Everyone is highly curios to have a glimpse at the future. A peek into the future not only provides excitement in general but also helps to in proper planning and laying out import strategies. At the minimal, future predictions give us an opportunity to be prepared for the bad times and minimize the losses. For example, a tool that helps a human resource manager to predict the employees who are at the risk of seeking voluntary termination from the organization is immensely helpful. This helps the manager in taking proper actions to retain high performing employees and minimize losses by reducing operating costs of the organization.
Development of methodologies and tools to predict future are of utmost importance. Predictive analytics is one such field of study at the junction of statistics, data mining, and machine learning that aims to provide future insights into various domains.
What is predictive analytics?
It is a process of analyzing historical and current facts to get an insight into future risks, events, and trends. Its goal is to generate actionable items for an end user to address future needs. It finds its use in all the field of sciences, marketing, healthcare, insurance, telecommunications, and other domains.
Difference from the state-of-the-art Business Intelligence practices
The state-of-the-art Business intelligence methods generate static reports from the historical data. For example, given a repository of sales transactions, a BI software generates different kinds of reports, in the form of documents, visual charts or spreadsheets, based on demographics, time, product categories and other criteria. This helps an end user to analyze the historical data and discover the factors that led to the observed sales data. In BI, onus lies with the end user to discover future trends and events from the reports. On the contrary, predictive analytics automatically generates the future trends and events by analyzing the historical data.
References
1. Wikipedia Article
2. Dean Abbott Blog
3. Forbes Article
4. Predictive analytics with data mining
Products
1. Oracle Data Mining and Predictive Analytics
2. IBM's SPSS
3. SAS
4. We Predict
5. Revolutionanalytics
Development of methodologies and tools to predict future are of utmost importance. Predictive analytics is one such field of study at the junction of statistics, data mining, and machine learning that aims to provide future insights into various domains.
What is predictive analytics?
It is a process of analyzing historical and current facts to get an insight into future risks, events, and trends. Its goal is to generate actionable items for an end user to address future needs. It finds its use in all the field of sciences, marketing, healthcare, insurance, telecommunications, and other domains.
Difference from the state-of-the-art Business Intelligence practices
The state-of-the-art Business intelligence methods generate static reports from the historical data. For example, given a repository of sales transactions, a BI software generates different kinds of reports, in the form of documents, visual charts or spreadsheets, based on demographics, time, product categories and other criteria. This helps an end user to analyze the historical data and discover the factors that led to the observed sales data. In BI, onus lies with the end user to discover future trends and events from the reports. On the contrary, predictive analytics automatically generates the future trends and events by analyzing the historical data.
References
1. Wikipedia Article
2. Dean Abbott Blog
3. Forbes Article
4. Predictive analytics with data mining
Products
1. Oracle Data Mining and Predictive Analytics
2. IBM's SPSS
3. SAS
4. We Predict
5. Revolutionanalytics
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