Pretty much everyone at some point in their life has had to deal with spreadsheets, but working with them every day is a different story. Love or hate: spreadsheets arouse strong feelings. Quick calculations and fast plots may spark our infatuation, but there are also some major drawbacks that may grate on your nerves over time and bring your love affair to a bitter end.
Superficially spreadsheets seem pretty user-friendly, but as you dig deeper, you’ll find yourself up against some major limitations. They lack the scalability needed to prepare large amounts of data, so analyzing large spreadsheets can become a nightmare: an ineffective, time-consuming, and error-prone nightmare.
Data analysis already plays a key role in the business world, as it helps organizations discover market trends, find data-driven actionable insights, and make informed decisions. And its role is expected to become increasingly strategic. Although spreadsheets are widely used in this field, there are far more suitable tools which greatly enhance job performance.
In this article we’ll go through 3 pain points of spreadsheets that are most likely slowing down your data projects. By asking ourselves some important questions, we’ll highlight the areas that could be improved.
1. Are you having a hard time unlocking key data?
Analyzing data and explaining the outcome to clients and stakeholders is not an easy task with standard spreadsheets. Data preparation often involves complex formulas, where you risk overwriting or modifying the original data, and it’s simply too easy to lose track of what you’ve done, and undoing operations can become a messy affair. Providing convincing data-driven decisions when you’re not sure yourself what happened, can be a struggle to say the least.
2. Are you sure you’re using the right tool for data consistency?
The risk of data inconsistency with spreadsheets is high. When spreadsheets are shared across teams, data may be updated or modified, consequently creating conflicting copies or duplicates. Human data entry is also a critical element of worksheets, as it’s easy to make mistakes, compromising the whole data integrity of the project.
3. Is your data preparation process resilient to change?
Data preparation with spreadsheets can get very complicated when your data is saved in multiple files, which may also be in different formats. The whole process is also not resilient to change, so whenever your data changes, you must make tricky manual adjustments or even start again from scratch.
It’s time to move on!
It’s time to make a clean break with spreadsheets and move on. When data preparation sucks up the majority of your time, you need to find a new approach. Rulex Platform offers you an innovative way to solve your complex data problems, with its flexible, scalable, and resilient software.
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Seeing the big picture
In Rulex Platform you can confidently explore data, knowing that the software is tracking your operations for you. If you don’t get the results you expected, you can easily roll back all operations, or even reinstate the original data with a few clicks. This flexibility means you can calmly explain how you reached your conclusions, while wowing your colleagues with interactive dashboards.
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Working safely with large volumes of data
Rulex Platform can effortlessly manage huge amounts of data, collecting and merging files from multiple formats and sources, so you can view and explore the results in a single environment.
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Make data preparation a piece of cake
Rulex data preparation is resilient to change, as it can be easily applied to new data without rewriting operations and formulas, so finally your team can stop wasting time on repetitive tasks.
Rulex platform in action
Actions speak louder than words, so let’s see how to perform some data prep operations in Rulex Platform.
To learn more about how Rulex Platform works, sign up to Rulex Academy and follow the course “Moving from spreadsheets to Rulex”. This course helps spreadsheet users smoothly transition to Rulex Platform and explains how the latter solves the most common data preparation issues that many analysts face.
