Tag Archive: data

1. What Is Data Visualization? 

   November 13, 2025 1:45 am Leave a Comment

   Understanding Data Through Visual Representation 

   

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   Introduction 

   In a world overflowing with information, making sense of large and complex datasets can be challenging. Data visualization is the key to unlocking insights and understanding from raw data. But what exactly is data visualization, and why does it matter? 

   Defining Data Visualization 

   Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, maps, and infographics, data visualization tools help people see and understand patterns, trends, and outliers in data. 

   Instead of sifting through endless rows of numbers or text, data visualization allows us to interpret data quickly and efficiently, making complex information accessible and actionable. 

   Why Is Data Visualization Important? 

   • Clarity: Visual representations clarify complex data, making it easier to comprehend. 

   • Efficiency: Humans process visual information faster than text or numbers, so visuals speed up data analysis. 

   • Decision-Making: Well-designed visualizations reveal trends and correlations, supporting informed decisions. 

   • Communication: Data visualizations aid in presenting findings to others, whether in business, science, or education. 

   Examples of Data Visualization 

   Common types of data visualization include: 

   Bar Charts: Compare quantities across categories. 

   Bar charts are especially useful in data visualization because they present categorical data in a straightforward, easily interpretable format. The length of each bar makes it simple to compare values side by side, highlighting differences and similarities among categories at a glance. Additionally, bar charts can reveal patterns such as the highest or lowest values, making them ideal for identifying trends or outliers within a dataset.

    

   Line Graphs: Show changes over time. 

   Line graphs are particularly valuable in data visualization because they effectively illustrate trends and changes over time. By connecting individual data points with lines, these graphs make it easy to observe upward or downward movements, spot cycles, and identify patterns such as peaks, valleys, and periods of stability. This makes line graphs ideal for tracking metrics like sales, temperature, or stock prices, helping users quickly assess historical performance and forecast future outcomes.

    

   Pie Charts: Display proportions of a whole. 

   Pie charts are useful in data visualization because they offer a clear and immediate visual representation of how different parts contribute to a whole. By displaying each category as a slice, pie charts make it easy to compare proportions and see which segments are dominant or minor within a dataset. They are especially effective when illustrating percentage breakdowns, helping audiences quickly grasp relative sizes and distributions without needing to interpret complex numbers.

    

   Heat Maps: Represent data values using color intensity. 

   Heat maps are useful in data visualization because they allow viewers to quickly identify patterns, trends, and anomalies in large datasets by leveraging color gradients. The intensity of color makes it easy to spot areas of high or low concentration, which is especially helpful when analyzing complex information like website activity, geographic data, or correlations between variables. This intuitive visual approach enables users to grasp distributions and relationships at a glance, making decision-making and deeper analysis more efficient.

    

   Scatter Plots: Show relationships between two variables. 

   Scatter plots are valuable in data visualization because they reveal the relationship between two variables, making it possible to detect correlations, clusters, or outliers within a dataset. By plotting individual data points on a two-dimensional graph, scatter plots help users see how changes in one variable may be associated with changes in another, which is essential for identifying patterns, trends, or potential causation. This visual approach is particularly useful for exploratory data analysis, statistical modeling, and understanding complex interactions between variables.

    

   Each type brings a unique perspective, allowing users to explore and interpret data in different ways. 

   Applications of Data Visualization 

   Data visualization is everywhere—from financial dashboards and scientific research to marketing reports and news media. Businesses use it to monitor performance, researchers to spot discoveries, and governments to inform the public. 

   Conclusion 

   Data visualization transforms raw data into meaningful insights by making information easy to see and understand. Whether for analyzing trends, spotting anomalies, or communicating complex concepts, data visualization is an indispensable tool in today’s data-driven world. 

    

2. Thought Starting Questions for 2022

   February 2, 2022 11:00 am Leave a Comment

   It’s that time of year again. Businesses are going about making confident predictions on what the next twelve months will hold for them. And yet, even at the best of times the accuracy of these predictions will be questionable by December. Given all that, perhaps the point of these predictions is less about being right, and more about creating engagement for the marketing folks?

   But as we all know, we are not living in normal times right now. No one can really see into the future, and it is not possible to be completely sure what the next year holds. Just as well, your time is valuable, so why waste it reading another set of bold predictions for 2022 that have questionable value just to boost someone’s marketing metrics?

   To this end, we humbly offer you a brief series of thought-starting questions that might actually be helpful and worth pondering if you are considering the year ahead. This will be the first of many blog posts to follow in the coming weeks, all of which will explore these questions a little more thoroughly.

   2022: What can we be confident about?

   There are three things we can be relatively sure of:

   • The Covid 19 pandemic, and its effects on business, are not over yet.
   • Specifically, supply chain issues and labor shortages are going to continue to be concerns for most of us for at least the next twelve months.
   • Higher inflation is definitely going to be an economic factor for the next twelve months, and possibly longer.

   How do these factors influence what I should be thinking about for this year?

   If you agree with the three driving points identified above, four key questions for 2022 stand out. These questions are linear and feed into each other. They are:

   #1 Does my organization have the right level of Data Literacy, and is it in the right areas and functions?

   There are some real-life “nuts and bolts” things that most businesses need to be thinking about and working on in this area.

   #2 Do we need to be considering Price Increases?

   And if they are needed, how do we actually implement them effectively, as opposed to just talking about them but not doing anything, or trying to implement them and just ending up upsetting our customers and / or not achieving any financial benefit.

   #3 Have we adequately reviewed business cost structures and all major business expenses in light of how business has changed?

   And what’s more, will it ever go back to how it was pre-pandemic?

   #4 Should we revisit (or visit for the first time) business investment cases for enhanced and increased automation?

   Historically, whether or not to invest in automation has been a decision based purely on ROI and ease of implementation. If wage inflation and labor shortages continue, increased automation may cease to be a choice and become a necessity to ensure operational viability, continuity, and long-term business sustainability. In other words, can you afford to not automate more?

   Exploring Question One

   This week, let’s dive a little deeper in thinking about the question, “Does my organization have the right level of Data Literacy, and is it in the right areas and functions?”

   What we already know:

   Once again, let’s take what we know about 2022, and then apply it to the question:

   • It’s generally accepted that inflation in the United States is currently running around 7%, and no one knows whether this is a short-term aberration, or if it will be a longer-term reality.
   • Based on current lifespans, average retirement age, and the law of averages, at least 80% of the current working population have no experience living or managing a business in a prolonged (or even relatively short) period of such high levels of inflation.
   • Likewise, most people working today are used to Just-in-time (JIT), Kaizen, and other lean manufacturing and supply chain practices (implemented across the eighties, nineties, and aughts, creating significant cost savings in a time when economies struggled) and either have not experienced or barely remember a time when things worked differently.

   Great, but so what?

   Well, the challenges of 2022 mean that having people who can work with current IT systems, pull data, and then report on and take standardized or rote actions just isn’t enough anymore.

   What businesses now need are people who can interpret the stories behind the data. People that can develop insights and actions that consider imperfect supply chains and labor availability are in high demand. These are people that can see the many complex dependencies and interdependencies, both internally and externally, that are either hidden or are not things that businesses previously had to worry about (pre-pandemic).

   Recognition of a deficit in required data literacy in many organizations is the reason why it is so hard to hire demand planners, business analysts, and supply chain specialists right now. This deficit isn’t just a supply chain and manufacturing issue. It spans across all functions, including administrative functions like HR (such as labor planning, scheduling, etc.) and finance (such as maintaining margins during times of extreme changes in expenses/revenue).

   The question, then, is as follows: Do you have the right people with the right skills to interpret and manage through a time of great variability and ambiguity? We’re in business territory that most people have never experienced, and businesses need skills and experience that perhaps they once had, but that have also likely atrophied after an extended period of disuse.

   Conclusion

   Your answers to this question will probably lead nicely to the next post on price increases. A subset of data literacy is the ability to pull together all the information needed to identify whether or not price increases are needed, how much they should be increased by, and the data that is needed to justify increases to customers. Even if you have this capability, when is the last time you asked for a price increase, and how well did it go?

   Here at Core Catalysts, we’ve helped multiple clients analyze their data literacy. In doing so, we’ve also helped clients identify issues and opportunities with meaningful impact to their top and bottom lines, and then helped them fill important gaps in organizational capabilities. This allowed them to take action to capitalize on available opportunities, spanning everything from IT system evaluation and implementation through identifying and hiring new employees. If you believe we could help your organization with this, why not reach out to us and schedule a call?

   In the meantime, we hope reading this article and thinking about these questions has been worth your time. We welcome comments and additional thoughts, and please reach out if you’d like to talk more about your current organizational data literacy and tackling some of these challenges!

   –Core Catalysts Team