Overcoming Data Preparation Challenges with Low-Code Tools


Low-code tools can automate tasks that have historically consumed a significant portion of data teams’ days liberating them to focus more on analysis and less on preparation.

Data teams face a critical challenge: the imbalance between the extensive amount of time spent on data preparation and the relatively limited amount of time available for the analysis and model training.

Data scientists spend the majority of their work days on data-curation activities like data preparation and cleansing. The problem is that the resource-heavy framework for data preparation impedes organizations’ ability to focus on the model training and data investigation that drives growth and innovation.

Think of the problem as an iceberg. Tasks like data standardization, integration and cleaning are a massive undertaking, but they’re all necessary for achieving data insights that provide real value to the organization. Too often, data analysis sits as a tiny suspended ice cap compared to the broad base of beneath-the-surface data engineering support it requires.

However, with the adoption of low-code platforms, organizations can reduce the time spent on data preparation tasks, freeing up their data teams to prioritize data analysis and the application of critical insights.

See also: Accelerating Digital Transformation: Low Code + Composable

Data preparation is a necessary but time-consuming task

Recognizing the need for high-quality data is the first step in the journey toward meaningful analytics. In the era of advanced AI, algorithms require quality data to generate meaningful insights. If the data isn’t clean, it can lead to inaccuracies and mistakes.

The challenge for data teams doesn’t stem from a lack of data. Instead, the problem lies in the labor-intensive process required to refine and render the vast quantities of available data into a state that is both clean and useful. 

Data preparation demands significant time and resources because it includes tasks like standardizing disparate data formats, identifying relevant data across myriad sources, and cleaning “dirty data” to distinguish between errors and outliers.

While often tedious and technical, these tasks are necessary for preparing data that’s clean, well-aggregated and informative — and capable of unlocking actionable insights that drive strategic decision-making.

This need for clean data is amplified by the data science talent shortage, which underscores the importance of adopting solutions like low-code tools. With low-code platforms, organizations can help fill the talent gap by enabling a greater percentage of their workforce to engage in data preparation. Data teams can then redirect their focus to extracting insights and generating value from the data.

3 ways low-code tools can create efficiencies and drive innovation

Low-code tools remove coding barriers, democratize data access, and streamline the data preparation process. By enabling your data team to focus more on analysis and less on preparation, your organization can more effectively apply key takeaways and drive meaningful business progress.

This strategic shift accelerates the data-to-insight cycle and enables data professionals to contribute more significantly to your organization’s strategic objectives.

With a low-code tool, you can:

1) Eliminate the coding barrier

When it comes to data preparation, math, and programming are barriers to participation in data from teams outside of IT/data teams. Low-code tools remove the coding barrier so any user who knows the domain and data can actively engage in the data preparation and analysis process. For skilled programmers, low-code platforms offer the advantage of saving time and, therefore, the chance to concentrate on the analysis and on the steps in the process.

While these tools eliminate certain barriers, they don’t eliminate the need for domain expertise and knowledge of data transformation operations. Employees who lack coding skills will still need a basic understanding of math to work with low-code tools. However, these tools make data more accessible and empower a wider range of professionals to leverage their industry-specific expertise. In turn, this accelerates the data preparation phase and drives innovation.

2) Improve automation and reduce errors

Low-code tools transform manual, repetitive tasks into automated processes, enhancing operational reliability and consistency. This shift optimizes workflow efficiency, reduces errors, and sets the stage for more advanced data handling techniques.

For example, low-code tools streamline the process of connecting to various data sources (e.g., files, databases with unique authentication procedures, cloud repositories, and even handwritten notes) by offering standardized and simplified connectors that mask complex details. They also facilitate fundamental data preparation tasks such as aggregations, filtering, joins, and concatenations through an intuitive drag-and-drop interface, eliminating the need for manual coding of indexes and functions.

Traditional methods like Excel spreadsheets need to be manually adjusted for varying data sets — leading to errors when data exceeds predefined parameters, like the number of days in a month. But low-code platforms automatically adapt to changes, processing all input data uniformly and accurately without the need for constant manual oversight.

3) Streamline recruiting processes

It’s not easy finding candidates with both advanced coding skills and deep domain knowledge. When organizations set this high bar, they limit the pool of suitable candidates and prolong the recruitment process, delaying critical data-driven projects. Low-code platforms make it easier to identify and onboard employees who can navigate the intricacies of data without the need to be proficient in more complex programming languages like Python.

By enabling individuals who are familiar with the data and its key performance indicators (KPIs) to perform preparation and analysis tasks, low-code tools can solve the recruiting problem. As a result, you can deploy resources more effectively and focus on leveraging domain-specific knowledge to drive insights and innovation without being constrained by the technical barrier of coding expertise.

Enable any user to intuitively work with data

Data preparation is often cumbersome and resource-intensive. But low-code platforms enhance the efficiency of the data preparation process and democratize data handling, so users across all skill levels can intuitively interact with data. Reducing the time and effort it takes to prepare data for analysis optimizes the time for workflow creation and expands your talent pool by lowering the barrier to entry for engaging with data.

Automating tasks that have historically consumed a significant portion of data teams’ days liberates them to focus more on analysis and less on preparation. This efficiency fosters innovation and enables teams to use the time they save to develop new ideas and propel their organization forward.

Dr. Rosaria Silipo

About Dr. Rosaria Silipo

Dr. Rosaria Silipo has been working in data analytics since 1992. Currently, she is Head of Data Science Evangelism at KNIME. In the past, she has held senior positions with Siemens, Viseca AG, and Nuance Communications and worked as a consultant on a number of data science projects. She holds a Ph.D. in Bioengineering from the Politecnico di Milano and a Masters in Electrical Engineering from the University of Florence; and is the author of more than 50 scientific publications, many scientific whitepapers, and a number of books for data science practitioners.

Leave a Reply

Your email address will not be published. Required fields are marked *