Difference 4: The Techniques
When we say advanced analytics, “advanced” refers to quantitative methods such as statistics, algorithms and stochastic processes. Although not all of the advanced analytics techniques are predictive, they are future-oriented since the key idea of the methods is to support data-driven decisions in the future. The advanced analytics techniques can be categorized into four functions:
Descriptive analysis aims to understand an underlying phenomenon or process. The analysis will answer questions like, “What are the typical characteristics of customers who tend to churn?”, or, “Which products do consumers usually purchase together?”
Similar to descriptive analytics, data science involves sifting through data to discover unknown patterns and correlations. It differs in that it tends to be an exploratory process with a loosely defined scope. The goal is to come away with questions that you would try to answer with further analysis.
Predictive analysis studies the hidden relationships between factors and outcomes and then forecasts or estimates an unknown value. For example, a predictive model will allow us to predict which customers are going to churn, or estimate how much revenue will be lost if temperatures drop 10 degrees.
Simulation and Optimization
Simulation imitates the operation and characteristics of a process and summarizes the outcome. Optimization prioritizes the decision options based on a key performance index. For example, if we want to design a drive-through route for a restaurant, we can simulate the traffic and ordering process, compare the simulation outputs for several options, optimize the design and select the best choice.