Data-Driven Decision Making: Exploring Biases and Tools

Unraveling the Myths of Pure Data Analytics: Exploring Biases, Tools, and Quantitative Intuition with Oded Netzer

In this episode of the Fast Leader Show, we are joined by Oded Netzer, a Columbia Business School professor and Amazon Scholar. Oded, an expert in data-driven decision making, discusses common biases like overconfidence, availability, and confirmation bias that can hinder our use of data.

He introduces the powerful IWIK (I Wish I Knew) tool, which focuses on identifying key questions rather than complex statistical analysis. We delve into the concept of quantitative intuition, emphasizing the value of precision questioning, contextualizing data, and synthesizing information for decision-making.

Oded reveals the importance of combining data with intuition and building teams that understand both business and data science. He also discusses his book, “Decisions over Decimals,” which promotes starting with a decision and embracing uncertainty.

Finally, we examine the role of context in data analysis and the dangers of data without context. Join us as we navigate the nuanced world of data-driven decision making.

Oded Netzer was born and raised in the Mediterranean city of Haifa Israel. He has an older brother and sister. As a kid, Oded enjoyed playing soccer in the street with goals being simple stones they found on the roadside and taking apart parts of machines his father brought back home from work, which he never bothered to reassemble.

His father is a German Jew born a few years before WWII started. He is a holocaust survivor who spent WWII fleeing Nazi Germany to Poland, Siberia, and Kazakhstan. After the war, he immigrated to Israel where he met Oded’s mother. Having skipped his entire education during the war, Oded’s father taught himself mechanical engineering.

Oded is a first-generation college student earning his industrial engineering degree from one of Israel’s top institutions, the Technion, Israel institute for technology. Being a first-generation college student, he took the whole thing seriously and stayed in college forever. As a recovered engineer after his undergraduate degree he started working in consulting, this is where he developed his passion for research, analytics, and business decision-making. He then decided to move to the other side of research to academia where he did his Master’s in statistics and PhD in marketing at Stanford. It used to take him 15 minutes to explain what he does for a living, and then Data Science became a household word. Oded then replaced the sun of California with the city life of New York where he is Arthur J. Samberg Professor of Business and the Vice Dean for Research at Columbia Business School, an affiliate of the Columbia Data Science Institute. He also splits his time between Columbia and Amazon where serves as an Amazon Scholar at Amazon’s advertising.

Oded is a world-renowned expert in data-driven decision-making in extracting meaningful insights from data. He has published dozens of papers and multiple book chapters on this topic and has won multiple awards both for his research and his teaching.

Teaching analytics to MBA students and executives at Columbia, he noticed that managers often fear using data for decision-making because they erroneously believe that you need to be top of your class in math to do so. That led him to write a book with Chris Frank, and Paul Magnone about data-driven decision-making called Decisions Over Decimals: Striking the Balance between Intuition and Information.

Oded and his wife Susan, an HR consultant, reside in Manhattan next to Columbia University with their three children, Talia, Ella, and Aviv, along with their Cavalier King Charles puppy, Toby.

Tweetable Quotes and Mentions

Listen to @OdedNetzer get over the hump on the @FastLeaderShow – Click to Tweet

“Now that we have data, it is actually the intuition to pour judgment into the data. It’s the intuition in order to combine actually our business acumen, our experience, our intuition with information that’s arriving at us.” – Click to Tweet

“The biases that affect our use of data is the fact that we tend to judge the book by its cover, the fact that we tend to use the most readily available information to judge an action as opposed to digging in deeper when needed.” – Click to Tweet

“Data without context is dangerous.” – Click to Tweet

“FOMO is exactly the reason why we looking for this perfect decision, this illusionary perfect decision, this illusionary certainty.” – Click to Tweet

“One important thing about risk and specifically related to FOMO is something we call a two-way door decision versus one-way door decision.” – Click to Tweet

“What is needed are exactly these skills to ask the right questions of the data, the precision questioning, the ability to contextualize the analysis, whatever the analysts provide, put in the context of the business, and finally synthesize it into making decisions.” – Click to Tweet

“What you need is data translators. It’s actually almost a new and emerging career path, those who understand enough of the business and understand enough of the data science to speak the language.” – Click to Tweet

“Our goal…was first to demystify the myth of the perfect decision…to introduce…this new set of skills, this new muscle that we call quantitative intuition, to see how we actually, truly move into a better world of data-driven decision making.” – Click to Tweet

Advice for others

The skill of patience, the skill of embracing the journey right as you are going through it, as opposed to just looking towards the end goal, realizing that a lot of what is happening is actually in the journey itself, not only in the outcome.

Holding him back from being an even better leader

The ability to synthesize information, the ability to actually combining thing and connecting dots.

Best Leadership Advice

Spend time with the problem. Before you jump into a solution mode, spend time with a problem.

Secret to Success / Tools

Context – my capacity to critically evaluate information, drawing from diverse knowledge and environments, to uncover connections that might initially seem unrelated – a human skill that surpasses machines.

Links and Resources

Decisions Over Decimals website: https://www.dodthebook.com/

Oded’s LinkedIn: https://www.linkedin.com/in/oded-netzer-700255/

Oded’s Columbia profile: https://www8.gsb.columbia.edu/cbs-directory/detail/on2110

Fast Leader Show on YouTube: https://www.youtube.com/c/FastleaderNet

Fast Leader Show on Apple Podcast: https://apple.co/364qAA2

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Fast Leader Show on Twitter: https://twitter.com/fastleadershow

Fast Leader Show on LinkedIn: https://www.linkedin.com/company/fastleader.net

Check out other episodes on data insights: https://www.fastleader.net/dei-strategies-data-backed-insights-leadership

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Podcast Transcript

0:00welcome to the fast leader podcast where we uncover the leadership life hacks that help you to experience breakout

0:06performance faster and rock it to success and now here’s your host customer and employee engagement expert

0:12and certified emotional intelligent practitioner Jim rimbach call center coach develops and unites

0:18the next generation of call center leaders through our e-learning and Community individuals gain knowledge and skills in the six core competencies that

0:25is the blueprint that develops High performing call center leaders successful supervisors do not just

0:30happen so go to call centercoach.com to learn more about enrollment and download your copy of the supervisor success path

0:37ebook now okay fast leader Legion today I’m excited because we’re going to have a great discussion on something that

0:43quite frankly is a massive opportunity for a lot of leaders at all levels and

0:48that’s to increase their business Acumen and abilities in regards to using data

0:54in their decisions you know oftentimes we think we do that effectively but we’re going to find out differently and

0:59but yet how to become more effective in using that because we have ODed netzer

1:04on the show today and ODed was born and raised in the Mediterranean city of

1:10Haifa Israel he has an older brother and sister and has a kid ODed enjoyed

1:15playing soccer in the street with the goal of being with with the goals being simple stones that they found on the

1:21roadside and taking apart parts of machines that his father brought back from home which he never bothered to

1:28reassemble his father is a german-born Jew a few years before World War II started he is a holocaust Survivor who

1:35spent World War II fleeing Nazi Germany to Poland Siberia and then Kazakhstan after the war he immigrated to Israel

1:42where he met oded’s mother having skipped his entire education during the war odette’s father taught himself

1:48mechanical engineering Odette is a first generation college student earning his industrial engineering degree from one

1:54of Israel’s top institutions the technion the Israel Institute of support technology being a first generation

2:01college student he took the whole thing very seriously and stayed in college forever uh I was a recovered engineer after his

2:09undergraduate degree he started working in Consulting and this is where he developed his passion for research

2:14analytics and business decision making he then decided to move to the other side of research to Academia where he

2:21did his Masters in statistics and PhD in marketing at Stanford it used to take him 15 minutes to

2:28explain what he does for a living and then data science became a household word odid then replaced the son of California

2:35with the city with the city life of New York where he is Arthur J Sandberg professor of business

2:42and the vice Dean for research at Columbia business school an affiliate of

2:48the Columbia data Science Institute he also splits his time between Colombia and Amazon where he serves as an Amazon

2:54scholar at Amazon’s advertising ODed is a world-renowned expert in data-driven

3:00decision making and extracting meaningful insights from data he has published dozens of papers and multiple book chapters on this topic and he’s won

3:07multiple awards for both his research and his teaching teaching analytics to MBA students and

3:13Executives of Columbia he noticed that managers often fear using data for decision making because they erroneously

3:18believe that you need to be top of your class in math to do so that led him to write a book with Chris

3:25Frank and Paul maggione about data-driven decision making called decisions over decimals striking the

3:32balance between intuition and information Odette and his wife Susan who is an HR consultant reside in

3:38Manhattan next to Columbia University with their three children telia Ella and Aviv along with their Cavalier King

3:46Charles puppy Toby oh dead are you ready to help us get over the hump for sure thank you Jim thank you for

3:52this very nice uh introduction and looking forward and thank you for hosting me in this podcast ah it’s great

3:58to have you no I I really as as I before we started recording I shared with you I’ve you know went through the book and

4:06I’ve actually been using some of the Frameworks um that that are in the book and to me

4:12it being someone who is you know analytically minded I I double

4:17majored in finance and real estate and you know you know I I get the thrill for

4:23a lot of the data-driv decision opportunities that exist and want to make sure I take advantage of myself and

4:30so I think this book is an opportunity for all of us to learn more but and so the thing that I like at the very

4:36beginning in the book you actually introduce something called quantitative intuition now being somebody who’s also

4:42certified in EQ emotional intelligence and and things like that I um I was really intrigued by that but I think

4:49it’s important for us to start with explaining what is quantitative intuition

4:54yeah thank you yeah quantitative intuition you know it sounds like an oxymoron right quantitative and

5:00intuition information intuition do not go together um with a you know with the arrival of

5:05Big Data you know the capital b capital D all of this flow of information that came to us there was this um hope and

5:13maybe belief that finally we’ll get rid of all of these biases that often come with making decisions based on intuition

5:20and finally we’ll get to this perfect to this certain decision and of course this is a myth the perfect decision does not

5:27exist there’s certain decisions do not exist we still need to pour in intuition into into decision making but it’s

5:34different than maybe the intuition we needed we needed to have when we didn’t have data at all now that we have data it is actually the

5:41intuition to poor judgment into the data it’s the intuition in order to combine

5:46actually our business Acumen our experience our intuition with a information that arriving at us and

5:53specifically we break down this notion of quantitative intuition into three pillars into three domains the first one

6:00is precision questioning the importance of very early on asking yourself what am I looking for because again with the

6:06flow of Big Data we can get easily lost in just jumping straight into solution mode into the

6:13data the second is this notion of a how do we interrogate the data how do we

6:20um how do we contextual the data how do we put it in the right context in order to arrive at some insights and finally

6:26in order to arrive at the decision how do we synthesize the information to to make a decision

6:33well okay so as you’re talking you know you hit the thing about the perfect decision being an illusion right so we

6:39have to get more effective at it um and we and and but as you were talking I also start thinking of the

6:45efficiency of it I mean for let’s talk about reality you know we’re we’re in a sound bite world you know we need to

6:51make quick decisions I mean so to me I think you’re you’re often looking at

6:57essentially the mental load associated with this and someone’s saying well you know I can go through this process I can

7:04go through this framework but heck man I need to get results you know so I mean what what’s the you

7:11know what’s the risk in thinking like that and essentially skipping what you’re talking about

7:16yeah I mean the risk is that that really we are spinning right um when when we start working on

7:21something particularly when data is is available and when that is available we want to jump straight into into the data

7:27we want to jump straight into analysis because it feels like working sitting seeing at home or thinking sitting in a

7:32meeting and thinking carefully about the problem doesn’t feel like working and jumping straight in the data sending the

7:39analyze the analyst to go and crank some some numbers let’s feel like we’re progressing we’re doing something but actually what we’re doing is spinning so

7:45really the risk of of looking for these certain decisions looking for this

7:50elusive certain decision is keep spinning with with um with more data you know there is we

7:57in the book we call them the cmors the seymours are the ones who in every meeting the only thing they ask is I

8:03want to see more data that’s exactly the spinning towards the perfect decision yeah we can always collect more data but

8:09at what point do you tell yourself I need to act we need to act faster and faster than ever it’s time to make the

8:16decision with the data we have and and pouring some judgment into it okay so

8:21then with that if we’re starting to talk and part of even what I mentioned about maybe considered a bias you met you talk

8:27about several different biases associated with us being able to make you know um decisions and that could

8:33affect our entire decision making process and I mean there’s a lot of different things that we can use in

8:40order to be able to describe that but what are some of the key biases that you often find people run into that are

8:46preventing them from moving forward moving forward more effectively yeah I think that kind of you can maybe

8:51categorize these biases into the biases that prevent us from using data or using

8:57it correctly and the biases that arise when data comes in so that actually both sides of biases so let’s start maybe

9:04with the ones that that prevent us from getting into data and probably chief of this is overconfidence

9:10we tend to be over overconfident in the things we don’t know and because we’re overconfident about our knowledge of the

9:15answer we don’t seek data we don’t seek information from others and I use your by the way data in the most General

9:21sense of the world data having a discussion with you I’m learning something I’m collecting a data

9:28overconfidence prevent us from doing that so we need to to overcome this to realize there are some things I know but

9:34I want to constantly check myself versus data another one that maybe is related to to looking at data is what is called

9:40availability bias the fact that we tend to judge the book by its cover the fact that we tend to

9:47um the the cover of a decision over decimal it can definitely judge it’s a great cover uh but the fact that we tend

9:54to use the most the most readily available information to judge an action as opposed to digging in deeper when

9:59needed the problem though when we get data there is a whole other set of biases and probably the most common common one

10:06there that we’ve all experienced is confirmation bias we tend to look for data where we already we tend to look

10:11under straight line we tend to look for data that supports exactly are preconceived a notion now unfortunately

10:19there is no Surefire way to avoid biases we are it’s so wired in in human

10:24behavior but there are few things you can do and and two things that I

10:30mentioned the first one is always contrast your intuition with data data is actually a good guard for biases

10:36because often come with intuition when I contrast this with data and particularly

10:41pay attention to the places where they diverge I can actually learn to avoid biases the second one is having diverse

10:48team and again I mean here diverse in the most General sense of the war diverse and

10:53data effectual unless they’re fake intuition is individual it’s personal so

10:58we all come with our own maybe a personal biases diverse teams helps put

11:04checks and balances on each individual bias of any any particular individual

11:10so I I think when you start thinking about a lot of our listeners are focused in on the customer and the customer

11:16experience the amount of data that we get in regards to customers experiences it’s just it’s absolutely immense and

11:23it’s continuing to grow at an ever increasing rate just like all other sources of data I mean okay so now let’s

11:29add the whole iot and internet of things and now I can get you know an end user

11:34you know data I can get all of this type of data uh you know at some point you know you really start needing to know

11:43that probably 95 of the data that you have access to is really irrelevant data

11:50you know so how I mean when I start thinking about one of the Frameworks that you have in the book it’s called

11:56iwik and there’s a lot of way I’d mentioned to you that I actually used it in a

12:01sales discussion call talking about iwik which is because a lot of times what we deal with well in a lot of different

12:08ways is a status quo bias right well hey this is what I’m doing and I’ll just keep doing what I’m doing right but but

12:15of did you know this about continuing to do what you’re doing right so if you could explain what is iwik

12:22yeah sure thank you well I mean first you know customer analytics this is at the heart of what I do for living both

12:28in my academic world I mean I’m a marketing Professor but the data scientist marketing Professor so exactly

12:34in this area and at Amazon dealing with again Amazon advertising talk about a place where a lot of decisions are being

12:41made based on customer analytics data and so very much a close to heart this this type of

12:48um environment and I week we call them it is iwik we call

12:53them iweek I wish I knew um and it’s a deceptively simple but

12:58extremely useful uh tool and for leaders for managers to hone in on the essential

13:05question to hone in on what is it that we really need to know in order to solve a problem and what we proposed during

13:12this approach is that at the start of a project and sometimes or the question

13:17that comes up a data data type question that comes up and sometimes throughout the process you go and actually ask both

13:24the person who asked for the the business question as well as sometimes those who are dealing with the data what

13:30is it that you wish you knew in order to solve that problem and that’s just I mean I mean a key in

13:37that that question is the word wish it’s such an opening question it’s not an inviting question I’m not asking you

13:42about statistical significance I’m not asking you about running Excel and and complex analysis what do you wish you

13:49knew to solve the problem and now I it helps me guide my process if I’m the analyst of looking where needed as

13:56opposed to jumping into the the you know the Fire Halls of data out there and looking for exactly the things that

14:03whoever is asking the business question a wish they knew we found it to be by the way very useful in going both

14:09hierarchically in the organization you can ask it all the way to top leaders what is it that you wish you knew we

14:15have a problem what is it that you wish you knew as well as a horizontally and and sometimes

14:20um even vertically a a more to people that work for you and and they they’re

14:26kind of two steps to this process to the ivic process first you ask the question by the way when we experience and then

14:32as you mentioned kind of picking up the book and and using it and first we plan this book not to be a one-time read but

14:38more one that you return to when exactly as you did when you say oh I have a sales call let me see how do I use this

14:44I week and first time you use it it’s kind of a sometimes gear isn’t a headlight I mean what do you mean I

14:50never thought about what I wish I knew right and Chris Frank he’s the the VP of a customer insights at American Express

14:57and of MX what’s called MX insights he runs the entire customer insights at

15:03American Express at this point it became so ingrained in the organization that people come to him and say here is what

15:10I I here’s the problem and here are my I weeks in the start it’s difficult but as you as you exercise it becomes easier

15:17and easier now the two steps to the process first you just collect all of the ibix at this point we are very

15:24expensive I’m willing to take any IV you want you want to have 20 30 40 often we

15:30get by the way to a list of easily 70 of these iwicks right the next step is super important now we’re going to put

15:36them on a kind of a two by two typical metrics of do I really need to know them in order to address the problem and do I

15:43already know them or not because you know Arabic for one person may be known for another and then we truly need to

15:49focus our efforts only on the one that I don’t know and I really need to know and that becomes much shorter list and and

15:55again helping us focusing our our exploration is something to be done early on in the Pro in the process for

16:01sure and sometimes throughout as you are crossing off some eye week saying you

16:06know this we already found and maybe adding new ones as you’re exploring new new topics okay you just hit on

16:13something that I think is one of the major flaws in people being able to be more effective in their decision making

16:19so let so what you just talked about is being able to separate out Divergent

16:24thinking and convergent thinking and what happens in the way that we’re trained in our educational system in the

16:30Prussian school system is that we Collide those two things together and so what ends up happening is as we start

16:36adding our eyewicks um we start refuting them and downplaying them and discounting them in

16:43the process and that’s a problem that’s a major flaw that’s prep that’s

16:48perfect exactly I mean our process is B is Divergent as you can in the first

16:54step do not worry at all about convergence right conversions would come later I I 100 agree with that uh that

17:00description the importance of not combining the two of separating the two processes well and then like I said

17:06unfortunately we’ve been trained to do that our entire educational life right right it’s like oh my gosh now I have to

17:12do something different yes you do indeed indeed you also talk about something that I refer to as retrospective

17:19engineering which is main which is basically okay this is the end this is the goal this is what we want to achieve

17:25now let’s work backwards from there right I refer to it as retrospective engineering you refer to it as something

17:31a little bit different if you could please explain that yeah and you know we often

17:39often making a what this referred to is more decision-driven data analysis let’s

17:45start not from the data but let’s start from the decision now from the decision I don’t mean the decision of we should

17:52do X because if you already know you should do X don’t look for data just do X if you’re convinced decision means I

17:58have at least two branches I I need to decide yes or no I need to decide go no go acquire not acquire merge not merge

18:06um did not doesn’t need to be two it can be you know five different branches should I Target these these so that segment should I invest in one of five

18:13different features in the product um five different features of a ux design all of these are our decisions to

18:21be made and now I’m going to ask my I weeks I’m gonna ask well what is it that I wish I knew in order to make that

18:26decision I even gonna create tables that are going to be what we call dummy tables or both tables empty tables with

18:33only headers to them saying if you give me these tables built in of course there will be nothing at them at the start of

18:39the process I can make the decision and now I truly guide the the path for the

18:44analyst to say fill in these tables if you fill in these tables I’ll feel confident to to make these that a

18:51decision and a company that does it by the way is Amazon Amazon definitely works backwards and in the sense that

18:58for example Amazon has this thing called PR FAQ and when they are thinking at Amazon

19:04about about introducing a new product both externally and internally a new algorithm and anything anything that is

19:10innovative within the company you write something called prfaq press release so

19:16it’s today but you write a press release for 2025 Amazon or team just introduced

19:21that um a product and then the FAQs are more the question that are likely to come these are the

19:27ivix so a very similar approach to to what we described that you mentioned that and kind of use

19:33that example I actually use that in a sales approach as well I wrote a press release it was a mock press release

19:39about partnering with a particular company and I sent it to some Executives and it got their attention exactly

19:48it’s amazing how that works uh okay so um um you you talk about also as part of

19:55this process that we need to become a fierce data Integra interrogator what is

20:00a fierce data interrogator yeah I mean I mean First Data interrogator is often not what people

20:05think it is remember we talked earlier on when you introduced me you talked about the fact that people are afraid of

20:10this um that they don’t approach data because they believe they need to be these data mavens these Excel experts or

20:18even python experts in order to deal with data and and it is a myth I mean not that we don’t need data analysts and

20:24again I’m both of my head both of my feet feet are within data I’m a data scientist but in order to interrogate

20:31data the skills rarely sit within um the data analyst job or within the

20:36books and statistics to interrogate data the the way to interrogate data is

20:41actually providing context to the data looking at the data from the context of the data and asking does it make sense

20:47and specifically what we mean by context is taking a number that you see in front

20:53of you and ask yourself how does it fare relative in absolute do I care about it does does this data

20:59matter that tons of things that are statistically significant and is really managerally totally irrelevant so is it

21:05relevant in absolute terms is it a um how does it look relative to

21:10history have been have you been fluctuating up and down five percent and you’re telling me now we are down three

21:16percent well that makes sense I mean that’s not new information right and ask yourself how does it look

21:22relative to competitors right I mean how often do we you know we go to the office celebrating you know our stock is five

21:28percent up and then someone asked the question how did the s p do today six percent right

21:33um compare the data put it in the context and and the reality is that the analysts rarely have good context

21:39because they work within their their data analysis team it is US leaders who often have the context to actually

21:46interrogate data so don’t be shy interrogating data because you actually do have the tools to do it by putting

21:52this this data in context I’ll give you an example of maybe the the wrong data interrogation

21:58um p-values or this confidence 95 level confidence right statisticians always

22:03look for these and I I can’t tell I can tell you how many times I had an analyst come to my office celebrating I found an

22:11a significant result or significant effect right now let’s think about what it means

22:16being 95 confident it means that whatever statement you’re making from a statistical point of view you’ll be

22:24wrong one out of 20 times think about a business decision making

22:29being wrong one out of 20 times pretty good no I mean most leaders would

22:34definitely buy being grown wrong one after 20 times wrong one at one out of ten

22:39not bad right I mean being right four out of five times

22:44statistician’s ears are burning when they hear point two but but what is the level of confidence I need

22:51in order to make the decision if you are sending a shuttle to space being wrong one out of 20 times that’s

22:57horrible you’re gonna blow this thing up if I’m recommending to you books and movies on Netflix or books on Amazon

23:05guessing it eight out of 10 times that’s beautiful so depending what is the decision we are making we should match

23:10the level of confidence so don’t be swayed necessarily just by the statistical ways of interrogating data

23:17context is is King data without context is dangerous you know as you say that uh here this

23:23week I’ve been doing a little bit of analysis on something where when you start thinking about our decision making

23:30and we start thinking about influences and Persuasions and you know we always we’ve heard the acronym many times since

23:36advertised a lot about fomo fear of missing out right um but when we start talking about a lot

23:41of these decisions that have to be made and like you talked about some of them being you know

23:47um let’s just let’s just say you know humanly important you know so people don’t die

23:53um is there’s a fomu factor fear of messing up uh so you know maybe this is part of the

24:00whole bias thing I mean maybe this is part of um you know not using Frameworks properly but what you know when I say

24:06fomu how does that play into you know decision making and decimals and and you

24:13know being able to move forward more effectively yeah I think it’s a crucial Point

24:18um fomo is exactly the reason why we looking for this perfect decision this

24:24illusionary perfect decision this illusionary certainty because once we get to the the third the Nirvana of

24:30certainty once we get to the Nirvana of the perfect decision there is no more formal but but we never get there now

24:36the reality is that um you know if you think about a personal life we feel very comfortable using intuition in our

24:42personal lives we get to to our our work life and then we are saying well there

24:48is data therefore I should not be it I could not have any more any fomo anymore right and again that’s that’s a myth we

24:54we should be focusing on the data we have and and leadingly leading us to the

25:00point in which we are confident enough to make that a decision and decisions

25:05are hard I mean decision by definition are a point of change and we are not

25:10wired to change that’s why people not organizations by the way but people fear decisions and what we are

25:19proposing in the book and that’s why it’s called decisions over decimals is start with the decision ask yourself

25:24what do I need in order to feel confident enough about that decision realize that a former former will exist at the end as

25:31well they will not have all of the information needed you need to get to a point that where you are confident about

25:36enough about making these and and and feel comfortable with that with that uncertainty

25:42um big part of the of the book and the tools we are providing in the book is is how to feel comfortable with that

25:47uncertainty that will will remain in the end well and you may have hit on this a little bit but maybe we need to kind of

25:53put a little bit more of a framing around it you talk about the decisions

25:58of uh dimensions of the decision moment so what are the different dimensions of

26:04decision making yeah and this is by the way very much related indeed to the previous point of

26:09of a how do we a handle fomo is indeed by thinking through the dimensions of

26:14the decision so and we talk about three Dem 3D and dimension to a decision it’s very important to sculpt your decision

26:21before you go in and and and collect the data or even start approaching it time

26:28that that’s one um Dimension risk is the second one and trust is the the third

26:34one right let’s talk first about that the first two time and and um risk right

26:40do I need to make the decision today or do I have time if you need to make a decision today you probably have less time to go and investigate a data if you

26:47are if you have a lot of time that’s often when we start getting into more of a you know group decision making and

26:53meetings and so on and so forth right risk is is super important right how

26:59risky is that a decision and again if a decision needs to be made with a short

27:04time and and little risk we often do not use data at all you know I need to choose whether I’m going to

27:10drink coffee or tea for lunch low risk low time need to be happened to

27:15air today and we don’t we don’t use any data if we mean decisions have high risk

27:21in in short time that’s where we truly need to focus on what is truly essential for me to do realizing I will not be

27:28certain I will not be certain at all about that decision but um I will I will collect the the

27:34appropriate data when we have both time and risk is where we are involved in these again these these meetings and and

27:40group decision making one important things about and think about risk and specifically related to formal is

27:48um something we call an Amazon calls it as well a two-way door decision versus one-way door decision

27:53you can categorize all decisions into is it reversible or not can I walk back on my decision and we we

28:02tend to treat all decisions and as one way door and that’s why we are so afraid of these decisions that’s why we collect

28:07so much data that’s why we have so much formal first of all ask yourself can it be a two-way door decision because

28:13that’s what would just lift the weight off your chest right I’m okay I’m making it but I can walk back now if you do

28:21find that that decision is unfortunately a one-way door meaning you cannot walk back ask yourself can I break the

28:27decision down into intermediate decisions that are still toward our decisions again buying you some time and

28:34some um and and comfort with the with making these decisions so a very

28:39important aspect of of decision making is one way versus two-way and that should guide the way

28:45you go about collecting data and and the depth in which you go you go about it

28:51um that yeah also is another one of those very vital points uh when you start talking about

28:57um you know first of all if if sometimes we can make things more you know talk

29:03about turning a mountain into a mohill a mohill into a mountain right sometimes we think it’s a massive decision but if

29:08we chunk it down there’s a lot of little decisions that could be made all along the way which would help to enable it to be something that we can back away from

29:15I think you know that’s why especially when you start talking about in the technology World they have pocs proof of

29:21Concepts we have you know you know I think the whole free trial thing is a totally different animal I

29:27agree yeah it’s um but I think that’s a that’s a great thing for us to always remember matter

29:33of fact um for those who’ve listened to the show you may know that I’m a varsity baseball pitching coach and one of my

29:39kids is actually a great football player and so he was talking he’s a senior and he was talking about his you know next

29:45opportunities and he was talking about different schools and things like that and I said look and then you can just I

29:50could just see his shoulders kind of sinking from the pressure and I’m like look I said they have the transfer portal I

29:57said you know they have all kinds of you know different opportunities um I said the thing for you is you need

30:02to know that this isn’t the the big the end of all things in regards to the choices that

30:07you make and you can just kind of see the relief right right and so that is

30:12your making decision about this school or the other ask yourself where do I move from there what is the can I is it

30:18a one-way door right a two-way door decision where I can actually move there’s some schools where you’re less able to do so versus others should

30:24something it should enter into a decision yeah yeah I mean and also you have all these other factors is like you

30:29know am I getting the education that I want and am I getting playing time I mean you know when you start talking

30:34about I wish I knew right it’s like oh I I start seeing applications of this in so many different ways both personally

30:40and professionally and I want to thank you and and your co-authors for definitely bringing it to light um okay so I start looking at and it’s

30:48part of kind of what we’re talking as well is yes there’s risks and decisions but how do we properly assess and do all

30:55that yes but I start thinking about what if I don’t do a better job

31:01of actually you know making and using my skills associated with Dread that is

31:07driven decision making what is the risk if I don’t improve my skills in this area yeah I think that the the risk is truly

31:14the there that um the world has changed on us right um you know back in the I don’t know if

31:20you uh remember the around 2010 there was this whole push towards STEM Science technology engineering and math high

31:27schools colleges data science programs I mean even just here at Columbia we have

31:32about five different data science programs and we have one in the business school one in the um we have a whole

31:37data Science Institute we have one in engineering with the School of Professional studies we have really

31:42infused a lot of these what I like to call nerds that like to sit in rooms without windows and really big computers

31:48and we infused into the world in in an area by the way they didn’t have before data scientists uh these data analytics

31:54skills uh it previously it was primarily Tech and finance now we start seeing them in in human resource in in CX which

32:03starts seeing them in in consumer package Goods but what we haven’t done is we haven’t

32:09trained the level of leaders to live in a world in which all of these exist and again I don’t mean here studying Python

32:15and understanding that they they you know the shiniest three-letter acronym for machine learning

32:20um and in fact I don’t know how many of you have played with a chat GPT by open AI that was released

32:26um recently I mean this type of of tools would

32:32replace actually a lot of the decoding aspect of things what is needed are exactly these skills to to ask the right

32:39questions of the data the Precision questioning the ability to contextualize

32:44contextualized analysis whatever the the analysts provide put it in the context of the business and finally synthesize

32:51it into into making decisions and if we fail to do to do that what what will happen is is pretty much spinning I

32:58think that’s that where we are finding ourselves often today spinning in meeting after meeting after meeting and

33:03in fact one of our Chris Paul and myself one of our our main objective behind

33:08writing this book is to have fewer and better meetings and eliminate a lot of

33:14that spinning that is happening well even when you say that you know you start talking about the proliferation of data science and stuff you know same

33:20things applying to baseball um so I actually was at a conference with a bunch of college coaches and pro

33:27pro coaches and Scouts and this this this one college coach said in reference

33:34to all the data and the data analytics he says but here’s one thing that we know about all this data and the analytics he goes

33:41you can’t teach a new dog old tricks yeah it was referring to is what you’re

33:48talking about you know I need to be able to take this data and synthesize it with you know the the experiences you know

33:55the the what we do know I mean and it’s like yeah we got to bring those two things together

34:01and that’s exactly what happened in Malibu a lot of these indeed they started moving all the way to the data and then realized these experts have a

34:08clean information let’s combine the two as opposed to just all in the data or

34:13only in the world of these experts who know how to recruit players well and then the other thing is you know and and

34:19we touched on it a little bit when you started talking about diversity of thought and diversity of thinking and diversity of perspectives well in the

34:25world of positive psychology they talk about uh when you put together those types of

34:31um teams if you want to just call it that you have to look for something what they call unlikely pairs

34:37um in order to be able to increase that ability of perspective taking into you

34:42know really even quite frankly blow out the iwik right right if but because if

34:48everybody thinks the same and they’re coming from the same perspectives my list of iwik may only be five where if I

34:54have unlikely Pairs and I put these people together that wouldn’t necessarily be you know collaborating

35:00together it’s like boom the number just kind of like it’s a 10x Factor right right and we talk in the book about the

35:06how do we build kind of a Qi team exactly what you’re saying the team sports of Qi not now not just the

35:11individuals and and you know people tend to think that that if I want to be data driven I need to again recruit a lot of

35:18these kids who like to sit in room with no windows and really big computers a lot of the data scientists a lot of the but you know yes you need them meaning I

35:26don’t know how to open a file that is more than million rows long because it doesn’t open Excel I do need a data

35:31scientist to help me but it’s a necessary and not sufficient condition right if I want to make decisions with

35:36data when they need is first by the way you need another um very much technical expertise which

35:42is the data Engineers the plumbers those who actually allow you to have access to the data in the data scientists cannot

35:48work without the data engineer right and then at the other end of it what you need is data translators exactly and you

35:55almost a new and emerging career path those who understand enough of the business and understand enough of the

36:02data science to speak the language right and actually a lot of our mdas here at

36:08Columbia business school I think are exactly on that path of um you know they’re taking python courses in fact we have at this point

36:15about 500 or even more than 500 of our students choosing an elective on python

36:20if you told me that a few years ago I would tell you out of your mind and but they realize that they’ll be serving in

36:26these roles of translators sitting between the group that does the analysis and the business unit and in in in in

36:32the same table you’ll have a data scientist an MBA a ux engineer a data

36:39engineer this is a type of discussion that are happening today and not only in tech companies but but starting to

36:45happen even in other companies so we’re getting there I mean this looks like you said 10 years from now I mean I

36:51think people will be even more Savvy in regards to you know doing better jobs of making teams that are

36:56um their Qi quotient goes way up right right good all right so when I start

37:01looking at you know this book the work that you’ve done uh the work that you’ve done the work that you are still yet to

37:07do I start wondering to myself what what is one of what is one of odette’s goals

37:12so what was one of your goals with what you’re doing and where you’re going right now yeah so I mean our my goal our

37:19goal at Chris Paul and myself uh with writing this book was was first to demystified demystified this myth of the

37:27perfect decision the demystify the myth of you need to be double progressed in math in order to make decisions with

37:33data to have fewer and and better meetings but also to to introduce indeed

37:39this new set of skills this new muscle and that we call quantitative intuition

37:44to see how we actually truly move into a better world of data-driven decision

37:49making to to have a better better data driven decision making and as you mentioned I mean one of the things you

37:55learned when you write a book is that you’re never done writing a book you at some point you know you publish it but

38:00you’re never done because you know the thinking keep keep evolving and and the leading to possibly the next book but a

38:07um it’s a it’s a definitely a continuous process and we very much enjoy it we are teaching it by the way at Columbia we’re

38:14teaching it to our um several executives education programs as well as to the executive mbas and so

38:20through this we also keep evolving our thinking as we are talking to more and more executive and the feedback loop of

38:27um how our decision look how do decisions look today right in today’s world of data and the fast deer Legion wishes you the

38:34very best all right here we go fast lead Legion it’s time for the home day oh now

38:42is a part of our show where you give us good insights fast so ask you several questions and your job is to give us

38:48robust yet rapid responses that are going to help us move onward and upward faster oh dead netzer are you ready to

38:54hoe down ready for it all right so what is holding you back from being an even better leader today

39:01what is holding me holding me back is um the ability to synthesize information the ability to actually combine anything

39:08and connecting dots I’m I’m improving at it but I think it’s something I can I can do even better and what is the best

39:15leadership advice you have ever received synthesize and going back but am the

39:21best I guess leadership advice I I received was um to to spend time with the problem before

39:29you you jump into into solution mode spend time with a um with a problem and what do you feel

39:36is one tool that you believe helps you in business or life context and the ability to criticize to

39:44look critically at at um information by trying to bring in

39:50Darth points of evidence that are not exactly there that coming maybe for different contexts from different

39:56environments and being well read well uh um knowledgeable about the environment

40:02truly help with things that seemingly seem um disconnected eventually you actually

40:08you see you see the connection that’s truly um why we are better than machines

40:14and what would be one book you’d recommend to our Legion and it could be from any genre of course we’re going to put a link to decisions over decimals on

40:21your show notes page as well yeah I am and everybody Lies by Seth davidovich

40:26um and of course apart from decisions of a decimal but um Everybody lies is an interesting book

40:32I mean Seth is a he’s a he was an economist at Google and what he he talks about in the book is what can we learn

40:39about humans about people from what they are searching in the privacy of their home in their Google bar

40:46um in the Google query right and his point is that we are as intimate with the Google query as we are with anyone

40:52in our life now of course he doesn’t talk about looking at each one of us personally but more in the aggregate

40:58there are tools the tool called Google Trends we can see the trends of what people are searching for and fascinating

41:04insights from that book okay fast literal engine you can find links to that and other resources at uh

41:11oded’s show notes Page by going to fastleader.net and just doing a search for Odin and that’s o-d-e-d

41:18um his last name of course is netzer but I think he’s the only oh dead that we have actually never ever had on the show

41:24and probably only be the one that ever will but ODed this is my last hump they held on question imagine you were given

41:30the opportunity to go back to the age of 25 and you’ve had the Knowledge and Skills that you have now but you can’t

41:35take it all back with you you can only choose one so what skill or piece of knowledge would you take back with you

41:40and why I guess the skill of

41:45um of patience the skill of a of embracing the the journey right as you are as you

41:53are going through it um as opposed to just looking towards towards the end goal realizing that a

41:58lot of what is happening is actually in the journey itself not not only in the outcome oh Dad I had a fast I mean I had a

42:06fabulous time chatting with you today how does the fast leader Legion connect with you and you can connect with me on

42:12a first around the book that’s on a DOD the book.com and you can find me on the

42:19Columbia website at uh on a dead nature and on the Colombian business school website or

42:26um through my LinkedIn page again Odette netzer as well as um my

42:31um my webpage again which you can get through the Columbia page or at um again

42:37or that nature at the Columbia business well Dad netzer thank you for sharing your knowledge and wisdom and helping us

42:42all get over the Hub thank you for joining me on the fast

42:48leader show today Recaps links from every show special offers and access to

42:53download And subscribe if you haven’t already head on over to fastleader.net so we can help you move

43:00onward and upward faster

Data-Driven Decision Making and the Biases That Affect Our Choices