What Are the Most Challenging Parts of Cleaning Data?
There can be multiple challenges associated with data cleansing. These are mainly related to extracting, merging and validating datasets from various sources. These all practices may infect your data with inconsistencies or typos.
Around 2.5 quintillion bytes of data are generated each day. With their oversizing, the associated problems are also mounting. The most common of them are concerned with data cleansing, which has many subsets like data enrichment, standardisation, typos removal, and more.
Here are the top 3 challenges that are related to data cleansing:
1. Merging Data from Various Resources
This problem appears when the location name does not exactly match with its original name. It happens when the name is translated from a local language into English or any other language. This is just one case. It can be the name of patients, reports, and other things.
You can avoid this problem by creating a master database that carries the original and accurate names of locations. Use it to call the accurate names from. If the issue does not resolve, codify scripts to extract the accurate match with all sorts of spellings using NLP algorithms.
Combining data from various sources can also be related to the difference in codes and terminologies within a database. It happens because of the standardization problem. Let's say, the format of data (12-09-2010) may match with vehicle numbers, whose use for the same purpose may mislead the decision.
The lack of standardisation may require many hours to remove imperfect entries. Creating tailored machine-learning models can help in early detection of the data variance on the basis of resources and distribution.
You may leverage the benefits of outsourcing data cleansing services or deploy tools to make it easier. This way, you can automatically find the exact and accurate data.
2. Invalid or Inaccurate Data
Data validation refers to examining the accuracy and quality of records. This is a part of data cleansing services and solutions. Typically, it is an exhaustive process.
You have to filter all errors in a database manually or automatically. However, tools use embedded codes to detect validity of any piece of information. Data scientists can also help you in creating validation algorithms as per set criteria. It can help in highlighting the errors automatically. This is how you can reduce manual efforts.
Many business process management companies emphasize building a model that can filter and match the data as per defined conditions for a given data point. This innovation can also simplify the process of data extraction from PDFs. The built models do the job by predicting the value and verifying the error accordingly.
3. Extracting Data from PDFs Reports
Extracting data from PDFs is no less than an uphill battle. Many businesses cannot skip this practice because it is necessary for analysing historic and recent datasets in the PDF reports.
However, you have the scripting option to extract a specific set of data from reports. But, this practice can require the investment of many hours in verification. If you don't have tailored solutions to address these problems, it can add a big burden.
Besides, there may be typos, lack of enriched values, duplicates, and likewise inconsistencies. You have to deal with them also. Tools like Wrangler and Google Refine can make it a piece of pie.