Today’s businesses are changing due to data. Everything depends on data, including daily operations and corporate decisions. And none of this is feasible without first turning raw data into information that is usable, especially when there is a lot of data and information coming from many sources. Tools for data modification are useful in this situation. It transforms data into the necessary format, making it simple to clean and map for insight extraction.
What is Data Manipulation?
Data manipulation is the process of modifying data to improve its readability and organisation. For instance, website maintenance is an illustration of data manipulation. Web server logs can be used by website owners to find the most popular web pages, traffic sources, and much more. Similarly, you can sort the data alphabetically to make it easier to identify relevant information. Stockbrokers manipulate data to predict movements in the stock market. Any retail and wholesale business entity performs the same to get KPIs.
Why to perform Data Manipulation?
Companies manipulate data to use it for things like trend forecasting, understanding consumer behaviour, boosting production, cutting expenses, etc. Unified and organised data helps business users make better judgements. Quick access to historical data can assist a company in making judgements about budget allocation, team productivity, and deadline projections, among other things. A firm can isolate and even eliminate external variables to help with the overall efficiency of the business by having more organised data.
Data Manipulation Language
Data manipulation language, or DML, allows for the better organisation and readability of data. It is a type of computer programming language used to add, remove, and update data in databases. It makes it simple to map and clean the data for additional research. Structured Query Language (SQL), which is used to update and retrieve data in a relational database using Insert, Select, and Update instructions, is a widely used language for data manipulation.
Data Manipulation Tools
You can change data using tools for data modification to make it easier to read or organise. Your data may contain patterns that are not immediately apparent without the aid of these tools. For instance, you can use a data manipulation tool to sort a data log alphabetically so that discrete items are simpler to find. Data manipulation is frequently confused with ETL and other transformation methods. Data manipulation, on the other hand, entails sorting, shifting, and rearranging data without really altering it. It entails actions to modify data in the format required for information display or to feed and train an analytics model. Not the data itself, but rather the relationship (either logical or physical) that one data item has with another, is the main goal of data manipulation. Row and column filtering, aggregation, join and concatenation, string manipulation, categorization, regression, and mathematical formulas are common processes used for data manipulation.
ETL, on the other hand, accomplishes something distinct. Before writing into the destination system, data must be extracted from the source system and made compatible with it.
Need of Data Manipulation Tools
Process optimization requires careful data manipulation. It converts data into a form that is useful and can be utilised to further develop insights. To make data compatible with the target system, data manipulation techniques are frequently employed during integration. Broadly, data manipulation may be advantageous in the following ways:
- Data Projection
- Data Consistency
- Duplicate Data Removal
- Data Interpretation
- Value Generation
Sequence of Data Manipulation
Five essential steps make up effective data manipulation:
- Data extraction from data sources is the initial phase.
- The data from the source system(s) should then be cleaned before being rearranged and restructured.
- Create and import a database that will be used as the staging area.
- Depending on business needs, combine or exclude certain information.
- Lastly, make use of the altered data to gain insightful knowledge.
