Data Prep for Data Visualization
The Double-Edged Sword of Data Visualization Projects:
As the world digitally transforms and becomes more data-driven, the role of data is changing and becoming increasingly important for companies looking to understand their consumers, spot opportunities, and improve decision-making. In order to maximize the potential of huge and complicated data environments, data visualization tools make it possible to discover and share the findings of the data clearly. Recent technological developments have greatly streamlined the process of converting raw data into interesting and insightful visualizations. Modern technologies and user-friendly platforms have emulated data visualization, making it appear as simple as pressing a few buttons to produce the visuals. Behind the veil of simplicity comes a significant and often overlooked step Data Preparation. As a data engineer, it is my job to prep the data, and although I’ve noticed how modern technologies can speed up the data visualization process, they cannot take the place of the meticulous, intentional work that goes into creating a successful visualization. It’s crucial to strike a balance between the advantages of these new technologies and a firm grasp of data visualization foundations.
Data Preparation: The Foundation of Effective Data Visualization:
Data preparation is a complex process of collecting raw data and then ensuring that the data is clean, accurately formatted, and suitably arranged for visualization or reporting. The role of prep work has a lot that goes into it. Often, it is just cleaning the raw data, but other times it’s figuring out how to build a model of data that can empower feature-rich dashboards to help enterprises enhance their analytics and decision-making capabilities. Improving the accuracy and dependability of your data visualizations is the ultimate goal of data prep. Essentially, I’m not the person that makes the final dashboard, but I am a critical reason that that dashboard is meaningful and insightful.
Data Preparation: Steps Involved:
The process of prepping data includes cleaning, organizing, validating, analyzing, and assessing the data. The process of data prep improves the effectiveness of data visualization.
Here’s a step-by-step of the data prep process:
- Collecting the data: relevant data is gathered from various sources like data warehouses/lakes, operational systems, marketing platforms, etc. The data comes in all different formats and compositions. It is important to understand what data is relevant to what you are trying to achieve and whether it is accurate. This helps to ensure the other steps run smoother.
- Profiling the data: after you conclude what data is needed, you need to know what needs to be done to prepare the collected data after exploring. This includes studying the data to recognize the structure and content and ultimately providing a deeper insight into the data collected. This stage also is a precursor to the next stage and is a necessary step.
- Cleaning the data: This stage entails removing mistakes and discrepancies in order to refine its accuracy and quality. This could entail replacing null or missing numbers, fixing errors and typos, getting rid of duplicates, or addressing outliers. The strategy for cleaning data is determined by the kind of data and the particular needs of the study.
- Transforming and integrating the data: Once the data has been cleaned, it is necessary to convert it into a uniform format that can be used for analysis. This might involve procedures like encoding category data, standardizing numerical data, or developing new variables based on preexisting ones. To offer a comprehensive perspective of the information, data from other sources must also be merged.
- Structuring the data: depending on the type of visualization required, data is modeled in a certain way to reflect the end visually. Data may be organized or restructured in a variety of ways, depending on the ultimate result. This could entail constructing tables, establishing schemas, or modifying the data to accommodate certain analytical needs. For data visualization, there are multiple ways of structuring the data including long structuring, hierarchical, categorization, aggregation, and so on.
Data Prep Delivers Accurate and Reliable Data Visualizations
Data prep is a measure taken to fix the missing gaps and inaccuracies that occur within the data. Data prep ensures that the information is accurate and reliable and that the data quality will be higher after the data is processed. When the quality of the data is high, the analysis can run smoothly, and organizations can trust the output. Data prep not only checks for errors but can create a consistent format and structure to help easily integrate into data visualization tools. Organizations that are more sophisticated with data prep and understand the value start creating a “semantic layer” to transform the data without ETL tools. It’s important that know and understand how the semantic layer works.
Semantic Layer 101
A semantic layer is essentially an abstraction layer between the raw data stored in data warehouses/lakes and users who wish to study the data. Semantic layers are good for presenting complex data in a simple and clear way based on the “Semantics” known and used by the organization. By translating data into this layer, it allows for anyone to access and understand it, create a consistent format, and allow for efficiency in creating reports and analysis. A semantic layer can reduce the amount of data prep that is needed and reduces time.
Key Takeaways + Workshop
I won’t lie, data preparation can be a challenging and time-consuming process. However, the benefits of it are worth it. These benefits include reliability, finding and identifying key issues that might not have been detected, reduction of long-term analytics costs, and more. I realize that the time and effort I put into data preparation always pays off in the end, producing visuals that are more powerful and accurate. If you’re new to the field of data analysis and visualization, my recommendation is to take your time with the data preparation process.
Should you require expert consultation on data preparation or seek methods to incorporate it into your organizational operations, we are pleased to offer our ‘Data Preparation for Data Visualization’ workshop. During this comprehensive session, we extensively discuss our Data Preparation Strategy, designed to empower your data visualization.
Talk to our Data Engineering team about Data Prep here.
Remember- a successful data visualization project is built on the foundation of well-prepared data.
