Data Enrichment Differs from Data Processing in Three Ways
While data enrichment isn’t that much different from data processing, these two aren’t exactly the same. In the world of data entry outsourcing, data enrichment plays an important role in adding value to unstructured data. In order for data processing to produce accurate and meaningful results, raw data has to be enriched first. Companies just starting to outsource their data entry work should learn at least three major differences between these two.
1. Data enrichment requires human intervention while data processing needs computing power.
Data processing mostly happens inside intelligent machines. It’s impossible for humans to solely process big data without the help of powerful technology. To produce meaningful and accurate results, data must be cleaned up, enriched, and organized first by human agents before it can be processed.
Machines may shorten the time it takes to update or clean up the data, especially when there’s a ready-to-use database of new information. But the ability to match new information with data that are available based on context clues can only be done by data entry workers laboring half a world away.
2. Data enrichment focuses on adding value to data rather than simply organizing and manipulating it.
Data gains value when it produces accurate and meaningful results. These are results that end-users — whether they are companies or people — will find useful. And, how does data gain that value? Through data enrichment, which involves several steps but eventually gives back life to old databases. Historical data, most of all, provides valuable insights into the past.
Those data enrichment steps include the following:
Data Cleansing – Your data may haven’t been updated in a long time. Some of that data may have become obsolete. One option is to have them deleted. Another option is to have them corrected and revised. Whatever option you choose will depend on the goals you wanted to achieve in your campaign.
It may be as simple as correcting minor data entry errors, typos or misspellings. Or, it may take a few more steps, such as searching for the correct and updated information from other sources. Your data entry workers may need to add a new column to the spreadsheet to make way for new information. Because information can have new layers to it as time goes by, your agents may need to reorganize all of the data to fit the requirements of your campaign.
Data Extraction – This step is also understood as data extrapolation. This is done using methods such as fuzzy logic and algorithms to find out what the relationships are between or among values in a data set. We call those relationships “patterns” or “trends.” They can be used to predict what the next value in a sequence will be or even predict a likely outcome of an event.
If your agents are using a spreadsheet, they can complete the job faster when they use macros and scripts. There are also statistical tools available to data scientists. There’s even a web-based tool that can extract data from graphs and maps.
Data Transformation – Most surveys opt to ask questions in multiple choice format because it’s easier to collect data that way. But sometimes, responses may vary in format, especially when the question is open-ended. This is where data transformation comes in.
Data entry workers can convert unstructured data into something more uniform in pattern. For example, what if survey respondents had to be grouped by their residential addresses? Since these addresses aren’t the same, the best option here is to find similarities in them. Data entry workers can group respondents based on the city they live in or their zip codes.
When you compare your current data to the old, you can come up with correlated information, such as how much your present data may have changed for better or worse. That will lead you to seek out what the factors could have been that contributed to those changes. And, how your company can take advantage of the information to improve your products and services, and thereby, increase sales.
3. Data enrichment is done before, during and after the processing of data.
Data enrichment actually follows a circular sequence of events. It happens before data is processed, during the actual processing, and after data has been loaded into storage. Cleanse, extrapolate, transform, repeat. The reason is that certain types of data have to be constantly checked and updated. For example:
- Geographic data like postal codes, county names, political districts, etc. tend to change or become outdated relatively quickly. This includes data from things like social media, mobile apps, and customer or marketing databases.
- Behavioral data like purchase histories, credit scores, and channels of communication.
- Demographic data such as income levels, marital statuses, educational levels, how many children people have, etc.
- Psychographic data like a person’s hobbies, personal interests, political leanings, etc.
- Census data like household data and data pertinent to a person’s community.
Data entry workers must match incoming records with existing data. For example, they must identify which person can claim for insurance coverage. Is it the principal or the beneficiary? And how much should the present claim be after a previous claim has been filed?
Another thing that data enrichment does is to correct invalid data based on the information that’s on record. For example, automated data feeds or data extrapolated from scanned documents may have errors that need corrections. Lastly, data can also be interpolated, which means missing values can be added based on other available data. For example, the age of a person can be quickly added based on his or her date of birth.
Data processing has many uses in our modern world, but data enrichment done by hand remains an important part of the whole process. Data can only become valuable once it’s been enhanced. The more details are provided and the more recent the data, the better for data management.