3 reasons spreadsheets could be holding back your financial analysis efforts

It’s 9:00 AM on a Monday. Your coffee is getting cold, and you’re staring at multiple spreadsheets that resemble a chaotic puzzle. While you’re painstakingly trying to piece together the data and ensure everything adds up, it makes you wonder if this is really the best use of your time. Accurate information seems elusive, tracking changes feels impossible, and you'd much rather enjoy a hot coffee than battle with endless cells and formulas.

It’s time to leave the manual grind behind. 

Here are 3 reasons why spreadsheets are hindering your financial analysis efforts and how working alongside low-code data science tools like the KNIME Analytics Platform can help you overcome these obstacles, streamline your processes, and enhance your data analysis and modeling processes.

Spreadsheets are useful for basic financial tasks, but they struggle to handle large volumes of data and perform complex operations. This makes them less effective as your financial data grows and your analysis needs become more sophisticated.

Let’s look at three of the main challenges to scaling with spreadsheets:

Finance teams often need to consolidate data from various sources, such as bank statements, ERP systems, CRM databases, and external market data. Each source may provide data in different formats. This requires manually adjusting and reformatting data coherently to ensure consistency. This is often time-consuming and repetitive, making it difficult to scale.

As the size of a spreadsheet grows, it can become very slow. This can cause delays in processing and analyzing data, making it difficult to respond quickly to financial inquiries or issues. Large, complex spreadsheets are more prone to crashes, which can result in data loss and require teams to spend time recovering or re-pulling data.Lack of collaboration and visibility

It is difficult to collaborate among team members with spreadsheets. Communication gaps and difficulties in coordinating efforts can lead to errors and inconsistencies in the final dataset or analysis. 

Spreadsheets don’t have comprehensive audit trails or detailed histories of data modifications. This lack of visibility can hinder transparency and accountability, making it difficult to trace the origins of errors, explain the analysis process, or verify the accuracy of data transformations over time. It’s also impossible to ensure version control if more than one person opens a file at the same time.

The versatility of spreadsheets is one of their biggest strengths. However, that versatility comes with inherent risks that can compromise accuracy and efficiency and lead to costly liabilities. Manual data entry, ambiguous data manipulation processes, and challenges in version control are three ways spreadsheet use can increase the incidence of errors. 

Although these factors may not be immediately apparent when using spreadsheets, the risks should be considered and mitigated.

Manually entering data into spreadsheets is labor-intensive, time-consuming, and error-prone. Especially as the volume of transactions increases. Human errors in data entry, such as typos, transposition errors, or incorrect data formats, are common, hard to keep track of, and can significantly impact the accuracy of financial records. 

Manual data entry is also not a high-value task, and moving away from it allows team members to spend time on more valuable activities like strategic financial analysis and decision-making.

Spreadsheets can help manipulate data and perform simple calculations. However, as tasks grow in complexity or dataset size increases, so does the manual effort required to manage them. This increases the likelihood of errors during data handling and also strains your capacity for thorough validation and error checking. 

There is no debugging tool or a testing framework to inspect whether all cells keep working as expected and having no error control makes them error prone. Even after manual validation, this journey remains challenging because tasks like data cleaning, preparation, and formatting recur every single day, week, or month. This process can be incredibly repetitive with errors creeping in but ultimately necessary.

When multiple people collaborate on a spreadsheet, it is important but difficult to maintain version control. Without robust version tracking mechanisms, you risk working with outdated data or conflicted documents. 

This can lead to confusion and errors in decision-making. Tracking changes manually or identifying the most current version of a spreadsheet can be challenging. This is especially true in fast-moving environments where updates occur frequently, such as in finance departments. Version control issues undermine the reliability of spreadsheet data and compromise the trustworthiness of analysis and any reporting derived from it.

Spreadsheets offer limited analytical capabilities which can limit in-depth analysis and decision-making. Although spreadsheets can help with organizing and manipulating data, their limitations become increasingly evident as data volumes and analytical demands grow. The two most significant challenges are spreadsheets’ lack of automation capabilities and the inability to provide real-time insights.

Spreadsheets heavily rely on manual input and processes such as copy and paste to transform raw data from systems into usable insights. While formulas and macros can automate certain calculations and repetitive tasks, they often fall short when it comes to more sophisticated data transformations and analyses. 

For instance, integrating data from multiple sources or performing monthly financial updates or reconciliations typically requires extensive manual effort and can be error-prone. If you want to take things one step further and involve machine learning processes to forecast cash flow, for example, you will quickly hit a ceiling with spreadsheets. 

This lack of automation slows down the analytical process and increases the risk of inconsistencies and inaccuracies in the results.

Spreadsheets are designed for static data analysis. This approach limits their ability to provide real-time insights into dynamic datasets or rapidly changing conditions. Decision-makers relying on spreadsheet-based reports may not have access to the most current information, leading to delayed or uninformed decisions.

Low-code analytics solutions like KNIME can help you work with spreadsheet data more reliably and efficiently – reducing error, manual labor, and allowing you to explore more advanced analyses. Transitioning from spreadsheets to a low-code analytics tool doesn’t mean abandoning what you know. Instead, it’s about building up your capabilities and making your data work for you more efficiently. 

KNIME allows you to create workflows that can be reused and scaled, ensuring consistency and saving time as your data and analysis needs grow. Here’s how it helps:

• Integrating Data from Multiple Sources: It is easy to connect and integrate data from various sources, including all your accounting and finance tools like SAP, Oracle, or others to eliminate the hassle of manual data consolidation.
• High Performance: It can handle large datasets more efficiently than traditional spreadsheets, reducing lag and performance issues that can slow down your work.
• Enhanced Collaboration and Visibility: Unlike spreadsheets, which can be difficult to manage and share in teams, KNIME offers robust collaboration features that make it easy to work together on data projects, improving visibility and teamwork.

Spreadsheets increase the scope for error with manual data entry and complex formulas, while KNIME’s automation and validation features help minimize these risks.

• Automate Data Entry: With KNIME, you can automate data imports and integrations, significantly reducing the time spent on manual data entry and the risk of human error.
• Clear Data Manipulation Process: The visual workflow interface provides clear and understandable data manipulation processes, making it easier to manage and reducing ambiguity.
• Effective Version Control: The workflow system helps manage version control more effectively, ensuring that changes are tracked and previous versions can be easily referenced.

Spreadsheets have limited analytical capabilities compared to KNIME, which offers advanced tools for deeper and more sophisticated analysis.

• Automated Processes: With KNIME, you can automate repetitive tasks, freeing up time for more valuable activities and reducing the risk of errors.
• Real-Time Insights: it enables real-time data processing and analysis, allowing you to gain insights as soon as the data is available.

Learn how finance departments like yours are overcoming the limitations of spreadsheets by downloading the E-guide on Why Finance Teams Are Moving from Spreadsheets to Low-Code Data Analytics.