In order to enhance, refine, or otherwise improve raw data — a process that’s collectively known as data enrichment, huge amounts of information must undergo different processes.

Sometimes, computer applications are used to perform these data enhancement 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 micro-tasking, 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.

Types of Data Enrichment Tasks

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 could grow from USD 7.62 billion in 2017 to USD 15.92 billion by 2022, especially in the Asia Pacific (APAC) region. Countries in this region will have the fastest growth in the data fusion market because of their smart cities and government initiatives.

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. Commonly used in marketing, segmentation benefits companies when launching specific email campaigns. For instance, businesses may target clients by age, address, or gender. They have the means to analyze data further at a much deeper level. For example, businesses can sort 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. So, this helps in filling in the missing information and contributes to better data analysis.

6. Data Characterization

Characterization makes data usable by establishing parameters that can describe their characteristics and behavior. Slicing or separating data into chunks make it more understandable. Data mining can be based on data characterization results.

Sustainable Outsourcing of Data Enrichment Tasks

Understanding what tasks you have to prioritize allows you to plan the number of people and hours required to complete each task. Moreover, it’s important to identify the challenges you may face, such as skills development and software requirements. It will provide insights into sustainably outsourcing the work and improving the workers’ performance. Once you’re successful in reaching these goals, you will see improvements in savings and efficiency.