Unleashing Your Data's Potential: How ETL is Reshaping Finance and Accounting

Published: Sep 11, 2023
By: Alex Cannellos
Topics: Artificial Intelligence
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In today's business landscape, the combination of technology within a firm’s finance and accounting department is critical for efficient reporting and analysis. Organizations that can automatically collect and prepare detailed operational and financial information are able to review their key metrics faster and as a result, make more informed decisions. The foundation of this data intake is the extract, transform and load (“ETL”) process, which involves:

Extracting raw data from diverse sources;
Transforming it into a usable format based on a set of rules; and
Loading the refined data to a designated destination for review or other analysis.

ETL is used by data analysts and data scientists for formal data warehousing and machine learning pre-processing, but this practice lends itself well to finance and accounting professionals for common tasks that are repetitive or pesky in nature. Users who need to perform tedious tasks over and over again – such as consolidating entities, calculating adjustments, parsing text or PDF files, looking up a value on another table or combining related tables – can use one of the many ETL tools available to create custom pipelines that reduce manual work. There are low-code and more advanced options available for different types of users, and a variety of helpful trainings available online for whichever tool fits one’s needs.

ETL Tools

ETL tools automate the flow of data from source to target location via a series of predefined steps. These transformations often involve filtering, sorting, summarizing, splitting and controlling data types. Raw data can be extracted from files (Microsoft Excel, CSV, text and PDF), web pages, systems, databases and other sources.

Each tool has its own user interface, and while there are some variations in function across the available options, many support a range of similar data manipulations and can integrate with artificial intelligence (“AI”) and other applications. Popular tools within accounting and finance are:

Power Query: While Excel reigns as king of accessible spreadsheet editing for professionals, Power Query is Microsoft’s data transformation and preparation tool that allows for ETL process creation via a low-code editor and a formula bar, either within Excel or Microsoft’s data visualization software, Power BI. With a very familiar interface, it’s easy to pick up the basics as an Excel user.
Alteryx: A comprehensive data analytics platform. It's popular for its ability to create robust workflows that can combine machine learning with other transform operations through a simple drag-and-drop, node-based interface.
IBM DataStage: A modernized data integration solution to cleanse and deliver data on and across cloud-based environments.
Wdata: Like Power Query and Power BI, Wdata is part of the Workiva platform and focuses on data preparation within that ecosystem. It allows users to clean, transform and combine data before ingestion into the cloud-based tool for financial reporting and compliance.
Programming languages like Python and/or SQL may also be used within a workflow as needed.

When to Workflow

Knowing when to employ an ETL workflow is essential, and it’s important to understand the expected return on investment when considering any automation project. When looking at which processes to workflow, users should focus on accounting and financial tasks performed frequently (daily, weekly or monthly), which demand significant human interaction or include disaggregated inputs needing cleansing or combination (e.g., many entities, time periods, currencies, locations). Not only will there be improvements in the ways end users can interact with the cleaned data, but there will be significant time savings found in offloading the work to the computer for those types of recurring tasks.

Other reasons to reach for an ETL solution would be to connect directly to live data, create a tailored centralized repository for specific business needs and improve scalability when more data is expected to be received later.

It’s still common to leverage these ETL tools for one-off tasks because of their enhanced functionality. And with the ability to copy and paste prior queries, you are able to share and reuse code for future needs.

Sample Use Cases

Below are a few additional examples where finance and accounting professionals can think to leverage these tools to enhance workflows and refocus efforts on more insightful analysis. Users can create workflows to:

Convert Messy General Ledgers: Process many general ledger text files from a legacy system for detailed account-level review. Users can put unprocessed text files in one folder, refresh the tool and output the cleaned data.
Combine Financial Statements: Review customized metrics and results for multiple offices or locations on a standalone or consolidated basis.
Connect Previously Disconnected Data: Calculate profit and margin by item, using sales and cost information from disparate sources.
Sort and Filter Transactions: Extract specific transaction detail by name or memo line to help calculate monthly journal entries.
Join Related Tables: Join customer demographic information to sales data to extract sales by state for state tax analysis.
Stack Related Tables: Append multiple accounts receivable or accounts payable aging reports to better view customer and vendor trends over time.
Perform Dynamic Time-Series Analysis: Review sales and purchase orders that occurred in the last 30, 60 or 90 days based on today’s date.
Quickly Summarize Information: Refresh the workflow to summarize and organize transaction detail from various banks for cash reconciliations.
Parse Data: Split information using simple or complex delimiter rules.
Enforce Data Type Consistency: Ensure data types are proper, so calculations and other analyses perform as intended.
Pivot and Unpivot Data: Flip information that isn’t normally presented in the format needed for analysis.
Allow Seamless Review and Iteration: Enable future editing in case there is a need to debug or further enhance the process.

Conclusion

The ETL process involves extracting data from various sources, transforming it into a usable format and then loading it to a target destination. ETL tools play a fundamental role in constructing pipelines or workflows that not only save time through automation but also create completeness and scalability when handling different forms of financial and operational data. This process can become a crucial application within audit and consulting practices, where large volumes of financial data need to be prepared for analysis on tight deadlines.

By employing ETL tools, auditors and consultants can more easily prepare system-generated trial balances, general ledgers, sales, receivables and payable data for analysis. This enables them to spend less time on manual data manipulation and focus more on deriving meaningful insights from the data.