Analytics is the discovery and communication of meaningful patterns in data.The goal of Data Analytics is to get actionable insights resulting in smarter decision and better business outcomes. Analytics often favours data visualization to communicate insight. We can find dependency and pattern in the historical data of the given problem.
data analytics means basic business intelligence (BI), reporting and online analytical processing (OLAP) to various forms of advanced analytics. Data analytics can help businesses increase revenues, improve operational efficiency, key performance indicators, business operations, optimize marketing campaigns and customer service
Organizations apply analytics to business data, to describe, predict and improve business performance using predictive analytics, enterprise decision management etc
data analytics: can be categorised into four types Predictive , Descriptive , Prescriptive , Diagnostic analytics
Predictive Analytics: Predictive analytics changes the data into valuable, actionable information. It uses data to determine the probable outcome of an event .
Predictive analytics apply various statistical technique like modelling, machine, learning, data mining and many more to analyze current and historical facts to make prediction future event
Descriptive Analytics: Descriptive analytics uses data and analyze past event for insight as how to work on future events. It looks at the past performance and understands the performance by mining historical data to understand the cause of success or failure in the past. most of the management reporting such as sales, marketing, operations, and finance uses this type of analysis.
Descriptive model quantifies relationship in data in a way that is often used to classify customers into groups. predictive model that focuses on predicting the behavior of single customer, whereas Descriptive analytics identify different relationships between customer and product. Prescriptive Analytics: Prescriptive Analytics applys big data, mathematical science, business rule, and machine learning to make prediction and then suggests decision
Diagnostic Analytics: In this analysis, we use historical data over other data to respond to questions.
Data Analytics Applications include : big data, Big data analytics ,Social media analytics, Accounting analytics, Financial analytics, marketing analytics, Management analytics, Business analytics, Supply chain analytics, Operations management analytics, Descriptive, predictive and prescriptive analytics
data analytics are of 2 categories - exploratory data analysis , which find patterns and relationships in data, and confirmatory data analysis , which applies statistical techniques to determine whether hypotheses is true or false.
Advanced types of data analytics include data mining, by sorting through large data sets to identify trends, patterns and relationships;
predictive analytics, which seeks to predict customer behaviour, equipment failures and other future events; machine learning and artificial intelligence using automated algorithms to churn through data sets swiftly than data scientists can do using conventional analytical modelling
Big data analytics applies data mining, predictive analytics and machine learning tools to sets of big data that contain unstructured and semi-structured data.
Text mining helps in analyzing documents, emails and other text contents.
In advanced analytics projects, starts with collecting, integrating and preparing data and then developing, testing and revising analytical models to ensure that they produce accurate results. Data from numerous source combined via data integration transformed and loaded into an analytics system. data quality problems can be reduced by running data profiling and data cleansing jobs to make sure that the information in a data set is consistent and that errors and duplicate eliminated. A data scientist builds an analytical model, using predictive modelling tools or other analytics software and programming languages such as Python, Scala, R and SQL
Big Data refers to very huge volumes of data that cannot be processed with the traditional applications that exist
3 V's of Big data are high-volume, high velocity and high variety data that demand cost-effective, innovative ways of information processing that enable enhanced insight, business decision making and process .
Big Data Applications include: Customer analytics, Compliance analytics, Fraud analytics, Operational analytics, Communications and retail
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