet, Salesforce, Hubspot, etc.
The biggest pros of automated data mapping are we don’t require programmers for complex coding.
As there are advantages, there are some significant disadvantages like a high price, i.e the investment in automated data mapping is high.
How to apply Data Mapping Techniques effectively and efficiently?
First of all, check that your data is clean, which means free of junk and unwanted files, and spam, the data which is considered for mapping should be robust and clarified.
Make sure what kind of mapping you are going to perform on what type of data, like sheets, documents, etc.
Take a trial on a small amount of data, and when everything works perfectly integrate your big data with tools, and choose what kind of data mapping is required like duplicating.
If you are using tools like WULT you can see the real-time mapping of data and also the errors encountered while data mapping, and also see the time required for mapping the remaining data.
Purpose of Data Mapping in Artificial Intelligence & Machine Learning:
As Artificial Intelligence works on neural networks, deep-learning thus it requires a large amount of sorted and arranged data, here comes the use of data mapping in Artificial Intelligence and Machine Learning.
Using Artificial Intelligence and Machine Learning in data mapping can significantly increase boost the performance of any company that relies on data mapping.
Artificial Intelligence and Machine Learning can work automatically on a huge amount of data and make improvements in the process of mapping the data. AI & ML can automate the time taking and stressful tasks, that required mapping data, so significantly it can boost the performance and profit margins of any business.
If you want to know about “FUTURE SCOPE OF ARTIFICIAL INTELLIGENCE IN BUSINESS” have a look.
Have a look at how AI-based Data Mapping works
What will be the Future of Data Mapping?
Technology is increasing day by day and thus the demand is increasing. Data Mapping is considered one of the important aspects to organize useful data that has been collected over a long period of time.
Believe me in the future Data mapping will be in great demand, due to many companies that are tracking users data, so the generation of big data will be continued to grow, and thus the demand for data mapping Technology and data mapping tools and data mapping engineers will be in great demand.
Till then Take Care, Be Bold Be Brave -Kunal Salekar