CEO's Guide to Data Extraction

Flipnode on May 04 2023

Data extraction serves as the foundation of a wide range of businesses, spanning from financial institutions to e-commerce companies and all industries in between. With the help of data extraction tools, businesses can automate a variety of tasks, resulting in cost, time, and resource savings. This article delves into the significance of data extraction, explores its use cases, evaluates various tools, and examines the associated challenges.

If you are contemplating whether data extraction is suitable for your company, this article provides all the essential details.

What is data extraction?

The Extract, Transform, and Load (ETL) process constitutes a critical part of data ingestion, wherein collected data is made immediately accessible or stored in a database for later use. This is a crucial aspect of any business strategy.

What is data extraction used for?

The purpose of data extraction, as defined, is to aggregate and refine information to facilitate its storage in a central location and transformation into the desired format.

Numerous businesses use data extraction as part of their operations. In this article, we will delve deeper into several use cases, but before that, let's learn more about data structures and extraction techniques. It's important to note that this article only addresses the extraction of public data. The scraping of non-public data is only permissible with the explicit consent of the owner or if you are the rightful owner of that data.

Types of data structures and extraction methods

• Unstructured data is data without any basic structure. Before extracting such data, it needs to be formatted or reviewed, which may involve cleaning up by deleting duplicate results, and removing unnecessary symbols, and whitespaces.
• Structured data is data that is already formatted for use and does not require additional manipulation.

Full extraction is used to extract data completely from the source and is often used when acquiring information for the first time. Some sources cannot identify changes, so to receive up-to-date information, the entire dataset needs to be reloaded.

Incremental extraction, on the other hand, involves tracking information changes and does not require extracting all the data from the target each time there is a change. However, this method may not detect deleted records.

Data extraction process

How is data extraction performed? Typically, the process of extracting data from a database or a software-as-a-service (SaaS) platform involves three primary stages:

1. Identifying structural changes, such as new tables, fields, or columns.
2. Retrieving the specified target tables (or fields, or columns) from the records as specified by the integration's replication plan.
3. Extracting relevant data, if discovered.

The collected data is subsequently uploaded to a data warehouse.

Why is data extraction important?

CEOs devote over 20% of their time to tasks that could be automated, according to McKinsey Digital report. Collecting data for status reports and analyzing operational data are examples of such time-consuming tasks. Data extraction can systematize information, automate repetitive tasks, and offer a variety of benefits to companies, including:

• Reduce human error and improve accuracy

Data extraction tools automate repetitive data entry processes, enhancing data input accuracy, and minimizing human errors.

• Increase productivity

Manual data entry is repetitive and monotonous, making it a daunting task. The task of manually entering data can be eliminated, freeing up employees to spend more time on more stimulating responsibilities.

• Increase data accessibility

Extracted and saved data can be readily available and visible to anyone on the team who requires it. Information can be accessed at any time, without having to wait for someone to upload it into the system manually.

• Save time and money for businesses

All of the benefits mentioned above culminate in one of the most important advantages of data extraction: it saves companies time and money. Automated processes necessitate fewer human resources, allowing personnel to focus on data analysis and other tasks.

Use cases: what companies utilize data extraction?

Here are some ways different companies use data extraction to automate tasks and make information available:

• E-commerce companies extract product and pricing information from various sites to stay competitive and implement dynamic pricing strategies to increase revenue.
• Financial firms use data extraction to generate financial reports and statements, which can be challenging due to diverse file formats. Accurate financial data is crucial for building predictive analysis.
• Government organizations that collect and store statistical data use data extraction to provide public statistical information.
• Data science-focused companies extract large amounts of information for machine learning training models, which use data to study patterns and build their own logic.

Data extraction tools

There are three primary categories of data extraction tools that offer different advantages to businesses:

• Batch data extraction tools:

These tools can be set to run on a schedule and as frequently as needed. They typically consolidate data in batches during off-peak hours to minimize disruption.

• Open source tools:

Open source tools require a certain level of infrastructure and expertise to implement but can be a cost-effective option for some companies.

• Cloud-based tools:

Cloud-based tools provide a comprehensive solution for all ETL processes, including data storage, analysis, and extraction. They do not require an in-house team of experts, making them a viable choice for smaller companies.

Data extraction challenges

• Extracting data from complex web pages

Extracting information from complex websites can be a challenge for most web scraping tools. To effectively gather public information from such websites, you need a powerful web scraper that can extract data without being blocked by the website's security measures.

• Joining data from different sources

Collecting a large amount of data often involves gathering information from different sources. However, combining data from structured and unstructured sources can be challenging and requires thorough planning to ensure accuracy and consistency.

• Data security

Extracted data may contain sensitive information, which means it may need to be regulated and secured. When moving data, additional security measures may need to be implemented, such as encrypting data during transit.

Conclusion

This article provides a comprehensive overview of data extraction for CEOs and other professionals in a company. It includes a discussion on the definition of data extraction, the advantages that businesses can gain from gathering public data, and the primary challenges associated with the process.