Big Data

Big data refers to extremely large and complex sets of data that traditional data processing applications are inadequate to handle. The term involves the volume, velocity, variety, and veracity of data.

Characteristics of Big Data

1. Volume: Big data involves a huge amount of data. This could be terabytes, petabytes, or even exabytes of information. The absolute size of the data sets requires specialized processing tools and techniques.
2. Velocity: This refers to the speed at which data is generated, collected, and processed. With the start of real-time data sources like social media, devices, and IoT devices etc.
3. Variety: Big data comes in various formats and types, including structured data (like databases), unstructured data (like text, images, and videos), and semi-structured data (like email, HTML, XML files). Handling this diversity of data types is a challenge in big data analytics.
4. Veracity: This aspect of big data refers to the quality and consistency of the data. Large datasets may contain inconsistencies, errors, or incomplete information. Dealing with the uncertainty and ensuring data value is a key concern in big data analytics.
5. Value: Ultimately, the goal of working with big data is to extract meaningful visions and value. Analysing and interpreting this huge amount of information can lead to better decision-making, improved business strategies, and increased scientific understanding.

Advantage of Big Data:

1. Business can utilize outside intelligence while taking decisions.
2. Improve customer services.
3. Early identification of risk to the product/service.
4. Better operational efficiency.
5. One platform carries unlimited information.
6. It helps in improving science and research.
7. Anyone can access huge information via surveys and deliver answer of any query.

Disadvantages of Big Data

1. Big Data analysis result is sometimes misleading.
2. Traditional storage can cost a lot of money to store big data.
3. Big data analysis violates principles of privacy.
4. Speedy updates in big data can mismatch real figures.
5. It can be used for manipulation of customer records.