Stop wasting time on data preparation

Stop wasting time on data preparation

August 30, 2021

Stop wasting time on data preparation – Rulex AI

It takes time to get to know your data. Understanding your data and preparing it for analysis is a critical step for any successful project.

But as any data analyst will tell you, data preparation is a time-consuming process, and often involves multiple software tools: one to extract data, one to transform and convert, and finally another tool to upload the results.

Tracking your activities further slows down this process, but is crucial for rolling back operations, or simply for explaining what you did.

How Rulex makes your life easier

1. Single environment

In Rulex all data import and preparation is performed in a single no-code platform where you can visualize results instantly.

Import your data, then manipulate them to get straight to the results you need: concatenation, discretization, time series analysis, statistics, formulas, reshaping … everything you could possibly need to not only understand your data, but also prepare them for advanced analytics.

Export your results from the same single working environment to local files, remote repositories, databases or even send files via email to a list of recipients each time you run the workflow.

2. Multiple sources and formats

Import your data from multiple different local and remote sources, in many different formats (XML, MS Excel, TXT, CSV, JSON etc), and merge them into a single dataset.

Instead of wasting time planning how datasets may interconnect in theory, based on their metadata, try it out in Rulex with your real data, thanks to extremely fast computational speed.

Check the results, tweak some changes, and once you’re satisfied, the job is done.

No additional implementation is required.

3. Rulex traces all your operations, so you don’t have to.

Thanks to Rulex’s automatic tracing of operations, rolling back in any part of the transformation process is a question of a few mouse clicks.

Technical and operational documentation can be generated from comments, so explaining and documenting the change process is quick and painless.


Data Manager – the heart of data preparation

The heart of data preparation phase is the Data Manager task, where you can:

  1. Explore your data through graphical tools, such as plots, curves and pies, and an array of single and bivariate statistics, to understand if the data at hand is appropriate for solving your problem. Plots can be saved and added to presentations to explain your results to team members.
  2. Filter, group and sort large datasets in seconds, vastly improving dataset usability and interpretation.
  3. Clean up your data to maintain high levels of data quality by:
    • removing unnecessary attributes,
    • identifying and removing outliers which can negatively impact results,
    • standardizing how missing values are expressed
    • correcting incorrect values whenever possible.
  4. Enrich your dataset with new values derived from existing attributes, thanks to the wide range of formulas.

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