Big Data is a combination of structured, semi-structured, and unstructured data collected by organisations that can be used for machine learning, predictive modelling, fraud detection, sentiment analysis, and other advanced analytics applications. Let us take a detailed look at it.
The number of companies, organisations and institutions that use Big Data solutions today has exploded in recent years, as has the amount of data collected. Some estimates put the total amount of data generated daily at 2.5 trillion bytes! Such a number is difficult to understand, let alone harness, and yet companies have eagerly embraced Big Data analytics for their lofty goals. We are only just beginning to understand how revolutionary Big Data could be, and with it really taking off, we can expect a gamut of changes with respect to how business is done in the coming years.
There are a variety of tools for analysing Big Data like NoSQL, Hadoop, and Cassandra. These tools allow us to collect different types of data from a wide variety of sources — digital media, Web services, business applications, machine log data, etc.
What is Big Data all about?
Big Data refers to the huge and diverse amounts of information available and growing each day. This includes the amount of data, the speed at which it is created and collected, and the variety of the data points collected. It comes from multiple sources and arrives in several formats (csv, tsv, html, json, parquet, avro). There’s a misconception that Big Data should be the size of a terabyte, zettabyte, or even exa-byte — but it isn’t.
The size depends on where the data is being used. For example, you have a 50MB file and want to email it as an attachment, but you can’t because it is too big. In this scenario, this ‘50MB’ file is referred to as Big Data.
Using Big Data has become easier with the increasing number of Big Data providers. Today we live in the age of Big Data, in which breakthroughs and revolutionary changes take place at regular intervals.
Companies use Big Data to improve their operations, make better decisions, provide better customer service, create personalised marketing campaigns based on specific customer preferences, and ultimately increase profitability. Companies that use Big Data have an advantage over those that don’t because they can make faster and better-informed business decisions
For example, Big Data can provide companies with valuable information about their customers that can be used to refine marketing campaigns to increase customer engagement and conversion rates.
Big Data allows companies to focus more and more on the customer. Real-time and historical data can be used to assess changing consumer preferences, allowing companies to update and improve their marketing strategies and be more responsive to customer wants and needs.
Medical researchers and physicians also use Big Data to identify risk factors for diseases as well as to help diagnose these in patients. Additionally, data derived from electronic medical records, social media, the Web, and other sources provides healthcare organisations and government agencies with up-to-the-minute information on infectious disease threats or outbreaks. In the energy sector, Big Data helps oil and gas companies identify potential drilling locations and monitor pipeline operations; similarly, utilities use it to monitor electrical grids.
Financial and insurance companies use Big Data for risk management and real-time analysis of market data. Manufacturers and logistics companies rely on it to manage their supply chains and optimise delivery routes. Other government uses include emergency response, crime prevention, and smart city initiatives.
Comparative analysis: This includes examining user behaviour metrics and observing customer engagement in real-time to compare a company’s products, services, and brand awareness with those of its competitors.
Listening to social networks: This is information about what people are saying on social media about a specific company or product that goes beyond what can be delivered in a survey. This data can be used to help identify target audiences for marketing campaigns by observing activity related to specific topics from various sources.
Marketing analysis: This information can be used to make the promotion of new products, services and initiatives more informed and innovative.
Customer satisfaction and feelings: All the information collected can reveal what customers think of a company or brand, how brand loyalty can be preserved if potential problems arise, and how customer service efforts can be improved.
Open source tools for Big Data analytics
This allows distributed processing of large data sets across clusters of computers. It is one of the best Big Data tools designed to scale up from single servers to thousands of machines.
- The cluster is highly scalable and fault tolerant.
- It is based on the ‘Data Locality’ concept.
- It allows for faster data processing.
The Apache Cassandra database is widely used nowadays for effective management of large amounts of data.
- Supports multi data centre replication. Data is automatically replicated to multiple nodes.
- Most suitable for companies that can’t afford to lose data, even when an entire data centre is down.
Hive allows programmers to analyse huge data sets on Hadoop. It helps with querying and managing large data sets quickly.
- It supports SQL like query language (called HQL – Hive Query Language) for interaction and data modelling.
- It supports partition, Bucket and tables.
- Hive is designed for managing and querying only structured data.
- It offers a Java database connectivity (JDBC) interface.
- Lack of understanding of Big Data: Companies fail in their Big Data initiatives because of a lack of understanding. Employees may not know what the data is, how it is stored, how it is processed, and where it comes from. While data professionals may know what is going on, the others may not have a transparent picture.
- Confusion in choosing which Big Data tool to use: Organisations are often confused when choosing the simplest tool for analysing and storing huge data. Is HBase or Cassandra the easiest data storage technology to use? Is Hadoop MapReduce okay, or is Spark a much better option for data analysis and storage? These are the questions that annoy companies, and sometimes they can’t find the answers. So they end up making the wrong decisions and choosing the wrong technology. This wastes money, time, effort, and man-hours.
- Data security: Securing this huge amount of data is a big challenge too. Companies are often so busy comprehending, storing, and analysing their data sets that they postpone action on data security, becoming victims of malicious hackers.
If you want to help your business achieve more, leveraging Big Data and AI is a must today or there is a clear danger of the organisation falling by the wayside.