We come across many different types of data visualization techniques during big data analytics courses. Today, pie charts, histograms, line charts, and other conventional statistical charts are still used, but not at the level in which they were known for back in the early 2000s and 2010s. The whole new worlds of conversion optimization tools powered by Scroll maps or Heat maps are now on a rise.
Heat Map data analysis, also known as HMD Analysis is a very popular technique used in the data visualization industry that helps gather relevant insights on the users’ activity on a screen. In the recent months, HMP analysis has garnered wide scale acceptance among business analysts trying to improve their interaction with customers who are spending significant time on the web pages looking for more information or interacting with relevant content such as blog posts, videos, product reviews, comments and so on. Most UI UX developers working with top grade content management systems supporting businesses for high density readerships such as e-commerce platforms owned by Walmart, Amazon, and others, or online VOD platforms such as Amazon Prime Videos, Netflix, Roku, or Disney Plus use this data analysis tool to improve overall site performance by ensuring better optimization of online content, reduced bounce rates, and superior experience during critical stages of product selection, filters, checkout, and payments. But that’s not the only way heat maps are getting used in the data analysis world. There are countless examples of how HMPs are used across the board to make better decisions pertaining to critical and non-critical operations, some of which have proved to be a lifesaver for many users.
In this article, we have provided a reader friendly overview of heat maps, their role in data analysis, and some remarkably advanced applications from the industry that could help you during the big data analytics courses.
What is a Heat Maps Analysis and how does Heat Mapping Work in Conventional Data Visualization Operations?
Heat Maps have been around for quite some time now, especially when it comes to representing data in a two dimensional format using various color schemes to showcase the spread of density of sample collection over the population. With the advent of computer generated graphics and interfaces used for data visualization, new ways of representing heat maps have emerged. This heat map analysis based on CGI shows the spread of data by virtue of color density or hue patterns providing a detailed visual cue on what data analysts could focus on, in certain categories of data analysis.
Types of Heat Maps
Different types of heat maps are used in the data visualization space. Most of these use clicks and scrolls, but for the physical display of phenomena such as pandemic, fire, and citizenship, a movement heat map and geospatial heat maps are also used.
So, common types of heat maps are:
- Mobile heat maps (moving data)
- Geographical heat maps (contours, weather data, country codes, etc)
- Attention heat map
- Contour lines
- Surface plotters
- Waterfall / cascading heat maps
- Pixel tracking heat maps – Scroll, drag, and click heat maps
There are different ways to work with HMD analysis, and a generic view of working with them is the use of color arrays or a mosaic plot organized in rectangular plots. Color schemes vary between gray scales, false colors, bathymetric maps, and choropleth maps. Some analysts argue that it’s best to distinguish between chloropleths and heat maps, but for big data analysts, we can use both satisfactorily, and in some cases interchangeably.
The Reason behind Sudden Demand for Heat Maps Data Analysis Tools
The biggest reason driving the HMP analysis adoption across the online industry is its ease of representation of data. Anyone can easily identify with the analysis and draw out near accurate inferences about the case studies that are represented by a heat map.
It is very easy to pick insights just by looking through a few heat maps for examples. As some data analysts point out, HMP analysis doesn’t require any kind of statistical background to work with these inferences, and in most cases, the results can be drawn out by merely looking at the charts and compare with the previous versions of data analysis drawn using charts, pies, and lines. Now, the second reason for HMP adoption is its closeness to healthcare trends that emerged during the COVID-19 pandemic.
If you have been following the COVID-19 pandemic’s spread, you can easily correlate what we are trying to explain here to illustrate the usefulness of Heat maps, along with the Dynamic Line Charts, explaining the spread of the pandemic country wise, and how each region has responded to the containment and lockdown norms over a period of time, such as days, weeks, months and quarters. These BI tools have proved useful in ensuring the safety and protection of many businesses, including healthcare services and logistics providers who rank among the highest employers of talented analysts with top grade business analytics certification.
There are many types of robust BI platforms that provide all kinds of different heat maps. Every BI analyst has a choice for these considering the customization and pre-templates available with the software.
Modern Trends of Heat Map analysis
The arrival of AI and automation with No Code and Low Code applications have allowed business analysts to come up with all kinds of heat map generation techniques for their varied purposes. Trends are heavily inclined toward the use of sequential color palettes (to represent coordination and the coherent relationship between data points) and divergent color palettes. In some cases, heat map analysts completely rely on using conventional tools such as contours, histograms, and lines to set up more detailed and clustered heat map analyses of big data samples.
Who Should Use Heat maps?
Other than data analysts, the heat maps are widely accepted as a tool for data inferences in healthcare, sports analytics, TV audience data management, city planning, census data interpolation, website programmatic advertising strategies, and landing page designing.
Some of the modern heat maps allow users to:
- Slice data in more than 2 different ways and filter out dynamic data for a more visually attractive and accurate data visualization
- Use AI to automatically develop and supervise advanced heat maps for superior decision making in real time scenarios, such as customer care interactions during peak hours of shopping, the density of localized warehousing for a particular product, and its delivery time within a radius of 10-20 kilometers
- Achieve social goals on platforms related to e-commerce shopping, social media intelligence, mobile bookings, and so on.
Why do data analysts love to work with HMD Analysis?
BI teams are demanding that their business analysis plotters have a good quality data analyst tool to measure the efficacy of heat maps generation. HMD analyses allow BI teams to quantify what aspect of data needs to be focused on, and what to be discarded. It also accurately narrates the plot wherein AI and machine learning algorithms can be deployed to gain a balance in data analysis while working with quantitative versus qualitative versus predictive data. From providing accurate analysis to delivering business recommendations, HMD analysts emphasize data investigations and forensics and don’t leave anything to assumptions or anomalies.