What are different types of analytics? Describe different types of analytics.

Analytics: 

The term analytics is used to characterize a vast array of methods that use data to help make better business decisions. And there are many ways to organize them into subcategories. The simplest way is to distinguish analytics into three large classes

• Descriptive Analytics
• Predictive Analytics, and 
• Prescriptive Analytics

Descriptive Analytics: The most basic form of analytics are descriptive analytics. The simplest way to define descriptive analytics is that it answers the question what has happened. Descriptive analytics typically condenses large amounts of historical or real-time data into smaller, more meaningful nuggets of information.

For example, in an internet marketing context, descriptive analytics could be used to summarize a large number of search, display and social media advertising campaigns into a smaller set of metrics that shows the average click-through rate, conversion rate, and the return on investment of each of these three advertising channels.

The main objective of descriptive analytics is to find out the reasons behind success or failure in the past. The vast majority of big data analytics used by organizations falls into the category of descriptive analytics.

Predictive Analytics: The next class of analytics, predictive analytics, uses data from the past to predict what will happen in the future.

For example, suppose we would like to predict the likelihood that a new prospective customer will respond to a promotional email campaign. By analyzing past data that includes situations where prospects responded and did not respond to similar campaigns in the past, analytics can help identify what distinguishes those prospects who responded from those who did not. On the basis of this data, a model can be built that can help assess the probability that the new prospect will respond to a future campaign. 

As we can see, predictive analytics is based on a solid understanding of what happened in the past. So most organizations are deploying them after they have mastered the art and science of descriptive analytics.

Prescriptive Analytics: Armed with models of the past and forecasts of the future, organizations can then venture to the more advanced level of analytics, prescriptive analytics.

This class of method is using optimization algorithms to determine the set of actions that optimize a desirable objective, given predictions of what is likely to happen. 

Referring to the previous example, assuming that we have built predictive models for a number of different campaigns, a prescriptive analytics model could be used to determine which campaign should be sent to which prospects in order to maximize our expected sales and stay within our marketing budget.

As organizations gain experience and skills with data-driven business decision-making, they typically progress from descriptive, to predictive, and finally to using prescriptive analytics to inform decisions and actions in a growing number of functions.

Roles in Business Intelligence and Data Analytics Team

Most data science projects, especially the ones of sufficient scale and complexity, are carried out by teams that consists of roles of different skill sets. Different organizations give different names to these roles. However, in most cases, these roles boil down to three broad categories that broadly correspond
to the steps of the analytics workflow.

Data engineers: are primarily responsible for the collection, transformation, and organization of data that is required for an analytics project.

Their skill set grows upon computer science and software engineering, and includes programming, data modeling, and database skills.

Data scientists: also known as statisticians, are responsible for developing and executing the analytical models that process data to derive the desired insights.

Such individuals are well-versed in statistical analysis, and increasingly, in the newer spectrum of powerful analytics techniques, such as machine learning, natural language processing, and social network analysis.

Data translators: are the interface between the analytics team and the rest of the business.

They understand the business side very well and have a broad, though not necessarily very deep, understanding of the technical steps of the analytics process.

They are responsible for defining the business questions that motivate the project, helping translate these into data questions, and developing a strategy for obtaining the right data.

At the other end, they are responsible for translating the results of the analysis into business insights and helping the business stakeholders use these insights to make decisions.

Manager/Leader: Analytics teams usually also include the manager role, who oversees the entire process and manages the relationships among the team members, as well as with the business stakeholders.

Data analysts: are typically more junior employees who help people from across an organization
answer relatively straightforward business questions by creating databases and data warehouses, and using business intelligence and data visualization software to construct charts and reports.