The increasing importance of data-driven decisions has changed how managers make strategic choices. A research study shows that companies that participate in data-driven decision-making experience a 5 to 6 percent growth in their productivity. However, the massive increase in unstructured data has made data management and extraction difficult as data to be converted into machine-readable formats is required for analysis.
Modern data extraction tools with built-in scheduling components help users to automatically pull data from source documents by applying a convenient extraction template and uploading structured data to the target destination.
What is data mining? How can it help companies?
In simple terms, data mining is the process of extracting data that is captured within semi-structured and unstructured sources, such as emails, PDFs, PDF forms, text files, social media, barcodes, and images. The enterprise data extraction tool makes business data from unstructured or semi-structured sources usable for data analysis and reporting.
For example, a real estate company may want to extract various data points, such as the tenant's name, building details, and the rental amount from the rental agreements. These agreements are generally in the form of unstructured PDF files - a mixture of free text and tabular data. This makes extracting information difficult as unstructured data sets are human-readable, and machines require structured information to process it digitally for further analysis or integration with other IT applications.
Form rental agreement
Why do you need to extract data?
Many companies are leveraging ETL tools to manage data and transform unstructured data to the organization. These data consolidation tools allow information users to break data warehouses, merge data from multiple sources, convert it into a fixed format, and upload it to a target destination. The first step in the ETL process involves data extraction so that the information held within different systems can be standardized and ready for further transformations and ETL teams can easily extract insights from the data.
Additionally, data extraction provides several benefits, including:
Better analysis and decision making
A study by Forrester revealed that only 0.5 percent of the world's data is analyzed and used . Data mining allows users to extract meaningful information hidden within unstructured data sources, such as customer trigger rate.
For example, a company is experiencing a decrease in revenue due to a shrinking customer base. The maintained spreadsheet shows the list of loyal customers and the change status for each month.
For trend analysis in volatility the manager wants to extract and aggregate the rows of volatility. This will help determine whether or not the company can retain its customers, and decide what measures needed, such as improvement in customer service, should be taken. With the help of the document data extraction tool, a 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 increasing the customer base rate.
Customer Data Form
Improved data quality
Many companies still rely on their employees to manually extract key information stored in PDF files. This can lead to 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.
Increase access to data
Forrester concluded that a 10% increase in data accessibility could lead to an increase in net income of over $ 65 million for the model Fortune 1000 company. An efficient data mining solution enables the users to have a complete view of the incoming data, thus simplifying data processing.
Improve data efficiency
People are an important asset to any business, and their productivity directly affects the organization's chances of success. Automated data extraction software can help free employees, giving them more time to focus on basic activities rather than repetitive data collection tasks. Automating data extraction allows to simplify the entire process from the time the data enters the work until the time it is stored in the data warehouse after it has been processed, eliminating the need for manual work.
Necessary elements in a data extraction software / tool
Choosing the right data extraction tool, that meets the data preparation requirements of an organization, is vital to data management. The tool must be able to convert incoming data into information that can generate actionable business insights.
Some of the important points which the organization must take into consideration include:
- Extract data and document information from multiple formats
Organizations receive data in structured, semi-structured, or unstructured formats from various sources. Organized formats can be addressed directly in most business intelligence tools after doing some cleanup. However, the ideal data extraction tool should also support popular unstructured formats, including DOC, DOCX, PDF, TXT, and RTF, allowing companies to benefit from all the data they receive.
- Real-time data extraction
Access to data at the right time is essential for making better decisions and smooth business processes. Many companies rely on bulk data extraction, which processes the data sequentially depending on the user's requirements. This means that the information available for analysis may not reflect the most recent operating data or significant business decisions must be based on historical data. Hence, an efficient data mining tool should enable real-time extraction with the help of automated workflows for faster data preparation for business intelligence.
For example, an employee is responsible for analyzing inventory levels at the time of sale at the end of the year. To achieve this, the company will need to extract real-time data points, including order ID, items sold, quantity, amount, etc.from sales invoices to keep checking current stock levels.
- Create reusable data extraction templates
The correct data extraction software used to build the extraction logic that can be applied to any document should allow the same layout. This compensates for the need to re-build the extraction logic for each incoming document.
- Embedded data quality and disinfection function
The data extractor should be able to identify any discrepancies and clean the data automatically according to the business rules set by the user. For example, if a company is using an extraction form to extract order quantities and order details from invoices, then the tool should be able to detect and delete any orders with negative quantity values.
- User-friendly interface
The tool should have an easy-to-use interface where business users can easily design various forms for data extraction. It should allow for easy data manipulation with little or no coding.
- Export data to widely used destinations
The intelligent data mining tool should allow users to export the converted data to popular destinations, such as SQL Server, Oracle, PostgreSQL and various BI tools such as Tableau, allowing companies to access meaningful information faster for timely decision making.
Astera ReportMiner - Automated data mining solution
Astera's ReportMiner automates unstructured data extraction to generate meaningful information and insights with features such as workflow coordination, email / FTP / folder integration, built-in task scheduling, automatic name and address analysis, and automatic creation of data extraction patterns. Moreover, Astera ReportMiner’s user-friendly interface simplifies data mining, allowing business users to build extraction logic in a completely code-free manner.
