Data analysts can spend around 20 hours each week preparing and analyzing data in spreadsheets. This significant amount of time could be better spent engaging in more creative work.

The challenges of spreadsheets

Microsoft Excel has been the go-to BI tool for many businesses for years because it’s easy to use and familiar to most employees. However, issues such as data accuracy, siloed work, human errors, and limitations with large datasets only begin to illustrate the frustrations associated with spreadsheets. As data usage increases, these challenges will likely worsen, impacting your daily work and the value you bring to the business.

Transitioning from spreadsheets to more advanced analysis tools won't happen overnight, but it's a crucial process that your company should start immediately. If an organization relies solely on spreadsheets, it risks falling behind competitors in making data-driven decisions and facing significant business-impacting consequences.

Limited scalability

If you've ever worked with large files in Excel, you've probably faced issues like freezing, crashing, or complete unresponsiveness. What if, for example, your company had to analyze millions of rows of data? You know…

Adding more formulas or functions to an Excel spreadsheet takes longer for the spreadsheet to recalculate itself. When you refresh your spreadsheet with new or updated data, Excel recalculates every function and formula over all the records in your file. Recalculating hundreds of formulas can take minutes, even with a modern computer.

A tool designed decades ago simply can't keep up with today's data operations demands.

Human errors

Many companies make major decisions based on spreadsheets that are pieced together by copying and pasting from other files and relying on outdated macros from nearly two decades ago.

A single incorrect entry or change in a cell can cause a chain reaction, messing up your data. To find and fix these errors, you need to create manual formulas to check if values are correct or duplicated. Managing and correcting errors becomes more tedious and time-consuming as your spreadsheet grows.

Difficult to automate

Every step of data preparation and cleaning in Excel isn't automatically recorded, so users often have to repeat their work if changes are needed. This lack of a recorded workflow leads to inefficiencies, as users must manually redo each step whenever modifications are required.

While Excel offers some automation for repetitive tasks through macros and VBA (Visual Basic for Applications), setting these up requires significant expertise. Creating effective macros and writing VBA code isn't intuitive and often demands strong programming skills. This complexity can be a barrier for many users, limiting the widespread adoption of these automation features.


Before analysis can begin, data often needs to be cleaned and prepared by removing duplicates, correcting errors, and formatting it consistently. In Excel, these tasks can be particularly time-consuming, especially with large datasets that require careful attention to detail.

Additionally, while Excel’s extensive library of formulas can perform various calculations, learning to use them well requires time and advanced expertise. Users often spend a lot of time troubleshooting errors and ensuring their formulas are correct.

Lack of data integration

Excel often requires users to manually import data from various sources, such as databases, applications, and other software tools. This increases the risk of errors and inconsistencies, potentially leading to inaccurate analyses.

To address these limitations, users frequently use external tools and add-ons to connect with different data sources. While these tools can enhance Excel’s capabilities, they add complexity and may cause compatibility issues. Additionally, relying on external tools can lead to higher costs and maintenance, making data integration with Excel less efficient.

The benefits of the modern data preparation approach

Automated analytics solutions can reduce manual workloads by up to 80%

The modern approach to data preparation and automation differs significantly from the methods used in spreadsheets. Here are the main principles you can expect from modern data preparation tools.

Structured datasets

Unlike spreadsheets, where data is a 2D grid, modern tools organize datasets as column-based tables. Each column represents a measure or dimension of the data, and each row represents a separate record. Each column has a strict data type, such as string (text), numeric, datetime, boolean, or even JSON. Tabula automatically determines and sets column types based on data.

Consistent formulas for big data

Big data - you've definitely heard the term. Tools like Tabula are built to handle enormous datasets with millions of rows. Instead of manually correcting such large datasets, you can transform all rows in bulk with formulas or transforms, saving time and reducing errors.

In spreadsheets, you manually enter formulas into cells and apply them to others by dragging and dropping, which can lead to hidden errors. In Tabula, you write your formula once, and it automatically applies to the entire column. This might seem similar to Excel's ARRAYFORMULA, but it's more robust. Adding new rows never breaks your transformations, and you can’t accidentally enter data in between to disrupt the formulas.

In Tabula, you can use the same familiar formulas as in Excel and even more - use autocompletion, in-place formula descriptions with examples, or extended documentation.

Responsive, user-friendly interface

Complex operations, like VLOOKUP, are simplified in Tabula with predefined custom interfaces. You should also expect an immediate result preview to check if you have entered all the parameters correctly. Even if you add new columns or change their order, these functions will continue to work without errors.

Visual step-by-step approach

Every transformation you made over your data in Tabula is saved as a separate step on a visual canvas. You can check the result tables after each action you made over the data. This also allows you to go back and make corrections without losing your later work. You only need to design your data processing once and then reuse it on new data with just one click.

Need scheduled updates? Set your data processing on a scheduler with a visual self-service interface and get fresh, real-time data for up-to-date analysis and decision-making.

Advanced reporting

While adding a new chart to a spreadsheet is easy, it’s not always suitable for powerful dashboards and reporting. Tools like Tabula allow you to create stunning reports with a few mouse clicks and share them with your colleagues.

Collaborative features

In spreadsheets, all intermediate actions are hidden, requiring extensive documentation to coordinate with colleagues, leading to lots of back-and-forth and lack of transparency. The visual dataflow approach eliminates the need for extensive documentation and adds unique transparency for your colleagues.

Share documents, dataflows, reports, and comments, or accept others’ work right inside the application.

Data enrichment

Modern tools often include features for data enrichment, allowing you to enhance your datasets with additional information from various sources, leading to more comprehensive analysis. In Tabula, you can use external API call transformation and then automatically convert JSON answers to columns and rows.

Built-in integrations

Expect native integrations with other apps like HubSpot or Stripe to seamlessly access and start working with your data. No more CSV downloads and manual data entry. Connect to 250+ data points directly from the Tabula and create a single source of truth for your team without a single row of code.

Scalability and portability

Being able to process over 2 million rows of data from separate datasets without writing any code was simply amazing.

Tabula is designed to work with millions of rows and data points, leveraging powerful stacks like data warehouses for almost infinite scalability.

Only use tools that generate portable data pipelines, meaning you can always take your work and move to a new data stack as your company grows.  No vendors lock-in. Start small with CSV and seamlessly move to any cloud storage, like Postgres or Snowflake, when needed.

Professional features

At Tabula, we believe that no-code tools can still offer advanced features. Professional features can be accessible and easy to use for anyone without requiring any coding knowledge.

Try it on practice, just download Tabula for free Trial (no credit card required).

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