Updated July 6, 2023
Introduction to Data Manipulation
The data present in the organization is not always easy to read and understand for outsiders, which makes it difficult for data interpretation. Hence, making the data into a readable format by inserting, deleting, and modifying the data present in the database is called Data Manipulation. This can be done with the help of Data Manipulation Language or DML, where we have commanded so that the data can be arranged in a structured manner. Moreover, this data can be mapped to proper containers, which helps in fetching information from the database directly when needed. Furthermore, structured data can be easily visualized in charts and graphs.
How to perform Data Manipulation?
- Data Manipulation Language: We have Data Manipulation Language that helps in inserting, deleting, modifying, and renaming the data present in the database. Commonly used Data Manipulation Language is Structured Query Language. This helps in all the data-related activities with simple queries. Any form of data can be modified and updated in the relational database with the help of SQL.
- Database: It is important to store the data, be it structured or unstructured, in a database so that it is easy to fetch the data as and when needed. This database helps modify the data with the help of queries and excel sheets. We have ETL tools that arrange the data in the required format per the user.
- Data Wrangling: An important step in data modification is data wrangling, where the obtained data is cleaned and manipulated for missing entries. Though it sounds easy, this is the step where data engineers spend 80% of the time arranging the data for data analysis. Rework of data appears in most cases, and the missing entries must either be ignored or filled with entries similar to the adjacent rows.
- Information: One should know what information is required from our data and how to fetch the same information without manipulating the database. The staging area serves this purpose from where we modify the data and move it to the target area. This restructuring happening between staging and target area helps to modify the data as per the requirement of the business and present it in the form of charts.
- Presentation: Presenting the data not in the form of numbers but in the form of beautiful charts helps anyone to identify the business trend and make changes accordingly. We have various tools to generate the graphs, such as Power BI and many others. These tools will present the graphs if fed with structured data. This helps in understanding the past trends and making a forecast of data from the given information.
Data Manipulation Methods
Different methods are mentioned below:
- Data Operations: If needed, we can use the same data without any modifications. This is possible only if the data is structured and if the data obtained is less. For a huge amount of data, it is important to perform arithmetic operations to understand whether the data shows a negative or positive impact. Also, we can combine this data either with old data or other data present in the organization for different products, which helps us understand the customer’s buying pattern.
- Testing: For any operations we do in the newly obtained data, it is important to perform testing to understand the data pattern. If any unwanted data is present in the database, we cannot understand the same without performing testing on the data. This helps to identify the pattern and avoid the data if necessary. Consistent data helps avoid all this confusion, but it is important to do the testing if the data is not continuous.
- Logical manipulation: Once the data is structured and testing is performed on the same, it is important to understand the data performance. Logical thinking is needed in this stage that helps us to recognize the pattern of data and the market drift towards certain products. This helps to increase the production of those products and avoid certain other products from the organization.
Advantages and Disadvantages
- Helping in Business: Organizations use data manipulation to understand where their business stand and how to move to the next step. For example, website owners can understand the traffic on their websites through data manipulation. The business forecast can be done easily with the data available, and new products can be launched by recognizing customer sentiments. This helps populate enough data when required and make decisions based on the same.
- Avoiding worthless data: We spend most of the time wrangling in data manipulation, so it is important to ignore unwanted data. This worthless data will take our time and business by identifying wrong trends. Instead, considering required results will make businesses move on the right path and help increase the organization’s productivity.
- Ignoring data Anomalies: When the available data is huge, data analysts usually avoid the anomalies. This will not make much impact for the initial years, but avoiding data anomalies makes it difficult to fetch smaller details about the product performance on the market and get insights on why the products have performed differently. Therefore, it is better to investigate all the data anomalies to make the data perfect for future business.
- Tools: Most of the tools will have some disadvantages. Query performance might not be great in some tools, and some might ignore small details. Data Manipulation tools should not be taken into confidence as each analyst should do their homework if any tool behaves abnormally.
Data is considered in a high position for existing businesses and new startups. This makes data manipulation unavoidable; hence, the career in data analysis and manipulation has increased significantly in recent years. Manipulating the data with proper tools and concluding it for further business development helps to grow the business at different levels.
This is a guide to Data manipulation. Here we discuss How to perform Data Manipulation and Methods and the advantages and disadvantages. You may also have a look at the following articles to learn more –