Data Preparation Tools & Best Practices

Enterprise data is inherently messy. Every system within an organization, including the CRM, accounting system, HR system, and every other system for that matter, stores data in a form that’s most suitable for its purpose. This basically means that even if you managed to collect all organizational data and store it in a single repository, you wouldn’t be able to perform effective analytics – that’s where data preparation comes in. Data preparation allows you to collect, clean, and transform data, making it more palatable to machine learning algorithms.

However, the data preparation process is often laborious and time-consuming, which ultimately prolongs time-to-insight from collected data. As organizations become more reliant on data-driven decision-making, they’re realizing the benefit of having accelerated data preparation strategies.

In that regard, data preparation tools have proven to be invaluable assets, owing to their ability to automate the data preparation process. Consequently, organizations that leverage these tools are able to deliver faster time-to-insight from collected data, giving them a competitive advantage.

In this article, we explore some of the most reliable data preparation tools, their importance, and some best practices when choosing a reliable data preparation tool.

What is the importance of data preparation?

At its core, data preparation aims to improve the quality and reliability of data, making it more suitable for analysis or predictive modeling.

This makes it a crucial aspect in any AI initiative as the performance, reliability, and accuracy of ML algorithms depend on the quality of their input data.

Here are some other reasons why the data preparation process is so important:

Fixing errors before processing

The primary purpose of data analysis is to find trends and insights in the data. If there are any inaccuracies in the data, your analysis’s results will be equally inaccurate. This makes data preparation not only advisable but also vital to ensuring the success of your analytics efforts.

Formatting data for ML models

Machine learning models get their training data from different sources. This data is typically presented in different formats, thus necessitating the need to transform it into a single format that the model can understand [1].

Reducing effort for multiple analyses

If you plan on using the same training data for multiple applications, it helps to prepare it well in advance to minimize your workload with subsequent applications. This is especially common in large language models, which can be trained on the same datasets.

Why are data preparation tools important?

There’s a saying in machine learning and artificial intelligence systems that states, ‘garbage in, garbage out.’ While some ML algorithms may technically function even with low-quality data, their analysis and subsequent predictions wouldn’t be quite reliable. That’s why it’s so important to adequately prepare your data before feeding it into a machine learning or business intelligence system.

Unfortunately, the data preparation process is often costly, labor-intensive, and time-consuming [2]. But, with data preparation tools, you can effectively automate and streamline the entire preparation process.

These tools have user-friendly interfaces that enable you to collect, clean, transform, and organize raw data into a consistent format for effective analytics.

Key features to look for in a good data preparation tool

Some data preparation tools are better than others, particularly when it comes to some specific use cases for dealing with different data formats and systems. To increase your chances of ending up with a well-rounded data preparation tool, here are some essential features to look out for:

Ability to connect to diverse data sources

A reliable data preparation tool should be able to connect to in-demand relational databases like Oracle, Azure, SQL Server, and Redshift. It should also be able to connect to different CRM systems and multi-structured sources like PDFs, images, log files, and text files.

Having built-in connectivity for different data sources allows for easier data extraction and integration. It can also lessen your workload as you will be able to retrieve complex data from multiple sources with just a few clicks.

Data security

Data security is a top priority for organizations and customers alike [3]. Over the past decade, instances of data breaches have increased considerably.

In 2023 alone, there were 3,205 data breach cases, up from 1,108 in 2020 [4]. Considering the cost and reputational implications of a data breach, it helps to have a tool with robust security measures.

Some of the best data preparation tools have access control features. These features restrict access to sensitive data. They can also be customized based on each user’s role or level of access needed.

By employing robust data security features in your preferred data preparation tool, you can effectively limit the chances of data breaches, ensure compliance, and enhance accuracy by reducing the risk of errors posed by tampering by unauthorized staff.

Ease of use

Legacy systems are code-intensive. Some systems require coding even for the simplest tasks. However, newer preparation tools allow users to transform and manipulate data without the need for extensive manual coding. This not only reduces your workload but also minimizes the risk of errors and other pitfalls commonly associated with manual coding.

Flexibility and adaptability

The ideal data preparation tool should be able to work with various data formats, sources, and platforms without compromising quality and performance. Some of the best tools available on the market can seamlessly adopt data from various infrastructures and integrate them with different service providers. Consequently, this increases workflow efficiency and makes it easier to derive data-driven insights from relevant sources.

Additionally, in a fast-paced business environment, where businesses are constantly evolving to meet ever-changing data requirements, an adaptable system is needed. As such, you should look for a data preparation tool capable of scaling and adapting to your business needs.

In that regard, the ideal tool should be able to handle increased data volumes, adjust to new technologies, and accommodate new business goals.

Top 5 data preparation tools for 2024

Proper data preparation is a vital component of effective analytics. To maximize your efficiency and effectiveness in data preparation efforts, you need robust tools that are perfectly suited to your unique business needs.

In that regard, here are some of the best data preparation tools shaking the market in 2024:

1. Integrate.io

Integrate.io is an advanced, easy-to-use data integration platform designed to simplify data preparation and management processes.

The platform features a wide range of capabilities that facilitate easy data collection, processing, and replication. For instance, it has CDC, ETL, and reverse ETL capabilities that allow for real-time sourcing and organization of raw data.

Integrate.io also supports integrations with various data sources and platforms including cloud storage, databases, and popular business applications, allowing seamless data collection and processing.

Besides its diverse capabilities and integrations, Integrate.io also features a drag-and-drop builder that allows non-technical users to create and manage complex data pipelines without having to write a single line of code.

2. Astera

Astera is arguably one of the most robust data management platforms available. The platform has advanced data preparation, extraction, integration, and API management capabilities, not to mention an easy-to-use UI that allows you to design and develop complex data pipelines without coding.

The platform’s built-in integrations allow you to connect to various data sources where you can take advantage of its rigorous data cleaning, preparation, and transformation features.

It also comes with a preview-focused interface that allows you to perform various data-cleaning activities seamlessly.

Once you’re done cleaning and manipulating the data, you can leverage the platform’s custom validation tools, verification checks, and data profiling tools to ensure reliability and consistency.

The platform also allows you to export organized data in different formats or integrate it with downstream systems for visualization, analysis, and consumption.

3. Alteryx

Alteryx is an AI-powered data preparation and analytics platform equipped with various data visualization and low/no-code features to facilitate seamless data preparation.

The tool features various interactions that allow you to connect to various data sources including warehouses, spreadsheets, and cloud applications.

As an analytics platform, Alteryx can conduct statistical, predictive, and spatial analysis of retrieved data. After your analysis, you can also utilize the platform’s extensive data exploration and profiling tools to validate your data and ensure consistency.

Depending on your specific business requirements, you can choose between cloud and on-premise installation.

4. Datameer

Datameer is a SaaS platform specially designed for utilization in the Snowflake environment. The platform facilitates seamless integrations with other cloud-based services, allowing seamless data collection, extraction, and integration.

The platform is pretty easy to use, with various data preparation tools, including SQL code and drag-and-drop interfaces that facilitate easy data ingestion and preparation. It also utilizes a graphical formula builder for data profiling and transformation.

Once you’re done manipulating data, you can export it to various business intelligence tools for further analysis through the platform’s various built-in integrations.

Microsoft Power BI

Microsoft Power BI is a component of the Microsoft Power Platform with impressive data preparation and visualization capabilities.

One of the platform’s key features is the self-service data preparation component, which facilitates seamless data ingestion, cleansing, transformation, and integration.

You can also enrich and schematize data, right on the platform with just a few clicks.

Final thoughts

The quality of your input data has a direct impact on the quality of your analysis results. This makes data preparation tools invaluable assets in your data preparation efforts as they streamline the process and significantly improve data quality.

You should, however, note that these tools aren’t created equal – some are just better than others. Some tools are also better suited to specific business needs than others.

Therefore, for maximum effectiveness, you should choose a tool that aligns with your business goals and the systems you already have in place.

References

[1] Towardsdatscience.com. Guide to File Formats for Machine Learning Columnar Training Inferencing and the Feature Store. URL: https://towardsdatascience.com/guide-to-file-formats-for-machine-learning-columnar-training-inferencing-and-the-feature-store-2e0c3d18d4f9?gi=4e3011da30b5. Accessed on July 16, 2024
[2] Techtarget.com. Top Data Preparation Challenges and How to Overcome Them. URL: https://www.techtarget.com/searchbusinessanalytics/feature/Top-data-preparation-challenges-and-how-to-overcome-them. Accessed on July 16, 2024
[3] Linkedin.com. Defending Your Data: Why Security Must be a Top Priority. URL: https://t.ly/NFJYJ. Accessed on July 16, 2024
[4] Statista.com. Data Breaches Recorded in the US. URL: https://www.statista.com/statistics/273550/data-breaches-recorded-in-the-united-states-by-number-of-breaches-and-records-exposed. Accessed on July 16, 2024