Master Data Extraction from SQL to Excel: Techniques for Efficient Data Transfer

SQL databases come with challenges such as steep learning curves, complexity, security concerns, and slower processing when handling large volumes of data. Hence, businesses convert this data to Excel format to integrate directly into various software, represent data visually, and perform complex calculations quickly. 

Although manual conversion is possible, businesses now prefer using automated data conversion solutions to save time, reduce costs, improve productivity, and avoid risks related to errors. These software solutions use advanced technologies to convert data from SQL to Excel with minimal human effort. 

This blog discusses the use cases of data extraction from SQL to Excel, challenges in conversion methods, preparation techniques, and a step-by-step process to convert data using the Docsumo platform.

Understanding the Importance of Data Extraction From SQL to Excel

Businesses convert SQL data to Excel, which offers various functionalities such as data visualization, manipulation and analysis, interactive dashboards, and integrations into accounting software. Here are some industries that use SQL data extraction for varied purposes:

a. Marketing

Marketers convert customer data from SQL to Excel to analyze key performance indicators (KPIs) and goals across different marketing channels, such as websites, email, and social media platforms. 

To get a visual overview, they represent data such as email signups, click-through rate, bounce rate, organic traffic, impressions, and page reach in charts, graphs, and heat maps. 

Excel data visualization enables marketers to monitor the performance of their campaigns and redefine their approaches according to how each platform works. Marketers also use Excel to compare competitors' marketing tactics in charts and perform a SWOT analysis to create a winning marketing strategy.

b. Healthcare 

In Excel, healthcare professionals consolidate patients' vital signs, such as height, weight, BMI, blood pressure, cholesterol, and glucose levels, to monitor their health and deliver personalized care.

They also collect outcomes from different treatment protocols and derive insights to personalize treatment procedures, reduce errors, and bring accurate health results in clinical cases. ‍

Excel spreadsheets also help medical institutions conduct research using advanced mathematical models and curate various health strategies in pandemic situations.

c. Accounting 

Importing data from SQL to Excel allows accountants to use native and third-party Excel templates to create budgets with data from different financial documents. They can manipulate the existing data and perform complex calculations effortlessly using VLOOKUP, SUM, AVERAGE, and more functions.

Accountants use Excel charts, graphs, and pivot tables to verify cash inflows, reconcile bank statements, track income and expenses, and create budgets for a particular month or year.  

Common Challenges in SQL to Excel Data Extraction

While converting SQL to Excel, businesses encounter the following challenges:

a. Data volume/Data complexity

A traditional data extraction method (manual extraction) may not be reliable when handling large volumes of data. It requires businesses to hire and train employees to perform business operations within a given timeframe. 

The challenges with high data volumes include time-intensive extraction processes, storage issues, increased costs, and data inaccuracy due to human errors.

b. Compatibility issues

Formatting and structural variance in two formats cause compatibility issues in extracted data. It disrupts business processes and demands rigorous testing and system upgrades to ensure compatibility.

c. Non-standard data extraction processes

Converting SQL data manually with diverse employees or using fragmented tools with basic technologies leads to significant errors in the converted data. It wastes time and increases costs as businesses must spend resources again for correction and compensation. 

Hence, enterprises employing humans or using standalone Optical Character Recognition (OCR) technology to extract data should choose standardized solutions to avoid such challenges.

d. No validation on converted data

Converted data demands validation to detect errors and inconsistencies, and businesses without a validation process may face challenges related to inaccuracies. Leaving errors undetected in the initial stages disrupts the entire business operations afterward.

e. Security and compliance concerns

Businesses converting sensitive information (financial and healthcare data) face challenges with security measures and protocols, as exposure of such data results in reputational damage, legal issues, and economic losses.

Businesses' security challenges vary according to the data extraction processes they choose. For example, manual data extraction methods involve multiple human touchpoints, which can lead to data theft, privilege misuse, and other data threats.

Conversely, businesses should prevent unauthorized access while using automation, and the software should comply with standards such as SOC-2, HIPAA, and GDPR to prevent data from cyberattacks.

f. Data integrity

Preserving data integrity while converting SQL data to Excel is another challenge, especially when processing documents at scale. Factors like human errors (accidentally deleting a column or row), storage attrition, spyware, malware, and transfer errors affect the completeness and reliability of data. 

Preparing Your SQL Database and Excel for Data Extraction

Here are six effective preparation processes to increase the security and accuracy of existing data. 

a. Backup and data recovery

Unexpected issues such as human errors, system failures, and technical glitches can cause data loss.

Hence, before converting to Excel, backup your data, keep multiple copies in different locations, and implement recovery strategies to avoid consequences related to data loss.

b. Data cleanliness in SQL database

Fix errors, incomplete, inaccurate, and duplicate data in advance to prevent additional mistakes during extraction and conversion processes. Use SQL's UPDATE statement to remove rows with missing values or input data in such rows. Eliminate irrelevant and identical data that holds no value for the current analysis.

c. Conversion of SQL to another format (PDF)

Before extracting, convert SQL database documents to another easily accessible format like PDF. Store the converted PDF documents in a single folder to ingest them into the conversion tool with less effort. 

d. Proper database connections

You can choose and connect with a proper database by entering the name of a specific SQL database in the drop-down list. Then, view the connection tabs on the left side, and an SQL worksheet will open automatically.

e. Permission settings

SQL's server database only allows authorized users to access, view, and modify data. Grant employees permission to perform the required tasks so they can import the data seamlessly without having to ask for and wait for authorizations to access different data.

f. Data import settings for Excel

If you're using Excel's import features to convert SQL to Excel, then make sure to optimize the settings. Click the data tab and import data files. Ask your organization's technical teams to handle issues related to SQL file access. Check how the preview looks, complete corrections in your data, and click "Load" to convert data to Excel.

Save Hours with Docsumo’s 99% Accurate AI

Extract data from complex documents & cut costs by 80% with AI data extraction.

Step-by-Step Guide to Data Extraction From SQL to Excel

Here's the step-by-step process to convert SQL data to Excel format. 

Step 1: Choose the right data extraction tool 

The most basic method for converting SQL data is copying data from the SQL database and pasting it into an Excel sheet. There are also free online tools for uploading SQL data documents and downloading extracted data in Excel formats. 

However, these processes don't offer a comprehensive solution for businesses looking to process large volumes of data with benefits like high accuracy, low processing time, and 100% automation.

Then, there are automated data conversion software solutions like Docsumo, which use OCR technology with Artificial Intelligence (AI) and Machine Learning (ML) algorithms to extract data with more than a 99% accuracy rate. Docsumo adapts and learns to different data formats and extracts data in less than 30 seconds, helping businesses make timely decisions.

Choosing the right data capture solution to extract data is crucial for businesses to avoid risks related to errors and safety measures. Before selecting the tool, understand your industry's security protocols, data volume, complexity, and accuracy rate. Analyze the factors, such as price, integrations, accuracy rate, adaptability, scalability, automation, and security protocols, to finalize the tool that fits your business needs.

Let's use Docsumo to discuss the step-by-step process of extracting data from SQL and streamlining business operations.

Step 2: Sign up on the Docsumo platform

Visit Docsumo's website and sign up for a free trial to process and extract data from 100 documents. Enter your name, phone number, and work email address and set a strong password to kickstart the data extraction.

Go to "Document Type" and click "Upload" to ingest SQL database documents from your system. You can also import data automatically from mail and Zapier onto the Docsumo platform.

Step 3: Ensure proper settings for SQL conversion

Customize the settings, such as accuracy levels, language recognition, and category tagging in the Docsumo platform. Also, choose the desired output format (Excel) to download the data automatically.

Step 4: Select which data to extract from SQL

Docsumo will begin its pre-processing techniques, such as deskewing, denoising, and contrast adjustments, to increase the quality of data present in the documents. Once this is done, Docsumo's trained API models extract key-value pairs and data from tables using neural networks and natural language processing algorithms.

Docsumo also allows you to train your API model and customize the data extraction according to your business requirements. Set key identifiers and line items by creating bounding boxes in respective data fields, and the tool adapts to extract the required data from the document.

Step 5: Edit the fields for extraction 

After Docsumo extracts data from documents, add or remove fields based on required data. Docsumo then follows and extracts the same fields from existing and new documents.

Step 6: Review extracted data

Docsumo validates the extracted data automatically with the existing documents. However, the best practice is to review the data manually until the tool extracts data with 99% accuracy.

Once the software processes data from a few documents, it extracts data accurately without human intervention. You can click "Approve" to save and download the document in Excel format.

Step 7: Automate data extraction for large volumes of data 

Docsumo is now set and ready to process and extract documents at scale. Upload multiple documents at once and extract data from them in 30-60 seconds. Automate the data extraction for large volumes of data and enjoy benefits such as 

• Increased accuracy rate to 99% 
• 95% straight-through processing rate
• Data extraction in less than a minute 
• Improvement in efficiency by 10X
• Advanced security and compliance as Docsumo complies with GDPR and SOC-2

Step 8: Integrate to workflow

Integrate the extracted data directly into numerous applications such as Zapier, Yardi, QuickBooks, Xero, Stripe, Chargebee, and more. This seamless integration ensures accuracy, reduces errors, and simplifies business operations.

Best Practices for Managing Extracted Data From SQL to Excel

The five best practices to manage and store the extracted Excel data are given as follows:

a. Extracted data updates

Update the extracted data by directly entering information in new cells in a spreadsheet. Represent data in charts and bar graphs and share them with other employees to understand the information quickly and make data-driven business decisions.

b. Conventions in naming

Consistency in naming files helps you to retrieve documents in seconds. Use the client's name, abbreviations, and date, and follow the same format for all records.

c. Data validation post-extraction

Advanced software solutions automatically carry out the validation process to ensure accuracy. Otherwise, post-extraction, you need to employ humans to validate the extracted data with original documents to avoid errors and inconsistencies.

d. Data Security

Give data access only to people who analyze the extracted data. Enforce robust passwords to prevent data leakages in cases of cyber threats and malware attacks. Backup your data regularly and use cloud storage options to ensure security.

e. Documentation

Documenting the extracted data helps existing and new users to interpret the data easily. Cite your data sources, monitor data lineage for visualization, and track the changes made during the workflow.

Enhancing Your Data Management With SQL to Excel Extraction

Converting data from SQL to Excel offers significant benefits for businesses, such as enhanced security, seamless integration, practical data analysis, and streamlined business operations. This guide outlines the preparation processes, conversion techniques, and the management of extracted data.

Docsumo is the ideal solution for businesses to convert data from SQL with a 99%+ accuracy rate. It automates the end-to-end data extraction processes and improves the business's productivity and efficiency by 10X. 

With Docsumo, capture data in 30-60 seconds, integrate the validated data into existing industry-specific software, and help employees concentrate on strategic tasks. Extract data from SQL to Excel using Docsumo with 100% automation and a 95% straight-through processing rate.