Data Analysis is the process of analyzing raw data to uncover hidden insights that guide strategic decision making in business.
It aid the business to optimize its performance to perform more efficiently, maximize its profits and make more data driven strategic decisions.
The Data analytics environment involves 4 main scopes:

1. Descriptive Analytics: Looking at what happened.
2. Diagnostic Analytics: why something happened.
3. Predictive Analytics: What is going to happen.
4. Prescriptive Analytics: what should be done next.
Data analytics relies on various tools and software ranging from spreadsheets, data visualization and reporting tools, data mining programs and various languages for effective data manipulation and analysis.

Importance of Data Analytics

1. D.A offers both estimation and exploration of data. This allows us to understand the market structure and process its current state offering a solid base for forecasting future expectations.
2. It helps businesses understand their current situation and change or enhance their processes for a new or enhanced product or services that meets market requirements.
3. Learning about consumer pains and what they want helps improve customer satisfaction which is done through data analysis. It enables the business to tailor targeted ads for enhancing customer experience. This overall improves efficiency of targeted ads.
4. It helps in streamlining operations through recognition of bottle necks and issues that affect the business performance and allows the owners and employees take actions on them.

Types of Data

We mainly have two types of data: Quantitative and Qualitative Data.
Qualitative is more about descriptions and characteristics of data. Example: Customer feedback, interview transcripts, focus group discussions, observational notes, descriptions of experiences or opinions. Used to understand experiences, perspectives, and motivations, providing in-depth insight
Quantitative is more about numerical data.E.g Height, weight, temperature, sales figures, test scores, survey responses with numerical scales. Used to establish relationships between variables, identify patterns, and make predictions.