Data Analysis

DATA ANALYSIS

  What is Data Analysis – Process Methods..

Data analysis is necessary for cost benefit analysis. System investigation and data collection is an assessment of current achievements. Our interest is in finding out how some steps are done with complete efficiency, how they can help in achieving the desired goals and how the cost of construction can be improved. Data analysis helps to make decisions more scientific and make the business operate effectively. It is being used in various business, science and social science domains.

The analysis identifies system design requirements. These features should be included in the candidate system to make necessary improvements.

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System requirements are as follows –

1. Quick notice preparation.
2. Better accuracy of billing.
3. Better Customer Service.
4. Rapid speed to retrieve information.
5.  Processing and operating improvements.
6.  Improving staff efficiency.
7. Compatible billing procedure to remove errors.

To achieve these objectives related to design, various options have to be explored. If there is usually more than one option. The analyst selects only those which are suitable from economic, technical and operational point of view. Each method has its advantages and disadvantages.

Read Also – What is Data ?

Process of Data Analysis 

• Data requirements

Data are required as inputs for analysis, which is made based on the needs of the analyst or customers (who will use the finished product of the analysis).

• Data collection

Data is collected from various sources.
Data can also be collected through various sensors, such as traffic cameras, satellites, recording devices, etc.
It can also be obtained through interviews, online downloads, questionnaires.

• Data processing

The data initially obtained must be processed or organized for analysis. For example, the received data can be kept as tables for which various spreadsheets or database management software can be used.

• Data cleaning

Once processed and organized, the data may be incomplete, duplicate, or contain errors.
Data cleaning is the process of preventing and correcting these errors. Common tasks include.

• Overall quality of existing data
• De-duplication – Under this, duplicate data is separated from the data.
• Column segmentation of data
• Record matching
• Identifying accuracy of data

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Exploratory data analysis

• Once the data spast or the data is clean, it can be analyzed.
• The analyst can use various techniques in the form of data analysis to understand the messages contained in the data.

Modeling and algorithms

• A mathematical formula or model called an algorithm can be applied to data to identify relationships between variables.

Data product

• A data product is a computer application that takes data as input and produces output.
• This can be based on a model or algorithm.
• For example, an application analyzes old purchases of customers and sees new products based on their needs.

Communication
Once the data is analyzed, users can be presented in multiple formats depending on their needs. It can be sent again for additional analysis as per the user feedback.

The analyst can use various data visualization techniques to communicate data clearly and efficiently to users. Like charts, tables etc.

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