Rooted in even basic human everyday needs to even high-scale industrial requirements, technology has revolutionized our way of living. It has wormed itself in every aspect of our lives. With this shift in paradigm, the value of data as a resource has increased tenfold. Large datasets fall under the category of Big Data which requires numerous types of analytics in Big Data for use.
With 2.5 quintillion bytes of data being generated on a daily basis via cell phones, streaming videos, social networks, and most importantly, the Internet of Things. The significant growth of data in recent years has given way to numerous types of Big Data analytics. Collecting, processing, and analyzing Big Data requires the expertise of professionals in this field who can impart the necessary information that can aid an organization in growing. With Big Data training, you can secure yourself a high-profile job in this field.
Keep reading if you want to know more about Big Data, types of data in Big Data analytics, types of digital data in Big Data, which type of clustering could handle Big Data, 4 types of data analytics, and more.
What is Big Data?
Big Data can be defined as a high amount of data that cannot be processed or stored with the help of standard processing equipment and data storage. A massive amount of data is produced daily, and interpreting and manually processing complex and expansive datasets are next to impossible. It requires modern tools and expert skills to interpret large volumes of data and provide them to organizations with valuable insights to help businesses grow.
Different Types of Big Data
Big data types in Big Data are used to categorize the numerous kinds of data generated daily. Primarily there are 3 types of data in analytics. The following types of Big Data with examples are explained below:-
1. Structured Data: Any data that can be processed, is easily accessible, and can be stored in a fixed format is called structured data. In Big Data, structured data is the easiest to work with because it has highly coordinated measurements that are defined by setting parameters. Structured types of Big Data are:-
- Credit/debit card numbers
2. Unstructured Data: Unstructured data in Big Data is where the data format constitutes multitudes of unstructured files (images, audio, log, and video). This form of data is classified as intricate data because of its unfamiliar structure and relatively huge size. A stark example of unstructured data is an output returned by ‘Google Search’ or ‘Yahoo Search.’
3. Semi-structured Data: In Big Data, semi-structured data is a combination of both unstructured and structured types of data. This form of data constitutes the features of structured data but has unstructured information that does not adhere to any formal structure of data models or any relational database. Some semi-structured data examples include XML and JSON.
Subtypes of Data
This form of data does not typically fall under the umbrella of Big Data. However, certain subtypes of data are relevant in the field of analytics. This type of data often refers to the origin of data. They are classified as follows:-
- Machine (operational logging)
- Social media or event-triggered
- Geospatial (locational)
Data subtypes can fall under other access levels, such as:-
- Open (i.e. open source)
- Dark/lost (siloed systems such as CCTV)
- Linked (transmitted web data through APIs and other methods)
Characteristics of Big Data
General characteristics of Big Data can be referred to as the five Vs: Volume, Velocity, Variety, Veracity, and Value. They have been elucidated below:-
- Volume: Volume is the size of a dataset processed and stored in the Big Data System and is known to be its most important and prominent feature. The size of data usually ranges from petabytes to exabytes and is processed with advanced processing technology.
- Velocity: Velocity is referred to as the data accumulation rate, which also helps analysts determine if it falls under the classification of regular data or Big Data. Data needs real-time evaluation, which requires well-integrated systems for handling the amount and pace of generated data.
- Variety: Variety is defined as the type of data format and the way it is organized and made ready to be processed. The data accumulation rate also influences whether the data is classified as Big Data or regular data. The speed of data processing essentially means that more data will be available than the previous set and also that the data processing rate will be high.
- Veracity: Veracity is the quality and reliability of the data in concern. Unreliable data devalues the authenticity of Big Data, especially when the data is updated in real-time. Therefore, data authenticity requires regular checks at every level of collection and processing.
- Value: Value is also worth considering in collecting and processing Big Data. More than the amount of data, the value of that data is important for acquiring insights.
- Variability: Variability is the characteristic of Big Data that enables it to be formatted and used for actionable purposes.
Benefits of Big Data
Collecting, processing, analyzing, and storing Big Data has several perks that adhere to modern-day conglomerate needs. Some of the added benefits of Big Data are as follows:-
1. Predictive analysis: This holds a significant amount of benefit in Big Data because it directly enhances businesses' growth via forecasting, better decision-making, ensuring maximum operational efficiency, and mitigating risks.
2. Enhanced business growth: With data analysis tools, businesses across the globe have improved their digital marketing strategies with the help of data acquired from social media platforms.
3. Time and cost saving: Big Data collects and stores data from variegated sources for producing actionable insights. Companies can easily save money and time with the help of advanced analytics tools for filtering out unusable or irrelevant data.
4. Increase profit margin: With the help of different types of Big Data analytics, companies can increase revenue with more sales leads. With the help of Big Data analysis, companies can determine how their products and services are faring on the market and how customers are receiving them. This can help them make more informed decisions about the areas that require investing time and resources.
Examples of Using Big Data
Although invisible, there is more to Big Data than what meets the eye. It is an integral part and parcel of our everyday lives. Some stark examples of using Big Data are described below:-
- Transportation: Big Data helps run GPS in smartphone applications which sources data from government agencies and even satellite images. Airplanes also generate a huge volume of data for transatlantic flights to optimize fuel efficiency, balance cargo and passenger weights, and analyze weather conditions in order to ensure the maximum level of safety.
- Advertising and Marketing: Big Data is a major constituent of marketing and advertising to target particular segments of the consumer base. Advertisers purchase or collect large volumes of data to identify what consumers like.
- Banking and Financial Services: Big Data plays an important role in the financial industry because it is used for fraud detection, managing and mitigating risks, optimizing customer relationships as well as personalized marketing.
- Media and Entertainment: Big Data is extensively used by the entertainment industry for gaining insights from reviews sent by consumers, predicting audience preferences and interests, and targeting campaigns for marketing purposes.
- Meteorology: Weather sensors and satellites all over the globe help collect large volumes of data to track climate conditions. Meteorologists extensively use Big Data to study the patterns of natural disasters, prepare forecasts of weather, and the like.
- Healthcare: Big Data has significantly impacted the healthcare industry at large. Healthcare providers and organizations have widely used Big Data for various purposes, including predicting outbreaks of diseases, detecting early symptoms of preventable diseases, e-records of health, real-time cautioning, improving patient engagement, predicting and preventing grave medical conditions, strategic planning, telemedicine and research, and the like.
- Education: Many educational institutions have embraced the usage of Big Data for improving curricula, attracting the best talent, and reducing rates of dropouts by improving student outcomes, targeting global recruiting, and optimizing the overall student experience.
Big Data has been extensively used in recent years in every aspect of our lives and every possible sector of global industries. It is one of the most valuable resources in the market, used to optimize any operational process. As an aspirant of data science, it is imperative to have the basic skills and knowledge about fundamental aspects of data analysis and to learn about the different types of data in analytics. You can take your first step into this lucrative career field by pursuing a reliable course or undergoing professional training. A great way to start is by taking part in KnowledgeHut’s Big Data training.
Frequently Asked Questions (FAQs)
1. What are the three types of Big Data classification?
Big Data can be categorized into three parts
- Structured Data
- Unstructured Data
- Semi-Structured Data
2. What are the 4 components of Big Data?
The 4 main components of Big Data are
- Load, analysis
3. What are the 6 characteristics of Big Data?
Big Data has the following 6 characteristics