The growing importance of data-driven decisions has changed how managers make strategic choices. A research study shows that businesses that engage in data-driven decision-making experience 5 to 6 percent growth in their productivity. However, the voluminous increase in unstructured data has made data management and extraction challenging as data needs to be converted into machine-readable formats for analysis.
Modern data extraction tools with built-in scheduler components help users automatically pull data from source documents by applying a suitable extraction template and load structured data to the target destination.
What is Data Extraction?
In simple terms, data extraction is the process of extracting data captured within semi structured and unstructured sources, such as emails, PDFs, PDF forms, text files, barcodes, and images. An enterprise-grade data extraction tool makes incoming business data from unstructured or semi-structured sources usable for analytics and reporting.
For example, a real estate business might want to extract various data points, such as the tenant’s name, premises details, and rental amount from rental agreements. These agreements are generally in the form of unstructured PDFs – a mix of free text and tabular data. This makes data extraction challenging as unstructured data is human-readable, and machines require structured information to process it digitally for further analyses or integration with other IT applications.
Why Do You Need Data Extraction?
Many businesses are leveraging ETL tools for data management. These tools allow information users to break data silos, combine data from multiple sources, convert it into a consistent format, and load onto a target destination. The first step in the ETL process involves data extraction so that information trapped within disparate systems can be standardized and made ready for further transformations.
In addition, extracting data offers numerous benefits, including:
Better Analysis & Decision Making
A study conducted by Forrester revealed that not more than 0.5 percent of the world’s data is analyzed and used. Data extraction allows users to extract meaningful information hidden inside unstructured data sources, such as customer churn rate.
For example, a company is experiencing a fall in revenue due to a shrinking customer base. The spreadsheet maintained shows the list of loyal customers and customer churn status for each month.
To analyze the trend in the churn rate, the manager wants to extract the rows with churn status and aggregate them. This will help identify whether the company can retain its customers or not and decide upon the necessary measures, such as improvement in customer service, that should be undertaken. With the help of a document data extraction tool, the business can easily extract this information and combine it with sales, product, marketing, or any other data to gain more insight into the reasons for the increasing customer churn rate.
Many businesses still rely on their employees to manually extract key information stored in PDF files. This can result in errors, such as incomplete records, missing information, and duplicates. Data mining not only guarantees valuable business insights but also saves time, money, and resources involved in manual extraction while ensuring data accuracy.
Increased Data Accessibility
Forrester deduced that a 10% increase in data accessibility could lead to a more than $65 million increase in net income for a typical Fortune 1000 company. An effective data extraction solution enables users to gain full visibility of incoming data, hence simplifying data processing.
Employees are a critical asset of any business, and their productivity directly impacts an organization’s chances of success. An automated data extraction software can help free up employees, giving them more time to focus on the core activities instead of repetitive data collection tasks. Automation makes it possible to streamline the entire process from the time data enters the business to when it is stored in a data warehouse after being processed, eliminating the need for manual work.
The Must-Haves in a Data Extraction Software
Opting for the right data extraction tool, which meets the data preparation requirements of an organization, is vital for data management. The tool should be able to transform incoming data into information that can generate actionable business insights.
A few important points that an organization should consider include:
- Extract Information from Common Document Formats
Organizations receive data in structured, semi-structured, or unstructured formats from disparate sources. Structured formats can be processed directly in most business intelligence tools after some scrubbing. However, an ideal data extraction tool must also support common unstructured formats, including DOC, DOCX, PDF, TXT, and RTF, enabling businesses to make use of all the data they receive.
- Real-Time Data Extraction
Having access to timely data is imperative for better decisions and smooth business operations. Many businesses are dependent on batch data extraction, which processes data sequentially depending on the user’s requirements. This means that the information available for analysis might not reflect the most recent operational data or crucial business decisions have to be based on historical data. Hence, an effective data extraction tool should enable real-time extraction with the help of automated workflows to prepare data faster for business intelligence.
For instance, an employee is responsible for analyzing the inventory levels at the time of a year-end sale. To make this possible, the business will need real-time extraction of data points, including order ID, items sold, quantity, amount, etc. from the sales invoices to keep a check on the current inventory levels.
- Create Reusable Extraction Templates
The right data extraction software should allow the user to build an extraction logic that can be applied to any document of the same layout. This compensates for the need to build extraction logic anew for each incoming document.
- Built-in Data Quality & Cleansing Functionality
The data extraction tool should be able to identify any variations and cleanse the data automatically according to business rules defined by the user. For example, if a company uses an extraction model to extract order quantities and order details from invoices, the tool should be able to detect and delete any orders with negative quantity values.
- User-Friendly Interface
The tool should have an intuitive interface where business users can easily design different data extraction templates. It should allow easy data handling with little to no coding involved.
- Export Data to Widely-Utilized Destinations
A smart data extraction tool should allow users to export the converted data to popular destinations, such as SQL Server, Oracle, PostgreSQL, and various BI tools like Tableau, enabling businesses to access meaningful information faster for timely decision-making.
Astera ReportMiner – An Automated Data Extraction Solution
Astera’s ReportMiner automates the extraction of meaningful information and insights from unstructured sources with features like workflow orchestration, email/FTP/folder integration, a built-in job scheduler, automated name and address parsing, and auto-creation of data extraction patterns. Moreover, the user-friendly interface of Astera ReportMiner simplifies data extraction, allowing business users to build extraction logic in a completely code-free manner.
Download a 14-day free trial and find out how you can streamline the extraction, transformation, and loading of data trapped in unstructured data files with Astera ReportMiner.