Data Extraction from Operating Statements: Enhancing Your Financial Reporting Process

Extracting data from operating statements is a crucial step for businesses seeking to gain a competitive edge. This data provides valuable insights into financial performance, revealing trends and patterns that can be leveraged to achieve critical objectives. 

By analyzing these trends, businesses can make informed decisions about cost-cutting strategies, optimize resource allocation, and develop data-driven financial plans.

Commercial real estate owners and businesses across industries are embracing automated data conversion tools. These intelligent tools streamline the extraction process,  adapting to various formats, templates, and layouts to capture the data you need with minimal human intervention and high accuracy.

This blog discusses the importance of efficiently extracting data from operating statements, challenges in extraction processes, a step-by-step guide to extracting data, and best practices for managing the extracted data efficiently. 

Understanding Operating Statements and Their Role in Data Extraction

Operating statements, also known as profit and loss statements, record a company's financial performance in terms of expenses, net sales, income tax, net income, interest expenses, and the number of outstanding shares. They act like a report card that shows the business’s financial health for a particular period. 

Operating statements help executives identify unnecessary expenses and devise strategies to increase revenue. Investors and stakeholders rely on operating statements to assess businesses' financial health and finalize their investing decisions. 

Key Data Points for Extraction from Operating Statements

Here are some vital data points that businesses extract from operating statements:

• Property name
• Owner details
• Revenue streams
• Total revenue
• Operating expenses: Employee salaries, rent, office utilities, maintenance and repairs, marketing and advertising 
• Net income
• Cost of Goods Sold (COGS)
• EBITDA (Earnings Before Interest, Taxes, Depreciation, and Amortization)
• Operating profit 
• Gross profit 
• Taxes 
• Interest expenses

Understanding the Importance of Efficient Data Extraction from Operating Statements

Some reasons why accurate data extraction is vital for operating statements: 

a. Enhances real-time analysis

Efficient data extraction from operating statements helps businesses analyze and interpret data to optimize operations. You can process documents in batches, derive insights, and make timely decisions to increase profits and revenue. 

b. Saves time 

Automated data extraction saves more time and enhances productivity by 10X. For instance, the time spent on manual data extraction can be utilized for more strategic tasks.

c. Facilitates trend analysis

Accurate data extraction from operating statements helps identify trends and patterns in the business's performance for a particular month, quarter, or year. It facilitates the creation of data-driven strategies to increase profitability and customer satisfaction. 

d. Supports compliance

Data extraction using advanced tools ensures compliance with industry-specific regulations and helps businesses avoid penalties and regulatory fines.

e. Benchmarking and industry comparisons 

Extracting accurate data from operating statements and comparing it with the industry's top players provides information about areas for improvement. This way, you can redefine your existing financial strategy to compete better and stabilize performance. 

6 Common Challenges in Operating Statement Data Extraction

Some common hurdles that businesses face while extracting data from operating statements: 

a. Variability in formats

Operating statements contain varying formats such as texts, paragraphs, complex tables, and charts to explain the financial transactions that happened in a particular period. 

Extracting rich data from these complex tables poses a major challenge for businesses. This is because manual extraction is more demanding than using trained technologies. 

At the same time, basic technology like OCR may not be reliable, as it cannot automatically adapt to varying layouts and formats, leading to incomplete and inconsistent data.

b. Data overload and volume

Extracting data from a handful of operating statements using manual extraction methods is possible in the initial stages. 

However, as you scale, extracting data from vast volumes of operating statements can get overwhelming. The challenges are non-standardized data extraction methods, time-consuming processes, and inaccuracies.

The result? 

Data overload affects the efficiency of the business, causing delayed decision-making.

c. Data inaccuracy 

Businesses using manual data extraction processes face challenges related to errors, inaccuracies, and inconsistencies in extracted data, which can lead to imprudent financial decisions, increased operational costs, and penalties. 

Moreover, stakeholders and investors might lose trust, which could lead to the loss of potential business opportunities and reputational damage. 

d. Increased costs

Data extraction from operating statements increases costs due to different factors: resolving errors, reconsidering financial decisions, and hiring and training employees hourly or daily. 

e. Data security and compliance

Data extracted from operating statements contains sensitive information, and theft can result in losses and regulatory fines. 

Therefore, ensuring security measures when sharing data with respective personnel is challenging for businesses. 

For instance, WBR’s study of 250 financial leaders shows that 45% of companies believe providing secure data access to employees is a significant challenge. Additionally, businesses face challenges meeting industry-specific compliance and regulations, such as GDPR and SOC 1 & 2. 

f. Data silos and interoperability 

Using different systems and databases may result in data silos and demands investing in proper technologies for seamless data integrations. This can lead to ineffective decision-making when multiple stakeholders analyze disjointed and fragmented data. 

Avoiding these challenges demands a robust data conversion tool, preparation techniques, and advanced data management practices. 

Preparing for Data Extraction from Operating Statements

Here are some preliminary steps that prepare your operating statements for data extraction processes: 

a. Scan with high-quality

Scan paper statements using a high-resolution scanner. Place the documents straight on a surface, ensure good lighting, and capture quality images of documents. 

b. Gather documents 

Collect all operating statements that require extraction and consolidate them into a single folder. Check file formats to see whether they demand standardization. 

c. Standardization of formats

To maintain consistency, convert documents in different formats to a single standardized format like PDF. This facilitates easy ingestion into the automation tool and ensures compatibility across various software applications. 

d. Pre-process operating statements

Pre-processing is a necessary process that helps the automation tool extract data accurately. It involves multiple steps, such as deskewing, denoising, and formatting adjustments. 

Deskewing corrects the alignment by fixing its skew angle. The noise removal removes unwanted dots, blur, shadows, boxes, and smudges. Finally, the contrast and density should be increased to make the data more transparent for the automation tool.

e. Data cleansing 

Verify the data present in the documents to eliminate errors, inconsistencies, and discrepancies. The process involves removing duplicate entries and irrelevant information.

f. Adoption of digital tools

You can use fragmented tools available in the market to complete the pre-processing techniques and prepare operating statements. However, investing in an automated data conversion tool completes these processes without human intervention, saving more time and reducing costs. 

Step-by-Step Guide to Data Extraction from Operating Statements

Efficient data extraction from operating statements relies on the capabilities of the automation tool you’re using, and hence, it's crucial to choose the right data capture tool. 

Most data capture solutions available in the market use standalone OCR technology to extract data from documents. However, it is not a reliable solution in the long run, as OCR technology has many limitations. 

The challenges with basic OCR technology are, 

• It struggles to extract data from special equations and symbols
• It lacks machine learning algorithms, so it cannot extract data with a high accuracy rate
• Employees need to create templates with rules for changing layouts 

The ideal data extraction software should extract data with a greater than 95% accuracy rate. Docsumo's AI-powered IDP solution uses machine learning and natural language processing algorithms to extract data with a 99%+ accuracy rate. It adapts to different layouts, templates, and fonts.

After analyzing the technology employed, check various parameters such as security, access controls, accuracy rate, automation percentage, and pricing to choose the best data extraction tool for your business. 

Now, let's use Docsumo as an example and understand how automated data extraction from operating statements improves the efficiency of your business: 

1. Sign up on the Docsumo platform

Visit Docsumo’s website and sign up for a 14-day free trial to extract data from operating statements. Provide details such as name, work email, phone number, and document type, and set a solid password to start the process. Once the registration is over, you can process 50 operating statements for free. 

2. Upload and organize documents

Organize documents in a separate folder to easily ingest them into Docsumo. Click ‘Upload’ and select ‘Operating Statements’ as the document type. Now, you can import documents from your system or enable automatic ingestion from your email. 

3. Edit the entries

Docsumo automatically pre-processes the operating statements for skew correction, noise removal, and adjustments in contrast and density. It then extracts key-value pairs from operating statements, such as property names and addresses, email addresses, and phone numbers. 

Docsumo then uses advanced neural networks to capture line items from complex tables and paragraphs. While it extracts data from operating statements, you can edit the fields and customize the data according to your requirements. Click ‘Edit’ to add or remove fields.

4. Data validation 

Once the data is extracted, Docsumo automatically validates it with available databases. This step increases the data's accuracy by finding errors, inconsistencies, and mismatching fields. 

5. Review and download the extracted data

After validation, verify the data manually to spot undetected errors. Human verification is crucial until Docsumo adapts and learns to extract data with 99%+ accuracy. 

If you find errors, review them and click ‘Approve’ to save the document. Then, download the extracted data in a preferred format, such as JSON, JPG, PDF, or Excel. 

6. Automating data extraction for large document sets

After practicing a few documents, you can start processing high volumes of operating statements. Docsumo extracts data from multiple files simultaneously and reduces the workload of your employees. 

Process operating statements at scale and watch how Docsumo extracts data with,

• 99%+ accuracy rate 
• 95% straight-through processing
• 100% automation

With Docsumo, you can:

• Reduce operational costs by 60-70% 
• Increase efficiency by 10X
• Improve security with GDPR and SOC-2 compliance
• Automatic validation that guarantees a 99%+ accuracy rate 
• Process documents in 30-60 seconds
• Make financial decisions quickly and streamline your business operations

7. Integration into workflow

Integrate with financial software applications such as QuickBooks, Chargebee, Xero, and Stripe for seamless and efficient data transfer. The data flows across systems without errors, and you can further process it directly. 

Best Practices for Managing Extracted Data from Operating Statements

Here are some valuable tips for managing the extracted data effectively: 

a. Data Verification

Automatic validation

If you use automated data conversion tools to extract data from operating statements, ensure the software validates data with your existing databases. 

After validation, review the data manually to ensure there are no errors. 

Human validation

Manual data extraction demands a robust validation process to improve accuracy. To do this, you should collect existing business records, such as accounting and bookkeeping documents. 

Match the extracted data with available databases manually and find missing values, errors, and discrepancies. 

b. File management 

Naming conventions 

Maintain consistency in naming files and make them more specific and crisp. The aim is to use names that imply what the file is about without having to check it. 

Include keywords, the date, the property’s name, and sequential numbers to find it easily using the search bar.

Folder hierarchy

To access files quickly, use folders and subfolders wherever possible and organize them using logical names. 

For instance, you can create a folder for ‘Property A’ and add extracted data from bank statements, operating statements, payroll, and inventory documents.  

c. Data security 

Backup and recovery 

Backing up data helps run business operations even during accidental data loss, corruption, disaster, and data breaches. It reduces downtime and prevents permanent data loss. 

You can have multiple copies of data using secure options such as external hard drives, cloud storage, USB flash drives, and Network-Attached Storage (NAS) devices. 

Data encryption

Thales’ 2023 cloud security study of 3000 IT professionals shows that only 45% of cloud data is currently encrypted. Data encryption converts files with sensitive information to coded texts, also known as encryption keys. 

This method adds a high level of security, allowing only users with the decryption keys to access the files.

Access control 

Restrict data access to unauthorized users by using passwords and multi-factor authentication methods. Use strong passwords and change passwords regularly by changing a few characters. 

Also, role-based access should be implemented, and data should only be shared with authorized personnel. 

Employee training

Training employees on secure storage and use of sensitive data can reduce cyber attacks in your organization. 

According to Fortinet’s 2023 security awareness survey of nearly 2000 cyber security leaders, 93% of decision-makers believe employee awareness training would reduce cyberattacks. The same report also states that 73% of organizations without a security awareness program want to incorporate one in their strategy to increase security. 

Educate employees about password policy, phishing attacks, remote work risks, social engineering, and malware infections. 

d. Maintenance and software updates

Regular audits

Periodic data audits help improve security and compliance. They monitor who has data access, delete duplicate files, and monitor network activities to detect suspicious behavior. 

Software updates

Updating your security software is one of the easiest ways to protect yourself from new cyber threats. Install the latest updates and fix bugs to prevent data exploitation. 

Enhancing Financial Reporting through Effective Data Extraction

Extracting data from operating statements and performing data analysis helps real estate businesses to track income, expenses, cash flow, and profitability. However, efficient data extraction is crucial as stakeholders rely on the extracted data to finalize business decisions. 

With Docsumo, data can be extracted automatically from operating statements with a high accuracy rate of 99%+. Docsumo validates the extracted data and sends it to underwriters for final review. 

Improve the efficiency and productivity of your business by 10X! 

Eliminate manual efforts, start processing multiple operating statements, and extract data with 100% automation. 

FAQs 

1. How can I ensure the accuracy of extracted data from operating statements?

Data extracted from operating statements may have a few errors, so verify the data manually with databases and correct them. Invest in a reliable tool like Docsumo, which ensures 99%+ data accuracy.

2. Can data extraction from operating statements be fully automated?

Yes. Advanced data conversion tools automatically ingest, extract, validate, and integrate data with software applications. 

3. What to do if extracted data from an operating statement is incorrect?

Validating the extracted data for corrections before further analysis is crucial. Looking for errors and making corrections manually helps the automation tool extract data accurately in future extractions.

4. Can I process multiple operating statements at once? 

Yes. Data automation tools improve your business's efficiency by helping to extract data from operating statements at scale. 

5. How can I ensure security for automated data extraction from operating statements?

Choose a tool that follows standard security protocols and complies with GDPR and SOC-2. To increase the security of extracted data, look for features such as role-based access and cloud storage.