Data Wrangling and Preparation

Your company has integrated data, transformed it into an usable structure and analyzed it, which added a lot of value. However, you soon realized data was not as reliable as you expected. Due to how it is gathered, it could be messy, incomplete or completely incorrect. This not only made obtaining useful insights harder, it decreased data ROI and even caused bugs in analytics tools that were not ready for those inconsistencies.

Data preparation can correct this. It is a method to recognize patterns in data, organize it and cleanse it, ensuring data quality. It aims to make data valid, complete, consistent, uniform and accurate, so its analysis can be fast, precise and efficient. It aims to make information contained in data easier to access and analyze.

But data preparation is no simple matter. It usually requires data experts, which may limit its effectiveness, since that makes final users dependent on IT teams. It means one more tool and process to integrate in an already complex data stack - which also means dealing with another supplier. Having another step in the data pipeline also increases risk of data being compromised, and since data is prepared before being used, it limits the end user’s view of how it changed from inception to analysis. All of this requires intense maintenance to mitigate, which means increased operational costs, to ensure data preparation doesn't have the opposite of the intended effect - diminishing analysis efficiency and data ROI.

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SlicingDice has powerful and intelligent data preparation capabilities


SlicingDice addresses these issues so organizations can maximize their analytics efficiency. By offering integrated data preparation capabilities, it ensures data quality without increasing complexity, costs and expertise requirements. This eliminates any risks related to integration or data movement, such as data loss or bugs due to inconsistency. We offer end users the option of working with prepared or unprepared data alike, which ensures visibility is complete, for everyone, all the time.

Our database technology supports many different data types and is near-schemaless, so you never have to deal with inconsistency issues again - it simply works. Prepared data is also machine-learning-ready and in place to be used with our module. This process can even be automated and refined with machine learning, meaning analytics gains improve over time. All of this can be powered on and never touched again, meaning teams are not dependant and can work on more critical business tasks. Organizations get value from data faster and more efficiently, meaning better data and overall ROI.


Check the Documentation Check our features comparison
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ETL & Data Preparation Module Technical Capabilities


Check below the most important features present on SlicingDice's ETL & Data Preparation Module.

Totally serverless ETL

No server to configure or monitor.

Many data sources

More than 150+ available data sources.

Automatic data loading

Connect your data sources and everything else will be synchronized.

Load data from other databases

Load from many different databases (like MySQL, MongoDB and others).

Load from online services

Load from many different online services (like Salesforce).

Many data sources

We enable querying straight from 150+ data sources.

Load from log files

Load from log files stored on object stores.

Connection to BI tools

Use any tool you want for BI (like Tableau and Qlikview).

Data transformation

Data transformation can be done using SQL commands.

Data sync

Automatic data synchronization from your data sources.

Data offloading

Automatic data offloading to other platforms and databases.

Visual data preparation

Data preparation done visually.

SlicingDice Advantage


Few but powerful reasons to start using SlicingDice's data preparation capabilities.

Prepare your data without using code.

Our module simplifies preparation, making it work without requiring any coding.

Find wrong and missing values.

Preparation can be used to refine data quality, by singling out outliers, such as wrong, missing or unwanted measures.

Understand the quality of your data.

Analyze the difference between prepared and unprepared data to know your data quality better, identifying possible failure points.

Prepare and clean your data for machine learning.

Data will be machine learning-ready, ensuring automation efficiency, to be used with our built-in module or any tool you prefer.

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