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What is Data Extraction: Techniques, Tools, and Best Practices
You collected all sorts of data for your business, but now it’s trapped! It is lying in your Facebook account, POS system, PDFs, website, and other databases. So, how do you feed this data into your analytics software and that too promptly? As important as it is to collect data, what matters more is how quickly you can extract it so it is ready for analysis. Did you know that 68% of business data is not utilized at all? This shows the importance of data extraction in any data-driven organization. If you can get this first step right, you can lay a strong foundation for the rest of your data pipeline.
What is Data extraction?
Data extraction is the process of retrieving or pulling data from various sources and converting it into a usable and meaningful format for further analysis, reporting, or storage. It is one of the most crucial steps in data management that allows you to feed data into applications or data analytics platforms downstream.
Data can come from various sources, including databases, spreadsheets, websites, application programming interfaces (APIs), log files, sensor data, and more. These sources may be structured (organized into tables or records) or unstructured (textual or non-tabular data).
Data extraction is relatively easy when dealing with structured data, such as tabular data in Excel files or relational databases. However, it is better to use data extraction software when it comes to unstructured data sources, such as PDFs, emails, images, and videos.
Importance of Data Extraction
A significant issue data extraction solves is that it improves data accessibility. Imagine a business with various data sources, and all that data is in different formats, and every department is trying to use that data according to their needs; the amount of mess it would create! Data extraction pulls all data together, allowing you to convert it into a standardized format and then put it in a centralized source for everyone to use as and when needed. The result is more empowered users who can use data without relying on IT resources.
Data Extraction Vs. Data Mining
Data extraction and data mining are often confused with each other. However, there is a difference between the two. As we explained earlier, data extraction is collecting data from different sources and preparing it for analysis or storage in a structured database.
Data mining, on the other hand, is the process of discovering patterns, trends, insights, or valuable knowledge from a dataset. It is all about applying various statistical, machine learning, and data analysis techniques to extract useful information from data. The primary goal of data mining is to uncover hidden patterns or relationships within data and then use them for decision-making or predictive modeling.
| Data Mining | Data Extraction | |
| Purpose | The focus of data mining is to derive actionable information from data. You can use data mining to discover relationships, make predictions, identify trends, or find anomalies within the data. | Data extraction aims to gather, cleanse, and transform data into a consistent and structured format so that users have a reliable dataset to query or analyze. |
| Techniques | Data mining often requires a deep understanding of statistical analysis and machine learning. It uses various techniques and algorithms, including clustering, classification, regression, association rule mining, and anomaly detection. | Data extraction typically involves data ingestion, parsing, and transformation techniques. Commonly used tools and methods used for data extraction include web scraping, document parsing, text extraction, and API-based data extraction. |
| Output | The output of data mining is actionable insights or patterns that you can use for making informed decision-making or building predictive models. These insights may include trends, correlations, clusters of similar data points, or rules that describe associations within data. | The output of data extraction is a structured dataset ready for analysis. It may involve data cleansing to remove inconsistencies, missing values, or errors. The extracted data is usually stored in a format suitable for querying or analysis, such as a relational database.
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| Timing | Data mining is performed after data is extracted, cleaned, transformed, and validated. | Data extraction is typically an initial step in the analysis, performed before any in-depth study or modeling. |
How does Data Extraction work?
Identifying Data Sources
The data extraction process starts with identifying data sources. You need to be clear on what data you need and where your data is located. It can be in documents, databases, or social media apps. Once you have identified your data sources, you need select the appropriate method for each source. For images, you might need OCR; for websites, you might need web scraping software, and so on and so forth.
Source Connection
Then, you need to establish a connection to selected data sources. The connection method may vary depending on the source type. You may use a database connection string, username, and password for databases. You may need to use APIs for web-based sources. Some data extraction software offers a complete solution with various inbuilt connectors so you can connect to all sources simultaneously.
Query or Retrieval
You can use SQL queries to retrieve specific data from tables for databases. Documents may require text extraction using OCR (or specific document parsers. Most data extraction tools are now code-free, which means all you need to do is just drag and drop a connector and connect to any data source without learning extensive SQL queries or programming languages.
Data Transformation and Loading
Once the data is extracted, it often doesn’t comply with the format required by the end destination or even for analysis. For example, you could have data in XML or JSON, and you might need to convert it into Excel for analysis. There could be multiple scenarios, which is why data transformation is essential. Some common transformation tasks include:
- Cleaning data to remove duplicates, handle missing values, and correct errors.
- Normalizing data by converting date formats or standardizing units of measurement.
- Enriching data by adding external information or calculated fields.
The transformed data is then fed into a destination, which varies according to the objective of the data. You could store data in flat files such as CSV, JSON, or Parquet files or put it in a relational database (e.g., MySQL, PostgreSQL) or NoSQL database (e.g., MongoDB).
Case Study
Ciena Corporation, a networking industry pioneer, receives purchase orders in PDF format and was facing delays in order fulfillment due to the manual effort required to transcribe and verify order details. To automate data extraction and save time, Ciena evaluated various solutions and found Astera ReportMiner to be the best fit. As a result, Ciena is now fulfilling customer requests 15x faster and can process purchase orders in just 2 minutes instead of hours.
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Data Extraction Techniques
There are various data extraction techniques; however, the most suitable technique for your organization depends on your particular use case. Here are some of the primary methods:
Web Scraping
Web scraping is used to collect data from various online sources, such as e-commerce websites, news sites, and social media platforms. Web scraping software access web pages, parse HTML or XML content, and extract specific data elements.
API-Based Extraction
Many web services provide APIs that allow developers to retrieve data from apps in a structured format. API-based extraction involves sending HTTP requests to these APIs and then retrieving data. It’s a reliable and structured way to extract data from online sources, such as social media platforms, weather services, or financial data providers.
Text Extraction (Natural Language Processing – NLP)
Text extraction techniques often use Natural Language Processing (NLP) to extract information from unstructured text data, such as documents, emails, or social media posts. NLP techniques include named entity recognition (NER) for extracting entities like names, dates, and locations, sentiment analysis, and text classification for extracting insights from text.
OCR
Optical Character Recognition (OCR) converts printed or handwritten text from documents, images, or scanned pages into machine-readable and editable text data. An OCR software analyzes processed images to recognize and convert text content into machine-readable characters. OCR engines use various techniques to identify feelings, including pattern recognition, feature extraction, and machine learning algorithms.
Document Parsing
Document parsing is when a computer program or system extracts structured information from unstructured or semi-structured documents. These documents can be in various formats, such as PDFs, Word files, HTML pages, emails, or handwritten notes. The parsing system identifies the document’s structure. Then, it extracts the relevant data elements, including names, addresses, dates, invoice numbers, and product descriptions, based on specific keywords, regular expressions, or other pattern-matching methods.
Types of Data Extraction
Once you have your data sources in place and you have decided which technique or techniques work, you need to set a system for your data extraction to work. You can choose from either manual data extraction, full data extraction, or incremental data extraction. Let’s see the pros and cons of each type of data extraction:
Full Extraction:
Full extraction, or a full load or refresh, extracts all data from a source system in a single operation. You can use this technique when the source data doesn’t change frequently, and a complete and up-to-date copy of the data is essential. Full data extraction, however, can be resource-intensive, especially for large datasets, as it retrieves all data regardless of whether the data has changed since the previous extraction. It is often the best choice as an initial step in data warehousing or data migration projects.
Incremental Extraction:
Incremental extraction, also called delta extraction or change data capture (CDC), is used to extract only the data that has changed since the last extraction. It is the best choice when dealing with frequently changing data sources, such as transactional databases. Also, it’s more efficient than full extraction because it reduces the amount of data transferred and processed. Common methods for incremental extraction include timestamp-based tracking, version numbers, or using flags to mark updated records.
Manual Extraction:
In the past, most organizations used to extract data manually. Some still copy and paste data from documents, spreadsheets, or web pages into another application or database. However, manual extraction is time-consuming, error-prone, and inevitably unsuitable for large-scale data extraction tasks. Still, it can be helpful for occasional or ad-hoc data retrieval when automation is impossible.
The Role of Data Extraction in ETL
ETL, which stands for extract, transform, load, is a comprehensive data integration process that includes extracting data from source systems, transforming it into a suitable format, and loading it into a target destination (e.g., data warehouse). Data extraction plays a crucial role in ETL pipelines.
Efficient and accurate data extraction is essential for maintaining data integrity and ensuring that the downstream ETL stages can effectively process and utilize the extracted information for reporting, analytics, and other data-driven activities.
Common Challenges for Businesses
You would think that with advancements in technology, data extraction might have become easier. However, businesses still need help with data extraction challenges. Here are some common challenges that you should keep in mind while implementing data extraction processes:
Data Source Heterogeneity:
Do you know that a business draws data from 400 sources on average? All these sources have a different format, structure, and access method, which makes it challenging to extract data and that too on time. According to a survey conducted by IDG, this explosion in data sources creates a complex environment that stalls projects; in fact, 32% of the people surveyed pointed out that they need help connecting to the data sources.
Data Volume:
There are around 4.95billion internet users who generate about 2.5 quintillion bytes of data every day. So, it is not just the variety of data sources that is a challenge, but data volume as well.
Moving large volumes of data from source systems to a central repository can take time, mainly if the organization’s network bandwidth is limited. Moreover, managing large volumes of data also means potential data governance issues.
Data Complexity:
We have talked about high volumes of data and a variety of data sources, but it doesn’t end there—data today is more complex than ever. Gone are the days when it was just stored in two tables in Excel. Today, you will find hierarchical data, JSON files, images, PDFs, etc. On top of it, all of this data is interconnected. For example, in social network data, individuals are connected through various types of relationships, such as friendships, follows, likes, and comments. These relationships create a web of interconnected data points. Now imagine extracting these data points, and then fitting them in a schema.
Error Handling and Monitoring:
Error handling and monitoring are crucial aspects of data extraction, as they ensure reliability and quality of extracted data. It is even more critical in real-time data extraction, when data requires immediate error detection and handling.
Scalability:
Many organizations require real-time or near-real-time data extraction and analysis. As data streams continuously, the systems must keep up with the pace of data ingestion, which is why scalability is essential. When setting up your infrastructure, you need to ensure that it can handle any growth in data volume.
Automation- The Need of the Hour
Given that data has become more complex, the only solution to solve data extraction woes is to employ a data extraction tool that can automate most of the tasks. Here are some of the benefits of using a data extraction tool over manual data extraction:
- Handle Multiple Data Sources: Data extraction tools come with built-in connectors, which make it easier to connect to all data sources at once, including websites, databases, spreadsheets, PDFs, emails, and APIs. Plus, data extraction tools today are now equipped with AI capabilities that can extract data from unstructured documents using powerful AI algorithms.
- Scalability: The best part about data extraction tools is that they can scale to handle large volumes of data efficiently. They can extract and process data in batches or continuously to accommodate the needs of businesses with growing data requirements.
- Data Quality: Many data extraction tools include data quality features, such as data validation and cleansing, which help identify and correct errors or inconsistencies in the extracted data.
- Automation: Data extraction tools can be scheduled to run at specified intervals or triggered by specific events, which reduces the need for manual intervention and ensures that data is consistently updated.
Top Data Extraction Tools
The tool you choose will depend on your particular use case. However, here are some of the best data extraction tools that you can consider:
- Astera Report Miner: Astera Report Miner is an enterprise-grade, code-free, AI-powered data extraction tool that allows users to extract data from unstructured data sources such as invoices and purchase orders within minutes. All users need to do is identify the fields they want to extract, and the advanced AI algorithms extract data without manual intervention. The tool also comes with in-built connectors, which make it super easy to connect to any data source. The data extraction tool has advanced data preparation features, using which you can quickly clean and validate data.
- Docparser: Docparser is a cloud-based data extraction tool that allows you to extract data from documents, PDFs, and images.
- Octoparse: Octoparse is a code-free web scraper. You can use the tool to extract data from any website and store it in a structured format. It is a point-and-click web scraper, meaning you can click on the website element, and it will extract the data for you.
- Mail Parser: Mail Parser is an excellent choice to extract data from your emails and attachments. You can convert the extracted into webhooks, JSON, XML, or download via Excel.
- Parsehub: It is an easy-to-use free web scraping tool that allows you to convert website data into a spreadsheet or an API. You can use it to scrape product reviews, pricing, or sales leads.
Parting Words
Data extraction is the fundamental step of the entire data management cycle. As technology advances and data sources grow in complexity and volume, the field of data extraction will undoubtedly evolve. So, it is essential to keep up with new tools and best practices in the industry. Want to get started with AI-powered data extraction? Download a free 14-day trial for Astera Report Miner and extract data from PDFs within minutes.
Authors:
Astera Analytics Team
