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The video from (Artificial Intelligence, 2020) explains how collecting Big Data helps organisations improve safety, efficiency, and service quality. According to (Mucci and Stryker, 2024), Big Data allows companies to personalise products and better meet the needs of their customers.
Why do Companies Need Data?
Why do Companies Need Data?
Companies collect Data to understand what is happening inside the business and how customers behave. This includes tracking what products people buy, how often they visit a website, what pages they click, or even their feedback on social media. All of this information helps companies make smarter decisions.
For example, a retail company can analyse customer shopping patterns to predict what items will be in high demand next month. by collecting data, they can also identify problems, like delays in delivery or low stock levels, and fix them faster.(Mucci and Stryker, 2024)
Types of Data
In data analysis, there are two main types of Data : quantitative and qualitative.
- Quantitative Data includes numbers that cam be measured or counted, such as age, price, income or numbers of items sold. For example, a retail store might track how many units of a product are sold each week.
- Qualitative Data includes words, categories, or descriptions, such as product names, colours, or customer feedback. A business might analyse reviews to find out how people feel about the product.
Both types of data can be further divided into :
Discrete Data (whole numbers, like number of employees)
Continuous Data ( data that can change and doesn't have to be a whole number, like a weight or time)(Hoskin, 2021)
In data analysis, it is important to understand how information is grouped. Some data is made up of numbers we can count or measure, like how much something costs or how many people visited a website. This is called quantitative data.
In data analysis, it is important to understand how information is grouped. Some data is made up of numbers we can count or measure, like how much something costs or how many people visited a website. This is called quantitative data.
Other data focuses more on descriptions, for example, what colour a product is, or how a customer feels about a service. That's know as qualitative data.
Other data focuses more on descriptions, for example, what colour a product is, or how a customer feels about a service. That's know as qualitative data.
These two types can also be broken down into discrete and continuous. Discrete data includes whole values, like the number of orders placed in a day. Continuous data can change smoothly, such as a person's height or time spent on a page. Understanding these differences helps businesses choose the right method to organise and analyse information. The video from (Simplilearn, 2022) explains this clearly by giving practical examples that connect statistical terms with real situations.
These two types can also be broken down into discrete and continuous. Discrete data includes whole values, like the number of orders placed in a day. Continuous data can change smoothly, such as a person's height or time spent on a page. Understanding these differences helps businesses choose the right method to organise and analyse information. The video from (Simplilearn, 2022) explains this clearly by giving practical examples that connect statistical terms with real situations.
5 Vs of Big Data
5 Vs of Big Data
Big Data is often described using 5 main characteristics, know as the 5 Vs. These help us understand what makes Data Big and why it needs special tools and technologies.
Volume ( Huge amount of data created every day, from social media, websites, devices, apps. Companies now deal with terabytes or even petabytes of data, which is to much for traditional systems).
Velocity ( Data is moving fast, every second, millions of emails, messages and searches are made around the world. Businesses need to process this data quickly to react in real time).
Variety ( Data comes in many formats such as numbers, text, images, video, or audio. It can be structured like a table or unstructured like a tweet or photo).
Veracity (This means how accurate and trustworthy the data is. Good decisions can only be made if the Data is reliable. That's why it is important to check the quality of the data before using it).
Value ( This is the most important part. Data is only useful if we can turn it into insights. Big Data helps organisations improve their services, increase profits, and solve problems by using the right information at the right time).
To get the most of out Data , organisations need to manage its all 5 Vs at the same time.(Chen, 2024)
When we talk about Big Data, the 5 Vs help us understand how complex and powerful it can be. These include Volume, Velocity, Variety, Veracity and Value. Each of them shows a different challenge when working with large datasets. For example businesses often receive huge amounts of data from many different sources( Volume and Variety), and this data comes in very fast (Velocity). But all of it is reliable(Veracity), and the main goal is to turn that data into useful insights(Value). (Chen, 2024)
When we talk about Big Data, the 5 Vs help us understand how complex and powerful it can be. These include Volume, Velocity, Variety, Veracity and Value. Each of them shows a different challenge when working with large datasets. For example businesses often receive huge amounts of data from many different sources( Volume and Variety), and this data comes in very fast (Velocity). But all of it is reliable(Veracity), and the main goal is to turn that data into useful insights(Value). (Chen, 2024)
As explained in the video from (TechnologyAdvice, 2023) these 5 Vs are the foundation of any data driven strategy used in business today.
As explained in the video from (TechnologyAdvice, 2023) these 5 Vs are the foundation of any data driven strategy used in business today.
Data Collection Methods
Data Collection Methods
To work with Data, we first need to choose the right way to collect it. There are many ways of collecting data depending on the purpose and situation.
Some common Methods include surveys, interviews, observation, focus groups and experiments.
Each method helps gather different types of information.
Choosing the right method is important to collect accurate and valuable Data.
These Data collection methods are essential in research and analytics. Survey are often used to gather information from a large audience, while interviews provide more detailed responses. Observation allows us to see real behaviours in natural environments, and experiments are great for testing ideas and finding patterns.
Using the correct method helps ensure that the data is accurate, relevant, and useful for making better decisions. As noted by (Duggal, 2021) , selecting the appropriate technique depends on the type of information needed and the goals of the analysis.(iCert Global, 2024)
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