What do you mean by data extraction?
Wikipedia defines data extraction as the process of retrieving data out of (usually unstructured or poorly structured) data sources for further data processing or storage.
So data extraction is the process of pulling out data from various sources (unstructured or poorly structured) in order to process it further and store it.
What are some data sources to extract data from?
A data source means the initial location data was discovered.
Sources of data include; emails, audio or video files, web pages, PDFs, social media, journals, health records, and images.
What’s the purpose of extracting data?
“The goal is to turn data into information, and information into insight.”
(Carly Fiorina, Former HP CEO)
1. To prepare data for further analysis.
When you extract data, its easier to analyse. Extracted data produces meaningful information for your business performance.
2. For improvement of your company’s performance.
Not only does data extraction make the analysis process easier, it also gives you the opportunity to improve your company’s performance.
With the information derived from extracted data, you can know your customers better, how they feel about your brands, their needs, and how your company can meet them.
According to PayPal Co-Founder Max Levchin,
“The world is now awash in data and we can see consumers in a lot clearer ways.”
3. To make informed decisions.
Your business is generating a lot of data (either via social media or your website). That data is filled with loads of useful information for your business.
The purpose of extraction is so that you can use that data to make informed business choices and decisions.
4. For ease of sharing
By extracting data, it becomes easy to share with your partners.
For example, if your company has to share data with partners and you don't want them to have all the data, through data extraction, you’re able to easily provide helpful but limited data access to your partners.
What are data extraction techniques?
There are two techniques of data extraction:
1.Logical data extraction.
Logical data extraction techniques extract the present data on the device via its interaction with the operating system and access to the file system.
Under logical data extraction we have: Full Extraction and Incremental Extraction.
Let’s take them one after the other.
A. Full Extraction
In the full extraction technique, data is extracted directly from the source at the same time.
Using this technique, there’s no need for any logical information. For example, if you wanted to extract data regarding payments made, the system extracts the company’s invoices/ receipts directly and at once.
B. Incremental Extraction
The Incremental extraction technique handles any changes in the data. This tool recognises any new updates or changes made in the data based on the dates and times.
While using this technique, the data engineer has to, first of all, add complex extraction logic to the source systems.
2. Physical data extraction
Data source systems are prone to limitations. For example, if the system used to store data is outdated, using logical extraction to draw data from it will be unfruitful.
Such data can only be extracted using physical extraction techniques. It has two types which include; online and offline extraction.
A. Online extraction
This process directly transfers data from the source system to the data storage platform. In order for the process to be seamless, the tools used for extraction must be directly connected to the source system or a transitional one (this is just more structured than the source system)
B. Offline Extraction
Unlike the online extraction technique, there’s no extraction directly from the source system here. In this technique, the extraction doesn’t take place inside the source system. Instead, it takes place outside of it.
The data used in this technique has either been structured prior or is structured via the various extraction routines. When it all comes down to it, businesses will need to invest in both logical and physical data extraction techniques.
Practical uses of data extraction and analysis software tools.
1. Churn prediction.
Data extraction and analysis software tools help your business to know why your customers are leaving and how to retain them.
2. Fraud prevention.
If you’re a financial service provider, you might have experienced the frustration of fraudulent transactions and dealing with annoyed customers.
With data extraction and software analysis tools, you’ll be able to target fraud issues and fight against fraudulent innovations by its ability to detect embedded security features on ID documents like passports.
Use the free trial of our data extraction tool to start improving speed and efficiency in your business process.
3. Insurance Claims Verification.
In evaluating the extent of damage done to a property, data extraction makes the process of scanning documents and extracting needed information error-free and fast.
This enables you to verify insurance claims and make the right decisions in line with your conclusion.
4. Automated Invoice Capturing.
Invoice capturing in many Accounts Payable departments is usually manual, time-intensive, and error-prone. This is because of the data extraction process involved.
With a data extraction and analysis software tool, you’re able to automatically and accurately extract the details from all important fields on an invoice and make it available to be stored or used for payments.