Big data is a complex volume of datasets that is computationally analyzed. The extracted information is useful to unveil trends and patterns for decision making in strategic business. Five V’s (Volume, Veracity, Variety, Velocity, and Value) make big data a considerable concern. Significant data hitches comprise data capturing, storing, analyzing, visualizing and querying, updating, sourcing, and data privacy. For such big data, handling is not a simple process to be done with traditional data processing software given such volume and value. At present, User behaviour analysis, predictive analysis, and other advanced data processing and analysis methods are used to extract a valuable, veritable, and large volume of various data with high velocity. This data is used by non-corporate business giants, medical researchers, STEM researchers, scientists, marketing, and government as well. 

Over time, big data capturing and storage is no longer an issue it can gather by various economical methods such as remote sensing, mobile devices, cameras, microphones, software logs, and wireless sensor networks. Every day 2.5 Exabyte data is gathered through the IoT (Internet of Things) devices. Since the past two decades, storage capacity per capita is doubling every 40 months. IDC estimated that in 2025, global data volume would swell by 163 zettabytes that are ten-fold of today’s total number. Being entitled as “Big Data” varies with users' capabilities of using numerous tools and exploring the data with their management system through data analysis.

Data exploration is the first step in data analysis. Data exploration is an immensely growing field of complex data ecosystem with various data types collected through multiple sources. For accurate data analysis, new data is collected from social media/IoT, and raw data is gathered from remote sensing. This vast amount of data is gathered in an unorganized manner in various formats (plain, JSON, text, RDF). The raw non-rigid data is processed and cleaned by formal statistical modelling and methods such as manual scripting and queries and automated actions.  

This metadata after data cleansing and data quality process is stored in a central warehouse or data warehouse. This approach is used to explore the tremendous amount of dataset to reveal the initial patterns, pattern spotting, trend spotting, statistical reporting, and characteristics. Data exploration is also known as ad hoc querying. It helps to visualize the hidden elements and relationships in the relevant data by converting the massive data into manageable data. This conversion takes a combination of steps comprises of manual and automated methods & tools like data profiling or data visualization, initial statistical charts, and reports. 

Data visualization is the graphical or pictorial representation of data. It helps big data to communicate efficiently. It not only conveys the message but also piques the interest. The interactive visualization may include the original data (numerical usually) or graphic elements (points or lines in charts). Mapping is a fundamental prowess in data visualization. It is both an art and science because it needs designing skills as well as statistical and computing skills to visualize data proficiently. Visual data exploration and data analysis facilitate information perception, manipulation and extraction, and interference for non-expert users. Visualization techniques used in modern systems provide an exploration of the data content, patterns identification, and infer correlations; that is not possible with traditional data analysis techniques. 

Data analysis is the process of systematic application of statistical models and techniques after cleansing, converting, modelling, and visualizing data. The goal of data analysis is to draw inductive interference from data, eliminate statistical fluctuations, extract practical information, evaluate and conclude scientific outcomes, and support decision making. Data analysis can be divided into confirmatory data analysis (CDA), exploratory data analysis (EDA), and descriptive statistics. Confirmatory data analysis primarily emphases on confirming or negating formed hypothesis and exploratory data analysis mainly concentrate on determining new data features. 

Various tools and techniques for Data Visualization & Analysis include:

• Graphical techniques (Histogram, Targeted projection pursuit, Odds ratio, Glyph visualization methods, Stem & Leaf Project, Pareto chart, Box plot, Scatter plot, Interactive versions of these plots, etc.)
• Dimensionality reduction (Principal Component Analysis, Multi-linear Principal Component Analysis, Multi-dimensional scaling, and Non-linear dimensionality reduction)
• Quantitative techniques (Trimean, Median Polish, and ordination)
• Predictive analysis
• Text analysis

These methods extract and classify information from unstructured data. Regardless of all the modern techniques and techniques, some challenges always exist in data visualization and data management due to the continuous growth of big data numbers. To overcome the issues, modern exploration and visualization systems are introduced that comply with scalable data management to command billion objects dataset and regulating the system response time in a few milliseconds.