Data Analytics and Applications

- Overview

Big data is a large and complex collection of data that businesses are constantly generating and collecting. Data analytics is the process of extracting meaningful information from data.

The main purpose of big data is to store and process large amounts of data. The main purpose of data analytics is to analyze raw data and discover insights.

Big data can help industries such as banking and retail by providing important technologies such as fraud detection and operational analysis systems. Data analytics can help industries such as banking, energy management, healthcare, tourism and transportation achieve new advances. 

Machine learning (ML) is a specific subset of artificial intelligence (AI) used to train machines how to learn. This makes it possible to quickly and automatically generate models that can analyze larger and more complex data.

Analytics and ML have been around for decades, but the use of ML has been limited due to the challenges associated with it. ML is processor-intensive and requires specialized skills and tools that are not yet widely available. However, the arrival of cloud computing has made it cheaper and easier for more organizations to obtain the computing power needed to run ML. 

The processing power of our smartphones and laptops makes it possible for almost anyone to access and use ML analytics and make smarter, data-driven decisions.

- Big Data Analytics

Big data analytics is the process of collecting, analyzing, and examining large amounts of data to find patterns, trends, and correlations. It uses tools, methods, and applications to extract insights from data sets that can come from many sources, such as mobile, web, email, social media, and networked smart devices. 

Big data analytics can help companies make better business decisions by discovering market trends and insights. The four main types of big data analytics are: Descriptive, Diagnostic, Predictive, Prescriptive.

Here are some examples of big data analytics:

• Predictive analytics: Uses data analysis, machine learning, artificial intelligence, and statistical models to find patterns that might predict future behavior.
• Data mining: Removes repetitive and noisy data and points out only the relevant information.
• Text mining: Analyzes text data from the web, like comments and likes from social media.
• Data cleansing: Fixes or removes incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset.

- Disciplines in Data Analytics

Dan Ariely, a well-known Duke economics professor, once said about big data: “Everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it.”  

This concept applies to a great deal of data terminology. While many people toss around terms like “data science,” “data analysis,” “big data,” and “data mining,” even the experts have trouble defining them. 

Data analytics is a discipline focused on extracting insights from data. It comprises the processes, tools and techniques of data analysis and management, including the collection, organization, and storage of data. 

The chief aim of data analytics is to apply statistical analysis and technologies on data to find trends and solve problems. Data analytics has become increasingly important in the enterprise as a means for analyzing and shaping business processes and improving decision-making and business results.

Data analytics draws from a range of disciplines — including computer programming, mathematics, and statistics — to perform analysis on data in an effort to describe, predict, and improve performance. 

To ensure robust analysis, data analytics teams leverage a range of data management techniques, including data mining, data cleansing, data transformation, data modeling, and more.

- Data Analytics vs. Data Analysis

As data rates continue to grow, extracting meaningful knowledge from raw data becomes critical. However, manually retrieving these pieces of information can be a very complex and cumbersome task for you and your organization. In this context, two terms that often pop up in your mind are profile analysis and profile analysis. Although they sound similar, there are subtle differences between them.

Data analysis and data analytics are often used interchangeably, but they are different concepts and have different values. Data analysis is a subset of data analytics, and while they are related, they are distinct processes with different purposes.

Data analytics is a broad field that includes the management of data and the methods and instruments used. Data analysis is a subset of data analytics that involves specific actions. 

Data analytics is a broad field that encompasses the entire process of extracting, transforming, and organizing raw data to glean valuable insights and inform decision-making. Data analytics involves analyzing raw data to make conclusions and find trends. It can help businesses optimize performance, make more strategic decisions, and maximize profit. 

Data analysis involves inspecting, cleaning, transforming, and modeling data. It involves applying statistical and logical techniques to describe, illustrate, condense, recap, and evaluate data. 

Data analysis involves examining data to understand what happened in the past and why. It's a narrower process that focuses on interpreting prepared data to derive insights for decision-making. Data analysis often uses descriptive statistics, visualization techniques, and hypothesis testing.

- Data Analysts vs. Data Scientists

While data analysts and data scientists both work with data, the main difference lies in what they do with it. 

Data analysts examine large data sets to identify trends, develop charts, and create visual presentations to help businesses make more strategic decisions. Data scientists, on the other hand, design and construct new processes for data modeling and production using prototypes, algorithms, predictive models, and custom analysis. 

The responsibility of data analysts can vary across industries and companies, but fundamentally, data analysts utilize data to draw meaningful insights and solve problems. 

They analyze well-defined sets of data using an arsenal of different tools to answer tangible business needs: e.g. why sales dropped in a certain quarter, why a marketing campaign fared better in certain regions, how internal attrition affects revenue, etc.

Data analysts have a range of fields and titles, including (but not limited to) database analyst, business analyst, market research analyst, sales analyst, financial analyst, marketing analyst, advertising analyst, customer success analyst, operations analyst, pricing analyst, and international strategy analyst. 

The best data analysts have both technical expertise and the ability to communicate quantitative findings to non-technical colleagues or clients.