Visual communications can alleviate problems related to your complex data deluge, extract key points from your data, and help you create a visual narrative. The sequence of events, influencing factors, and unknown truths are examples of individual data stories that can become clearer with visual communications. Visualizations tell stories with charts to address a variety of needs starting with your own needs to evaluate data, communicate to peers, convince the board, present to clients, or report regulatory compliance. This chapter evaluates the current data state and presents a paved path toward a future with hardware and software advancements that you can use to create your own visual narrative.
Over the years we (a design research team) have partnered to create improved data displays that solve information challenges for our clients. In 2010, for instance, we worked on a risk platform—a proprietary technology software solution—for one of the world’s largest bank holding companies. The platform, focused on structured products, was geared to provide research analysts with standard risk scenarios for fixed income securities.
The most informative feedback we received about the platform came from an analyst named Dave (not his real name), whose job it was to sort through the data to identify the major themes and highlights and publish weekly market perspectives for the firm. As part of his work, Dave reviewed information on market data rates, detail holdings, historical prepays, current prepayment models, rates of return, and color commentary across a set of securities. In addition, he read 12 daily news feeds, including them in his reports as needed. His reports were used all across the organization to make key decisions about fixed-income securities. In other words, his work was company-critical information.
As such, Dave needed to be in a position to access and analyze enormous amounts of data and distill it into a few key points to tell a clear story. In our conversation with Dave, he said that to maintain his position as a thought leader, he needed to remain “in constant discovery mode….” The value he provided lay in his ability to separate the signals from the noise and offer relevant insights. To deliver on that, Dave needed to improve his data displays. With that in mind he revealed that his main complaint was needing even more data display space despite using four separate computer screens, and he often found himself scrolling across vast spreadsheets to access that data. Here is what Dave told us he needed:
Having worked as a research analyst for 15 years, Dave knew what he needed from his data displays, and it had everything to do with improving his digital experience. During our interview he even held up his smartphone, pointing to its tiny screen and then back to the four large monitors. According to Dave, despite their limited screen size, the apps were more efficient than the monitors.
Dave is far from alone in wanting and needing a paved path toward better visual communication. The proliferation of apps on phones and tablets has created a new generation of users with higher expectations for all their digital experiences. Mobile apps created for play are pushing people to expect more from the applications in their work environment. Today’s market demands immediacy, simplicity, and aesthetic appeal from mobile and desktop applications.
Tens of thousands of people just like Dave are out there in the world. Research analysts and others in finance and many related fields study complex data sets. They deliver weekly reports that are largely qualitative but with quantitative supporting details. An ever-expanding universe of data, plus regulatory requirements and greater global reach all contribute to the world’s data complexity.
Bloomberg’s market data, for example, is a huge and complex data source. The popular Bloomberg terminals provide data-driven insight into 52,000 companies, with more than 1,000,000 individuals consuming the 5,000 news stories published every day. Each terminal employs more than 30,000 command functions to navigate through the information. Analysts like Dave spend a significant portion of their time in Bloomberg.
As shown in Figure 1-1, other data sources, among the thousands that exist, include news and economic commentary as well as transactional, fund, portfolio, custodian, and accounting data to be presented within the context of investor client data. And the list goes on and on. Data sources continue to increase in type and size.
The number of additional elements that compound the complexity of data is astounding: risk and compliance rules set by firms, corporate actions set by the market, and investor mandates set as client guidelines for engagement, to name just a few.
Figure 1-1: Various Data Sources
Regulatory pressures in the industry are another necessary but complicating factor. They impact the type, format, frequency, and volume of reports issued. For example, firms must configure reports for their clients that meet regulatory requirements and disclosures set by the Dodd-Frank Act. Varying additional jurisdictional boundaries create added requirements to comply with regulations by state bureaus and local foreign regulators. Outside of the United States, MiFID II regulates how trillions of euros worth of stocks, bonds, derivatives, and commodities are traded, settled, and reported. Influences of globalization layer still more factors of complexity into available financial data.
Likewise, clients from various regions of the world, each with their own language, currency, and culture, have differing expectations of how data should be presented (see Figure 1-2). They may expect data to be grouped, subgrouped, filtered, tallied, and organized into grids, pivot tables, or charts.
Figure 1-2: Globalization and Rule Demands
On the one hand, these factors increase the amount and complexity of the data. On the other hand, advancements in technology enable us to handle these increasing amounts of data more readily and present them in numerous different ways (see Figure 1-3). Improved processing power combined with cheaper data storage, faster transfer, and mobility are what make more data readily available. The upside is that this increased availability enables us to dig deeper and learn more. Technological advancements for gathering, storing, and sharing larger data sets increase our capabilities with interactive data visualizations. However, we must be careful to create visual communications that are accurate and insightful.
Figure 1-3: Hardware Capabilities
The world’s ability to store digital information has roughly doubled every 40 months since the 1980s (see the following note). Major improvements in graphics cards and high-resolution displays have enabled the software side of the industry to create more sophisticated visuals without overburdening the computer systems, such as Business Intelligence (BI) and data visualization software (see Figure 1-4) now considered standard tools. In addition, we have powerful programming languages, open source charting libraries, technical computing packages, online visualization tools, and visualization research labs.
Figure 1-4: Software Capabilities
We are at a point in which hardware and software can be used to present data, but we need to consider how best to represent it. Today, we can spend less time gathering and aggregating data and more time visually organizing data accurately. Although technology has enabled us to do more with our charting capabilities, demands in the marketplace push us to achieve higher standards. Because of technology, we can now move far beyond the traditional bar, line, and pie chart to create more sophisticated versions or introduce completely new visual concepts.
As the standard for a sophisticated and accurate digital experience increases, the bar for communicating financial data to more diverse audiences is set ever higher. Firms often ask us, “What can we do to improve our investment communications?” Soon thereafter, individuals at those firms often come back to us and ask, “How do I best present this data to my peers at a meeting I have next week? And by the way, the following week, I need to present this data to the review board and audit committee.”
The presentation of data needs to encourage relevant conversations across audiences. Each audience group will require a slightly different set of questions and therefore needs a different perspective of the data (see Figure 1-5).
Figure 1-5: Solutions for Multiple Audiences
A relationship manager, for example, may want to know which of the portfolios she covers are at risk for redemption, whereas senior management would like to know the firm-wide view of accounts at risk, trends, and coverage for those accounts. From an individual presentation to a firm-wide risk perspective, the narrative needs to adjust and tilt to meet the needs of the audiences.
Dave struggled with the amount of data on his screens as well as the presentation of the data in his reports. As a result, a well-designed visual narrative was missing from the key points Dave presented, and he found it difficult to neatly connect each influencing factor back to the supporting data in his reports. His story, and others like it, has informed our work. Since then, there continues to be an increase in data, globalization, rules/regulations, and hardware and software capabilities. These increases influence the need and ability to create visual explanations of the data. As a result, we have focused our efforts on providing a much broader range of visualization solutions. Our analysis of current information needs across different audiences has effectively paved a path toward visual communications.
Visually interacting with data can provide multiple perspectives and serve multiple audiences. We have moved away from a visual narrative that provides a single perspective and shifted toward those that provide many viewpoints from the lowest level of details to the highest level of aggregation. We have more choices in how we display our data, and it is our job to present the clearest chart and most informative graph. We need to optimize how we visualize data to maximize our comprehension. In the following chapters we present a number of effective and innovative ways to chart complex data, leveraging technology, and addressing the needs of a variety of audiences.
Communicating with data visualizations is not just about being more effective by replacing text and numbers. A thoughtfully crafted visualization increases our understanding of the data. It reveals patterns, quantities, changes over time, or recurring themes at a glance. It makes data so much easier to comprehend that it can elicit “aha” moments, instant epiphanies, from your audience. Fellow theoretical physicist John Wheeler attributed to Albert Einstein the statement, “If I can’t picture it, I can’t understand it.” Einstein’s quote advocates for the use of visual aids to create understanding. He understood the power of a visual and used drawings and charts throughout his notebooks to explain his observations and formulas.
What types of challenges can data visualizations of charts and graphs address? What are the inherent qualities of a chart and how can we leverage them? This chapter covers these questions as we address the overall benefits of data visualizations.
You can rely on data visualizations to see outliers, trends, correlations, and patterns. First, consider the case of outliers in the data. An exception report identifies instances in which some threshold was breached. Let’s say these data points or outliers are the focus of interest. Maybe they show errors in a system to highlight specific work to be corrected. Maybe you want to assign levels of priority to such work on the basis of the number of outliers. But what else could you do with the outlier data? You could track associations between the exceptions and the data to see if the exceptions are increasing or decreasing. Are they above or below your yearly averages? Is there a pattern in the outliers that may help identify their root cause? Does their timing correlate to other patterns in the data? Do they, for example, map to market volume, season, or something else?
This inquiry leads you to realize that reviewing this one exception report is not enough. You need to look at the data from different angles. Every question leads to another.
In the exception report example just mentioned, you started with an unprioritized list of exceptions or outliers. You ranked them by priority for course correction. You then looked for trends by comparing outliers over time. Finally, you looked into correlations in the data to see if associations in the exceptions track to time, market volume, or another variable. Patterns in the dataset help you to draw conclusions that in turn enable you to effectively predict and prevent future errors.
Data visualizations need to anticipate and address follow-up questions. Understanding why you rely on data helps you design visualizations to meet an array of questions. Each data visualization has a stated purpose but also goes beyond its immediate purpose and serves as an entry point to multiple views. Data visualizations enable you to discover things beyond the reach of their initial intention. They prime you to compare, connect, and create your own conclusions.
Data visualizations distill data. They reduce the effort required to understand comparisons, as calculations, and results are represented directly as visual content. To compare a table of numbers with 10 rows and 10 columns would require you to make roughly 4,950 calculations to understand the relationship between the 100 individual data points. Instead a simple comparison can be made by scanning the visual representation of this same data set.
Types of comparisons vary widely. You can compare like values or introduce context to provide fresh perspectives of the data. Typically, comparisons reveal rank, compare attributes, or show how an event might unfold over time. Consider the following types of comparisons:
Figure 2-1: Rank Example
Figure 2-2: Attributes Example
Figure 2-3: Time Series Example
Data visualizations that provide comparisons can help you answer questions regarding what and when. What is the top performing fund for this year? What were the most prevalent attributes of the fund? Have those attributes changed over time, if so when?
Relationships between data sets enable you to understand connections in the data. Is a data set part of a subset? Does one data set impact the results of another set and how? A visualization that connects the big picture with corresponding details enables you to inspect the data at various levels. Pointillist painting is a technique that uses small distinct dots of color and applies those dots in patterns to form a coherent picture. Individual dots in a painting are like individual data points in a visualization. Similar in effect to Pointillist painting, our individual data points, like the distinct dots in the painting, have little meaning, but when combined they form a vibrant picture. Your eyes make those connections and allow you to see the big picture and still inspect each dot or section. Data visualizations can show connections in the data by using the techniques of drill down, networks, and correlations.
Figure 2-4: Drill Down Example
Figure 2-5: Network Example
Figure 2-6: Correlations Example
Showing connections can answer questions as to “how and where.” For example, drill down visualizations can reveal how an aggregated total is composed of underlying line items. Network visualizations can show how an event in one part of the world impacts the price of a commodity in another part of the world. The effect, influence, and means of linking datasets across locations can be shown and explained with networks. The third type of data visualizations that show connections are correlations. For example, correlations can explain if and how the size of a fund affects performance or if there exists a relationship between firm size and beta values.
Data visualizations enable you to draw your own conclusions and help you solve complex questions. A well-crafted visual system provides a set of answers that facilitate deeper evaluation. You can formulate theories on the basis of patterns, themes, and calculations. Visualizations that lead to conclusions can provide the mechanisms for you to advance your understanding by confirming a conclusion based on a tested hypothesis. For example, a pattern can help you to predict outcomes; groups of categories, and subcategories enable you to see themes; and complex formulas can be visualized to show you the results of a calculation.
Figure 2-7: Pattern Recognition Example
Figure 2-8: Theme/Categories Example
Figure 2-9: Visual Calculations Example
The benefit to these types of visuals is that the answer is based on the raw data, so we are able to see the results and not just ask why but also begin to understand why. Why do small cap value funds outperform large cap core funds? Why did a financial model provide such volatile results? These are a sample of questions that represent conclusive types of inquires that can help you understand why an event occurred.
We as humans have evolved robust visual processing skills to interpret our environment. Unless blindness has forced us to rely on other senses, we gather and process visual information during every waking moment. Consider a simple errand to a store across the street. Before you cross the street, you scan for the motion of cars to make sure it’s safe. You read the cross-walk signal, avoid the pothole in the street, and navigate the crowd. You assess the flow of traffic and make sure a car is not turning onto the street. Within seconds you are able to cross the street untouched. This is a simple example of our visual data processing skills. Throughout the day we are able to make well-timed decisions based on seeing and assessing what is in front of us. Yet we rarely stop to think how visual data processing and analysis gets us through the day.
Imagine being limited to gathering information about your surroundings via raw numbers and text. Your ability to react and make a next move would require vast amounts of time and effort simply to consume the data. Assessing such data would require long calculations. Making sure it is safe to cross the street would turn seconds of scanning into minutes. The tasks that take you a few minutes may take hours and would lead to a higher risk of making the wrong choice. This text-and-numbers-based world seems unreasonable.
The fact is much of our work is spent in this text-and-numbers-based world. Spreadsheets of data including but not limited to cash-flow models, balance sheets, earnings valuations, investment returns, research notes, and news all consume how we navigate our work. We consume textual and numeric information, review the data, make our assessments via calculations, and decide on the appropriate next steps. However, how we communicate should reflect our visual cortex strengths and be designed around our ability to see and comprehend.
Communications should be optimized knowing the gifts of our extremely evolved powers of visual perception. Our eyes have the power of seeing visual misalignments; tracking motion, color, texture, depth; and recognizing visual patterns. Knowing our strengths and building upon these strengths can help us better navigate the world of text and numbers. The more we replace text and numbers with visual objects, the more we can set up a system that works with our own visual system of seeing. But that is not all; the characteristics of a strong visual communication are many:
Figure 2-10: Locations Across the Globe
Figure 2-11: NASDAQ 1Y Chart
Figure 2-12: Concise Line Charts
Figure 2-13: Calendar Chart
Figure 2-14: Memorable Childhood Illustration
Figure 2-15: Revealing Cluster Chart
Figure 2-16: Multiple Outputs
Data visualizations enables us to turn on the light switch and see what is available. They shine light in a way that enables you to reveal data that would otherwise stay hidden. Making data visible can provide immediate gratification to audiences around the globe. You can represent your data with concise charts and evoke an inviting or memorable display. Visualizations can also be versatile and adjust to fit different needs. You can leverage these qualities of a visualization to make the data work for you.
The ability to encode the data into a visual picture is a communication strength that enables you to understand, explain, teach, convince, and reach larger audiences. Pictures of data that carefully communicate the information needed by your audience are important and provide considerable value to compare, see connections, and make conclusions. Charts cross regional boundaries and can be used across the globe to quickly and concisely communicate large amounts of data. They can pique and keep your interest to draw you in and be remembered. You can learn, make discoveries from charts, and reuse charts across different audiences and delivery mechanisms. Data visualizations produce solutions that do much of the work for you regardless of volumes, real-time updates, or additional variations. You just need to take advantage of their capabilities.
