Data is generated every time you use your smartphone, chat and shop with family and friends on Facebook. All this data is very complex and comes quickly in real time. All of these datasets are called “big data.”. Refers to large or complex data that is difficult or impossible to process using traditional methods. Although big data does not have a specific volume, data distributions are usually made in terabytes (TB), petabytes (PB), and exabytes (EB). they use big data to provide customer service, create personalized offers based on customer preferences and ultimately increase their profitability. Companies using big data use the data accumulated in their systems more effectively and can take the steps to be taken more consciously and quickly.. Thus, big data helps to increase customer engagement and conversion rates.
Using big data enables companies to work customer-centered and focused.. Real-time data can be used to evaluate customers’ evolving preferences. As a result, businesses are empowered to update and improve their marketing strategies and become more responsive to customer needs.
Big Data also helps oil and gas companies in the energy industry identify potential drilling locations and monitor pipeline operations.. Financial services firms use big data systems for risk management and real-time analysis of market data. Manufacturers and shipping companies rely on big data to manage their supply chains and optimize delivery routes. Other uses include emergency response, crime prevention, and smart city initiatives.
Analytical branches that can be made using customer data and information found in Big datasets include:
- Comparative Analysis: Used to compare a company’s products, services and brand authority with its competitors. It happens by examining user behavior and observing real-time customer engagement.
- Social Media Review: Information about what people are saying about a particular business or product on social media is the part of the analysis. The data received can be used to help define the target customer base for marketing campaigns.
- Marketing Analysis: To make the promotion of new products, services and initiatives more innovative Marketing analysis should be done to obtain information that can be used for the purpose. This is the section that discusses whether any potential problems will arise, how to maintain brand loyalty and improve customer service efforts.
Big data is often characterized by 3V. 3V is defined by Doug Laney as Volume (volume), Velocity (speed) and Variety (variety). New Vs have been added to big data that has become popular over time.
Big Data Vs:
- Volume: Volume is the most common of big data is the feature specified as. Volume defines how much data we have.
- Velocity: Speed represents the speed at which data is available. With the growth of the Internet of Things, data flows to companies at a rapid pace and must be handled in a timely manner.. Large datasets are updated in real or near real time, rather than the daily, weekly, or monthly updates done in many traditional data stores.. Big data analytics applications receive, correlate and analyze incoming data and then generate a response or result based on an overarching query. Data scientists and other data analysts need a deep understanding of available data. As big data analytics expands into areas such as machine learning and artificial intelligence (AI), managing data rate is also important, as analytical processes automatically find patterns in collected data and use it to generate insights.
- Variety: Diversity defines one of the biggest challenges of big data. Applies to large datasets that are generally less consistent than traditional transactional data, may have multiple meanings, or may be formatted differently from one data source to another.
- Veracity: Data accuracy refers to how accurate the data in datasets is. Raw data collected from multiple sources, such as social media platforms and web pages, can cause serious data quality issues that can be difficult to detect. You must make sure that the data is correct. Poor quality data without validation can cause problems. Uncertain data can lead to inaccurate analysis and lead to poor decisions. As a result, you should always validate your data and make sure you have enough correct data to produce valid and meaningful results.
- Value: After addressing volume, speed, variety, variability, realism, and visualization that takes a lot of time, effort, and resources, it’s necessary to make sure your organization gets value from the data.
Data Types:
There are three types of data; Structured, Semi-structured and Unstructured data. Used in different projects of all types.
- Structured data: Fixed format and usually numeric. In most cases, it is handled by machines, not humans.. This data type consists of information managed in SQL databases and spreadsheets.
- Unstructured data: Data that does not enter a predetermined format.
- Semi-structured data: Contains data forms such as web server logs or data from sensors you set up. Although it is not classified under a specific database, it contains important information that distinguishes the individual elements within the data.
- Apache Lucene, full-text indexing and search Since the software uses libraries, it can be used for any recommendation engine.
- Apache Zeppelin is an incubation project that provides interactive data analysis with SQL and other programming languages.
- Elasticsearch is more of an enterprise search engine.
- TensorFlow is a software library that is getting more and more attention as it is used for machine learning.
Tools You Can Use:
As Big Data is an ever-growing resource, it goes along with it. The tools intended to be used must also always evolve.. Tools such as Hadoop, Pig, Hive, Cassandra, Spark, Kafka are frequently preferred depending on the requirement of the organization. Apache is Hadoop.
Another tool is Apache Spark. One of Spark’s strengths is that it can store most of the rendering data in memory and on disk.. Can work with Spark, Hadoop, Cassandra, OpenStack Swift and many other storage solutions. One of the best features of Spark is that it can run on a local machine.
Apache Kafka allows users to publish and subscribe to real-time data streams. Kafka’s main task is to bring the reliability of other messaging systems into the data stream.