Making big decisions can be difficult and scary. Here are a few steps to make that big step more manageable: •Fact-check the information that enables the decision to be made •Broaden the sources of data (i.e. don’t just look at success stories, but also examine failures or missteps) •Trusting data but not treating it as evidence due to the lack of universality TL;DR: Fact check information, broaden your sources of information, and trust the data but don’t treat it as a universal success story. https://lnkd.in/eNFXepFm #decisionmaking #consulting #data #management
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Each of us makes many decisions each and every day. Some have greater impact that others. Have you and your team reflected on how impactful decisions are made? What data is relied on and how it is tested? Take a moment to read this from the Harvard Business Review. Share it with your team and have a discussion. Consider regular reminders, a checklist or a new and better informed process.
How to Vet Information Before Making a Decision
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Group Chief Data Officer, PureHealth | Data, Technology, and Innovation for a Better World | Culturally Curious | Global Citizen | x-Microsoft | x-Mozilla | Harvard MPA | Chicago Booth MBA | UChicago PhD ABD
Wonderful piece in the Harvard Business Review by Alex Edmans, Professor of Finance at London Business School, on how to critically interrogate information before making decision. In a world increasingly awash with information and misinformation, this perspective is timely and needed. A few key principles to take away: 1. A statement is not fact, because it may not be accurate 2. A fact is not data because it may not be representative 3. Data is not evidence, because it may not be conclusive 4. Evidence is not proof, because it may not be universal #Evidence #DataDriven #EvidenceBasedDecisions https://lnkd.in/dQrGp32s
How to Vet Information Before Making a Decision
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Strategies to overcome Categorical Thinking in Business: Categorical thinking can limit decision-making and hinder progress. To combat this, Harvard Business Review's article "The Dangers of Categorical Thinking" offers some strategies for businesses: 1) Increase Awareness: Encourage a mindset that respects ambiguity, subtlety, and complexity in decision-making. Make people aware of the biases and oversimplifications that come with thinking in categories. 2) Continuous Data Analysis: Cultivate the ability to analyze data continuously to prevent categorical thinking-related mistakes in decision-making. Ensure that staff members receive adequate training on data interpretation to avoid misinterpreting information due to categorical biases. 3) Audit Decision Criteria: Promote a more adaptable and sophisticated method of decision-making that takes a wider variety of considerations into account. Make sure choice criteria aren't dependent on strict category distinctions or arbitrary thresholds by frequently evaluating them. 4) Challenge Categorical Structures: Actively challenge and question current category frameworks to avoid fossilization and encourage adaptation. Promote an attitude that is receptive to updating and reevaluating categories in light of fresh data or evolving conditions. By implementing these strategies, businesses can improve decision-making and overcome the limitations of categorical thinking. #BusinessStrategy #MarketSegmentation #ProblemSolving #CriticalThinking
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This resonates. A useful reminder of why data-driven decision-making is important for all types of organizations, including how to avoid its pitfalls. Some of these themes were also explored in my piece in Alliance magazine from a couple of years ago: https://lnkd.in/e8FbviCD What other aspects of data-driven decision-making need to be understood better? https://lnkd.in/dK73qAqk
Where Data-Driven Decision-Making Can Go Wrong
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Amy Edmondson makes some very important points here, and in this excellent article. As she says, evidence is rarely definitive – and often clouded by cognitive bias. Lazy leaders like to approach decision making as though they were flipping a light switch. On or off. Yes or no. But we live in a world with many shades of gray, and getting comfortable with ambiguity is the only way to navigate it successfully. #decisionmaking #choices #criticalthinking #redteaming #redteamthinking
"When considering internal data or the results of a study, business leaders frequently take the evidence presented as gospel or dismiss it altogether. Both approaches are misguided." In our current world, nuance is too often disregarded in favor of black-and-white thinking, especially regarding data analysis. But evidence is rarely definitive, and taking the time to discuss the nuances of analyses is vital to understanding how evidence can—or can’t—inform a specific decision. My new Harvard Business Review piece with colleague Michael Luca outlines five pitfalls to look out for when discussing data, plus how to make thoughtful choices based on information you have or might obtain. 📖 Read now: https://lnkd.in/gurXxYVX
Where Data-Driven Decision-Making Can Go Wrong
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Yes… nuance and context matters…
"When considering internal data or the results of a study, business leaders frequently take the evidence presented as gospel or dismiss it altogether. Both approaches are misguided." In our current world, nuance is too often disregarded in favor of black-and-white thinking, especially regarding data analysis. But evidence is rarely definitive, and taking the time to discuss the nuances of analyses is vital to understanding how evidence can—or can’t—inform a specific decision. My new Harvard Business Review piece with colleague Michael Luca outlines five pitfalls to look out for when discussing data, plus how to make thoughtful choices based on information you have or might obtain. 📖 Read now: https://lnkd.in/gurXxYVX
Where Data-Driven Decision-Making Can Go Wrong
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"When considering internal data or the results of a study, business leaders frequently take the evidence presented as gospel or dismiss it altogether. Both approaches are misguided." In our current world, nuance is too often disregarded in favor of black-and-white thinking, especially regarding data analysis. But evidence is rarely definitive, and taking the time to discuss the nuances of analyses is vital to understanding how evidence can—or can’t—inform a specific decision. My new Harvard Business Review piece with colleague Michael Luca outlines five pitfalls to look out for when discussing data, plus how to make thoughtful choices based on information you have or might obtain. 📖 Read now: https://lnkd.in/gurXxYVX
Where Data-Driven Decision-Making Can Go Wrong
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Another resonating read | “Collective intelligence is best when mechanisms are in place to promote active and diverse participation. […] Encouraging dissent and constructive criticism can help combat groupthink, make it easier to anticipate unintended consequences, and help teams avoid giving too much weight to leaders’ opinions. Leaders also must push people to consider the impact of decisions on various stakeholders and deliberately break out of siloed perspectives.”
"When considering internal data or the results of a study, business leaders frequently take the evidence presented as gospel or dismiss it altogether. Both approaches are misguided." In our current world, nuance is too often disregarded in favor of black-and-white thinking, especially regarding data analysis. But evidence is rarely definitive, and taking the time to discuss the nuances of analyses is vital to understanding how evidence can—or can’t—inform a specific decision. My new Harvard Business Review piece with colleague Michael Luca outlines five pitfalls to look out for when discussing data, plus how to make thoughtful choices based on information you have or might obtain. 📖 Read now: https://lnkd.in/gurXxYVX
Where Data-Driven Decision-Making Can Go Wrong
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Brilliant article from Amy Edmondson which is very timely. Boardroom discussions so often revolve around data 'as evidence' with Directors latching on to the data which 'supports' what they want to happen - known as #confirmationbias The article included the following questions which are really helpful: - Could you please provide more details about the context of the research to help us assess its relevance to our situation? - How do our wages compare with those offered by other employers competing for similar workers, and how does this relate to the findings of the study? - Was an experiment conducted? If not, what method was used to determine whether increased wages were driving the productivity changes or merely reflecting them? - What measures of productivity were utilised, and over what period were the effects observed? - Are there any other analyses or data that might be pertinent to our understanding? Read more to ensure effective decision-making in your organisation. #Directors #Boardrooms #PsychologicalSafety #Culture #ThinkOrganisation
"When considering internal data or the results of a study, business leaders frequently take the evidence presented as gospel or dismiss it altogether. Both approaches are misguided." In our current world, nuance is too often disregarded in favor of black-and-white thinking, especially regarding data analysis. But evidence is rarely definitive, and taking the time to discuss the nuances of analyses is vital to understanding how evidence can—or can’t—inform a specific decision. My new Harvard Business Review piece with colleague Michael Luca outlines five pitfalls to look out for when discussing data, plus how to make thoughtful choices based on information you have or might obtain. 📖 Read now: https://lnkd.in/gurXxYVX
Where Data-Driven Decision-Making Can Go Wrong
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Scrum Master at Agile Defense | Adjunct Professor at Saint Louis University | Chief Petty Officer at U.S. Navy Reserve
Statistical Diagnostics of a Healthy Business In my latest paper, I explore how Christian leaders can leverage statistical diagnostic tools to ensure the health and sustainability of their organizations. Rooted in biblical principles like wisdom from Proverbs 2:6, I discuss the importance of ethical, evidence-based decision-making in today’s data-driven world. Tools like Apache Hadoop and DeepDive offer solutions for managing dark data, a growing challenge in our information age. As Christian leaders, we must uphold ethical standards and integrate faith into our leadership practices to navigate these complex issues successfully. #Leadership #BusinessAnalytics #FaithIntegration #DataManagement #ChristianLeadership #EthicalLeadership
Statistical Diagnostics of a Healthy Business
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