Big Data Analysis refers to the process of collecting and interpreting large and diverse data sets in order to determine previously unknown correlations, relations, trends in an industry, and the preferences of consumers. They are essential as:
They aid businesses in making key operational decisions based on available data.
They make organizations more efficient by highlighting inefficiencies that need correcting.
They promote tailored services to consumers by analyzing how different customers behave.
They assist in forecasting and maintaining competitive leverage over rivals.

There are different data types that can be analyzed by Big Data Analytics for instance: 
First, there is Structured Data. Structured data includes any data that is arranged in an orderly manner and is easily searchable, for example Databases and Spreadsheets. 
The second type of data is known as Unstructured Data and this includes all types of data that can’t be put into a proper format for example text files, twits, and multimedia data such as images and videos. 
Another type of data is called Semi-Structured Data. It includes data that is not bound by any format but has some form of tags or markers separating the different elements such as xml or json files. 
Finally, there is Real-Time Data. This type of data is both generated and consumed without any time latencies on it for instance sensor data, data in transition and transactions that are live.

A number of key resources and techniques are at our disposal as we carry out Big Data Analytics, namely:
Hadoop Ecosystem: A software framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models.
Apache Spark: An open-source unified analytics engine for large-scale data processing.
NoSQL Databases: Systems such as MongoDB, Cassandra, HBase which are used for storage and retrieval of data that cannot be organized within a tabular structure.
Data Warehousing Solutions: Implementations including Amazon Redshift, Google BigQuery and Snowflake that serve millions of records and can be queried in fractions of a second.
Machine Learning Platforms: http://en.wikipedia.org/wiki/Machine_learning Machine learning techniques applied using such developer libraries; for example, TensorFlow, Scikit-learn, PyTorch to create forecast models.
Visualization Tools: Used for producing engaging and creative graphics, software such as Tableau, Power BI, and D3.js are a few of the examples.

The use of Big Data Analytics comes with a high number of advantages to the business, such as:
Improved Decision Making: Decision-making is more precise and faster with analysis-driven data.
Improved efficiency: Looking for areas of wastage and improving them to cut on time and resource expenditure.
Better Customer Insights: Understanding customer habits and tastes more in order to enhance targeting and personalization efforts.
Managing Risks – Recognizing the risk of crimes or fraud through data mining in the search for deviations from normal patterns.
Creativity and Expansion – Finding and exploring new possibilities and markets to foster creativity and economic development.

Data protection and privacy are of utmost importance to us. Below are the steps we have taken to ensure this.
Data Encryption: Protecting the data from any unauthorized access by encrypting it in transit and at rest.
Access Controls: Keeping the sensitive data under the lock and key access of specific authorized individuals to avoid data breach.
Compliance: Following the best practices, updating the standards and practices, putting the data privacy laws in CCPA, GDPR, HIPAA etc.
Regular Audits: Reviewing the security practices and conducting vulnerability testing at regular intervals of time, eliminating the threat.
Data Anonymization: Disguise individual records in a database to disclose useful information without revealing any individual record.