The ROI of Moving from Spreadsheets to Low-Code/No-Code Data Analytics - Business Use Case

Sep 13, 2026

Upload by Fahsai

Spreadsheets are ubiquitous in business, but they become fragile and costly as data volume grows, leading to slow performance, frequent crashes, and error-prone manual copy-paste and formula management. Low-code/no-code data analytics platforms address these challenges by enabling reliable enterprise-scale insights, automating processes, and supporting advanced analyses. The document highlights visual workflows that improve transparency, collaboration, and error spotting, while repeatable, schedulable logic reduces manual work, preserves process history, and accelerates reporting across teams and tools.

LanguageEnglish

CategoryPresentations

FormatPDF

Pages12

Reading Time4 min

AccessFree to download

The ROI of Moving from Spreadsheets to Low-Code/No-Code Data Analytics

Why spreadsheets become costly at scale

Shifting the mindset: rows and columns to visual workflows

Repeatability: from data crunching to value

The ROI of Moving from Spreadsheets to Low-Code/No-Code Data Analytics The use of spreadsheets is widespread across business operations. Excel is the default tool most of us turn to without really thinking about whether it is the right fit for the task. Project management? Excel. RoAS report? Excel. Capacity planning? Excel. Budgeting and forecasting? Excel. This is in part because most of us have grown up with Excel. We are proud of our Excel expertise and the sophisticated formulas we know. Excel is the first data analytics tool that business users across finance, marketing, and other functions were exposed to. It is true that spreadsheets are very convenient and valuable for collecting and crunching small amounts of data. However, as the data gets large, spreadsheets quickly become unwieldy. Their performance slows down and they start crashing more often, errors are introduced frequently with all the manual copy pasting and tricky formula coverage. The costly fragility of using spreadsheets for complex, large scale projects is evident through plenty of real-world examples, be it the $6 billion loss at JP Morgan Chase, the London Olympics committee selling 10,000 tickets for non-existent seats, MI5 bugging one thousand wrong phone numbers, or TransAlta losing $24 million. No business, no matter how secure or how sophisticated, is impervious to the inevitability of costly, reputation-damaging errors. There is no doubt that an overreliance on spreadsheets can expose businesses to unacceptable levels of risk. So, what’s the alternative? Low-code/no-code data analytics platforms are increasingly gaining popularity for enterprise-scale insights as they enable businesses to work reliably with large amounts of data, automate manual processes, and perform advanced analyses beyond data wrangling, all while mitigating the typical spreadsheet challenges. While you might not be able to replace spreadsheets entirely, it is important to have an assortment of modern tools in your toolbelt to be able to use the best possible technology that is relevant to your scenario and get the most out of your data and time. Shifting the Mindset from Rows and Columns to Visual Workflows The key characteristic of a low-code/no-code data analytics platform is the workflow. Instead of sifting through menus or typing out formulas as you would in Excel, you drag and drop nodes to create an easy-to-follow visual workflow. In Excel, it can be hard to decipher formulas that sometimes extend over multiple rows. Thereis also always the danger of your formulas breaking when you accidentally hit the wrong key. A change in a single cell can trigger a chain reaction that alters the output of your worksheet entirely. The worst part is that the analysis can continue being used for a long time before someone figures out something went wrong. There is no debugging tool or testing frame to inspect whether all cells keep working as expected. In a visual workflow, on the other hand, arrows take you from node to node; explanatory notes can be inserted underneath each step of the process and longer annotations can be added around multiple nodes. You can easily view each step of the analysis process to quickly spot, and correct errors, without the need to start the analysis from scratch. A workflow also allows all stakeholders to follow the process you have created, even those with a non-technical background. The inherent visual documentation of workflows provides transparency, easier sharing and reapplication of processes throughout the team. Additionally, with a low-code/no-code data analytics platform like KNIME, they can centrally set access permissions at the data, node, and workflow level, so users can either reuse a prebuilt workflow or build on it further, all without any loss of security or data integrity. Moreover, low-code/no-code platforms let you perform far more advanced analysis on your data than the limited number of operations that yo

FAQs

Why do spreadsheets become risky or inefficient as data grows?

Spreadsheets slow down, crash more often, and are prone to errors caused by manual copying, pasting, and complex formula management. Small changes can trigger cascading output changes that remain undetected for a long time.

What is the key advantage of low-code/no-code data analytics platforms in workflow design?

They use visual workflows where users drag and drop nodes and connect steps with clear structure. This makes it easier to insert explanations, inspect each step, and correct errors without restarting the full analysis.

How does low-code/no-code support repeatability and reporting efficiency?

It preserves both the process and the resulting table, so analysts can rerun or reschedule logic instead of rebuilding steps each time. This automates data pulling, merging, cleaning, and delivery of reports to stakeholders.

Sep 13, 2026

Upload by Fahsai