Introduction
Data is compelling to organizations as it helps optimize their growth and profit. What is data analytics then? Data analytics is the process of gathering data and using data, techniques, and various tools to identify trends and generate useful information that helps in decision-making. Most workspaces utilize data to develop strategic decisions that drive the organization. A deeper insight into customer satisfaction, availability of new business opportunities, and optimized processes is achieved then.

Data Lifecycle
a) Data collection and storage
b) Data processing
c) Data analysis and visualization

Types of data analytics

1. Prescriptive Analysis
   This type of analysis combines the insight from all analyses done previously. This helps to determine which action to take in a current problem. This analysis is more efficient as data performance is improved and it helps to analyze current problems and make decisions. Examples of prescriptive analysis are: Lead scoring in Sales, fraud detention in banks, and email automation in marketing.

2. Diagnostic Analysis
   This analysis answers the question 'Why?'. This helps to find the cause from the insight found in Statistical Analysis. This analysis is useful for identifying behavior patterns. If a new problem arises, then one can have a look at this analysis to help find similar patterns of that problem.

3. Statistical Analysis
   This type of analysis is done using past data in the form of dashboards and it answers "What happens?". The process includes collection, analysis, interpretation, presentation, and data modeling. A set or sample of data can be analyzed using this method. In statistical analysis, there are two main categories used i.e., descriptive and inferential. In descriptive analysis, a sample of or complete numerical data is summarized and used to calculate the mean and deviation for continuous data and frequency and percentages for categorical data. In inferential analysis, a sample from complete data is used and different summaries are made by selecting different samples.

4. Predictive Analysis
   This type of analysis answers the question 'What will happen?'. This question is answered using previously collected data. Predictions of future outcomes are made using current or past data. For instance, current data indicates that the use of AI is high and is expected to increase over the years. Most people have found AI tools efficient and time-saving. This accuracy is measured and it depends on how much detailed the information collected is.

Benefits of data analytics

1. Helps in understanding how data is compiled.
2. Data analysis aids in recognizing patterns and trends which help in informed decision-making.
3. Improves productivity.
4. Helps in mitigating any risk that might occur.
5. Aids various organizations to reach their targets and goals.
6. Identifying sources for a competitive advantage.

Data analytics skills
This field needs one to have a specific set of skills to be a successful data professional. These skills comprise both technical and soft skills. Soft skills are essential in every field of work and they include communication, story-telling, and presentation, attention to detail, organization, time management, and problem-solving.
Technical skills include Python, data analytics and visualization software such as SQL, Excel and Power BI, machine learning algorithms and models, and database management.