Big Data Analytics is the complex method of examining large and diverse data sets to reveal relevant information including hidden patterns, unknown correlations, market trends and client preferences that can assist organizations to make informed business decisions.
Big Data Analytics includes mainly gathering data from various sources, mungling it in such a manner that it becomes accessible to analysts and users to consume.
Improve customer integrations: Big Data Analytics can be used to improve customer interactions and integrations. You can aggregate structured, semi- and unstructured data from touchpoints your customer has with your company to gain a 360-degree view of your customer's behavior and motivations for improved tailored marketing. Data sources can include social media, sensors, mobile devices, sentiment and call log data.
Detect and mitigate fraud: You can monitor transactions in real-time, proactively recognizing those abnormal patterns and behaviors indicating fraudulent activity. Using the power of big data along with predictive/prescriptive analytics and comparison of historical and transactional data helps companies predict and mitigate fraud.
Drive supply chain efficiencies: Gather and analyze big data to determine how products are reaching their destination, identifying inefficiencies and where costs and time can be saved. Sensors, logs, and transactional data can help track critical information from the warehouse to the destination.
The benefits of Big Data Analytics include:
Data Procurement: A data analyst can gather and store enormous amounts of information from different sources such as webpages, directories, etc. In order to use these data in a snap as at when needed, a well-guided system must be in place. Big Data Analytics helps in data procurement.
Data Segmentation: There are times when a real estate agent wishes to distribute her information on the basis of various parameters such as Gender, Age, Income Group, Location, Budget, segment customers, market segmentation, product segmentation, etc. Big Data Analytics helps in proper data segmentation.
Checks Variability: Data flows may be extremely incompatible with regular peaks in relation to growing information speeds and data variants. So Big Data Analytics helps to check this variability.
Checks Complexity: The information today comes from various sources, making it hard to connect, match, clean and convert information across systems. However, relationships, hierarchies, and various information linkages need to be connected and correlated or your information can spiral out of control rapidly. Big Data Analytics helps in checking data complexity.
Business Intelligence: This is a technology-based process for analyzing data and presenting actionable information to help executives and managers, including corporate end-users, make informed business decisions.
Identify New Opportunities: It helps the organization to harness their data and use it to identify new opportunities.
Decision Making: It helps the organization to make better, informed and faster decisions.
Market Predictions: It helps organizations to be able to predict market outcomes and how it will affect organizational goals and objectives.
Competitive Edge: It gives the organization a competitive edge over others.
Accurate Measurement: It offers accurate measurement of data.
Data Visualization: It allows the researchers to be able to visualize data.
Saves Cost/Time: It helps to save cost and time.
Market Conditions: It helps to understand market conditions and directions in order to plan ahead.
Predictive Applications-Identity Management: (or Identity and Access Management) is the method of managing organizations that have access to your information.
Real-time Reporting: collects information minute by minute, typically in an intuitive dashboard format, and relays it to you.
Security Features: It is essential for a successful company to keep your system secure.
Analytics Features: The provision of tools for users with a multitude of analytics packages and modules.
Data Processing Features: This involves collecting and organizing raw information in order to generate significance.
Technologies Support: It supports a variety of technologies and tasks that may be useful to you.
Types of Big Data Analytics include:
1. Descriptive Analytics (past): These tell what happened. They 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.
2. Predictive Analytics (future): Predictive analytics tools use highly advanced algorithms to forecast what might happen next. They make use of artificial intelligence and machine learning technology to predict events.
3. Prescriptive Analytics (environmental Analytics): A step above predictive analytics, prescriptive analytics tell organizations what they should do in order to achieve the desired result.
4. Diagnostic Analytics (failure and success rate of an event): These explain why something happened. More advanced than descriptive reporting tools, allow analysts to dive deep into the data and determine root causes for a given situation.
The Tools used in Big Data Analytics are:
1. Hadoop: Data Processing and Storage.
2. Kafka: Data Warehousing.
3. Apache H – Base: No – SQL Database.
4. Splunk's: Log Analytics Platform.
5. Talend: Software Integration.
6. Apache Spark: Real-time Processing.
Big Data Analytics Skillset includes:
1. Basic Programming
2. Data Visualization
3. Statistical and Quantitative Analysis
4. Specific Business Knowledge
5. Computational Frameworks e.g. Hadoop.
6. Data Warehousing e.g SQL and NO SQL.
In the Full Course, you will learn everything you need to know about Big Data Analytics with Certification upon successful completion of exams.
Big Data Analytics - Introduction/Overview
Big Data Analytics - Data Life Cycle
Big Data Analytics - Methodology
Big Data Analytics - Core Deliverables
Big Data Analytics - Key Stakeholders
Big Data Analytics - Data Analyst
Big Data Analytics - Data Scientist
Big Data Analytics - Problem Definition
Big Data Analytics - Data Collection
Big Data Analytics - Cleansing data
Big Data Analytics - Summarizing
Big Data Analytics - Data Exploration
Big Data Analytics - Data Visualization
Big Data Analytics - Introduction to R
Big Data Analytics - Introduction to SQL
Big Data Analytics - Charts & Graphs
Big Data Analytics - Data Tools
Big Data Analytics - Statistical Methods
Big Data Analytics - Machine Learning for Data Analytics
Big Data Analytics - Naive Bayes Classifier
Big Data Analytics - K-Means Clustering
Big Data Analytics - Association Rules
Big Data Analytics - Decision Trees
Big Data Analytics - Logistic Regression
Big Data Analytics - Time Series
Big Data Analytics - Text Analytics
Big Data Analytics - Online Learning
Big Data Analytics - Exams and Certification
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