Introduction
We have covered big data and analytics in some of our previous posts. This article will focus on IBM’s solutions for big data.
Have you ever wondered how big companies such as Facebook and YouTube can handle such enormous amounts of data? We use the term “Big Data” to denote the large amount of data processed to serve an organisation's specific purpose.
This article will discuss the basics of Big Data and the different solutions provided by IBM to handle Big Data.
After you reach the end of this article, you will clearly understand big data and the IBM Big Data Analytics features.
An Overview of Big Data
What exactly is big data? It can be characterised as data sets that are too large or complex for typical relational databases to collect, maintain, and process promptly. Businesses can acquire fresh insights and take action by accessing an enormous volume of data and analysing a wide range of data sources.
We may define the characteristics of big data using the 5 V’s as follows:
- Volume: Volume is the size of or amount of data. This data may be structured or unstructured.
- Velocity: It relates to the rate at which information gets accumulated. This is primarily due to social media, mobile data, IOTs, and other factors.
- Variety: It refers to the various kinds of data (data types, formats, and so on) that we are interested in analysing. The data may be structured, semi-structured, or unstructured due to various data sources generated by humans or robots.
- Value: The term "value" refers to how valuable the data is in making decisions.
- Veracity: It relates to the assurance of the data's quality, integrity, credibility, and accuracy.
( 5 V’s of Big Data, Source: techentice )
Applications
Big Data enables businesses to make better and faster decisions by providing them with more information to solve problems and more data to test their hypotheses on.
- Customer Experience
With the introduction of Big Data, a significant field, customer experience, has been changed. More data on customers and their preferences is being collected than ever before. This data is being used to benefit customers by providing customised recommendations and offers. - Machine Learning
Another subject that has benefited immensely from the growing popularity of Big Data is machine learning. More data implies we have more datasets to train our machine learning models, and a more trained model (usually) performs better. - Demand Forecasting
With more and more data about customer transactions being collected, demand forecasting has grown more precise. This enables businesses to create forecasting models to predict future demand and scale production accordingly.
Big Data Analytics
Big data analytics uses advanced analytic techniques for massive, heterogeneous big data sets, containing structured, semi-structured, and unstructured data and data from many sources and sizes ranging from terabytes to zettabytes.
Big Data analytics is used to uncover hidden patterns, relationships, market trends, and client preferences from large amounts of data. Big Data analytics has several advantages, including improving decision-making and avoiding fraudulent actions.
Advantages of Big Data Analytics
Businesses that combine big data with advanced analytics benefit in a variety of ways, including:
- Low cost and Operational Efficiency
When it comes to storing vast amounts of data, big data technologies like cloud-based analytics can dramatically lower costs (for example, a data lake). Furthermore, big data analytics aids businesses in finding more efficient methods of operation. - Making smarter decisions in a shorter amount of time
When combined with the capacity to analyse new data sources, such as streaming data from IoT, in-memory analytics allows businesses to analyse information quickly and make well-informed decisions. - New product and service development and marketing
Analytics allows organisations to gauge client requirements and satisfaction, providing customers with precisely what they want and when. More businesses are using big data analytics.
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