Today, businesses require every advantage and edge they can. Due to challenges like shifting markets and economic uncertainty, changing the political landscape, unpredictable consumer behavior as well as global pandemics, today’s businesses have fewer margins of mistakes. In this blog, we will introduce what is data analysis and its process.
Businesses that wish to remain in business, but also flourish will increase their chances for success through making intelligent decisions when answering the question: “What is data analysis?” How does an individual or a company decide on these options? They do this by acquiring as much actionable, useful information as they can and making better-informed choices!
This is common sense and can apply to your both personal and business. It is impossible to make important decisions without knowing the stakes, the benefits and drawbacks as well as the possibilities of results. Also, no business which wants to grow must make decisions based on flawed information. Companies require information, they require data. That’s where analysis of data is a part of the picture.
Before we get deep into the specifics about methods of data analysis we must first comprehend the concept of data analysis.
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What Is Data Analysis?
Although a myriad of groups, organizations and experts have their own methods of approaching data analysis, the majority are able to be reduced into a common definition. Analyzing data is the method of cleaning, changing the raw data and extracting relevant, actionable data that can help businesses make well-informed decisions. This process helps to reduce the risks associated with decision-making by providing valuable information and statistics that are often displayed in charts, images table, graphs and charts.
An easy illustration of data analysis can be observed whenever we make an important decision in our everyday lives, by looking at the events of the past or what might occur if we take that decision. In essence, it is an analysis of the future or past in order to make a choice on the basis of the results of that analysis.
What Is the Data Analysis Process?
Data Analysis is the method of collecting, processing clean, and modeling data in the hopes of obtaining the information needed. The outcomes are then communicated to the public, allowing for conclusions and aiding the decision-making process. Data visualization can be utilized to present the data to ease of identifying important patterns that exist in the data. The words Data Modeling and Data Analysis are the same.
Data Analysis Process consists of the following steps that have an iterative nature
- Data Requirements Specification
- Data Collection
- Data Processing
- Data Cleaning
- Data Analysis
Data Requirements Specification
The data needed for analysis is derived from an inquiry or experiment. Based on the needs of the people who are who are conducting the analysis, the data that is required for the analysis are determined (e.g. The population of persons). Particular variables pertaining to a particular population (e.g. Age, Income and age) can be identified and then gathered. The data can be either quantitative or categorical.
Data collection is the process of gathering data on specific variables that are consider to be data requirements. The focus is on ensuring an accurate and reliable collection of data. Data Collection makes sure that the information collected is reliable and the resulting decisions are accurate. Data collection provides an objective measure of the baseline and an objective to increase.
The data gather from a variety of sources, ranging from databases of organizational nature to information found on websites. The data gathered could not be well-organized and could include irrelevant data. Thus, the information collected must undergo Processing and Cleaning. Processing as well as Data Cleaning.
The data taken must be processed or arranged to be analyzed. This means organizing the data in a manner that is required for the applicable Analysis Tools. For instance the data may need to be arranged into columns and rows in the table of an Spreadsheet or statistical Application. In addition, the Data Model might have to be constructed.
The data processed and organized may not be complete, contain duplicates or mistakes. The Data Cleansing process is the method of fixing and preventing the mistakes. There are a variety of kinds of Data Cleaning which are based on the kind of data. For instance, when cleaning financial data certain numbers could compare with credible published figures or thresholds. In the same way, quantitative data techniques are a good tool for detecting outliers, which would later exclude in an analysis.
The data that has clean, processed and organized is ready for analysis. There are a variety of data analysis techniques available to comprehend, interpret and draw conclusions based upon the requirements. Data Visualization can also be utilized to analyze the data in a visual format, in order to gain more understanding of the messages contained in the data.
Statistics Data Models like Correlation, Regression analysis,, and Correlation can utilize to discover the relationships between variables in the data. These models that provide a description of data can help in facilitating analysis and communicating the results.
The process may need additional Data Cleaning or Data Collection, so these processes are the result of an iterative process.
The outcomes of the data analysis should publish in the format needed by the users to aid in their decision-making and subsequent actions. The user feedback could lead to additional analysis.
The data analysts are able to select visualization methods for data like charts and tables, which aid in communicating information effectively and clearly to users. Analytic tools offer the ability to display the necessary information by using color codes as well as formatting of charts and tables.