Predictive analytics uses historical data to predict future events, including in the areas of finance, meteorology, security, business, insurance, logistics, mobility and marketing. In general, historical data is used by predictive analytics to create a mathematical model that captures important trends. This predictive model is then applied to current data to predict what will happen next or to suggest actions that can achieve optimal results. Predictive analytics has received a lot of attention in recent years as there have been great advances in assistive technologies, especially in the areas of big data and machine learning.
Business analytics tools can be used to collect and analyze large amounts of data in companies to subsequently take further precautions to optimize business processes. The terms business analytics and advanced analytics are keywords that should stand for improved or extended business analytics. It is said that the improved forms would place a stronger focus on the forecast of future developments.
Predictive analytics is a Subset of Business intelligence (BI)
Predictive analytics is a subset of business intelligence (BI) and Business Analytics (BA). BI and BA are often used interchangeably, although there are differences in methodology. In principle, business analytics represents a more advanced evolutionary stage of BI. However, business intelligence is often used as a generic term for all forms of data analysis in the company.
The Differences are in the Questions they Answer
The difference between business intelligence and business analytics from one view is through the questions they answer. Business intelligence tends to focus on descriptive analytics.
On the other hand, business intelligence (BI) does also prioritizes descriptive analytics (that provides a summary of historical and present data). But, business intelligence (BI) answers the questions WHAT and HOW. It answers questions about what happened (what happened when?), the quantity, frequency or causes of an event. Tools for this are, for example, reporting (KPIs, metrics), automated monitoring (alarm when thresholds are exceeded or exceeded), dashboards, ad hoc requests or OLAP (Online Analytical Processing). OLAP, for example, works deductively, i.e. makes hypotheses and specifically queries information to confirm or reject the assumption.
That means you can replicate what works and change what does not work. Business analytics can focus on predictive analytics as well (which uses data mining, modelling and machine learning).
With Business Intelligence (BI), companies can answer questions about the current economic situation by systematically collecting, evaluating and presenting company data. BI deals mainly with the events in the past and their effects on the present.
Predictive analytics forms a sub-discipline of business analytics. It starts where OLAP or reporting stops. Instead of just analyzing the existing situation, predictive analytics uses data models to make predictions about possible events in the future.
Predictive analytics is required to build models using statistical and machine learning techniques to predict. It requires statistical techniques and tools like SAS, R, SPSS, Matlab. A business analyst role is more diverse. This requires knowledge of the software development process, knowledge of technology when you are working with software products from SAP, Oracle, etc. I guess that both terms have been used and abused of late. But generally predictive analytics is a subset of data science.