How are various steps in data analytics assisting businesses in making better-informed decisions in less time?

There is a lot of data being collected through various sources, and the performance of any business is largely determined by how well and when such data is analyzed.

Data analysis is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, hidden patterns, unknown correlations, and supporting decision-making. So in simple terms, Data analysis is a process for obtaining raw data and converting it into information useful for decision-making by users. Data is collected and analyzed to answer questions, test hypotheses, or disprove theories.

The process of making such decisions begins with data collection and ends with data interpretation into information. Below are the steps required for end-to-end data analysis with meaningful insights.

1. Required data Analyze

Data are required as inputs to an analysis, which is defined by the needs of those who conduct the analysis or customers. Data can be numerical, such as product amount, revenue, or costs, or categorical, like the product category, region, or client type.

2. ?Data collection

Data is collected from a variety of sources. The requirements may be communicated by analysts to custodians of the data. The data may also be collected from sensors in the environment, such as traffic cameras, satellites, recording devices, etc. obtained through interviews or downloads from online sources.

3. Processing of data

Data must be processed or organized before it can be analyzed. For example, arranging data into rows and columns in a table format for further analysis, such as within a spreadsheet or statistical software, could be one of these.

4. Data Cleaning

Once processed and organized, data may be incomplete, contain duplicates, or contain errors. Data cleaning is the process of preventing and correcting these errors. Processes and checks are usually applied while cleaning data such as record matching, duplication, and column segmentation. Such data problems can also be identified through a variety of analytical techniques.

5. Data analysis methodologies

There are two main types of Data Analysis methods. Descriptive Analysis - Describe the main features of a large collection of data. Confirmatory Analysis - Confirm or negate a hypothesis. Exploratory Analysis - Find previously unknown relationships in the data. Causal Analysis - Find out what happens to one variable when you change another.

6. Communication & Evaluation

Once the data is analyzed, it may be reported in many formats to the users of the analysis to support their requirements. The users may have feedback, which results in additional analysis. As such, much of the analytical cycle is iterative and needs to be supported by multiple sources.

As a result, data analysis is altering business by allowing for faster and more accurate decision-making. The goal is to make the corporate activities more time-efficient by streamlining them.

Here are some glimpses from my most recent data analytics project, which focused on attrition rate analysis and recommendations for retaining employees or lowering attrition rates.