What is Big Data Analytics?
Big data analytics is the process of examining large and complex data sets to uncover information such as hidden patterns, correlations, market trends, and customer preferences that can help organizations make informed business decisions. Through data analytics technologies and techniques, organizations can analyze data sets and gather new information to answer basic questions about business operations and performance. Overall, big data analytics is a form of advanced analytics that involve complex applications with elements such as predictive models, statistical algorithms, and what-if analysis powered by analytics systems.
An example of big data analytics can be found in the healthcare industry, where millions of patient records, medical claims, clinical results, care management records, and other data must be collected, aggregated, processed, and analyzed. Big data analytics is used for accounting, decision-making, predictive analytics, and many other purposes. This data varies greatly in type, quality, and accessibility, presenting significant challenges but also offering tremendous benefits. Compared to traditional business intelligence, big data has several differences.
Why is Big Data Analytics Important?
Organizations can use big data analytics systems and software to make data-driven decisions that can improve their business-related outcomes. The benefits can include more effective marketing, new revenue opportunities, customer personalization, and improved operational efficiency. With an effective strategy, these benefits can provide competitive advantages over competitors.
How does Big Data Analytics Work?
Data analysts, data scientists, predictive modelers, statisticians, and other analytics professionals collect, process, clean, and analyze growing volumes of structured transaction data, as well as other forms of data not used by conventional BI and analytics programs. The big data analytics process involves the following steps:
1. Data collection from various sources such as internet clickstream data, web server logs, cloud applications, social media content, and more.
2. Data preparation and processing to organize, configure, and partition data properly for analytical queries.
3. Data cleansing to improve its quality by scrubbing the data using scripting tools or data quality software.
4. Data analysis using various tools such as data mining, predictive analytics, machine learning, and artificial intelligence, among others.
Types of Big Data Analytics
There are several different types of big data analytics, each with their own application within the enterprise:
1. Descriptive analytics – analyzes data for general assessment and summarization.
2. Diagnostic analytics – determines why a problem occurred.
3. Predictive analytics – predicts what comes next.
4. Prescriptive analytics – provides recommendations after diagnostics and predictions.
Key Big Data Analytics Technologies and Tools
Many different types of tools and technologies are used to support big data analytics processes, including the following:
– Hadoop
– Predictive analytics hardware and software
– Stream analytics tools
– Distributed storage data
– NoSQL databases
– Data lake
– Data warehouse
– Knowledge discovery and big data mining tools
– In-memory data fabric
– Data virtualization
– Data integration software
– Data quality software
– Data preprocessing software
– Apache Spark
– Microsoft Power BI and Tableau
Big Data Analytics Uses and Examples
Big data analytics can be used to assist organizations in various ways, including:
– Customer acquisition and retention
– Fraud detection
– Risk assessment
– Marketing analysis
– Operations improvement
Overall, big data analytics is a crucial tool that organizations can use to gain insights from large and complex data sets, drive informed decision-making, and achieve competitive advantages in the marketplace.