What is Big Data Analytics | How Big Data Analytics are Implemented

                   Big Data Analytics


A. Big Data Analytics: What it is

Big data analytics is the process of examining large and varied data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful information that can help organizations make more-informed enhanced insight and business decisions.

There are four different types of Big data analytics tools:

Ø  Descriptive Analytics: These tools create simple reports and visualizations that show what occurred at a particular point in time or over a period of time. These are the least advanced analytics tools.
Ø  Diagnostic Analytics: Diagnostic tools are more advanced than descriptive reporting tools. It is allow analysts to dive deep into the data and determine root causes for a given situation.
Ø  Predictive Analytics: Predictive analytics tools use highly advanced algorithms to forecast what might happen next. Often these tools make use of artificial intelligence and machine learning technology.
Ø  Prescriptive Analytics: Prescriptive analytics tell organizations what they should do in order to achieve a desired result. These tools require very advanced machine learning capabilities, and few solutions on the market today offer true prescriptive capabilities.

B. How Big Data Analytics is implemented presently across various industries:

Primary goal for most of the enterprises is enhance various business benefits through big data analytics, including new revenue opportunities, more effective marketing, better customer service, improved operational efficiency and competitive advantages.
Key industries using big data analytics:
Banking and Securities
Stock Exchange Authorities or Banking Authorities are using big data analytics to ensure that no illegal trading happens by monitoring the stock market/finance market.
Big banks, hedge funds and other financial institutions uses big data for trade analytics used in high frequency trading, pre-trade decision-support analytics, sentiment measurement, Predictive Analytics etc.
This industry also heavily relies on big data for risk analytics including; anti-money laundering, demand enterprise risk management, "Know Your Customer", and fraud mitigation.
Communications and Media
Since consumers expect rich media on-demand in different formats and in a variety of devices, some big data challenges in the communications, media and entertainment industry include:
·         Collecting, analyzing, and utilizing consumer insights
·         Leveraging mobile and social media content
·         Understanding patterns of real-time, media content usage

Organizations in this industry simultaneously analyze customer data along with behavioural data to create detailed customer profiles that can be used to:
·         Create content for different target audiences
·         Recommend content on demand
·         Measure content performance
In healthcare, large amounts of medical data have become available in various healthcare organizations (providers, pharmaceuticals etc.). This data could be an enabling resource for deriving insights for improving care delivery and reducing waste. The enormity and complexity of these datasets present great challenges in analyses and subsequent applications to a practical clinical environment.
Using public health data for faster responses to individual health problems and identify the global spread of new virus. Health Ministries of different countries incorporate big data analytic tools to make proper use of data collected after Census and surveys.
Big data in education used to measure teacher’s effectiveness to ensure a good experience for both students and teachers. Teacher’s performance can be fine-tuned and measured against student numbers, subject matter, student demographics, student aspirations, behavioural classification and several other variables.
To increase productivity by using big data to enhance supply chain management. Manufacturing companies use these analytical tools to ensure that are allocating the resources of production in an optimum manner which yields the maximum benefit.
For everything from developing new products to handling claims through predictive analytics. Insurance companies use business big data to keep a track of the scheme of policy which is the most in demand and is generating the most revenue.
When it comes to claims management, predictive analytics from big data has been used to offer faster service since massive amounts of data can be analyzed especially in the underwriting stage. Fraud detection has also been enhanced.
The public sector generates a huge amount of data in transactions, employment, education, manufacturing, and agriculture, to name a few. Big data analytics applications can significantly help the government to achieve efficiencies, combat fraud, bring transparency, foster the economy, and spike productivity and growth.
Using big data analytics the security agencies and police can analyze the disparate sources and respond to crime, attacks, and other such situations in the country.
Retail and Whole sale trade
Big data from customer loyalty data, POS, store inventory, local demographics data continues to be gathered by retail and wholesale stores. Retail and wholesale traders can utilize big data for analytics and for other uses including:
·         Optimized staffing through data from shopping patterns, local events, and so on
·         Reduced fraud
·         Timely analysis of inventory
·         Customer loyalty
For better route planning, traffic monitoring and management, and logistics. This is mainly incorporated by governments to avoid congestion of traffic in a single place.
Applications of big data analytics by governments, private organizations and individuals include:
·         Governments use of big data: traffic control, route planning, intelligent transport systems, congestion management (by predicting traffic conditions)
·         Private sector use of big data in transport: revenue management, technological enhancements, logistics and for competitive advantage (by consolidating shipments and optimizing freight movement)
·         Individual use of big data includes: route planning to save on fuel and time, for travel arrangements in tourism etc.
Energy and Utilities
By introducing smart meters to reduce electrical leakages and help users to manage their energy usage. Load dispatch centers are using big data analysis to monitor the load patterns and discern the differences between the trends of energy consumption based on different parameters and as a way to incorporate daylight savings.
In utility companies the use of big data also allows for better asset and workforce management which is useful for recognizing errors and correcting them as soon as possible before complete failure is experienced.

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