In this article, and related podcast, we are addressing a sensitive issue: racism and other forms of discrimination in management consulting.
It is important there is honesty in this discussion because while discrimination is a major topic in management consulting, it is usually hijacked for personal reasons and personal causes. Many take events and try to convert them into poster cases for their own causes.
It is better to examine each case on an individual basis as well.
We will talk about a McKinsey consultant, lets call her Abbie, whom we helped place into the firm a few years ago. She was one of our very first clients. Personally, she was one of my favourite clients, and remains one of my favourite clients.
Over the last few months we’ve had a series of discussions with Abbie who felt that due to her racial profile she is being passed up for promotional opportunities and her salary is not advancing at the rate it should.
Abbie perceived this primarily as a racial discrimination issue. And she has positioned it that way. Abbie was seriously considering taking legal action when she approached us for advice. I believe some over-zealous lawyers also played a role in convincing her of this path to take.
I will talk you through the way we analyzed this problem for Abbie. This analysis helped her understand her options and whether or not she is a victim of racial prejudices.
And I am not convinced she is. I’ll show you why.
Consultant’s background: Abbie is quite young. She is definitely a fast-tracked consultant. She has an undergraduate degree and an MBA from a prestigious Ivy League school.
Abbie is not an American. She grew up in a European country and works in a European-ish McKinsey office. Abbie became a McKinsey consultant after her MBA and was moved around quite rapidly and promoted unusually fast.
She attended the right schools, worked with the right professors for her MBA, interned at the right places, joined the right clubs, is part of the right network and up to now she was a quite successful McKinsey consultant.
She is someone who’s done remarkably well for herself.
This is obviously a topic that is so sensitive that I was initially considering not addressing it. What changed my opinion was that the process of analysis with this McKinsey consultant, in both our opinions, saved her career.
At the beginning Abbie was very emotional about this. She was angry. She was not seeking advice on what to do. She wanted to know how to sue. Period.
Yet, by rationally talking her through the way I saw it, she swung around 180° and started going in a different direction, where she became increasingly engaged and her consulting career actually swung back on track.
Abbie presented four pieces of evidence to support her view she is being held back for racial reasons. And my job, well she did not ask me really, but my job was to make sure she would not obliterate her career by misreading these data points. We do not allow clients to take actions that will harm them.
The four data points this McKinsey consultant presented are outlined below.
1st data point – consultant’s salary. Basically what she had shown is that her salary initially exceeded that of peer consultants. Yet, over time her peer group overtook her with regards to salary. These are not big numbers. Her annual salary was exceeding salaries of peer consultants by $5,000-$10,000 and over time they caught up and overtook her by a similar amount. The firm typically pays within a very tight band.
So this is the first data point Abbie had, based partly on anecdotal evidence and partly on pay stubs she seen from a few peers, since she has a good relationship with them.
Only some of this data is corroborated by the scans she emailed in. But let’s assume all is true.
2nd data point – performance reviews of peer consultants. The second piece of information Abbie collected is peer performance reviews. What Abbie has done is look at some of her peers’ performance feedback at the end of the consulting projects and the end of the year. She also compared and contrasted the performance of peer consultants against her own performance.
Here Abbie is partly relying on the written feedback from the firm. Second she’s relying on her observations of colleagues on a study.
3rd data point – her performance reviews. Third, Abbie is relying on the feedback from McKinsey about her performance. Abbie kept quite detailed notes and she thought that based on the feedback she received verbally and in writing there was no reason why her salary would suddenly lag behind peers or her promotions would slow down.
The lack of a promotion is a key reason for the lawsuit, as it always tends to be.
4th data point – comments in the corridor. Finally, Abbie is relying on alleged comments made by McKinsey consultants and partners that she has heard in the office and which she considers to be prejudicial to her ethnicity.
Based on those four data points Abbie has concluded that she has a strong case to sue McKinsey for racial discrimination.
After analyzing each data point I do not feel this is a racial discrimination issue or a gender issue. I believe it is a performance issue. And I will talk you through how I arrived to this conclusion.
I feel that there are many people in management consulting today who, for whatever reason, worry that they may be discriminated against. It could be gender discrimination issues. It could be racial discrimination issues. It could be anything.
Yet, what I want you to be particularly wary of is drawing linkages between what is at best a terribly weak data set.
There is also a big difference between something happening and proving something happened.
And if you can prove it happened, you need to prove it was institutionalized and sanctioned by management. That is a tall order.
But first, Abbie’s story must be logical. Can it pass that logic test?
I will go through each data point Abbie presented. However, before I go through each data point, I will talk you through the lens Abbie uses to analyze this issue.
The first step was to get Abbie to realize she is biased. Everyone is biased and it is important to control for this. If I was unsuccessful in getting this McKinsey consultant to realize she is biased then she would never understand why she’s only seeing this situation from her side.
This is the way I did it.
1st bias – no certainty about basis for discrimination. Abbie has concluded that it is a racial discrimination issue but she cannot prove it is not a gender discrimination issue.
Forget about whether or not McKinsey is discriminating against her. In fact, lets even assume McKinsey is discriminating against her. I’m not saying they are, but just for the sake of argument lets assume they are discriminating against her.
Abbie has picked a form of discrimination that can be supported with the data points she has. Yet, for all she knows McKinsey is discriminating against her, but they are discriminating against her since she is a female, not because she is a minority.
Abbie is basically saying, “They are discriminating against me because I am a minority since I actually have data that shows that. The comments, etc”.
Yet, if you look at the four data points she has: salary information, peer performance reviews, feedback on her performance and comments she heard in the corridor, only one of those data points, the comments in the corridor, is loosely, and I would say very loosely, linked to her racial profile.
After listening to the comments verbatim, it could really mean anything.
The rest of the linkages are not clear. Abbie could be discriminated against because she is a female. We do not know.
So the first point I wanted Abbie to understand is that if she picked a form of discrimination that fits the data point then it is reasonable to conclude that the form of discrimination that she is open to selecting is basically open for discussion depending on how one interprets that/those data points.
And if it is open for discussion and if she is willing to change it so easily then what certainty is there that there is this form of discrimination?
I am not saying that there is no discrimination. I am saying there is no certainty about why McKinsey may be discriminating against her.
You may think that this is a minor technical point. But it is not a minor technical point because if you cannot determine the source of the discrimination you cannot prove there is discrimination.
And that’s what it comes down to. Being able to prove that what you are claiming is true.
So this was the first argument I made. And Abbie accepted it. She said, “What you are saying is true because I don’t know if it is because I am a female”. Good, she is a rational thinker.
2nd bias – the positive bias. The second thing I pointed out to her is what is called the positive bias. Look at the way Abbie analyzed her salary. She said, “At the beginning I was earning more than my peer group. And now it just changed and I am earning less than other consultants at my level. So I must be discriminated against”.
But I pointed out to Abbie, “Why do you assume that you earning more occurred because you earned it? What if there were too few female minorities in the office. So the firm decided to pay you a higher salary to guarantee you would join? And then, over time, maybe your performance was unsatisfactory and they decided to normalize your salary”.
If there is racial discrimination why was she initially paid more than peers? Why was it not discrimination when she was paid more? If Abbie accepted the system before, why does she not accept the system not that it penalizes her?
And that is really the problem we are having when we get into these discrimination discussions. We always see us being a victim when discrimination is negative. But we always see us being rewarded for good work when the discrimination is positive.
And I wanted Abbie to understand that it is not easy for us to prove that she was earning more than other consultants at her level due to her good work and earning less due to discrimination. There can be a number of extenuating circumstances here.
What I am trying to get her to understand is that I am not saying she is wrong. But there may be multiple reasons why something had happened. And I am not saying that racial discrimination wasn’t taking place. I am saying that if she wants to go down this path she needs to be able to prove certain things.
Because until she can prove it, they are rumors. And it’s going to be settled out of court for a sum that will not please her. A sum of around a million dollars, which is likely what she will get, is not going to please her in the long term because, given her profile, she can earn it in a few years if she left management consulting.
Yet once she goes after her employer, her profile will lose at least some of its luster to other perspective employers.
3rd bias – withholding economic right. The third bias is best explained with an example.
Lets assume you are a minority. You are minority A. But you are wealthy. You have access to everything you want. And there’s another wealthier group ahead of you which controls the country. This is majority B. And below you there is a poor group who does not have control of anything. Everyone laughs at them because they have no money and you see them doing the lowest paid jobs, such as cleaning the streets. They are minority C.
Now this is a simple test which you can run yourself. You are in the middle – wealthy and well educated – but you are still a minority.
You, from A, are walking on the street and someone from the poor minority C laughs at you or does not want to serve you at a restaurant. Nine times out of ten it will not bother you. You will dismiss.
It will not bother us because when we are discriminated against by someone whom we feel is inferior to us or does not have something we want, like access to a high-paying job, we don’t worry about that form of discrimination. Discrimination only makes headlines when it’s a form of discrimination whereby you feel you’re being withheld access to some economic gain.
Some people will jump up and say, “That is not true!”. But it is true. It is absolutely true.
You only hear about discrimination in the press when some weaker group feels some stronger group is withholding an economic right they have. Because this economic right is being withheld they call it discrimination.
My point is that I want Abbie to have true and sincere values as a leader. She cannot complain only when she feels shortchanged in a system, yet be extremely happy when that very same system benefits her.
That is wrong. She needs to either accept or reject the system. She cannot have it both ways.
Summary of 3 biases:
I wanted Abbie to understand that these are the biases she has:
Realizing these biases was an important learning point for Abbie. She had come in thinking, “Oh, my God! I am being discriminated. I have got to sue these people. I have a strong case”. And it’s completely wrong.
I wanted her to understand that this is a gray area. It’s a gray area not only because it’s open to interpretation but because of the way she is handling it. In a trial the judge, and jury, will look at both the allegations and her response to those allegations.
She is forgetting to consider her response to those allegations.
Those three biases definitely exist. I could clearly see them in the way Abbie discussed her situation with me.
If she would go to arbitration/court she would be facing much more difficult questions about these biases. And even if she would not be questioned about the biases she would need to prove the biases do not exist. Because all her opponent had to do is to say the biases may exist and there is a reasonable doubt.
Now let’s go through each of the data points and I will explain to you why we make this mistake of assuming discrimination.
Analyzing 1st data point – consultant’s salary
I have spoken about the salary data point. We always assume that when we earn more it is due to hard work but when we earn less it is because we are being discriminated. And there is a reason for this in management consulting.
When people perform well, McKinsey will go out of their way to tell you, “You did a great job. You are amazing”. Companies do this because it’s easy to give feedback when feedback is positive.
When your performance is unsatisfactory companies, even McKinsey and BCG which are known for issuing obliterating criticism, tend to avoid and sugar-coat negative feedback.
So what happened is that Abbie was doing well for a long time, which resulted in extensive positive feedback, promotions and salary increases. She can observe a correlation: lots of positive feedback is followed by promotions and a salary increase.
And she now thinks, “I’m not hearing I am doing badly. Yet, my promotion is postponed and my salary is lower than salaries of other consultants at my level. What is up with that? Where is the correlation?”.
But it is simple. We like to tell people they did well because they are not going to argue with us. So when they are doing relatively well we say, “You are doing great!”. And people see this correlation: positive feedback is followed by rewards.
When their performance is inadequate, we tend to withhold criticism so they don’t see the correlation. They see very little negative feedback followed by a salary drop or postponed promotion.
Due to a lack of negative feedback, when rewards subside or turn into “punishments” we assume there is no cause. And, therefore, many may assume it must be discrimination.
When your performance is inadequate most of the time the criticism will be muted to not hurt your feelings. And this is a perfect example where the lack of a correlation does not imply there is a lack of causality.
McKinsey is particularly good at giving criticism. They give it to employees in such a nice way that even when they are basically insulting consultants and calling them stupid, most actually feel good about it. No firm has perfected that like McKinsey.
As a result, you may think, “They told me only once I am doing poorly. They told me 15 times I am doing well. I must be doing well”.
And that is the mistake. Luckily Abbie understood that. Not easily, but she understood that.
Once we tackled this data point, the others were quite easy to debunk.
Analyzing 2nd data point – peer performance reviews
The next data point we had to analyze is peer performance. Performance of peer consultants is difficult to analyze because we don’t know the context of that performance.
This McKinsey consultant collected data about peer performances basically from observing how her peers are doing on consulting projects. And second, she was reading some of her friends’ actual performance reviews.
I would say she doesn’t have the right sample size. She is basically self-selecting a group that either supports her or, for whatever reason, is reinforcing the behavior.
Yet, even if they are not reinforcing the behavior, the sample size is not appropriate. It’s not unfiltered and it’s not a random sample. That is on the written feedback.
Observing people is a ridiculous system to use. When you are stuck in your own work and you can’t even pay your bills on time, let alone buy a Valentine’s Day gift for your boyfriend, how in the world is a consultant going to be attuned enough to pay attention to what is happening with everyone else on the study?
Abbie could not have done that well.
So when someone says, “I observed performance of my colleagues during consulting engagement”, there are immediate biases. One just cannot do that on a consulting project.
You can observe a few things but you don’t know why they are happening. You can see consultants who are slacking off but for all you know they are just brilliant and they don’t have to work as hard. You can see consultants getting negative criticism but for all you know they responded to that negative criticism and they do exceptionally well at the end.
When you are observing people you don’t know the context. For all you know, the consultants that you think are weak are actually quite strong. You don’t know the discussions these consultants are having with leadership. You don’t know the quality of their work.
Yes, some of these consultants you are observing can be outright failures. Those are negative outliers. If you had to plot consultants’ performance on a bell curve the outliers are the positive outliers who do especially well and negative outliers who do particularly badly. And it is relatively easy to spot them.
However, it is much harder to identify the performance of consultants who sit in the middle of the bell curve. You don’t know if they are doing well or poorly. So when Abbie told me, “I observed consultants at the same level as me and I am better than them”, my immediate question was, “How do you know you are better than them?”.
This is a crucial question to answer.
Initially, Abbie told me she thought she was doing well all her career. Then the firm signaled her she is not doing well. So she is not a good judge of performance.
So how can she say, “I am not a good judge of performance when it comes to my work. But I am going to use the fact that I am a good judge of performance when it comes to other consultants’ work to make the case that I am being discriminated against”. The logic fails there.
Either she is or she is not a good judge of performance. She cannot have it both ways.
Analyzing 3rd data point – consultant’s performance reviews
Finally, I have read her official written performance feedback and I feel her feedback is consistent with what a consulting partner should be giving to this consultant.
What’s happened here is that Abbie does not understand the firm sugar-coats things. The feedback is still quite brutal but it’s done in a positive way.
And one of the things Abbie does not realize is that the firm never really said, “We are going to promote you in six months”. The firm only said, “You are doing well and if you continue at this rate you will be promoted”.
Abbie further failed to comprehend the economy also impacts the decision here. The financial crisis in Europe impacts salary levels and promotions. The firm is unlikely to say this because they don’t want it to look like they’re being affected by economic conditions. Nevertheless, Abbie needs to recognize that economic conditions may be impacting the decision.
If I look at her feedback it won’t hold up in any arbitration because the firm is not committed to anything. All McKinsey said is Abbie is doing well and I do notice over the last 6-7 quarters there has been consistent feedback about the fact that she needs to improve her ability to manage teams. And I can see she has not eliminated that worry.
If I was looking at her file I would say, “Look, they are talking about this consultant’s lack of ability to manage teams. She has not fixed that. Isn’t it fair to conclude that she can’t be promoted until she will fix that?”.
So on the feedback side no promises were made. If some promises were made that she will be promoted in six months, maybe there is a case there. But even then, if the firm made a promise to promote her in six month, I would not rely on it too much because things can change.
And while the firm generally will not admit to it, things do change.
Analyzing 4th data point – comments in the corridor
And finally when it comes to the comments being made in the corridor, the one thing I have learned about comments is that it is important to understand the context.
Just because you overhear something when people pass you by in the corridor does not mean you understand what came before it to grasp the real meaning of the comment. Let’s look at an extreme example of that. I am not saying this is what has happened. But this could have happened.
For all she knows, she could have overheard two colleagues talking about the latest episode of the TV show they saw and someone was quoting one of the characters. He may not have actually meant what he quoted. It may not have been his words. But he was just quoting the character and she overheard it.
My point is we don’t know what happened.
When Abbie recounted the quote, it was very hard to know if they where specifically referring to her. It could have referred to something else.
We went through the problem solving process. We gathered, analyzed and considered all key data points and uncovered biases which shed more light on how weak Abbie’s case was.
Now, with the hardest part of the decision making process behind us, the truth was staring us in the face.
The best I can see here is it is a performance issue in Abbie’s case. Therefore, going after the firm is fraught with significant risk and will likely ruin Abbie’s career. In fact, after doing the analysis Abbie’s idea to sue the firm is ridiculous.
In the past this McKinsey consultant was paid a lot. She was told she will be promoted. And she doesn’t see any reason why this has changed. She is looking for reasons why her salary is lower than that of peer consultants and why her expected promotion was pushed back.
Abbie again here self-selected data points to fit her story. In her feedback it is clear that managing teams is a problem, yet she ignored that data point until I brought it to her attention.
Management of teams is a clear weakness for Abbie. She needs to be able to manage small case teams. And she has not done that well enough. Falling short in such an important skill has undermined her career with the firm. Abbie got distracted with gathering data to prove that she was discriminated and allowed her reputation as an exceptional consultant to slip away.
She needs to bring it back.
The good news here is that Abbie had basically shelved all talk about fighting with the firm. She is focused on her career. Until I see her next performance review it is hard to be certain that her star is rising again at McKinsey, but I think she is doing better.
This is based on the emails we have received from her since these discussions were held.
At some point in your life you are going to feel like you are being mistreated or mishandled and the most important thing you can do there is not to overreact. There are times when you are being mistreated. Of course it happens.
But make sure that it is a real example of being discriminated because once you pull this trigger, you can’t back away from it. And in this case Abbie is quite extreme. She wanted to sue the firm.
If you pull the trigger, for the rest of your life you will be the person who accused the firm of doing something they may or may not have done. And it will most likely always be an accusation because the firm will do everything they can for this not to go to trial.
Even if you’re completely right the firm will take it to arbitration and pay you a settlement, basically admitting no wrong doing.
And if you want to sue them and you want to make it stick it will have to be a law suit or class action lawsuit. For the latter, this means you will need to encourage other people to sign up, which is a certain suicide because if you are trying to convince people to sign up the firm will find out and you will be asked to leave.
And if you don’t convince people to sign up you don’t have a job and you don’t have a case.
Even if you have the case it does not mean you can actually take it to trial and win because I can tell you now the firm will settle because they don’t want any negative publicity.
In this post I wanted to show you how data is usually misinterpreted to make a case that shows that we are being “punished” for some reason. I also wanted to introduce you to the concept of positive biases when it comes to discrimination. They exist. We all have them. We tend to cherry pick data points to support the point we are making. I specifically wanted to show you examples in this particular case.
When you get into these situations where your career is off track, it is tempting to say, “Well it’s not my fault. I’m being discriminated against”. Yes, it could be happening but you have to be able to prove it. And sometimes it’s so hard to prove it you might as well just say, “You know what, if it can’t be proved why don’t I simply figure out how to make this work for me”.
That is the strategy we followed for Abbie. She could not prove to us she had a case so we convinced her to pursue a strategy to salvage her career. She desperately wanted to leave, which is the easy choice since she had mentally checked out, but I convinced her to stay.
This is key. When you mentally checkout you sometimes want to leave the firm even when it is the worst possible option for you.
A career can be turned around. It may be painful. It may be uncomfortable. You may need to confront some scary truths. But you know what, you need to just do it.
And this sometimes is the best strategy which many people don’t ever think about. I said this before and I will say this again, once you pull the trigger off claiming some kind of discrimination – racial, sexism or whatever it is – you are forever going to be tagged with that label.
And you want to make sure you don’t get tagged with that label. There are always alternative options to get what you want without having to go through that path.
I hope this article, and related podcast, will help you think through some of the slights you may be experiencing and some of the ways you may be magnifying them where there is not actually a problem.
This is obviously an exceptionally sensitive and important topic. We don’t talk about these things much because they are particularly delicate subjects. Yet, almost everyone I ever spoken to, whether they are a minority or majority of the population, has always worried about how they are being treated.
While discrimination does take place in management consulting, things are not always what they seem. Before you claim discrimination make sure you have carefully analyzed and considered all key data points, while avoiding the many biases to which decisions are prone.
Only then select the option that is best for you, keeping in mind that outright warfare is rarely, if ever, a viable strategy for an individual going after a major organization.
QUESTION(S) OF THE DAY: How should Abbie have reacted to her circumstances? How would you have reacted? Please let us know in the comments.
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