Data Analytics is converting and analyzing Raw Data into insights which uses various tools and technologies to set conclusions about the information efficiently. It improves the decision-making of a business and helps it grow easily. It is the gathering, reconstructing, and arranging of informative data to set conclusions. It is used multimedia in different sectors and industries.
Importance of Data Analytics
- Strengthens decision-making ability- Data Analytics gives companies the power to build new and better business decisions. It helps in setting up the organization in a more effective way.
- Predict Future Trends- It helps the business to predict future trends using data insights. It allows businesses to identify their target audience and launch new products/ services according to their audience’s tastes and preferences.
- Strengthens Security- The Data Scientists keep the information or data of the business secure which helps minimize the risk of cybercrimes and data stealing.
- Creates strategies and market campaigns- It helps in creating business marketing strategies with new and innovative ideas and targeting more traffic.
- Gain greater insights into target markets- Businesses learn to target their audience after understanding their preferences which helps to boost sales.
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How Does a Data Analytics Work?
- Collect and Store Data- The first step is to identify the data you need and collect it. Identify and combine different kinds of data using various tools and techniques.
- Data Quality- The next step is to correct the quality of the data according to the standards and requirements.
- Building Models- After that, the next step is to build different models using various programming tools and languages like Python, HTML, C++, and SQL.
- Presentation- The final step is to present business models and executives.
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Types of Data Analytics
There are 5 different types of data analytics that are used to convert data into insights. These data approaches are different from each other and are used mostly. Let’s discuss all of them in detail:
1) Descriptive Data Analytics
This is the analysis of past and historical data and predicting what could happen in the future. It means understanding data insights using historical data and identifying future-related trends. It helps to identify problems in companies. Descriptive data analysis is used in different sectors including Business, Marketing, Healthcare, Finance, Education, and Logistics.
The process of Descriptive Data Analysis includes:
- Data Collection
- Data cleaning and Preparation
- Data Exploration
- Data Analysis
- Data Visualization
- Data Interpretation and Reporting
2) Diagnostic Data Analytics
This data helps to identify the root cause/ problems raising the question ‘Why did this happen’. It is the advanced data used in implementing some specific tools for deeper analysis. The key concepts of Diagnostic Data Analysis include Correlation, Comparative Analysis, Hypothesis Testing, Comparative Analysis, Data segmentation, Root Cause Analysis (RCA), and Anomaly Detection.
The process of Diagnostic Data Analysis includes:
- Data Collection
- Data Preparation
- Exploratory Data Analysis
- Drill-Down Analysis
- Correlation Analysis
- Hypothesis Development and Testing
- Root Cause Analysis
- Reporting and Communication
3) Predictive Data Analysis
Predictive Data Analysis uses different statistical tools and techniques to predict future outcomes of events, behaviors, and data. It uses different mathematical methods and algorithms for input data. It helps in changing some positive factors of any company. This Data Analysis has been used in different sectors of Sales, Business, Finance, Supply Chain Management, Manufacturing, Forecasting, and Fraud Detection.
The process of Predictive Data Analysis includes:
- Problem Definition
- Data Collection
- Exploratory Data Analysis
- Modeling
- Model Evaluation
- Model Deployment
- Prediction and Forecasting
- Data Interpretation
4) Prescriptive Data Analysis
Prescriptive Data Analysis is the use of every data method and selecting the best solution from all the available options. Every algorithm, machine learning, and statistical approach is used to improve business outcomes. It has been used in various sectors and industries including Healthcare, Energy, Finance, Logistics, Marketing, and Manufacturing.
The key components of the process of Prescriptive Data Analysis include:
- Collection of Data
- Data preparation
- Data Modeling
- Data Analysis
- Data Outcome
- Data Implementation and Monitoring
5) Real-Time Data Analytics
Real-Time Data Analytics is a type of data that is analyzed on the spot as soon as it enters the database. It is used to deliver fast actions and responses. Logical and Mathematical tools are applied to this data. It is used in different industries like Healthcare, Retail & E-commerce, Manufacturing, Finance, and Logistics and Transportation.
The key components of Real-time data analytics are:
- Data Source
- Data Storage
- Data Ingestion
- Data Processing
- Performances
- Machine Learning
- Decision-Making and Actions
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Conclusion
In conclusion, Data analysis is a process that uses different tools, techniques, and methods to provide the best results. The 5 types of Data Analysis are- Descriptive, Diagnostic, Predictive, Prescriptive, and Real-time Data Analysis. These data analyses are different from each other and have different techniques and processes.
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