Big data is a term that refers to datasets that are so large and complex that they cannot be processed using traditional data processing techniques. Big data analytics is the process of extracting valuable insights from these large and complex datasets.
There is no doubt that big data is here to stay. With the ever-growing amount of information being generated every day, businesses and organizations need powerful tools to make sense of it all. Enter artificial intelligence (AI). AI is uniquely suited to help turn data into insights that can guide decision-making. And as data continues to grow in volume and complexity, the demand for AI will only increase.
Data and AI are symbiotic; each is essential to the other. AI depends on data for its “fuel”; without data, AI cannot function. At the same time, mastering data is becoming increasingly difficult without the help of AI. The sheer volume of data makes it impossible for humans to process and analyze it all. And as data becomes more complex, with more variables and making more connections, AI is needed to sift through it all and identify patterns and relationships.
Characteristics of big data
McKinsey introduces the four characteristics of big data which is often called the 4V characteristic of big data. Later, IBM adds the 5th V.
- Volume: Big data typically refers to datasets that are too large to be processed using traditional methods. For example, a dataset with millions of records would be considered big data.
- Velocity: Velocity refers to the speed at which new data is generated. For example, if you are collecting data from social media sites, the velocity would be the rate at which new posts are made.
- Variety: Variety refers to the different types of data that are included in a big data dataset. For example, a dataset could include text, images, video, and audio.
- Value: The result of big data analytics should be the creation of business value. For example, if you are using big data to improve customer service, you should see an increase in customer satisfaction.
- Veracity: Veracity refers to the accuracy of the data. When dealing with big data, it is important to consider the quality of the data. For example, if you are using social media data to make business decisions, you need to be aware of the potential for false or misleading information.
Benefits of big data to AI
There are many benefits of big data to AI, but some of the most important ones include:
- Big data provides a larger dataset for training AI models. The more data that is available, the more accurate the models will be.
- Big data can help identify patterns and relationships that would be difficult for humans to find.
- Big data can be used to improve the performance of AI models.
- Big data can help reduce the need for human intervention in decision-making.
- Big data can help organizations make better decisions by providing insights that would otherwise be unavailable.