Big data analysis includes the use of advanced analytical techniques in very large and complex data sets, which include structured, unstructured and semi-structured data from various sources and from terabytes to zettabytes in different quantities.
Big data is a concept used for data sets whose size or form is beyond the ability of conventional relational databases to store, handle, and process low-latency data. Big data includes one or more of the following characteristics: high volume, high velocity, wide range. Artificial intelligence (AI), web, social, and the Internet of Things (IoT) push data sophistication across new ways and data sources.
Big Data Analytics helps analysts, researchers, and business users to make smarter and quicker decisions using data that was previously unavailable or unusable. Businesses may use sophisticated computational technologies such as text analytics, artificial learning, predictive analytics, data analysis, statistics, and natural language processing to derive fresh information from previously untapped data sources separately or in combination with current business data.
Cases for Big Data Analytics
- Improve customer integrations
Connect organized, semi-structured, and unstructured contact point data that your consumer has with the business to obtain a 360-degree view of your customer’s actions and inspiration for better-targeted marketing. Data sources can involve social networking, cameras, mobile apps, sentiment, and log info.
- Detect and mitigate fraud
Track transactions in real time to recognize suspicious patterns and behaviors in a proactive manner that indicate fraudulent activity. The power of big data, predictive / prescriptive analysis and evaluation of past and transactional data allow businesses to detect and prevent fraud.
- Drive supply chain efficiencies
Collect and evaluate big data to assess how goods reach their destination, to find inefficiencies, and where costs and time can be saved. Sensors, logs, and transaction data can be used to track critical information from the warehouse to the destination.
Big Data Analytics vs. Business Intelligence
Business Intelligence is sometimes referred to as the first two descriptive and predictive phases of four major data measures. BI is also stored in a data warehouse where data is very organized and only shows “what, where, and how” something happened. Such data are also used to track and gain insights into popularity patterns and interactions based on recent events.
Big Data Analytics is taking this step further, as technology can access a range of structured and unstructured datasets (such as user actions or images). Big data analytics Malaysia software may combine this data with historical knowledge to assess the likelihood of an occurrence happening based on experience.
The position of the Big Data Analyst is not limited to raw data analysis, but also the participation of data engineers in ETL operations, working with reporting and visualization software, and machine learning algorithms. The work roles of the data scientist and the analyst sometimes overlap unless the data scientist is a mathematical or statistical purist. A smart way to learn specific skills is to engage in real-time project training using the same range of methods and methodologies commonly used in the industry.