Analysis of Data

Introduction

Structured data is traditionally easier for Big Data applications to digest, yet today's data analytics solutions are making great strides in this area. New tools are available to analyze unstructured data, particularly given specific use case parameters. Most of these tools are based on machine learning. Structured data analytics can use machine learning as well, but the massive volume and many different types of unstructured data requires it.
Until a few years ago, analysts used keywords and key phrases to search through unstructured data and get a decent idea of what the data involved. But, unstructured data has grown so fast and so huge, that users need to employ analytics that not only work at compute speeds, but also automatically learn from their activity and user decisions. Natural Language Processing (NLP), pattern sensing and classification, and text-mining algorithms are all common examples, as are document relevance analytics, sentiment analysis, and filter-driven Web harvesting.
Unstructured data analytics with machine-learning intelligence allows organizations to analyze digital communications for compliance. Pattern recognition and email threading analysis software searches massive amounts of email and chat data for potential noncompliance. It can track high-volume customer conversations in social media. Text analytics and sentiment analysis lets analysts review positive and negative results of marketing campaigns, or even identify online threats.
This level of analytics is far more sophisticated compared to simple keyword search, which can only report basics like how many Facebook posts mentioned the name of a given company. New analytics also include context – if the mention positive or negative? Were the posters reacting to each other, or were they independent posts? It can capture the tone of reactions to announcements?
The analytics help understand the market pulse. AI analytics tools work quickly on massive amounts of documents to analyze behavior of customers. For example, a magazine publisher can apply text mining to hundreds of thousands of articles, analyzing each separate publication by the popularity of major subtopics. Then they can extend analytics across all their content properties to see which overall topics got the most attention by customer demographic. Such analytics can help in obvious ways. There was no way of doing such analysis on structured data – that would have missed capturing most of the information available.

Types of Analytics

There is no derth of information in the data available to us. There are four major fields in Data Analytics that focus on extracting different kinds of information from the available data.

Descriptive Analytics

This tells "what" happened. It helps create simple reports, visualisations that help you understand what happened at a given point in time or a period of time. It is the least advanced in terms of algorithms.

Diagnostic Analytics

This helps explain why something happened. More advanced than descriptive analytics, it allows analysts to dive deep into the data and determine root causes for a given situation.

Predictive Analytics

Among the most popular big data analytics available today, predictive analytics involves highly advanced algorithms to forecast what might happen next. IT is based on various artificial intelligence and machine learning technologies

Prescriptive Analytics

This goes a step beyond predictive analytics. After predicting what is going to happen, prescriptive analytics tell how it should be enhanced or avoided (depending upon the desired results). Of course this requires very advanced machine learning capabilities, and few solutions on the market today offer true prescriptive capabilities.