There are at least 4 can’t miss reasons to start extracting data, and they can make a real difference to almost any business today. 

Read on to find out more.

But first, what is data extraction?

Data extraction as the process of retrieving data out of various data sources for further processing or storage. These days, it’s almost a must for a competitive business to invest in some form of data extraction. 

What do data sources look like?

The problem - and opportunity - with a lot of data is that it’s often unstructured. It’s also just about everywhere.

Sources of data include; emails, audio or video files, web pages, PDFs, social media, journals, health records, and images.

 

Your 4 can’t miss reasons for 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, it becomes easier to analyze. It’s the first step to introducing some order into what can be a chaotic, fragmented data landscape.

2. For getting meaningful insights

It might seem obvious, but data extraction can make it easier for you to get business insights that matter. It also gives you an opportunity to improve your business results.

For example, you can get to know your customers better, from buyer sentiment to purchasing behavior. And that can help you make useful predictions about how to steer your business. 

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 better data-driven decisions

Once you have data insights, you can choose whether or not to act on them. And that’s when the value of your data can start becoming more clear. Maybe you find unexpected patterns in customer behavior that allow you to better tailor your offerings. Or maybe you uncover an opportunity to cut costs without compromising quality.

Your data-driven insights will be unique to your business. And until you actually extract and analyze your data, you won’t know what you don’t know.

4. For ease of sharing  

When you extract data, it becomes easier to share with your partners.

You can also choose how you want to apply your own rules of data governance. For example, it might make sense to only share a portion of your data with certain partners. And smart data extraction allows you to do just that. 

There are 2 main techniques for data extraction

There are two techniques of data extraction:

1. Logical

2. Physical

Data extraction techniques.
Image: VoyanceHQ

1. Logical data extraction

Logical data extraction techniques extract the present data on a device via its interaction with the operating system and access to the file system.

There are 2 types of logical data extraction: Full Extraction and Incremental Extraction.

Let’s have a quick look at both.

A. Full Extraction

With full extraction, all data is extracted directly from the source at a given point in time.

B. Incremental Extraction

The Incremental extraction technique handles changes in data. This tool recognizes new updates or changes made in the data based on dates and times.

When using this technique, a data engineer often has to 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 extract data from it may not be an option.

Such data can only be extracted using physical extraction techniques. These include online and offline extraction.

A. Online extraction

This process directly transfers data from the source system to a 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 (which is more structured than the source system).

B. Offline Extraction

With 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.

 

Practical uses of data extraction and analysis software tools

1. Churn prediction

Data extraction and analysis software tools can 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 frustrated customers.

With data extraction and software analysis tools, you’ll be better able to target fraud. For example, it's possible to detect embedded security features on ID documents like passports and use that information in fraud prevention.

3. Insurance Claims Verification

In evaluating the extent of damage done to a property, data extraction can make the process of scanning documents and extracting needed information nearly error-free and fast.

This enables you to verify insurance claims and make better data-driven decisions, both for your business and your customers.

4. Automated Invoice Capturing

Invoice capturing 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.

Wrap up 

If you're running a business, you've got data. And if you're growing, your data probably is too.

Data extraction can help give you insights that can change your business decision making. It can both reduce uncertainty and unveil new opportunities.