By Why Data Analysis Is So Important?
How much a company’s sales will grow depends on how much data that company has. The problem is that data is available to all companies, and a particular company needs to rise above others to increase its sales and branding. In today’s era, data collection has become so easy and technology has expanded so much that it is often difficult to outbid other companies and build a strong niche for your company. Even maintaining the current sales volume is a struggle.
All companies have data, so how can I distinguish myself? In the age of data availability, data analysis is required to advance the sales of your company.
From my point of view, some facts can be followed in this case:
- It is essential to have adequate data on market demand.
- User data (what users like, dislike, how much they use)
- User volume.
- Financial status of the user.
- Product differentiation.
- Product types of rival companies.
- Pros and cons of rival companies’ products.
- Price of rival companies’ products.
- All detailed data about our products.
- Sales volume per day, per week, per month, and per year.
- Places that fail to generate business.
These data should be collected in sufficient quantity, which is the first step of data analysis called data collection. The next step after data collection is data preprocessing and data cleaning. This is important to:
- Ensure the quality and reliability of the data.
- Enhance the performance and accuracy of machine learning models.
- Enable meaningful and accurate insights from data analysis.
Data cleaning is a time-consuming task. Data cleaning involves identifying and correcting errors, inaccuracies, and inconsistencies in the data. The main tasks include:
- Handling Missing Values:
- Removing Missing Values: Deleting rows or columns with missing data if they are not critical.
- Imputing Missing Values: Replacing missing data with estimates (mean, median, mode, or using algorithms to predict missing values).
- Removing Duplicates:
- Identifying and removing duplicate records to ensure each observation is unique.
- Correcting Errors:
- Identifying and correcting errors in the data, such as typos, incorrect data entries, and outliers.
- Standardizing Data:
- Ensuring consistency in data formats (e.g., date formats, categorical labels) and units (e.g., converting all weights to kilograms).
Data Preprocessing
Data preprocessing involves transforming raw data into a format suitable for analysis. The main tasks include:
- Data Integration:
- Combining data from different sources into a coherent dataset.
- Data Transformation:
- Normalization: Scaling data to a specific range (e.g., 0 to 1) to ensure comparability.
- Standardization: Transforming data to have a mean of 0 and a standard deviation of 1.
- Log Transformation: Applying a logarithmic transformation to reduce skewness in the data.
- Data Reduction:
- Dimensionality Reduction: Reducing the number of variables using techniques like Principal Component Analysis (PCA).
- Feature Selection: Selecting a subset of relevant features for analysis.
- Encoding Categorical Variables:
- Converting categorical data into numerical format using techniques like one-hot encoding or label encoding.
- Data Splitting:
- Splitting the data into training, validation, and test sets for model building and evaluation.
