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Difference Between Data and Predictive Analytics
Data analytics is a process which is used to reach the conclusion of the given information. This process is done by filtering the raw data. Predictive analytics is a process in which predictions are made about the results with the help of the current information. In this article, we will discuss the difference between Data and Predictive Analytics.
Data Analytics
Data analytics is a process in which the raw data is analyzed and conclusions are drawn accordingly. A systemic computational process is needed along with cleaning, aggregation, visualization, and interrogation of the data. The output of the data analytics is in the form of a presentation, dashboard, report, etc.
Importance of Data Analytics
Data analytics is a very important process in data-driven companies. It helps the executives, managers, and front-line workers to make decisions The analytics is applied to the data so that the performance of the business can be improved.
Predictive Analytics
Predicative analytics is a process in which historical data is used to predict the future. Data mining, data modeling, machine learning, and deep learning algorithms are used in predictive analytics to provide the future.
Working of Predictive Analytics
The steps that are used to perform predictive analytics are as follows −
Problem Definition
Define the problem on the basis of several factors like fraud detection. Inventory levels, severe weather, etc. The definition of a problem will help in doing predictive analytics.
Acquiring and Organizing Data
A large amount of data is available so there is a need to acquire the data. Data flows should be checked which will help in creating the data models. This will help in organizing the data in a data warehouse.
Data Pre-Processing
Raw data has to be cleaned to create the data models. If any data is missing, it can create problems which can cause errors while predicting the future.
Predictive Models Development
There are different types of tools and techniques which are used in the development of predictive data models. These models are developed on the basis of the problem.
Result Validation and Deployment
The accuracy of the model is checked and then the data is sent to the stakeholders with the help of an app, a website, or a data dashboard.
Techniques used in Predictive Analytics
Many techniques are used to perform predictive analytics and we will discuss them here in detail.
Regression Analysis
Regression analysis is a technique in which relationships between two variables are estimated. This analysis is performed so that a correlation can be determined between the inputs. Regression analysis is best for those data flows that are continuous.
Decision Trees
Decision trees are used to classify the data into different categories on the basis of different variables. This is a method which is used to understand the decisions made by individuals. This model is in the form of a tree which consists of branches that denote potential choice. The result of the decision is denoted by the leaf. Decision trees are useful in cases when there are many missing variables in the datasets.
Neural Networks
Machine learning methods are included in the neural networks. These networks are used in predictive analytics when complex relationships are to be modeled. Nonlinear relationships are determined with the help of neural networks.
Difference between Data Analytics and Predictive Analytics
The table below will display the difference between data analytics and predictive analytics.
| Data Analytics | Predictive Analytics |
|---|---|
| Data analytics is a process which is used to derive conclusions from a given data. | Predictive analytics is a process in which current and past data are examined and predictions for the future are made. |
| Data-driven decisions can be made. | Risk evaluation is done along with prediction of the future outcomes. |
| Deep insights on the data are created on the basis of mechanical processes and rational algorithms. | Forecast on the data is made on the basis of advanced algorithms and computational models. |
| Raw data is used and filtered to provide the conclusion. | Clean data is used to provide the forecast. |
| Customer requirements are the basis of data analytics. | Hypothesis testing and assumptions are used to generate a predictive model. |
| Strong statistical knowledge is needed to perform data analytics. | Strong technical and basic of statistical knowledge is used to perform predictive analytics. |
| Data analytics deals in the detection of fraud and risks along with digital advertisements, customer interaction, etc. | Predictive analytics deals with crisis management, sales forecasting, clinical decision support systems, etc. |
| Data analytics is useful for the verification of hypotheses and theories. | Predictive analytics is used for making predictions with the help of specialized models. |
| Data collection is used in the data analytics and then the analysis is performed for one or more uses. | The things included in predictive analytics are project definition and statistical modeling and these are the bases which are used to predict the outcome. |
Following are the steps of data analytics −
|
Following are the steps of predictive analytics −
|
| The results of the data analytics cannot be predicted. | Predictive analytics helps in predicting the outcome of the future. |
Conclusion
Data analytics and predictive analytics are useful for every business. Data analytics is used to make decisions while predictive analytics is used to predict the future. Data analytics is based on the current dataset while predictive analytics uses historical data along with the current one.
FAQs on Data Vs. Predictive Analytics
1. What type of analysis is done in data analytics and predictive analytics?
Businesses use data analytics to make decisions while predictive analytics is used to provide the future of the business according to the current and historical data.
2. What type of data is used in data analytics and predictive analytics?
Raw data is used in data analytics while clean data is used in predictive analytics.
3. What type of outcome is provided by data analytics and predictive analytics?
The results of data analytics are used to make decisions while the result of predictive analytics is used to decide the future.
4. What type of knowledge is required for both types of analytics?
Strong statistical knowledge is needed for data analytics while strong technical and basics of statistics is needed for predictive analytics.
5. Which type of analytics is used for fraud detection?
Fraud detection is done through data analytics.
