Big data analytics utilizes advanced analytic methods against enormous, various flowing data sets from diverse sources that can be monitored digitally, from terabytes to zettabytes. Data sources can be computers, mobile devices, machine sensors, Internet of Things (IoT) devices, security cameras, satellites and so on… This data is used by organizations to drive decisions, improve processes and policies, and create customer-centric products, services, and experiences that was previously unapproachable or impracticable.
The benefit of Big Data is quantified by the intensity to which you can analyze and comprehend it. Artificial Intelligence (AI), Machine Learning (ML), and modern database technologies allow for Big Data visualization and analysis to deliver actionable insights in real time. Big Data analytics help companies to maximize their data to recognize new opportunities and create business models.
There are three typical categories of big datasets, structured data, unstructured data and semi-structured data.
It is the easiest to manage and search. The data can consist of information like stock reports, machine or device logs, and statistical details. A spreadsheet, with its arrangement of pre-defined columns and rows, is an example of structured data. Components are clearly grouped, allowing database designers and administrators to define simple algorithms for search and analysis.
This can be surveillance data, weather data, social media posts, audio files, images, and geo-spatial data. This type of data cannot be simply taken in standard row-column interactive databases. Traditionally, companies that intended to comb, control, or examine enormous amounts of unstructured data
It is a fusion of structured and unstructured data. It is considered information that does not reside in a relational database but that does have some organizational properties that make it easier to analyze. Examples of semi-structured are CSV, XML and tab delimited files are semi structured documents, NoSQL databases are also deemed as semi structured.
The volume of big data generated comes from three key sources, social data, machine data and transactional data.
originates from social media platforms, the Likes, Comments, Video Uploads, and conventional media that are uploaded and shared on social media platforms. The nature of the data offers beneficial concepts into user performance and outlook which is effective in marketing analytics. In addition, the internet is an excellent source of social data, various web analytical tools can be used to boost the quantity of big data.
is characterized as generated information through manufactured equipment, internal sensors, medical devices, satellites and machine logs can track user performance. Sensors such as, smart meters, road cameras, satellites, games and Internet Of Things will produce value, volume and variety of data in the very near future.
is generated from the day-to-day transactions both online and offline. Receipts invoices, purchase orders, and storage records are categorized as transactional data.
Big Data Analytics can foster data-driven decision making and empower your employees in ways that increase value to your organization.
Promote new business strategies, products, and services.
Discover the power of data-driven marketing.
Pinpoint systems and business processes issues in real-time.
Predict and monitor business and the market information in real-time.
Influence business decisions by discovering critical points buried in large datasets.
Optimize complex decisions for unpredicted events and dormant risks.
Create customized products, services, and offerings.
Provide speedy fulfillment of products and services.
Differentiate revenue to increase company earnings.
Accumulate data from numerous sources, including external third-party sources.
Provide real-time response for customer support and increase customer experiences.
Big Data architecture maps the essential processes to administer Big Data across data sources, to data storage, then on to Big Data analysis, and analyzed results are produced as business intelligence.
Apache Hadoop works on an open-source framework for controlling supplied Big Data processing across a system of numerous associated computers. Hadoop clusters many computers into vast scalable network and analyzes the data in parallel. This method normally applies a programming model called MapReduce, which coordinates Big Data processing by organizing the distributed computers.
Data Warehouse is a type of data management system that is created to facilitate and support business intelligence (BI) activities and especially analytics. A data warehouse unifies and consolidates enormous amounts of data from multiple sources.
Data lakes are next-generation data management solutions which is a highly scalable environment that supports extremely large data volumes, gathering petabytes of structured, semi-structured and unstructured data in its native format from a variety of sources. Data lakes supplement data warehouse and business intelligence solutions. They deliver the framework for machine learning and real-time advanced analytics in a cooperative environment.
Apache Hadoop works on an open-source framework for controlling supplied Big Data processing across a system of numerous associated computers. Hadoop clusters many computers into vast scalable network and analyzes the data in parallel. This method normally applies a programming model called MapReduce, which coordinates Big Data processing by organizing the distributed computers.
Big Data is utilized throughout the Securities Exchange Commission (SEC) to monitor the financial market. They use network analytics and natural language processors to catch illegal trading activity in the financial markets. Retail traders, Big banks, hedge funds, and others in the financial markets also use Big Data for trade analytics (High-frequency trading, pre-trade decision-support analytics, sentiment measurement, and predictive analytics). Big Data is widely used for risk analytics that includes anti-money laundering and demand enterprise risk management.
Big Data analysis aids healthcare professionals to make more precise and evidence-based conclusions. Big Data also helps hospital administrators identify trends, manage risks, and reduce excessive spending, enabling the highest possible budgets to be allocated to areas of patient care and research. In healthcare systems Big Data is collected from a cell phone apps, from millions of patients, to grant doctors to use evidence-based medicine as opposed to administering several medical lab tests to all patients who go to the hospital.
Big data is considerably used in higher education. They now can utilize a Learning and Management System that tracks when a student logs onto the system, it can quantify how much time is devoted on different pages in the system, as well as the complete progress of a student over time. Big Data is also used to measure teacher’s effectiveness to ensure a shared enjoyable experience for both students and teachers. Teacher's performance can be tweaked and quantified against student behavioral classification, student demographics, student aspirations, subject matter, and several other quantifiable variables.
According to the U.S. Bureau of Labor Statistics, utility companies spend over US$1.4 billion on meter readers and typically rely upon analog meters and intermittent manual readings. Smart meter readers deliver digital data several times a day and, with the benefit of Big Data analysis, this intelligence can enlighten more efficient energy usage and accurate pricing and predicting. When field workers are liberated from meter reading, data capture and analysis can benefit more promptly to reallocate them where repairs and upgrades are immediately needed.
Various companies in the media and entertainment industry are facing new business challenges to create, market and distribute their content. Consumers require on-demand search and access of content anywhere, any time, on agnostic devices. Big Data provides actionable points of intelligence about millions of people. Publishing environments are now customizing advertisements and content to attract customers. These insights are collected over a variety of data-mining behaviors.
