Updated March 6, 2023
Introduction to Predictive analytics tools
Predictive Analytics is the branch of data analysis, where, based on historic and current data, the predictions for future are made. The “future” here can be the next minute, hour, day, month, year or 10 years. The tools used for conducting predictive analysis are defined as predictive analysis tools. These can be software available for your local machine or software available as a cloud service. Statistical modelling is used here to draw the insight, which holds sharp precision. Predictive Analytics is where all the data related operations like Data mining, big data, statistical modelling and machine learning comes into play and the tools help in implementing these operations in real time, with real time data
Tools for Predictive Analysis
There are various tools, that use these techniques and operate on the available data, which maybe historic or current. The prediction for future is drawn from it, which helps in strategy and planning to move ahead. Predictive analysis is spread across fields like legal, automotive, environmental, academic research and aerospace.
We understood the basic meaning of Predictive Analysis. The next thing is how to conduct predictive analysis, where the very first step is to select the tool perfect for our usage. There are many tools available in the market. Let us now, have a look at some of the top tools in the industry currently being used for predictive analytics.
1. IBM SPSS:
SPSS is short for Statistical Package for Social Sciences and this tool is used for a very wide range of data analysis. This tool was launched in 1968 by SPSS Inc. However, it was later acquired by IBM. This tool is used in market, education, health, government bodies, etc. for data analysis and estimation of future possibilities. This tool is highly used for the analysis of data accumulated in surveys. This is a helpful way for predictive analytics. There are four key functions of this tool, namely, Statistics, Programming the modeller, text analytics and designing visuals. While the first feature helps in statistical analysis of the data, modeller is how a developer can design and creative an analytical model with help of SPSS tool. The text analysis features help in analysis of surveys and draw conclusions for future possibilities with certain precision. Lastly, the visualization designing helps in assembling the collected data in visual forms like pie charts, bar charts, etc.
2. Tableau
Tableau is a tool which is highly known for its analytical capabilities and predictive model. The forecasting model is implemented in Tableau, with help of which, the users are able to make a prediction with a sharp precision, with the available historic and current data. The interface is highly user friendly and simple drag and drop techniques can be used to create visual representation of the available data. The analytics of Tableau is completely attached to front end thus providing ease of use to beginners.
3. Alteryx
Alteryx is another world known tool, used in predictive analysis. It provides a friendly interface for the user to extract, transform and load the data. This tool is most generally used in combination with Tableau, where Alteryx transforms and readies the available data. This data is sent as input to tableau, which in return provide insightful visualisation of the data and help in predicting the future trends. Hence, while Alteryx is used for analysis of data, Tableau provides the appropriate representation of the data. The interface for both Tableau and Alteryx is simple and do not require any professional assistance for performing petty tasks.
4. SAS Analytics
SAS is a powerful software for business intelligence and helps in reports, predictive analysis and visualization. It comes with many good features like interactive dashboards, insightful, exploration of visual data, easy integration with Microsoft tools. This tool very keenly keeps the data consistence and this is managed centrally, using relevant components of the tool like security and shared metadata.
5. SAP Analytics Cloud
This software as a Service (SaaS) tool is also abbreviated as SAC and combines the features and properties for Planning, Predictive Analytics and Planning. This tool was created in 2015 simply for planning. Later a layer of Business Intelligence was implemented which was followed by introducing the capabilities of predictive analysis in the tool. This tool helps in data visualisation and centres around reports. For the predictions, there are some artificial intelligence techniques implemented in the tool like zeroing in on important characteristics, pointing out the things that do not fall into pattern, taking current data to predict future to a good level of precision. The tool has implementation of machine learning for analysis which is done using smart discovery.
6. RapidMiner
RapidMiner is another tool, which focuses on analysis of data for non-programmers. It offers a low code interface that is platform-independent and can be run across numbers of OS like iOS, Windows, etc. It offers basic properties of data visualisation, data analysis, data cleaning. In addition to these, it also offers a wide variety of machine learning techniques that are helpful in predictive analytics. This is a tool that gives all the properties on one single platform. It allows the users to create models of machine learning and later deploy them to web, such that they can access it whenever needed, from mobile or desktop. This tool is very useful for people with no programming knowledge. Hence, it is widely used in the field of research by analysts and researchers.
Thus, in this article we read about what is Predictive Analytics some of the top tools in the current industry. The intent is to give the basic idea to the end-user, as to what does predictive analytics means and how it can be used to predict good (or bad) results depending on the historic data observation. Based on the features of various tools listed above, the users can decide which one is the best for them and use the same in analysis of their data for prediction purposes.
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