🧭Business Intelligence: Perspectives (Part XV: From Data to Storytelling III)

Business Intelligence Series 

As children we heard or later read many stories, and even if few remained imprinted in memory, we can still recognize some of the metaphors and ideas used. Stories prepared us for life, and one can suppose that the business stories we hear nowadays have similar intent, charge and impact. However, if we dig deeper into each story and dissect it, we may be disappointed by its simplicity, the resemblance to other stories, to what we've heard over time. Moreover, stories can bring also negative connotations, that can impact any other story we hear. 

From the scores or hundreds of distinct stories that have been told, few reach a magnitude that can become more than the stories themselves, few become a catalyst for the auditorium, and even then they tend to manipulate. Conversely, well-written transformative stories can move mountains when they resonate with the auditorium. In a leader’s motivational speech such stories can become a catalyst that moves people in the intended direction.

Children stories are quite simple and apparently don’t need special constructs even if the choice of words, structure and messages is important. Moving further into organizations, storytelling becomes more complex, upon case, structures and messages need to follow certain conventions within some politically correct scripts. Facts become important to the degree they serve the story, though the purposes they serve change with time, becoming secondary to the story. Storytelling becomes thus just of way of changing the facts as seems fit to the storyteller. 

Storytelling has its role in organizations for channeling the multitude of messages across various structures. However, the more one hears the word storytelling, the more likely one is closer to fiction than to business decision-making. It's also true that the word in itself carries a power we all tasted during childhood and why not much later. The word has a magic power that appeals to our memories, to our feelings, to our expectations. However, as soon one's expectations are not met, the fight with the chimeras turns into a battle of our own. Yes, storytelling has great power when used right, when there's a story to tell, when the business narratives are worth telling. 

The problem with stories is that no matter how much they are based on real facts or happenings, they become fictitious in time, to the degree that they lose some of the most important facts they were based on. That’s valid especially when there’s no written track of the story, though even then various versions of the story can multiply outside of the standard channels and boundaries. 

Even if the author tried to keep the story as close to the facts, the way stories are understood, remembered and retold depend on too many factors - the words used, the degree to which metaphors and similar elements are understood, remembered and transmitted correctly, the language used, the mental structure existing in the auditorium, the association of words, ideas or metaphors, etc.

Unfortunately, the effect of stories can be negative too, especially when stories are designed to manipulate the auditorium beyond any ethical norms. When they don’t resonate with the crowd or are repeated unnecessary, the narratives may have adverse effects and the messages can get lost in the crowd or create resistance. Moreover, stories may have a multifold and opposite effect within different segments of the auditorium. 

Storytelling can make hearts and minds resonate with the carried messages, though misdirected, improper or poorly conceived stories have also the power to destroy all that have been built over the years. Between the two extremes there’s a small space to send the messages across!

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🧭Business Intelligence: Perspectives (Part XIV: From Data to Storytelling II)

Business Intelligence Series

Being snapshots in people and organizations’ lives, data arrive to tell a story, even if the story might not be worth telling or might be important only in certain contexts. In fact each record in a dataset has the potential of bringing a story to life, though business people are more interested in the hidden patterns and “stories” the data reveal through more or less complex techniques. Therefore, data are usually tortured until they confess something, and unfortunately people stop analyzing the data with the first confession(s). 

Even if it looks like torture, data need to be processed to reveal certain characteristics, trends or patterns that could help us in sense-making, decision-making or similar specific business purposes. Unfortunately, the volume of data increases with an incredible velocity to which further characteristics like variety, veracity, volume, velocity, value, veracity and variability may add up. 

The data in a dashboard, presentation or even a report should ideally tell a story otherwise the data might not be worthy looking at, at least from some people’s perspective. Probably, that’s one of the reason why man dashboards remain unused shortly after they were made available, even if considerable time and money were invested in them. Seeing the same dull numbers gives the illusion that nothing changed, that nothing is worth reviewing, revealing or considering, which might be occasionally true, though one can’t take this as a rule! Lot of important facts could remain hidden or not considered. 

One can suppose that there are businesses in which something important seldom happens and an alert can do a better job than reviewing a dashboard or a report frequently. Probably an alert is a better choice than reporting metrics nobody looks at! 

Organizations usually define a set of KPIs (key performance indicators) and other types of metrics they (intend to) review periodically. Ideally, the numbers collected should define and reflect the critical points (aka pain points) of an organization, if they can be known in advance. Unfortunately, in dynamic businesses the focus can change considerably from one day to another. Moreover, in systemic contexts critical points can remain undiscovered in time if the set of metrics defined doesn’t consider them adequately. 

Typically only one’s experience and current or past issues can tell what one should consider or ignore, which are the critical/pain points or important areas that must be monitored. Ideally, one should implement alerts for the critical points that require a immediate response and use KPIs for the recurring topics (though the two approaches may overlap). 

Following the flow of goods, money and other resources one can look at the processes and identify the areas that must be monitored, prioritize them and identify the metrics that are worth tracking, respectively that reflect strengths, weaknesses, opportunities, threats and the risks associated with them. 

One can start with what changed by how much, what caused the change(s) and what further impact is expected directly or indirectly, by what magnitude, respectively why nothing changed in the considered time unit. Causality diagrams can help in the process even if the representations can become quite complex. 

The deeper one dives and the more questions one attempts to answer, the higher the chances to find a story. However, can we find a story that’s worth telling in any set of data? At least this is the point some adepts of storytelling try to make. Conversely, the data can be dull, especially when one doesn’t track or consider the right data. There are many aspects of a business that may look boring, and many metrics seem to track the boring but probably important aspects. 

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🧭Business Intelligence: Perspectives (Part XIII: From Data to Storytelling I)

Business Intelligence Series

Data is an amalgam of signs, words, numbers and other visual or auditory elements used together to memorize, interpret, communicate and do whatever operation may seem appropriate with them. However, the data we use is usually part of one or multiple stories - how something came into being, what it represents, how is used in the various mental and non-mental processes - respectively, the facts, concepts, ideas, contexts places or other physical and nonphysical elements that are brought in connection with.

When we are the active creators of a story, we can in theory easily look at how the story came into being, the data used and its role in the bigger picture, respective the transformative elements considered or left out, etc. However, as soon we deal with a set of data, facts, or any other elements of a story we are not familiar with, we need to extrapolate the hypothetical elements that seem to be connected to the story. We need to make sense of these elements and consider all that seems meaningful, what we considered or left out shaping the story differently. 

As children and maybe even later, all of us dealt with stories in one way or another, we all got fascinated by metaphors' wisdom and felt the energy that kept us awake, focused and even transformed by the words coming from narrator's voice, probably without thinking too much at the whole picture, but letting the words do their magic. Growing up, the stories grew in complexity, probably became richer in meaning and contexts, as we were able to decipher the metaphors and other elements, as we included more knowledge about the world around, about stories and storytelling.

In the professional context, storytelling became associated with our profession - data, information, knowledge and wisdom being created, assimilated and exchanged in more complex processes. From, this perspective, data storytelling is about putting data into a (business) context to seed cultural ground, to promote decision making and better understanding by building a narrative around the data, problems, challenges, opportunities, and further organizational context.

Further on, from a BI's perspective, all these cognitive processes impact on how data, information and knowledge are created, (pre)processed, used and communicated in organizations especially when considering data visualizations and their constituent elements (e.g. data, text, labels, metaphors, visual cues), the narratives that seem compelling and resonate with the auditorium. 

There's no wonder that data storytelling has become something not to neglect in many business contexts. Storytelling has proved that words, images and metaphors can transmit ideas and knowledge, be transformative, make people think, or even act without much thinking. Stories have the power to seed memes, ideas, or more complex constructs into our minds, they can be used (for noble purposes) or misused. 

A story's author usually takes compelling images, metaphors, and further elements, manipulates them to the degree they become interesting to himself/herself, to the auditorium, to the degree they are transformative and become an element of the business vocabulary, respectively culture, without the need to reiterate them when needed to bring more complex concepts, ideas or metaphors into being.  

A story can be seen as a replication of the constituting elements, while storytelling is a set of functions that operate on them and change the initial structure and content into something that might look or not like the initial story. Through retelling and reprocessing in any form, the story changes independently of its initial form and content. Sometimes, the auditorium makes connections not recognized or intended by the storyteller. Other times, the use and manipulation of language makes the story change as seems fit. 

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📊Graphical Representation: Overreaching in Data Visualizations

Graphical Representation Series 

One of the most important aspects to stress in the context of graphical design is the purpose of graphical representations and the medium in which they are communicated. For example, one needs to differentiate between the graphics propagated on the various media channels that target the public consumption and potential customers (books, newspapers, articles in paper or paperless form, respectively blog posts and similar content) and graphics made for organizational use (reports, dashboards or presentations).

If the former graphics are supposed to back up a story, the reader being led into one direction or another, the author having the freedom of choosing the direction and the message, in the latter, unless the content is supposed to support, persuade or force a decision, the facts and data need to be presented in an equidistant manner, in a form that support insights, decision making or further inference. This applies to data professionals as well to the business users preparing the data.

Data visualization authors tend to use the title and subtitle to highlight in reports and dashboards the most important findings as per their perception, sometimes even stating the obvious. One of the issues with this approach is that the audience might just pick up the respective information without further looking at the chart, missing maybe more important facts. Just highlighting an element in the graphic or providing explanatory headlines is not storytelling, even if it helps in the process. Ideally, the data itself as depicted by the visuals should tell the story! Further information with storytelling character should be provided in the presentation of the data and taylored accordingly for the audience!

With a few exceptions, the information and decisions shouldn't be forced on the audience. There are so many such examples on the various social networks in which data analysts or other types of data professionals seem to imply this in the content they share and this is so wrong on many levels!

No matter how deep a data professional is involved into the business and no matter how extensive is his/her knowledge about the systems, data and processes, the business user and the manager are the closest to the business context and needs, while data professionals might not be aware of the full extent. This lack of context makes it challenging to interpret the trends depicted by the data, respectively to associate the changes observed in trends with decisions made or issues the business dealt with. When such knowledge is not available the data professional tends to extrapolate instead of identifying the chain of causality together with the business (and here annotation capabilities would help considerably). 

Moreover, it falls on management's shoulders to decide which facts, data, metrics, KPIs and information are important for the organization. A data professional can make recommendations, can play with the data and communicate certain insights, gaps or courses of action, though the management decides what's important and how the respective information should be communicated! Overstepping the boundaries can easily lead to unnecessary conflict in which the data professional can easily lose, even if the facts are in his favor. It's enough to deal with missing or incorrect information for the whole story to fall apart. 

It's true that some of the books on graphical design use various highlighting techniques in the explanation process, but they are intended for the readers to understand what the authors want to say. Unfortunately, there are also examples improperly used or authors' opinion diverge from the common sense. Independently of this, the data professional should develop own visual critical thinking and validate the techniques used against own judgement! 

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🧭Business Intelligence: Perspectives (Part VII: Insights - Aha' Moments)

Business Intelligence Series

On one side scientists talk about 'Insight' with a sign of reverence when referring to the processes, patterns, models, metaphors, stories and paradigms used to generate and communicate insight. Conversely, data professionals seem to regard 'Insight' as something trivial, achievable just by picking and combining the right visualizations and storytelling. Are the scientists exaggerating when talking about insight, or do the data professionals downplay the meaning and role of insight? Or maybe the scientific and business contexts have incomparable complexity, even if the same knowledge toolset are used?

One probably can't deny the potentiality of tools or toolsets like data visualization or data storytelling in providing new information or knowledge that leads to insights, though between potential usefulness and harnessing that potential on a general basis there's a huge difference, no matter how much people tend to idealize the process (and there's lot of idealization going on). Moreover, sometimes the whole process seems to look like a black box in which some magic happens and insight happens.

It's challenging to explain the gap as long as there's no generally accepted scientific definition of insights, respectively an explanation of how insights come into being. Probably, the easiest way to recognize their occurrence is when an 'Aha' moment appears, though that's the outcome of a process and gives almost no information about the process itself. Thus, insight occurs when knowledge about the business is acquired, knowledge that allows new or better understanding of the data, facts, processes or models involved. 

So, there must be new associations that are formed, either derived directly from data or brought to surface by the storytelling process. The latter aspect implies that the storyteller is already in possession of the respective insight(s) or facilitates their discovery without being aware of them. It further implies that the storyteller has a broader understanding of the business than the audience, which is seldom the case, or that the storyteller has a broader understanding of the data and the information extracted from the data, and that's a reasonable expectation.

There're two important restrictions. First, the insight moments must be associated with the business context rather than with the mere use of tools! Secondly, there should be genuine knowledge, not knowledge that the average person should know, respectively the mere confirmation of expectations or bias. 

Understanding can be put in the context of decision making, respectively in the broader context of problem solving. In the latter, insight involves the transition from not knowing how to solve a problem to the state of knowing how to solve it. So, this could apply in the context of data visualization as well, though there might exist intermediary steps in between. For example, in a first step insights enable us to understand and define the right problem. A further step might involve the recognition of the fact the problem belongs to a broader set of problems that have certain characteristics. Thus, the process might involve a succession of 'Aha' moments. Given the complexity of the problems we deal with in business or social contexts, that's more likely to happen. So, the average person might need several 'Aha' moments - leaps in understanding - before the data can make a difference! 

Conversely, new knowledge and understanding obtained over successive steps might not lead to an 'Aha' moment at all. Whether such moments converge or not to 'Aha' moments may rely on the importance of the overall leap, though other factors might be involved as well. In the end, the emergence of new understanding is enough to explain what insights mean. Whether that's enough is a different discussion!

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🧭Business Intelligence: Perspectives (Part VI: Data Soup - Reports vs. Data Visualizations)

Business Intelligence Series

Considering visualizations, John Tukey remarked that ‘the greatest value of a picture is when it forces us to notice what we never expected to see’, which is not always the case for many of the graphics and visualizations available in organizations, typically in the form of simple charts and dashboards, quite often with no esthetics or meaning behind.

In general, reports are needed as source for operational activities, in which the details in form of raw or aggregate data are important. As one moves further to the tactical or strategic aspects of a business, visualizations gain in importance especially when they allow encoding data and information, respectively variations, trends or relations in smaller places with minimal loss of information.

There are also different aspects of visualizations that need to be considered. Modern tools allow rapid visualization and interactive navigation of data across different variables which is great as long one knows what is searching for, which is not always the case.

There are junk charts in which the data drowns in graphical elements that bring no value to the reader, in extremis even distorting the message/meaning.

There are graphics/visualizations that attempt bringing together and encoding multiple variables in respect to a theme, and for which a ‘project’ is typically needed as data is not ad-hoc available, don’t have the desired quality or need further transformations to be ready for consumption. Good quality graphics/visualizations require time and a good understanding of the business, which are not necessarily available into the BI/Analytics teams, and unfortunately few organizations do something in that direction, ignoring typically such needs. In this type of environments is stressed the rapid availability of data for decision-making or action-relevant insight, which depends typically on the consumer.

The story-telling capabilities of graphics/visualizations are often exaggerated. Yes, they can tell a story though stories need to be framed into a context/problem, some background and further references need to be provided, while without detailed data the graphics/visualizations are just nice representations in which each consumer understands what he can.

In an ideal world the consumer and the ‘designer’ would work together to identify the important data for the theme considered, to find the appropriate level of detail, respectively the best form of encoding. Such attempts can stop at table-based representations (aka reports), respectively basic or richer forms of graphical representations. One can consider reports as an early stage of the visualization process, with the potential to derive move value when the data allow meaningful graphical representations. Unfortunately, the time, data and knowledge available seldom make this achievable.

In addition, a well-designed report can be used as basis for multiple purposes, while a graphic/visualization can enforce more limitations. Ideal would be when multiple forms of representation (including reports) are combined to harness the value of data. Navigations from visualizations to detailed data can be useful to understand what happens; learning and understanding the various aspects being an iterative process.

It’s also difficult to demonstrate the value of insight derived from visualizations, especially when graphical literacy goes behind the numeracy and statistical literacy - many consumers lacking the skills needed to evaluate numbers and statistics adequately. If for a good artistic movie you need an assistance to enjoy the show and understand the message(s) behind it, the same can be said also about good graphics/visualizations. Moreover, this requires creativity, abstraction-based thinking, and other capabilities to harness the value of representations.

Given the considerable volume of requirements related to the need of basis data, reports will continue to be on high demand in organizations. In exchange visualizations can complement them by providing insights otherwise not available.

Initially published on Medium as answer to a post on Reporting and Visualizations. 

📈Graphical Representation: Infographic (Definitions)

"an infographic is defined as a visualization of data or ideas that tries to convey complex information to an audience in a manner that can be quickly consumed and easily understood." (Mark Smiciklas, "The Power of Infographics: Using Pictures to Communicate and Connect with Your Audiences", 2012)

"Tools and techniques involved in graphical representation of data, mostly in journalism, art, and storytelling." (Anna Ursyn, "Visualization as Communication with Graphic Representation", 2015)

"Use of visual images such as charts, graphic organizers, diagrams, photos, etc. in teaching and learning." (Esther Ntuli, "Active Learning Strategies in Technology Integrated K-12 Classrooms", 2015)

"Information graphics that are visual representations of data or information." (Julie A Delello & Rochell R McWhorter, "New Visual Literacies and Competencies for Education and the Workplace", 2016)

 "Information or data represented as a visual image in a chart or diagram. An infographic can be an excellent way to conceptualize dense text or numbers in ways that appeal to the eye and engage the reader." (Kindra Cotton et al, "Leveraging New Media as Social Capital for Diversity Officers", 2016)

"A short-form, visual representation of information, data, or knowledge presented through simple images that highlight patterns, trends, or insights. Simplified from the term information graphic." (Jonathan Ferrar et al, "The Power of People: Learn How Successful Organizations Use Workforce Analytics To Improve Business Performance", 2017)

[Infographic Map:] "Diagrammatic 'visualizations' of the space of knowledge and its associative logics that allows to use its own 'form' as a tool to 'act' on complex systems of knowledge." (Alessandra Cirafici & Alessandra Avella, "A Virtual Museum of Pompeii 'ex Votos': Design Strategies", 2020)

"Graphic visual representations of information, data or knowledge intended to present complex information quickly and clearly." (Jing Zhou, "Connecting Art, Culture, Science, and Technology", 2021)

"a form of communication that uses visual language and text. Both languages are complementary, part of a whole, and therefore can’t be understood when separate" (Jaime Serra)

"An infographic is a visual form of content used as a medium to represent and share information, knowledge, and data." (Infographic World)

"An infographic is an edited, summarized presentation of data selected by a designer to tell a story. A visualization is a display designed to explore data so every reader will be able to extract his or her own stories" (Alberto Cairo)

✏️Brent Dyke - Collected Quotes

"A random collection of interesting but disconnected facts will lack the unifying theme to become a data story - it may be informative, but it won’t be insightful." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

"An essential underpinning of both the kaizen and lean methodologies is data. Without data, companies using these approaches simply wouldn’t know what to improve or whether their incremental changes were successful. Data provides the clarity and specificity that’s often needed to drive positive change. The importance of having baselines, benchmarks, and targets isn’t isolated to just business; it can transcend everything from personal development to social causes. The right insight can instill both the courage and confidence to forge a new direction - turning a leap of faith into an informed expedition." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

"Analysis is a two-step process that has an exploratory and an explanatory phase. In order to create a powerful data story, you must effectively transition from data discovery (when you’re finding insights) to data communication (when you’re explaining them to an audience). If you don’t properly traverse these two phases, you may end up with something that resembles a data story but doesn’t have the same effect. Yes, it may have numbers, charts, and annotations, but because it’s poorly formed, it won’t achieve the same results." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

"Are your insights based on data that is accurate and reliable? Trustworthy data is correct or valid, free from significant defects and gaps. The trustworthiness of your data begins with the proper collection, processing, and maintenance of the data at its source. However, the reliability of your numbers can also be influenced by how they are handled during the analysis process. Clean data can inadvertently lose its integrity and true meaning depending on how it is analyzed and interpreted." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

"Before you can even consider creating a data story, you must have a meaningful insight to share. One of the essential attributes of a data story is a central or main insight. Without a main point, your data story will lack purpose, direction, and cohesion. A central insight is the unifying theme (telos appeal) that ties your various findings together and guides your audience to a focal point or climax for your data story. However, when you have an increasing amount of data at your disposal, insights can be elusive. The noise from irrelevant and peripheral data can interfere with your ability to pinpoint the important signals hidden within its core." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

"Data storytelling can be defined as a structured approach for communicating data insights using narrative elements and explanatory visuals." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

"Data storytelling gives your insight the best opportunity to capture attention, be understood, be remembered, and be acted on. An effective data story helps your insight reach its full potential: inspiring others to act and drive change." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

"Data storytelling involves the skillful combination of three key elements: data, narrative, and visuals. Data is the primary building block of every data story. It may sound simple, but a data story should always find its origin in data, and data should serve as the foundation for the narrative and visual elements of your story." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

"Data storytelling is transformative. Many people don’t realize that when they share insights, they’re not just imparting information to other people. The natural consequence of sharing an insight is change. Stop doing that, and do more of this. Focus less on them, and concentrate more on these people. Spend less there, and invest more here. A poignant insight will drive an enlightened audience to think or act differently. So, as a data storyteller, you’re not only guiding the audience through the data, you’re also acting as a change agent. Rather than just pointing out possible enhancements, you’re helping your audience fully understand the urgency of the changes and giving them the confidence to move forward." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

"Data storytelling provides a bridge between the worlds of logic and emotion. A data story offers a safe passage for your insights to travel around emotional pitfalls and through analytical resistance that typically impede facts." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

"Even with a solid narrative and insightful visuals, a data story cannot overcome a weak data foundation. As the master architect, builder, and designer of your data story, you play an instrumental role in ensuring its truthfulness, quality, and effectiveness. Because you are responsible for pouring the data foundation and framing the narrative structure of your data story, you need to be careful during the analysis process. Because all of the data is being processed and interpreted by you before it is shared with others, it can be exposed to cognitive biases and logical fallacies that distort or weaken the data foundation of your story." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

"Even though data is being thrust on more people, it doesn’t mean everyone is prepared to consume and use it effectively. As our dependence on data for guidance and insights increases, the need for greater data literacy also grows. If literacy is defined as the ability to read and write, data literacy can be defined as the ability to understand and communicate data. Today’s advanced data tools can offer unparalleled insights, but they require capable operators who can understand and interpret data." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

"First, from an ethos perspective, the success of your data story will be shaped by your own credibility and the trustworthiness of your data. Second, because your data story is based on facts and figures, the logos appeal will be integral to your message. Third, as you weave the data into a convincing narrative, the pathos or emotional appeal makes your message more engaging. Fourth, having a visualized insight at the core of your message adds the telos appeal, as it sharpens the focus and purpose of your communication. Fifth, when you share a relevant data story with the right audience at the right time (kairos), your message can be a powerful catalyst for change." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

"Forward-thinking organizations look to empower more of their workers with data so they can make better-informed decisions and respond more quickly to market opportunities and challenges." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

"In addition to managing how the data is visualized to reduce noise, you can also decrease the visual interference by minimizing the extraneous cognitive load. In these cases, the nonrelevant information and design elements surrounding the data can cause extraneous noise. Poor design or display decisions by the data storyteller can inadvertently interfere with the communication of the intended signal. This form of noise can occur at both a macro and micro level." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

"The intended endpoint or destination of a data story is to guide an audience toward a better understanding and appreciation of your main point or insight, which hopefully leads to discussion, action, and change. However, if you have several divergent findings and try to combine them into a single data story, you may run the risk of confusing your audience or overwhelming them with too much information. To tell a cohesive data story, you must prioritize and limit what you focus on. Sometimes an insight deserves its own data story rather than being appended to the narrative of another insight." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

"The one unique characteristic that separates a data story from other types of stories is its fundamental basis in data. [...] The building blocks of every data story are quantitative or qualitative data, which are frequently the results of an analysis or insightful observation. Because each data story is formed from a collection of facts, each one represents a work of nonfiction. While some creativity may be used in how the story is structured and delivered, a true data story won’t stray too far from its factual underpinnings. In addition, the quality and trustworthiness of the data will determine how credible and powerful the data story is." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

"The success of your narratives will depend on your ability to effectively perform the following tasks and responsibilities as the data storyteller: Identify a key insight. [...] Minimize or remove bias. [...] Gain adequate context. [...] Understand the audience. [...] Curate the information. [...] Assemble the story. [...] Choose the visuals. [...] Add credibility." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

"There are eight audience considerations that can influence how you approach your data story: (1) Key goals and priorities. [...] (2) Beliefs and preferences. [...] (3) Specific expectations. [...] (4) Opportune timing. [...] (5) Topic familiarity. [...] (6) Data literacy. [...] (7) Seniority level. [...] (8) Audience mix." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

"When narrative is coupled with data, it helps to explain to your audience what’s happening in the data and why a particular insight is important. Ample context and commentary are often needed to fully appreciate an analysis finding. The narrative element adds structure to the data and helps to guide the audience through the meaning of what’s being shared." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

"When visuals are applied to data, they can enlighten the audience to insights that they wouldn’t see without charts or graphs. Many interesting patterns and outliers in the data would remain hidden in the rows and columns of data tables without the help of data visualizations. They connect with our visual nature as human beings and impart knowledge that couldn’t be obtained as easily using other approaches that involve just words or numbers." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

"While a story can act as a powerful delivery agent for sharing facts, the intent of data storytelling should never be to deceive an audience. Just like falsifying data is unacceptable, using narrative in a manipulative manner is similarly irresponsible. Instead, data storytelling should be viewed as a means of making insights more compatible with the human mind and more conducive to comprehension and retention." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

"While visuals are an essential part of data storytelling, data visualizations can serve a variety of purposes from analysis to communication to even art. Most data charts are designed to disseminate information in a visual manner. Only a subset of data compositions is focused on presenting specific insights as opposed to just general information. When most data compositions combine both visualizations and text, it can be difficult to discern whether a particular scenario falls into the realm of data storytelling or not." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

"You no longer need to have the words 'data' or 'analyst' in your job title to be immersed in numbers and be expected to use them on a regular basis. Data is now everyone’s responsibility. In fact, the Achilles’ heel of any analyst is a lack of context - something most business users have in spades. A sharp analyst can miss something in the data that is easily spotted by the seasoned eyes of a business user, who can draw on years of domain expertise. Data doesn’t care who you are or what your analytical skill level is - it’s willing to yield up insights to whoever is diligent and curious enough to find them. Greater data access means valuable insights can be discovered by people of all backgrounds - not just technical ones." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

✏️Cole N Knaflic - Collected Quotes

"Beyond annoying our audience by trying to sound smart, we run the risk of making our audience feel dumb. In either case, this is not a good user experience for our audience." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

"By combining the visual and verbal, we set ourselves up for success when it comes to triggering the formation of long-term memories in our audience." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

"Concentrate on the pearls, the information your audience needs to know." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

"Exploratory analysis is what you do to understand the data and figure out what might be noteworthy or interesting to highlight to others." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

"First, to whom are you communicating? It is important to have a good understanding of who your audience is and how they perceive you. This can help you to identify common ground that will help you ensure they hear your message. Second, What do you want your audience to know or do?" (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

"Further develop the situation or problem by covering relevant background. Incorporate external context or comparison points. Give examples that illustrate the issue. Include data that demonstrates the problem. Articulate what will happen if no action is taken or no change is made. Discuss potential options for addressing the problem. Illustrate the benefits of your recommended solution." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

"Having all the information in the world at our fingertips doesn’t make it easier to communicate: it makes it harder. The more information you’re dealing with, the more difficult it is to filter." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

"Highlighting one aspect can make other things harder to see one word of warning in using preattentive attributes: when you highlight one point in your story, it can actually make other points harder to see. When you’re doing exploratory analysis, you should mostly avoid the use of preattentive attributes for this reason. When it comes to explanatory analysis, however, you should have a specific story you are communicating to your audience. Leverage preattentive attributes to help make that story visually clear." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

"I almost always use dark grey for the graph title. This ensures that it stands out, but without the sharp contrast you get from pure black on white (rather, I preserve the use of black for a standout color when I’m not using any other colors). A number of preattentive attributes are employed to draw attention to the Progress to date trend: color, thickness of line, presence of data marker and label on the final point, and the size of the corresponding text." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

"If I had to pick a single go-to graph for categorical data, it would be the horizontal bar chart, which flips the vertical version on its side. Why? Because it is extremely easy to read. The horizontal bar chart is especially useful if your category names are long, as the text is written from left to right, as most audiences read, making your graph legible for your audience." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

"If you do succeed in persuading them, you’ve only done so on an intellectual basis. That’s not good enough, because people are not inspired to act by reason alone." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

"If you simply present data, it’s easy for your audience to say, Oh, that’s interesting, and move on to the next thing. But if you ask for action, your audience has to make a decision whether to comply or not. This elicits a more productive reaction from your audience, which can lead to a more productive conversation - one that might never have been started if you hadn’t recommended the action in the first place." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

"In the field of design, experts speak of objects having 'affordances'. These are aspects inherent to the design that make it obvious how the product is to be used. For example, a knob affords turning, a button affords pushing, and a cord affords pulling. These characteristics suggest how the object is to be interacted with or operated. When sufficient affordances are present, good design fades into the background and you don’t even notice it." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

"My base color is grey, not black, to allow for greater contrast since color stands out more against grey than black. For my attention-grabbing color, I often use blue for a number of reasons: (1) I like it, (2) you avoid issues of colorblindness that we’ll discuss momentarily, and (3) it prints well in black-and-white. That said, blue is certainly not your only option (and you’ll see many examples where I deviate from my typical blue for various reasons)." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

"One thing to keep in mind with a table is that you want the design to fade into the background, letting the data take center stage. Don’t let heavy borders or shading compete for attention. Instead, think of using light borders or simply white space to set apart elements of the table." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

"Short-term memory has limitations. Specifically, people can keep about four chunks of visual information in their short-term memory at a given time. This means that if we create a graph with ten different data series that are ten different colors with ten different shapes of data markers and a legend off to the side, we’re making our audience work very hard going back and forth between the legend and the data to decipher what they are looking at." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

"Sometimes bar charts are avoided because they are common. This is a mistake. Rather, bar charts should be leveraged because they are common, as this means less of a learning curve for your audience." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

"[...] tables interact with our verbal system, graphs interact with our visual system, which is faster at processing information." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

"The 3-minute story is exactly that: if you had only three minutes to tell your audience what they need to know, what would you say? This is a great way to ensure you are clear on and can articulate the story you want to tell." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

"The unique thing you get with a pie chart is the concept of there being a whole and, thus, parts of a whole. But if the visual is difficult to read, is it worth it?" (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

"There is a story in your data. But your tools don’t know what that story is. That’s where it takes you - the analyst or communicator of the information - to bring that story visually and contextually to life." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

"Using a table in a live presentation is rarely a good idea. As your audience reads it, you lose their ears and attention to make your point verbally." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

"What do you need your audience to know or do? This is the point where you think through how to make what you communicate relevant for your audience and form a clear understanding of why they should care about what you say." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

"What would a successful outcome look like? If you only had a limited amount of time or a single sentence to tell your audience what they need to know, what would you say? In particular, I find that these last two questions can lead to insightful conversation." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

"When we’re at the point of communicating our analysis to our audience, we really want to be in the explanatory space, meaning you have a specific thing you want to explain, a specific story you want to tell - probably about those two pearls." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

"When you have just a number or two that you want to communicate: use the numbers directly." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

"Will you be encountering each other for the first time through this communication, or do you have an established relationship? Do they already trust you as an expert, or do you need to work to establish credibility? These are important considerations when it comes to determining how to structure your communication and whether and when to use data, and may impact the order and flow of the overall story you aim to tell." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

"You should always want your audience to know or do something. If you can't concisely articulate that, you should revisit whether you need to communicate in the first place." (Cole N Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals", 2015)

✏️Kate Strachnyi - Collected Quotes

"As beautiful as data can be, it’s not an al fresco painting that should be open to interpretation from anyone who walks by its section of the museum. Make bold, smart color choices that leave no doubt what the purpose of the data is." (Kate Strachnyi, "ColorWise: A Data Storyteller’s Guide to the Intentional Use of Color", 2023)

"Blue is a nice color for a lot of things, but it’s tough for people to tell the difference between shades of blue in a report. Light blue and dark blue and royal blue and navy blue have a tendency to run together, so differing shades are not going to make that big of a difference for audience members trying to unspool what’s being presented. The same goes for other colors: it’s not that easy for humans to tell the difference between varying shades of the same color (unless they are drastic)." (Kate Strachnyi, "ColorWise: A Data Storyteller’s Guide to the Intentional Use of Color", 2023)

"Colors and numbers are much more similar than we think. Using contrasting colors on different forms of information allows your audience to make a very clear delineation between the two, even when the setup and style are completely the same." (Kate Strachnyi, "ColorWise: A Data Storyteller’s Guide to the Intentional Use of Color", 2023)

"Color is by far the most abused and neglected tool in data visualization. We abuse it by making color choices that make no sense, and we neglect it when we populate our hard work with software default settings, which are a good place to start but can be customized to suit your needs. [...] Color - if used prudently - makes our visualizations more digestible and more informative." (Kate Strachnyi, "ColorWise: A Data Storyteller’s Guide to the Intentional Use of Color", 2023)

"Data becomes more useful once it’s transformed into a data visualization or used in a data story. Data storytelling is the ability to effectively communicate insights from a dataset using narratives and visualizations. It can be used to put data insights into context and inspire action from your audience. Color can be very helpful when you are trying to make information stand out within your data visualizations." (Kate Strachnyi, "ColorWise: A Data Storyteller’s Guide to the Intentional Use of Color", 2023)

"Data storytelling is a method of communicating information that is custom-fit for a specific audience and offers a compelling narrative to prove a point, highlight a trend, make a sale, or all of the above. [...] Data storytelling combines three critical components, storytelling, data science, and visualizations, to create not just a colorful chart or graph, but a work of art that carries forth a narrative complete with a beginning, middle, and end." (Kate Strachnyi, "ColorWise: A Data Storyteller’s Guide to the Intentional Use of Color", 2023)

"Data visualization is the practice of taking insights found in data analysis and turning them into numbers, graphs, charts, and other visual concepts to make them easier to grasp, understand, learn from, and utilize.[...] The visualization of data can be thought of as both a science and an art in that the way it is displayed is often as important to its understanding as the actual information that is being displayed." (Kate Strachnyi, "ColorWise: A Data Storyteller’s Guide to the Intentional Use of Color", 2023)

"Good data stories have three key components: data, narrative, and visuals. [...] The data part is fairly obvious - data has to be accurate for the correct insights to be achieved. The narrative has to give a voice to the data in simple language, turning each data point into a character in the story with its own tale to tell. The visuals are what we are most concerned about. They have to allow us to be able to find trends and patterns in our datasets and do so easily and specifically. The last thing we want is for the most important points to be buried in rows and columns." (Kate Strachnyi, "ColorWise: A Data Storyteller’s Guide to the Intentional Use of Color", 2023)

"One tip to keep an audience focused on your story without overwhelming them is to reduce the saturation of the colors [...] When you lower the brightness and intensity, you are reducing the cognitive load that your audience has to bear. [...] Regardless of what combinations you decide on, you need to avoid pure colors that are bright and saturated." (Kate Strachnyi, "ColorWise: A Data Storyteller’s Guide to the Intentional Use of Color", 2023)

"Our machines are helpers, not decision makers. Their insights are not the final word in the discussion, merely the work of our most nimble observers who can ramp up time spent on analysis by factors that our counterparts even a generation ago would have a hard time believing." (Kate Strachnyi, "ColorWise: A Data Storyteller’s Guide to the Intentional Use of Color", 2023)

"Sometimes, adding a divider to a visualization can help transform it from something that’s difficult to understand into a more effective visual." (Kate Strachnyi, "ColorWise: A Data Storyteller’s Guide to the Intentional Use of Color", 2023)

"The lack of focus and commitment to color is a perplexing thing. When used correctly, color has no equal as a visualization tool - in advertising, in branding, in getting the message across to any audience you seek. Data analysts can make numbers dance and sing on command, but they sometimes struggle to create visually stimulating environments that convince the intended audience to tap their feet in time." (Kate Strachnyi, "ColorWise: A Data Storyteller’s Guide to the Intentional Use of Color", 2023)

"The practice of finding relationships between different sets of data - also known as correlations - is the bread and butter of what data analysis, and by proxy data visualization, is all about." (Kate Strachnyi, "ColorWise: A Data Storyteller’s Guide to the Intentional Use of Color", 2023)

"Visualizations can remove the background noise from enormous sets of data so that only the most important points stand out to the intended audience. This is particularly important in the era of big data. The more data there is, the more chance for noise and outliers to interfere with the core concepts of the data set." (Kate Strachnyi, "ColorWise: A Data Storyteller’s Guide to the Intentional Use of Color", 2023)

"When the colors are dull and neutral, they can communicate a sense of uniformity and an aura of calmness. Grays do a great job of mapping out the context of your story so that the more sharp colors highlight what you’re trying to explain. The power of gray comes in handy for all of our supporting details such as the axis, gridlines, and nonessential data that is included for comparative purposes. By using gray as the primary color in a visualization, we automatically draw our viewers’ eyes to whatever isn’t gray. That way, if we are interested in telling a story about one data point, we can do so quite easily."  (Kate Strachnyi, "ColorWise: A Data Storyteller’s Guide to the Intentional Use of Color", 2023)