Both small and large businesses alike rely on data analytics to generate useful insights aimed at mitigating risks, improve operational efficiency, and deliver remarkable customer experiences. However, this can never come to pass if you do not access all needed data. Things tend to be even more stressful when the data is dark and unstructured. So how can you prevent this from happening and access the required data hassle-free?

The secret lies in settling for a robust, automated data extraction process. That way, you can easily locate and convert data into clean, usable information. Read on and unearth two useful tips for successful data extraction.

Exploration

By now you should be aware of the fact that the more data is at your disposal, the better and more accurate the results. So, exploring all the data within an organization’s repository is a vital step when it comes to data extraction. Unfortunately, most organizations have to make do with unstructured data.

For you to understand the type of data landscape you are dealing with, be sure to crawl through all your data sources. Remember to figure out each piece of data after which you can eliminate the redundant data.

Cluster Collected Data

Once you find the required data, cluster it based on the similarities. To pull this off successfully, collect all the files and convert them into PDFs of the highest quality. The only way for you to perform an effective data extraction is when the documents are grouped together. With your data clustered as per the similarities, you can perform the automated Optical Character Recognition (OCR) process.

Just in case you do not know, this process involves the use of OCR software to process pixels within digital documents. It is then that they’ll be turned to machine-consumable data. Be sure to factor in the pros and cons of this approach before making a decision.

Extracting data successfully is easier said than done especially when doing it for the first time. The good news is you can seek the help of experts in the field when going through a hard time.