Data consists of all the valuable information for a business or project. The enormous amount of data consists of the information required to be analysed. Studying and analysing the data is known as data analysis, and hence it narrows down the information from a vast pool of data. Despite the colossal volume of data, just a small percentage of data provides the necessary information. The data analysis and interpretation is the process of cleaning, narrowing, and processing the raw data, extracted into actionable value, and hence use the same in making an informed decision. The procedure provides valuable insights for processing all the necessary information.
What is the data analysis process?
The process of data analysis is not a one-step process; it consists of many steps, which ultimately lead to the refinement of the information.
- Data requirement – The first step is understanding the purpose of the analysis. This includes the type of data used for the analysis and the data required for analysing and narrowing down the information.
- Data collection – As part of exploratory data analysis, it is crucial to figure out a process for collecting the data from the right source. There are various sources and ways to collect the data, including interviews, direct observation, surveys, focus groups or simply experimentation.
- Data Cleaning – All the collected data may not be useful, so it is crucial to eliminate unnecessary data. Cleaning up removes duplicate records and errors and refines the data. Before you begin data analysis and interpretation, it is necessary to clean up the data.
- Data Analysis – Many tools and software help understand and interpret the data. The data analysis tools assist in concluding.
- Data interpretation – After analysis, data is interpreted based on the results from the data.
- Data Visualisation – This is the final step in the tedious and lengthy process of data analysis and interpretation; it finally helps present the information visually. It is presented in charts, maps, graphs, etc. It provides valuable insight and gives necessary comparison and observation.
Types of Data Analysis
What is data analysis? It is a method that helps in capturing useful information through the process of inspecting, cleansing, transforming, as well as modelling the dataset. It can be further categorised into many different types:
In this type of analysis of data, the insight about the data is provided using a numerical process, and hence numerical variables are summarised. This type of data analysis helps provide excellent and high-level information on data with the help of terminologies including mean, mode, median, standard deviation, variance etc.
Exploratory data analysis
This type of analysis provides a visual way to analyse the data. The data is first understood with numerical values and later explored through a visual procedure, which can be further divided into two parts:
- Uni variate analysis is done while exploring the characteristics of the single variable.
- Multivariate analysis is done by comparing multiple variables, and hence it helps in comparing the relationship between two or more variables.
Hence, the data is analysed with the help of plots and graphs in a visual manner. Bar plots, histograms, box plots, violin plots, etc., are mostly used for univariate exploratory data analysis. However, scatter plots, contour plots, multi-dimensional plots, etc., are commonly used for multivariate analysis.
- It is used to provide a visual way of describing the data and clearly identifying its characteristics.
- The exploratory data analysis is crucial for dealing with and analysing the high dimensional data.
One of the best ways to reduce errors and minimise issues is by making predictions about errors. Predictive data analysis is a scientific way of predicting future outcomes and analysing historical events. Hence, it helps in estimating the chances of a particular result. It answers the question – “What is most likely to happen in the future?”.
This type of data analysis helps predict the referential outcome for many sectors. It is impossible to get each data, so the result can be inferred scientifically from the samples and statistical analysis.
Importance of Data Analysis
- One of the prime importance of data analysis is researching from a huge pool of data and collating useful information. Hence, it helps distil the information into a more presentable and accurate manner.
- It opens up the researchers to a vast selection of tools that sequentially filters the data and gives valuable insight or information.
- It helps the researchers to analyse and study the data better.
Data analysis is a process that helps in transforming the available raw data into something significant, meaningful, and valuable for the business. The data analysis and interpretation process includes several steps, from specifying the data required to finally creating visual reports of the data. It is used by many businesses, companies, and projects to decide from a large pool of data and take necessary action. The article includes comprehensive information on the sequence of essential steps used in data analysis, its types, and their importance.