- In the Big data, it can be text data, digital image data, digital video data, digital audio data and more. Consequently with a larger amount of data types comes a wider range of data cleansing methods. There are techniques that verify that a digital image observation is ready for processing and specific approaches exists that can ensure the audio quality of your file is adequate to proceed.
- Dealing with missing values. This step is a crucial one as big data has big missing values which is a big problem to exemplify.
- Some case specific techniques for dealing with Big Data. Text data mining represents the process of deriving valuable unstructured data from a text. Think of the huge amount of text that is stored in digital format. There are many scientific projects in progress which aim to extract specific text information from digital sources. For instance you may have a database which has stored information from academic papers about marketing expenditure. The main topic of your research you could find the inforamtion you need without much of a problem. If the number of sources and the volume of text stored in your database was low enough. Often though the data is huge. It may contain information from academic papers, blog articles, online platforms, private Excel files and more. This means you will need to extract marketing expenditure information from many sources. In other words, big data not an easy task which has led to academics and practitioners developing methods to perform tasks data mining.
- Data masking: If you want to maintain a credible business or governmental activity you must preserve confidential information. However, when personal information is shared online it doesn’t mean that it can’t be touched or used for analysis. Instead you must apply some data masking techniques so you can analyze the information without compromising private details like data shuffling data. It conceals the original data with random and false data allowing you to conduct analysis and keep all confidential information in a secure place. An example of applying data masking to big data is through what we called confidentially preserving data mining techniques.
- Big data in the social media and financial trading data.
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