🪙Business Intelligence: Decision-Making (Just the Quotes)

"Charts and graphs are a method of organizing information for a unique purpose. The purpose may be to inform, to persuade, to obtain a clear understanding of certain facts, or to focus information and attention on a particular problem. The information contained in charts and graphs must, obviously, be relevant to the purpose. For decision-making purposes, information must be focused clearly on the issue or issues requiring attention. The need is not simply for 'information', but for structured information, clearly presented and narrowed to fit a distinctive decision-making context. An advantage of having a 'formula' or 'model' appropriate to a given situation is that the formula indicates what kind of information is needed to obtain a solution or answer to a specific problem." (Cecil H Meyers, "Handbook of Basic Graphs: A modern approach", 1970)

"The more any quantitative social indicator is used for social decision-making, the more subject it will be to corruption pressures and the more apt it will be to distort and corrupt the social processes it is intended to monitor." (Donald T Campbell, "Assessing the impact of planned social change", 1976)

"The greater the uncertainty, the greater the amount of decision making and information processing. It is hypothesized that organizations have limited capacities to process information and adopt different organizing modes to deal with task uncertainty. Therefore, variations in organizing modes are actually variations in the capacity of organizations to process information and make decisions about events which cannot be anticipated in advance." (John K Galbraith, "Organization Design", 1977)

"So we pour in data from the past to fuel the decision-making mechanisms created by our models, be they linear or nonlinear. But therein lies the logician's trap: past data from real life constitute a sequence of events rather than a set of independent observations, which is what the laws of probability demand. [...] It is in those outliers and imperfections that the wildness lurks." (Peter L Bernstein, "Against the Gods: The Remarkable Story of Risk", 1996)

"Delay time, the time between causes and their impacts, can highly influence systems. Yet the concept of delayed effect is often missed in our impatient society, and when it is recognized, it’s almost always underestimated. Such oversight and devaluation can lead to poor decision making as well as poor problem solving, for decisions often have consequences that don’t show up until years later. Fortunately, mind mapping, fishbone diagrams, and creativity/brainstorming tools can be quite useful here." (Stephen G Haines, "The Manager's Pocket Guide to Strategic and Business Planning", 1998)

"Blissful data consist of information that is accurate, meaningful, useful, and easily accessible to many people in an organization. These data are used by the organization’s employees to analyze information and support their decision-making processes to strategic action. It is easy to see that organizations that have reached their goal of maximum productivity with blissful data can triumph over their competition. Thus, blissful data provide a competitive advantage.". (Margaret Y Chu, "Blissful Data", 2004)

"Dashboards and visualization are cognitive tools that improve your 'span of control' over a lot of business data. These tools help people visually identify trends, patterns and anomalies, reason about what they see and help guide them toward effective decisions. As such, these tools need to leverage people's visual capabilities. With the prevalence of scorecards, dashboards and other visualization tools now widely available for business users to review their data, the issue of visual information design is more important than ever." (Richard Brath & Michael Peters, "Dashboard Design: Why Design is Important," DM Direct, 2004)

"Decision-makers process priors incorrectly in several ways. First, people tend to assess probability from the representativeness of an outcome rather than from its frequency. When supporting information is added to make an outcome more coherent and congruent with a representative mental image, people tend to judge the outcome more probable, even though the added qualifications and constraints by definition make it less probable. […] Second, humans often judge relative probability of outcomes by assessing similarity rather than frequency. […] Third, when given worthless evidence in a Bayesian framework, people tend to ignore prior probabilities and use the worthless evidence." (Leland Wilkinson, "The Grammar of Graphics" 2nd Ed., 2005)

"Human decision-making in the face of uncertainty is not only prone to error, it is also biased against Bayesian principles. We are not randomly suboptimal in our decisions. We are systematically suboptimal. (Leland Wilkinson, "The Grammar of Graphics" 2nd Ed., 2005)

"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)

"All human storytellers bring their subjectivity to their narratives. All have bias, and possibly error. Acknowledging and defusing that bias is a vital part of successfully using data stories. By debating a data story collaboratively and subjecting it to critical thinking, organizations can get much higher levels of engagement with data and analytics and impact their decision making much more than with reports and dashboards alone." (James Richardson, 2017)

"An actionable task means that it is possible to act on its result. That action might be to present a useful result to a decision maker or to proceed to a next step in a different result. An answer is actionable when it no longer needs further work to make sense of it." (Danyel Fisher & Miriah Meyer, "Making Data Visual", 2018)

"Business intelligence tools can only present the facts. Removing biases and other errors in decision making are dynamics of company culture that affect how well business intelligence is used." (Cindi Howson, "Successful Business Intelligence: Secrets to making BI a killer App", 2008)

"The problem is when biases and inaccurate data also get filtered into the gut. In this case, the gut-feel decision making should be supported with objective data, or errors in decision making may occur." (Cindi Howson, "Successful Business Intelligence: Secrets to making BI a killer App", 2008)

"Most discussions of decision making assume that only senior executives make decisions or that only senior executives' decisions matter. This is a dangerous mistake. Decisions are made at every level of the organization, beginning with individual professional contributors and frontline supervisors. These apparently low-level decisions are extremely important in a knowledge-based organization." (Zach Gemignani et al, "Data Fluency", 2014)

"Apart from the secondary benefits of digital data, which are many, such as faster and cheaper information collection and distribution, the primary benefit is better decision making based on evidence. Despite our intellectual powers, when we allow our minds to become disconnected from reliable information about the world, we tend to screw up and make bad decisions." (Stephen Few, "Signal: Understanding What Matters in a World of Noise", 2015) 

"Probabilities allow us to quantify future events and are an important aid to rational decision making. Without them, we can become seduced by anecdotes and stories." (Daniel J Levitin, "Weaponized Lies", 2017)

"The goal of data science is to improve decision making by basing decisions on insights extracted from large data sets. As a field of activity, data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting nonobvious and useful patterns from large data sets. It is closely related to the fields of data mining and machine learning, but it is broader in scope." (John D Kelleher & Brendan Tierney, "Data Science", 2018)

"The second rule of communication is to know what you want to achieve. Hopefully the aim is to encourage open debate, and informed decision-making. But there seems no harm in repeating yet again that numbers do not speak for themselves; the context, language and graphic design all contribute to the way the communication is received. We have to acknowledge we are telling a story, and it is inevitable that people will make comparisons and judgements, no matter how much we only want to inform and not persuade. All we can do is try to pre-empt inappropriate gut reactions by design or warning." (David Spiegelhalter, "The Art of Statistics: Learning from Data", 2019)

"Information relevance refers to the extent to which information is appropriate for the decision-making situation facing the manager. Extraneous or extra information distracts the decision-maker from the assigned task and information overload frustrates the decision-maker and impairs the decision-making process. Relevant information must pertain to the problems, decisions and responsibilities of the recipient." (C S V Murthy, "Data and Businesss Analytics", 2020)

"Information that is complete means information that covers key issues and is sufficient to support the decision-making situation at hand without critical omissions. The more complete a body of information, is obviously, the more expensive it is to develop and maintain. Care must also be taken not to provide extra information than needed, due to its expense, and not to provide so much information that the recipient will suffer from information overload (information indigestion)." (C S V Murthy, "Data and Businesss Analytics", 2020)

"The concept of programmed decisions is important because the ultimate (and unachievable) goal of information systems is to provide purely programmed decisions. Because this is not possible, we seek to provide the optimum type of information to the human decision-maker, who then makes non-programmable decisions. Decisions lend themselves to programming techniques if they are repetitive and routine, and if a procedurs can be worked out for handling them so that each is neither an ad hoc decision nor one to be treated as a new situation each time it arises." (C S V Murthy, "Data and Businesss Analytics", 2020)

"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)

"The goal of using data visualization to make better and faster decisions may lead people to think that any data visualization that is not immediately understood is a failure. Yes, a good visualization should allow you to see things that you might have missed, and to glean insights faster, but you still have to think." (Steve Wexler, "The Big Picture: How to use data visualization to make better decisions - faster", 2021)

"Current decision-making in business suffers from insight gaps. Organizations invest in data and analytics, hoping that will provide them with insights that they can use to make decisions, but in reality, there are many challenges and obstacles that get in the way of that process. One of the biggest challenges is that these organizations tend to focus on technology and hard skills only. They are definitely important, but you will not automatically get insights and better decisions with hard skills alone. Using data to make better data-informed decisions requires not only hard skills but also soft skills as well as mindsets." (Angelika Klidas & Kevin Hanegan, "Data Literacy in Practice", 2022)

"Decision-makers are constantly provided data in the form of numbers or insights, or similar. The challenge is that we tend to believe every number or piece of data we hear, especially when it comes from a trusted source. However, even if the source is trusted and the data is correct, insights from the data are created when we put it in context and apply meaning to it. This means that we may have put incorrect meaning to the data and then made decisions based on that, which is not ideal. This is why anyone involved in the process needs to have the skills to think critically about the data, to try to understand the context, and to understand the complexity of the situation where the answer is not limited to just one specific thing. Critical thinking allows individuals to assess limitations of what was presented, as well as mitigate any cognitive bias that they may have." (Angelika Klidas & Kevin Hanegan, "Data Literacy in Practice", 2022)

"Data literacy is something that affects everyone and every organization. The more people who can debate, analyze, work with, and use data in their daily roles, the better data-informed decision-making will be." (Angelika Klidas & Kevin Hanegan, "Data Literacy in Practice", 2022)