Big data has created unrivaled opportunities for businesses. It aids them in achieving faster and deeper insights that can strengthen the decision-making process and improve customer experience. It can also accelerate the speed of innovation to give companies a competitive advantage. Given that we live in a time of data and information overload, we really need to have better mechanisms to make sense of it all. Enter data visualization, which supports the transfer of information to knowledge by illustrating hidden issues and opportunities in big data sets.
A significant amount of the human brain is dedicated to visual processing. This results in our sight having a sharpness of perception far surpassing our other senses. Effective data visualization shifts the balance between perception and cognition. The visual cortex is used at a higher capacity, and the viewer understands the presented information much quicker to make better decisions based on the findings.
Businesses are increasingly turning to data visualization to organize the overwhelming amount and variety of data cascading into their operations and to eliminate the struggle of just storing the data, shifting instead to analyzing, interpreting, and presenting it in a meaningful way. The trend toward data visualization is worth delving into by any business seeking to derive more value from big data.
Tackling big data usually involves the four Vs: volume, velocity, variety, and veracity. However, we must emphasize a fifth V that requires attention, namely visualization. Even with the use of business intelligence (BI) tools and the exponential increase in computing power, the need to consume information in a meaningful manner exceeds the ability to process it.
Visualization plays a key role starting from the raw input of big data, where structures and underlying patterns that may be held within the data can be observed or formed, resulting in a visual representation that presents key insights in an efficient and digestible way.
Crafting data visualization is more than simply translating information in a visual display. It ought to communicate information effectively, with the prime purpose of presenting data in a quick, accurate, powerful, and long-lasting manner.
The main problem with big data involves complexity. Data is growing exponentially with time, as an increasing amount of it is made available on the internet. Furthermore, the number of insights, opportunities or hypotheses hidden in a dataset is exponential to the size of the datasets.
Key performance indicators (KPIs) can be used to achieve efficiency and ensure the comprehensibility of visual representations resulting from big data, attain the goal of graphical excellence, and add value to the result. Big data visualization requires skills and principles that must be carefully learned. Each big data visualization created should follow a clear path to success: attain, define, structure, extract, load, display, refine the data, and interact with it.
KPIs add value to the entire process by ensuring clarity in developing the strategy of the project, focus on what matters and requires attention, as well as improvement by monitoring the progress towards the desired state.
When developing a big data visualization project, the process should follow a cursive and pre-defined flow in accordance with the project needs and the requirements of the end-user. These recommended stages are:
Acquiring the Data
This is usually how the process starts, unrelated to the platform that provides the data. In the process of big data collection, there is also the issue of data selection. Instead of just throwing it all in, one should focus on selecting high-quality data that is relevant to the project’s objective and does not add noise to the end result.
The noisier the data is, the more difficult it will be to see the important trends. It is suggested to have a clear strategy for the data sources required, as well as for the subsets of data relevant to the questions the project wants to answer.
Structuring the Data
The next phase in the process is structuring the acquired data. This includes the process of organizing the data to align it to one standard. The data store might be comprised of a combination of structured, semi-structured, and unstructured data.
At this stage, it is easier to identify the common aspects in each set of data and to find relationships between the data at hand. This includes translating system-specific data coding to meaningful and usable data (the platform where the data will be aggregated does not know that the set of data labeled “Customer No.” is the same as “# Customer” or “ID-Customer”).
Loading and Visual Mode Selection
After cleaning the data, filtering through enormous amounts of data, and replicating the application logic to make the data self-describing, the process continues with loading the data in the preferred platform and choosing the visual mode of representation.
In this stage, we can determine if the background data is very noisy, as the emerging visual representation will be hard to read or irrelevant to the strategic objective of the project.
By implementing KPIs along the project and linking them to the project objectives, the increased value will be added in the form of:
Better quality of the visual representations
Fewer project delays
Less rework along the way
Improved productivity
Greater contribution to the visuals’ value
Enhanced growth and innovation of the visual representation
As 2025 rolls in, we want to give you, our readers, a little treat. We scoured our database and took a good long look at the articles we published in 2024 to come up with a list of what stood out the most and resonated with you each month of last year. Entry into the prestigious list was determined using three criteria: value (how informative and useful the article is to the strategy and performance management community), readability (how easy it is to understand the thesis and concepts presented within), and reach (how widely the article was viewed). Without further ado, here are the top 12 Performance Magazine web articles for 2024.
Beyond Remote Work: Insights and Strategies for Enhancing Employee Productivity and Performance
Promoting diverse work options is important because enabling employees to work in ways that align with their preferences is essential for maintaining and boosting productivity.
Key Safety Considerations for Generative AI Adoption in Business
With the advancement of generative artificial intelligence (GenAI), security risks are becoming a growing concern. What precautions should organizations take to protect their data before adopting GenAI?
Leveraging Effective Performance Management Systems for Real Estate Success
A robust performance management system (PMS) is key to cultivating a high-performance culture. How can real estate companies tailor their PMS to align with their goals and secure a competitive advantage?
Establishing a data team from scratch can seem daunting due to its complexity—considering there is no one-size-fits-all solution. What key areas should organizations focus on to truly build a strong data team?
Business Process Reengineering: The Path to Maximum Efficiency
Business process re-engineering is a strategic approach that reshapes and optimizes workflows to boost efficiency and agility in the evolving business landscape.
Choosing the right key performance indicators (KPIs), according to the State of Strategy Management Practice Global 2023 Report, ranks as the second most significant obstacle in strategy planning.
How Data-Ink Ratio Imposed Minimalism on Data Visualization
Data-ink ratio seeks to maximize the proportion of informative elements in a chart, with a ratio of 1 being the ultimate goal. It only includes data-representing elements, free of decorations or redundancies.
Rethinking Business Ecosystems Part 1: What Systemic Issues Are Undermining Your Holistic Growth?
Leaders must foster innovation within the business ecosystem due to the unpredictable global market and growing societal expectations—allowing them to identify gaps and develop relevant solutions.
The State of Sustainability Reporting: Key Insights for Businesses
The sustainability report, as Global Reporting Initiative (GRI) CEO Eelco van der Enden puts it, “is the end of a long journey of transactions and actions that define the company’s approach to sustainability.”
Do ESG Strategies and Performance Measurement Truly Matter to Sectoral Investors?
As environmental awareness grows, environmental, social, and governance (ESG) strategies are gaining more importance in the investment community. Thus, industry-specific KPIs become all the more crucial to implement effective sustainability measures.
How Strategy Management in MENA Is Shaping Up: Key Insights from TKI’s 2024 Report
Organizations in the Middle East and North Africa (MENA) region are adapting their strategic approaches to navigate current challenges and capitalize on emerging opportunities.
Charting a Course: A Step-by-Step Guide to Deliberate Strategic Planning
Deliberate strategic planning is a methodical approach where organizations set clear objectives and craft strategies to achieve them. It thrives in a stable environment, ensuring alignment between goals and actions.
Isn’t it fascinating how charts can dissolve divides and turn disconnect into discoveries? It’s a purposeful medium where even the fragments and imbalance in the world are always part of the story. By unifying or restructuring grand and small narratives in a more distilled expression, charts make complex truths accessible to a wider audience. Consequently, the audience become not just spectators but also collaborators, peeling meaning from their respective perspectives or contributing to a common understanding. However, producing charts with this level of effectiveness doesn’t just happen through the mere union of analysis and aesthetics.
It starts with choosing the right chart for your data. That choice should be driven by intuitive wisdom and structured control, and both are anchored on purpose. By defining what you need to bring out of the data, you eliminate the unessential and get your message across as quickly and as clearly as possible.
What’s Your Purpose?
Comparison is a common purpose for data visualization, and the bar chart is the tool to make this happen. Bar charts use one categorical variable and one numerical variable like income data across departments. For more nuanced comparisons, clustered bar charts can be used to introduce another categorical variable, like seniority levels within departments. Bar charts, as shown in Figure 1, have horizontal bars, while another type, the column chart, uses vertical bars.
Figure 1. A bar chart (left) and a column chart (right) | Image Source: Financial Edge
Another purpose for data visualization is to show composition, specifically the contribution of parts to a whole. This type of visualization generally involves one categorical variable and one numerical variable. Composition charts in a matched pair usually take the form of a pie chart or donut chart (see Figure 2). An example of what they can demonstrate is the percentage of customers who prefer one product over another.
Figure 1: A pie chart (left) and a donut chart (right) | Image Source: Medium
More complex data with multiple categorical variables will benefit more from using stackedbar charts, which are similar to bar charts butwith multiple categorical variables represented as additional parts stacked alongside each other (see Figure 3). Alternatively, complex data can be illustrated using a treemap, which involves dividing a rectangular area into smaller rectangles to represent hierarchical data such as income distribution across regions and cities (see Figure 4).
Figure 3. A stacked bar chart | Image Source: Medium
If your purpose is to show change over time in charts, it can be done with line charts or area charts (see Figure 5). The line chart works better when it comes to a numerical variable with a time series, such as monthly revenue. The continuity of the line allows the viewer’s eye to easily notice trends and changes over time. The area chart goes one step further by shading the area under the line, emphasizing both the change and its magnitude. For more compact visualizations within tables, sparklines can be used. These are small charts that may be placed in a sheet’s individual cells and can highlight trends in big data sets or point out maximum and minimum values (see Figure 6).
Figure 5. A line chart (left) and an area chart (right) | Image Source: Edraw
As for visualizing relationships between variables, go with a scatter plot or bubble chart for numerical data and a heat table for categorical data. A scatter plot displays data points to show the correlationof two numerical variables, while a bubble chart is similar to a scatter plot, in which the x- and y-axes consist of two numerical variables, but the bubbles (or circles) in it vary in size to encode a third numerical variable (see Figure 7).
Figure 7. A scatter plot (left) | Image Source: Medium | and a bubble chart (right) | Image Source: Medium
For categorical data, a heat table has one categorical variable placed in rows and another placed in columns. The cells of the table are then coded with numerical values, often by way of different intensities of color. This is a particularly useful way to identify patterns or relationships between categorical variables, such as countries and performance scores (see Figure 8).
When you’re working with geospatial data, you might find a choropleth map more suitable (see Figure 9). It plots a numeric variable like population density over a geospatial variable such as regions or countries. This type of map illuminates the perception and realization of the spatial pattern by shading particular regions with differing tones.
The right chart for your data isn’t always immediately obvious. By establishing your purpose first, you narrow down your choices. You avoid overcomplicating or underrepresenting information. Another layer of factors to consider is the type of data you have and its size. And beneath all of these is your audience. From their familiarity with charts to the complexity of your data, decision-making always involves the people you are creating a chart for in the first place.
To learn more about data visualization, check out more articles here.
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Editor’s Note: This article was written in collaboration with Islam Salahuddin, data consultant at Systaems.
Welcome to the first chart from The Uncharted: Chart of the Week!
This week, we look at how greenhouse gas (GHG) emissions from the heat and electricity sector—more than any other industry—are skyrocketing in China and India.
The electricity and heat sector, which consists of electricity plants and energy generators that use fossil fuels, is the main source of GHG emissions compared to other sectors, recording 15.2 billion tonnes of carbon dioxide-equivalents in 2020, up from just 8.7 billion tonnes in 1990.
By transitioning to renewable energy and improving energy production and consumption efficiency, 51 out of the examined 169 countries have managed to curb their GHG emissions over the past three decades, including the United States(US), Russia and the United Kingdom (UK).
However, the exact opposite happened in China and India. While China was the third most intensive emitter of GHG in 1990 with 0.7 billion tonnes, the emissions escalated to a staggering 5.7 billion tonnes—an almost seven-fold increase.
In India, the electricity and heat sector was the top eighth in GHG emissions in the world in 1990, with 0.2 billion tonnes. Emissions increased by almost four times as much in 2020 with 1.1 billion tonnes.
In both countries, the surge of GHG emissions is attributed to major economic developments, rapid industrialization, and substantial population growth, in addition to their dependency on coal and fossil fuels, despite their efforts to diversify their energy mix.
The figures show how maintaining ambitious economic development while curbing emissions for lasting sustainability can still practically be an unsolved challenge, even for some major developed countries.
Fortunately, curbing GHG emissions is possible, and it starts with your business by monitoring and managing its emissions using the relevant KPI. That is why The KPI Institute published a full documentation for the # Greenhouse gas emissions KPI, available for free here.
“If communication is more art than science, then it’s more sculpture than painting. While you’re adding to build your picture in painting, you’re chipping away at sculpting. And when you’re deciding on the insights to use, you’re chipping away everything you have to reveal the core key insights that will best achieve your purpose,” according to Craig Smith, McKinsey & Company’s client communication expert.
The same principle applies in the context of data visualization. Chipping away is important to not overdress data with complicated graphs, special effects, and excess colors. Data presentations with too many elements can confuse and overwhelm the audience.
Keep in mind that data must convey information. Allow data visualization elements to communicate and not to serve as a decoration. The simpler it is, the more accessible and understandable it is. “Less is more” as long as the visuals still convey the intended message.
Finding the parallel processes of exploratory and explanatory data visualization and the practice of sculpting could help improve how data visualization is done. How can chipping away truly add more clarity to data visualization?
Exploratory Visualization: Adding Lumps of Clay
Exploratory visualization is the phase where you are trying to understand the data yourself before deciding what interesting insights it might hold in its depths. You can hunt and polish these insights in the later stage before presenting them to your audience.
In this stage, you might end up creating maybe a hundred charts. You may create some of them to get a better sense of the statistical description of the data: means, medians, maximum and minimum values, and many more.
You can also recognize in exploratory if there are any interesting outliers and experience a few things to test relationships between different values. Out of the 100 hypotheses that you visually analyze to figure your way through the data in your hands, you may end up settling on two of them to work on and present to your audience.
In the parallel world of sculpting, artists do a similar thing. They start with an armature-like raw data in designing. Then, they continue to add up lumps of clay on it in exploratory visualizations.
Artists know for sure that a lot of this clay will end up out of the final sculpture. But they are aware that this accumulation of material is essential because it starts giving them a sense of ideal materialization. Also, adding enough material will ensure that they have plenty to work with when they begin shaping up their work.
In the exploratory stage, approaching data visualization as a form of sculpting may remind us to resist two common and fatal urges:
The urge to rush into the explanatory stage – Heading to the chipping away stage too early will lead to flawed results.
The urge to show all of what has been done in the exploratory stage to the audience, begrudging all the effort that we have put into it – When you feel that urge, remember that you don’t want to show your audience that big lump of clay; you want to show a beautified result.
Explanatory Visualization: Chipping Away the Unnecessary
Explanatory visualization is where you settle on the worth-reporting insights. You start polishing the visualizations to do what they are supposed to do, which is explaining or conveying the meaning at a glance.
The main goal of this stage is to ensure that there are no distractions in your visualization. Also, this stage makes sure that there are no unnecessary lumps of clay that hide the intended meaning or the envisioned shape.
In the explanatory stage, sculptors use various tools. But what they aim for is the same. They first begin furtherly shaping the basic form by taking away large amounts of material. It is to ensure they are on track. Then, they move to finer forming using more precise tools to carve in the shape features and others to add texture. The main question driving this stage for sculptors is, what uncovers the envisioned shape underneath?
In data visualization, you can try taking out each element in your visualization like titles, legends, labels, colors, and so on. Then, ask yourself the same question each time, does the visualization still convey its meaning?
If yes, keep that element out. If not, try to figure out what is missing and think of less distracting alternatives, if any. For example, do you have multiple categories that you need to name? Try using labels attached to data points instead of separate legends.
There are a lot of things that you can always take away to make your visualization less distracting and more oriented towards your goal. But to make the chipping away stage simpler, C there are five main things to consider according to Cole Nussbaumer Knaflic as cited in her well-known book, Storytelling with Data:
De-emphasize the chart title; to not drive more attention than it deserves
Remove chart border and gridlines
Send the x- and y-axis lines and labels to the background (Plus tip from me: Also consider completely taking them out)
Remove the variance in colors between the various data points
Label the data points directly
In the explanatory stage, approaching data visualization as a form of sculpting may remind us of how vital it is to keep chipping away the unnecessary parts to uncover what’s beneath, that what you intend to convey is not perfectly visible until you shape it up.
Overall, approaching data visualization as a form of sculpting may remind us of the true sole purpose of the practice and crystalize design in the best possible form.
Deepen your understanding of processing and designing data with our insightful articles on data visualization.
Do ESG Strategies and Performance Measurement Truly Matter to Sectoral Investors?
