To enhance, refine, or otherwise improve raw data, collectively known as data enhancement, huge amounts of information must undergo different processes.
Sometimes, computer applications are used to perform these data enrichment tasks. But because of the tremendous amount of information that’s available on the internet, traditional data-processing software is no longer sufficient to handle big data. As in the case of microtasking, there are times when human perception and judgment are necessary.
The following are different data enrichment tasks you could outsource to speedily produce high-quality data.
1. Data Fusion
Data fusion is the process of combining raw data from more than one source to create new raw data that are more consistent, accurate, and useful.
The global data fusion market is expected to grow from USD 7.62 billion in 2017 to USD 15.92 billion by 2022. With their smart cities and government initiatives, countries in the Asia Pacific (APAC) region are forecasted to have the fastest growth in the data fusion market.
2. Data Entity Recognition
Also known as “entity chunking” and “entity extraction,” entity recognition aims to locate and sort entities into categories such as the names of persons, places, values, etc. Implementing a powerful data entity recognition method simplifies retrieving information drastically.
3. Data Disambiguation
‘Ambiguous’ means “unclear or inexact.” Raw and unstructured, a lot of data can be misunderstood, or be prone to cause errors. The process of data disambiguation ensures that words are clear and understood in its intended context.
For instance, words especially from highly technical fields, such as healthcare, can be challenging both in meaning and spelling. Moreover, all language processes, such as speech recognition or text-to-speech software, must use disambiguation to produce accurate results.
4. Data Segmentation
Segmentation is the method of categorizing data into groups according to similar traits. This is commonly used for marketing such as when launching specific email campaigns, for instance, targeting clients by age, address, or gender. Data can be analyzed further at a much deeper level like sorting clients by their spending habits or internet browsing behavior.
5. Data Imputation
Missing data can lead to biases, reduced efficiency, and errors in data analysis. When one or more values are missing, some systems result in deletion. However, missing data may be replaced in some cases with values estimated from other available information. When the missing values are imputed, the data set can be recognized as complete and analyzed.
6. Data Characterization
Characterization is used to make data usable by establishing parameters that can describe their characteristics and behavior. Data can be sliced or diced to make it more understandable. Data mining can be based on data characterization results.
Understanding what task you have to prioritize and which you can sustainably outsource to other workers will translate to savings and efficiency.