4 Tips for Visualizing Big Data for Actionable Insights
Big Data Interviews
4 Tips for Visualizing Big Data for Actionable Insights
Are you ready to unlock the secrets of visualizing big data for actionable insights? In this article, industry-leading experts from the roles of CEO and Senior Data Scientist share their top tips. The discussion kicks off with advice on focusing on data simplification and wraps up with strategies to prepare a gold-layer dataset, among a total of four expert insights. These valuable perspectives will transform how big data is approached in your organization.
- Focus on Data Simplification
- Use Segmented Visuals
- Visualize Samples of Dataset
- Prepare Gold-Layer Dataset
Focus on Data Simplification
You need to focus on two things, and two things only: data simplification and story-driven design. Think about the decision-makers who will be looking at your data—a clear, impactful story helps them see patterns and trends without being overwhelmed by volume. Doing this isn't easy because you must first understand the core message or insight you want the visualization to convey, as well as understand your audience well enough to know what will resonate with them most closely. This will vary wildly. Think of a simple heatmap, for example. It's great for identifying high- and low-intensity areas in large datasets, such as customer purchase behaviors across regions or time periods, and this information will be very immediately useful to marketing and sales, marginally useful to finance, and practically useless to HR. In terms of visualization techniques that I prefer, a time-series heatmap for analyzing customer behavior trends over time is a fairly good one. It's not super complicated, as it is just mapping data points across months and years, but I find that it really helps people wrap their heads around patterns, seasonal trends, and anomalies in customer engagement.
Use Segmented Visuals
A pro tip from me would be that when you're trying to effectively visualize big data, you need to simplify complex information into segmented visuals, as this is what is going to allow your viewers faster interpretation and more actionable insights. Take, for example, a Sankey diagram. These are an excellent and effective way for tracking user flows on websites by clearly showing how traffic moves between pages and where users exit. This visualization quickly highlights drop-off points, making it easier to strategize for higher engagement. There are other options, certainly, but the crux of the matter is that choosing techniques that break down large datasets helps present insights in a straightforward, digestible manner, especially for non-technical stakeholders, which is what you always need to be keeping in mind.
Visualize Samples of Dataset
Big data poses at least two challenges for visualization. First, the volume of data can simply be prohibitively large—either by poor scaling of the visualization algorithm or simply by outnumbering the available pixels on which to display the visualization. Second, big data is often high-dimensional, which poses a challenge for visualizing the data in a two-dimensional medium (like a computer screen).
To overcome these challenges, one must visualize samples of the full dataset instead of attempting to visualize the entire dataset and, if the dataset is high-dimensional, use an embedding method to reduce the dimensionality of the data. Sampling is best done randomly, and this process lends itself nicely to experimentation: Make a hypothesis or build a model using one sample, then test the model or hypothesis on another sample. My favorite embedding technique is Multi-Dimensional Scaling (MDS). It is particularly useful because of its flexibility toward the distance metric. In high-dimensional spaces, measuring distance in a meaningful way is challenging, so it's nice to be able to quickly see the impact of your choice of metric.
One good example of using this technique is illustrated here (https://jtjohnston.github.io/art-of-science/#neutron-squid-2019). The goal was to build a model to determine the crystal structure of barium titanate from a diffraction pattern—essentially, to classify a high-dimensional vector. I randomly sampled the data and applied MDS to the samples with varying distance metrics. From that resulting visualization, we learned that the particular distance metric we used would result in distance-based classifiers (like k-nearest neighbors) that work very well for three of five classes but would likely confuse the remaining two classes. The visualization not only helped us assess the efficacy of any given distance metric, but it also provided us insight into where our models would likely fail, beyond what a single measurement of accuracy might suggest.
Prepare Gold-Layer Dataset
My top tip for effectively visualizing big data is to first ensure you have a well-prepared "gold-layer" dataset that's optimized for consumption by your BI tool. People often ignore data engineering tasks, but it's the most important thing for effective visualizations. To make sure your gold data is ready, focus on thorough data cleaning to eliminate errors and inconsistencies, and perform data transformation to unify formats and data models/structures. Implementing data validation processes and indexing can also enhance data quality and retrieval speed. Choosing the right BI tool is crucial, and selecting one that supports big-data capabilities like Apache Spark can greatly improve processing efficiency. For example, integrating Databricks with Power BI has worked well for me, as it combines scalable data processing with robust visualization features. This setup allows for the creation of interactive dashboards that provide actionable insights without compromising performance.