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tech.desacanggu.id article about Business Intelligence Reviewed; What is Big Data? Why to Understand? Applications
By James Charles

Big data is a term that refers to extremely large and diverse sets of information that are generated at ever-increasing rates from various sources. Big data can be analyzed using specialized software tools to reveal patterns, trends, and associations, especially relating to human behavior and interactions.

But what exactly makes data big? And why is it important to understand it? In this blog post, we will explore the definition, characteristics, and uses of big data, as well as some of the challenges and opportunities it presents.

The Definition of Big Data

There is no single, universally accepted definition of big data. However, one way to describe it is by using the three Vs: volume, velocity, and variety.

• Volume: This refers to the amount of data that is produced and stored. Big data can range from terabytes to petabytes or even exabytes of data. For example, according to Oracle, Facebook handles more than 500 terabytes of data every day, including user posts, likes, comments, photos, and videos.
• Velocity: This refers to the speed at which data is created and collected. Big data can be generated in real time or near real time from sources such as sensors, mobile devices, social media platforms, online transactions, and web logs. For example, according to IBM, Twitter generates about 12 terabytes of data per day from its 500 million tweets.
• Variety: This refers to the types and formats of data that are available. Big data can be structured (such as numerical or categorical data that can be easily organized and stored in databases or spreadsheets) or unstructured (such as text, audio, video, images, or geospatial data that require more processing and analysis to derive meaning). For example, according to Forbes, Google processes more than 100 petabytes of data per month from its various services, including search, YouTube, Gmail, and Maps.

The three Vs help us understand the scale and complexity of big data. However, some experts have added more Vs to describe other aspects of big data, such as:

• Veracity: This refers to the quality and reliability of data. Big data can come from various sources with different levels of accuracy, completeness, consistency, and timeliness. For example, according to Harvard Business Review, poor data quality costs the US economy about $3.1 trillion per year.
• Value: This refers to the potential benefits and insights that can be derived from data. Big data can provide valuable information for decision making, innovation, optimization, and prediction. For example, according to McKinsey Global Institute, big data analytics could generate up to $3 trillion in value per year across various sectors such as health care, retail, manufacturing, and education.
• Variability: This refers to the changes and fluctuations in data over time. Big data can be dynamic and unpredictable due to factors such as seasonality, trends, events, or user behavior. For example, weather data can vary based on location and time of year, and consumer behavior data can vary based on cultural events or economic conditions.

Why is it important to understand Big Data?

Understanding big data is important for several reasons:

1. Business decision making: Big data can provide valuable insights for businesses to make informed decisions, improve processes, and optimize performance. For example, analyzing customer data can help businesses understand consumer preferences, behavior, and needs, which can inform marketing strategies and product development.
2. Innovation and research: Big data can fuel innovation and research in various fields such as medicine, engineering, and social sciences. For example, analyzing medical data can help identify new treatments, diagnose diseases, and monitor patient outcomes.
3. Personalization: Big data can enable personalized experiences for individuals, such as personalized recommendations, offers, and services. For example, analyzing user data can help companies provide customized content and products based on user preferences and behavior.
4. Social impact: Big data can have a significant impact on society by addressing important issues such as climate change, poverty, and inequality. For example, analyzing environmental data can inform policies and actions to mitigate the effects of climate change, and analyzing demographic data can inform social policies to reduce inequalities.

Applications of Big Data

Big data has numerous applications in various industries and fields, including:

1. Health care: Big data can be used for medical research, drug discovery, personalized medicine, disease diagnosis, and patient monitoring.
2. Finance: Big data can be used for risk management, fraud detection, investment analysis, and customer relationship management.
3. Retail: Big data can be used for customer segmentation, personalized marketing, inventory management, and supply chain optimization.
4. Manufacturing: Big data can be used for quality control, predictive maintenance, supply chain management, and process optimization.
5. Education: Big data can be used for student assessment, personalized learning, and educational research.

Conclusion

In conclusion, big data is a vast and rapidly growing field that offers many opportunities and challenges for businesses, researchers, and society as a whole. Understanding the characteristics and applications of big data can help individuals and organizations make the most of this valuable resource.