Following the positive reception of our recent article on fundamental artificial intelligence concepts, we’ve recognized your keen interest in delving further into AI. As such, we’ve decided to curate a series of beginner-friendly posts where we’ll explore in greater depth the key terms and ideas central to artificial intelligence.
In this specific article, our focus shifts to Big Data. This piece promises to be an enlightening read for anyone seeking to unravel the mystery behind this ubiquitous term and its implications in the business world.
What is Big Data?
Let’s start with the theory or an explanation of Big Data.
As the name suggests, the term Big Data refers to massive, complex data sets that are difficult or impossible to process using traditional methods due to their size, speed of generation, and diversity.
One of the more popular terms for Big Data is the definition created by Doug Laney, an industry analyst who, in the early 2000s, defined Big Data using the so-called “3V model.”
- Volume (Volume): Technological advances have made it possible to collect and store data on a previously unattainable scale. The first V thus refers to volume as understood by huge volumes of data.
- Speed (Velocity): In the age of the Internet of Things and instant communication, data is generated and transmitted incredibly fast. The second V, therefore, stands for the lightning-fast speed at which data must be processed and analyzed.
- Variety (Variety): Refers to the variety of sources and forms from which data is collected. Data can include numbers, text, audio, video, graphics, and more. Each data type requires specific tools to collect, store, and analyze it.
Some sources provide an expanded version of this definition, indicating two more elements, such as verification of the integrity of the data held (veracity) and its relevance to the user (value).
How Does Big Data Work?
To achieve the benefits of Big Data, you need to prepare it properly. Working with big data involves several critical steps, each contributing to achieving this outcome. These steps may vary based on the specifics of the data and the practices of the company processing it. Below you will find the most common activities related to Big Data.
1. Data Collection
The first step is the generation and/or collection of data. This process varies from organization to organization, as each has unique methods for data acquisition. It includes collecting unstructured (refer to glossary) and structured data from various sources, such as the cloud, mobile apps, IoT sensors, and more.
2. Data Organization
As mentioned above, Big Data encompasses structured and unstructured data. While the former is typically easier to organize, the latter must be prepared appropriately to yield correct information.
3. Data Cleaning
To obtain reliable results and enhance the quality of the data, it needs to be cleaned. This process involves removing duplicates and unnecessary data that could lead to distorted results and incorrect conclusions.
3. Data Analysis
Data on its own holds no value until it is analyzed and valid conclusions are drawn. To do this, you need to use advanced analytical techniques such as data mining, predictive analytics, and deep learning.
Key Benefits of Big Data
Collecting, analyzing, and making informed decisions based on processed data can provide your company with a wealth of benefits. Below, we’ll outline the key ones. Given our specialization, we’ll primarily focus on examples from the tourism industry.
Customer Acquisition and Retention
Big Data allows companies to understand customer preferences, needs, and purchasing behaviors deeply. A notable example is Amazon, which uses big data to personalize shopping experiences and suggest products based on customers’ past purchases. One look at any product recommendations when visiting the platform should illustrate this point perfectly 🙂
Like Amazon, travel companies can use Big Data to personalize their services, recommending attractions or accommodations to customers based on their past choices or the preferences of others with similar interests.
Personalized Offers and Higher ROI
Analyzing Big Data enables companies to target specific groups with personalized offers, leading to more effective promotional campaigns and a higher ROI (Return on Investment).
By analyzing large data sets, companies can forecast future trends, enabling them to better prepare for changes and stay ahead of the competition.
Identifying Potential Risks
Big Data is an excellent tool for risk management – regardless of the industry. In the case of tourism, it could involve monitoring weather conditions, local events, airline worker strikes, or other factors impacting travel.
Big data analytics is perfect for adjusting prices based on demand, availability, and other factors such as seasonality or weather. It can help optimize pricing strategies and increase profits.
Big Data: Key Concepts and Terminology
Structured Data : Data that is organized in a specific manner, thus making it straightforward to store, process, and analyze. It can, for example, be organized in tables, where each column represents a specific field (like first name, last name, address), and each row represents a single record (like a specific individual or product).
Unstructured Data: Data lacks a specific form or organization, complicating its storage and analysis through standard tools. Examples of unstructured data include the text of emails, social media posts, audio and video files, images, PDF documents, and many others. These data are often information-rich but require more advanced tools and techniques for processing and analysis.
Data Mining: the process of uncovering patterns, correlations, trends, anomalies, or dependencies in large data sets that are often hidden or non-obvious. Data mining usually includes several steps, like data selection, data processing and cleaning, and the evaluation and interpretation of results. In the context of Big Data, data mining is particularly critical as it converts vast amounts of disordered data into valuable insights.
Predictive Analytics: Process that forecasts future outcomes and trends based on historical and current data. Companies can utilize predictive analytics to anticipate sales trends, customer behaviors, financial performance, and even risk, among other factors. This data-driven approach enables better business decisions.
Deep Learning: A subset of machine learning that focuses on implementing neural networks modeled after the human brain’s structure and functions.
Within the context of Big Data, Deep Learning is employed to process a massive quantity of complex and unclassified data. Deep neural networks can learn from raw data, like text, images, audio, or sequences, without manually labeling or classifying this data.
Machine Learning: A branch of artificial intelligence that involves using algorithms to analyze data, learn from it, and make predictions or decisions based on patterns within the data.
Machine learning is key for processing and analyzing enormous data sets within the Big Data context. Machine learning algorithms can assist in extracting useful information, detecting trends, identifying patterns, and even predicting future events.
Data Science: A wide field concentrating on conducting research, analyzing, and interpreting information from data. It employs various techniques and tools like machine learning to gain valuable insights from large data sets.
Data Scientist: A specialist in data science who employs statistical, mathematical, and programming knowledge to analyze and interpret large data sets. They create predictive models, analyze trends, and solve complex business issues.
Data Engineer: An engineer tasked with building, testing, and maintaining data infrastructure, like databases and large data processing systems. They work on designing, building, and integrating techniques to gather, store, process, and analyze large volumes of data.
Data Analyst: An analyst who utilizes statistical and mathematical techniques to convert large data sets into useful information. Their role involves data analysis to aid organizations in making data-driven business decisions.
We trust this article has provided illuminating insights into Big Data. If you have any questions on concepts related to artificial intelligence, or if you’re considering integrating AI into your travel business, don’t hesitate to contact us.
We’re always eager to impart our knowledge and guide you on how to successfully embark on the journey of implementing intelligent solutions within your organization.