Two weeks ago, I published a research paper Ethnic Divisions Within Unity: Insights into Intra-Group Segregation from Israel’s Ultra-Orthodox Society. It is my first paper in so many aspects

• First paper in a long while
• first paper I authored by myself
• first paper in social studies (I’m a former pharmacist, remember)

I have a confession to make. When I concluded the research phase, I stumbled upon an open question that I couldn’t answer. I really wanted to publish what I already had, so instead of trying to deemphasize the problem or write far-fetched theories, I decided to put the problem under a spotlight and declare it loud and clear, emphasizing that this publication is a chance to deal with publication bias — a phenomenon that leads scientific journals to favor positive or significant findings while neglecting studies that yield negative or inconclusive results.

This is not the end

Once the paper got published, I recalled that a sizable audience of my podcast (This Week in the Middle East) belongs to the Haredi society - the society that was the subject of my study. I recorded a video presentation (here: it’s in Hebrew), asking my podcast audience to share their inner knowledge and propose explanations. What do you know? I got several very interesting ones - that will serve as the basis for further research.

THE END

I recently came across a financial update with a plot that looks like the one below: a bar plot of revenue with a line plot of growth rate. The growth rate is on a secondary y-axis, and the x-axis is shared between the two plots. This type of plots are very common in financial reports, and they are often used to show the relationship between two variables that have different scales. However, they are also often criticized for being misleading, as the two variables are not directly comparable. In this post, I will show a better way to visualize this type of data. But first, what’s wrong with the plot below?

1. The secondary y-axis is not aligned with the primary y-axis. This makes it difficult to compare the two variables.
2. Two points on a graph that lie one above the other do not necessarily have a relationship of proportionality or a fixed ratio. This is because the two y-axes have different scales.
3. Navigation is difficult. The reader has to constantly switch between the two y-axes to understand the relationship between the two variables.

I wrote about double scales a lot in this blog. See the ‘double scale’ tag to read more

The solution: split the plot into two subplots

Let’s split the graph into two subplots, one for each variable, and share the x-axis. This way, the reader can easily compare the two variables, and the relationship between them is clear.

Attempt 1: a bar plot and a line plot

Nice, the two plots are aligned, it’s easy to know what’s happening in the revenue and the growth rate. Note, how I emphasized the zero line in the “growth rate” subplot.

However, showing the evolution of the revenue using bars is not ideal: the bars are not continuous, and the reader may think that the revenue is constant between two months. Let’s try another approach.

Attempt 2: two line plots

The advantage of the line plot is that it allows zooming in on the data, which is not possible (FORBIDDEN) with a bar plot. However, the colored area that the bars provided gave us the information about the total revenue over the time (recall that total revenue is the area under the curve). Now, this information is lost.

Let’s try to add this information back to the plot.

Attempt 3: line plot with shadowed area

This is the best of both worlds: we have the continuous line plot that gives a proper visualization, and we have the shadowed area that gives us the information about the total revenue over time. The two subplots are aligned, and the relationship between the two variables is clear. The reader can easily see that the revenue is increasing while the growth rate is fluctuating.

Conclusion

The fact you can do something doesn’t mean you should. Dual-axis plots are often misleading and difficult to read. Splitting the plot into two subplots is a much better way to visualize this type of data. It allows the reader to easily compare the two variables and understand the relationship between them. The code to generate the plot is below.

This document outlines why HR departments in large organizations benefit from a dedicated data science approach, highlighting impacts beyond recruitment. In short, my thesis is as follows: as organizations scale, so does the complexity of understanding their internal dynamics. Data tools become essential to analyzing large organizations, as they enable HR to identify patterns and insights that can drive strategic improvements across key areas.

Enhancing communication: Data science improves internal communication by identifying key influencers and assessing the effectiveness of HR initiatives.

Strengthening company culture: Using tools like sentiment and language analysis, data science reveals emerging trends and super-communicators who can drive cultural change.

Boosting employee retention: Predictive modeling enhances retention by identifying at-risk employees and addressing sources of dissatisfaction.

Ensuring fair compensation: Finally, data-driven analysis supports fair, competitive, and equitable pay practices within the company, fostering trust and motivation among employees.

Before exploring the details, it’s crucial to reinforce the importance of maintaining strict ethical standards and ensuring employee privacy. Data science in HR should be supervised by HR executives who understand the company’s culture, with data scientists clearly communicating any limitations and potential biases in their analyses.

Understanding scale: why large organizations need data science

In small companies, leaders and HR teams often have a clear, intuitive sense of the organization’s dynamics. They can easily recognize patterns in communication, cultural shifts, or employee dissatisfaction because these factors remain within a manageable scale. However, as organizations grow beyond a certain size—often exceeding Dunbar’s number of around 150 stable social relationships—these dynamics become more complex and harder to track. In large organizations, where direct observation and informal communication are no longer sufficient, a dedicated data science approach becomes essential to reveal insights that would otherwise remain hidden.

Enhancing communication using social network analysis

Social network analysis (SNA) provides valuable insights into the informal networks within an organization. By mapping communication patterns, SNA reveals key influencers, information brokers, and opinion leaders who shape company culture and drive change initiatives. These insights allow HR teams to identify potential ambassadors of change, enhance the effectiveness of internal communications, and reduce departmental silos.

In a recent project for the head of talent and development at a top multinational consulting firm, I analyzed collaboration patterns among managers across regional offices. This analysis identified interaction gaps within the management community, resulting in a de facto split in the team. Addressing this issue led to a measurable increase in manager collaboration over several months, and we also identified key influencers who could further drive change.

Strengthening company culture

Data science plays a pivotal role in fostering a positive company culture. Sentiment analysis on internal communications helps HR identify recurring sources of negativity, enabling targeted interventions to address concerns and boost morale. For this analysis, open communication channels like P2, Basecamp, or Slack are to be used, while private communications should never be included.

Keyword analysis allows HR to track emerging cultural trends within the organization, enabling a proactive response to shifts in employee sentiment. Additionally, language analysis—combined with other methods—can identify “super-communicators,” employees who excel in clear and engaging communication. These insights allow HR to target communication training effectively, ensuring consistent and cohesive language across the organization.

Several years ago, a colleague gathered internal communications to investigate complaints of toxic communication from certain executives. The analysis validated some complaints and disproved others, allowing HR to present findings to the relevant executives, resulting in improved communication culture and reduced employee frustration.

Enhancing employee retention

Predictive models that forecast employee attrition empower HR to take proactive steps before an employee decides to leave, making retention efforts more effective. By identifying individuals at risk of departing, HR can plan targeted interventions and strategically allocate resources to improve engagement. These models also uncover sources of dissatisfaction, such as issues with work-life balance, career growth opportunities, or management styles. By addressing these concerns early, HR can foster a supportive work environment that encourages long-term employee commitment.

Conducting compensation analysis

Data science can be instrumental in ensuring fair and competitive compensation practices within an organization. Mining external data allows HR to benchmark salaries against industry standards, helping design compensation packages that attract and retain top talent. Additionally, internal pay data can reveal disparities across departments, roles, and demographics, promoting a more transparent and equitable pay structure. Addressing these disparities reinforces a culture of fairness and inclusivity, reducing the risk of dissatisfaction related to perceived inequities and helping maintain a motivated workforce.

In-house or external data scientist?

While it is crucial for HR-related data science projects to be overseen by HR executives who understand the company’s culture and values, the choice between hiring an in-house data scientist and outsourcing the work depends on specific needs.

An in-house data scientist provides ongoing support to HR teams, tailoring analyses to the company’s unique needs and ensuring that insights are integrated effectively into HR practices. This approach fosters a deeper understanding of the company’s specific challenges and opportunities, enabling HR to make decisions aligned with strategic goals.

An external consultant, by contrast, offers objectivity and is less affected by company politics or internal biases, which can enhance the neutrality of analyses. External consultants also bring a wealth of experience from various organizations, offering fresh perspectives and innovative solutions to HR challenges.

Conclusion

With a dedicated data science approach, whether through in-house expertise or external consultation, HR departments gain the ability to make informed, data-driven decisions across communication, culture, retention, and compensation. Each of these areas contributes to a healthier, more cohesive organizational culture. By leveraging insights that reveal informal networks, track employee sentiment, predict turnover, and ensure fair compensation, a data science approach enables HR to address critical challenges proactively, ultimately fostering a stronger, more resilient organization.

I performed my first A/B tests ten years ago. Here are the most common mistakes I made

1. Doing an A/B test in the first place

Yes, the first mistake is doing the A/B test in the first place. An A/B test is an experiment. Many changes in products or services are not part of an experiment. They can be driven by business decisions, tech limitations, or shifting values. In these cases, managers decide to perform A/B tests to ease their conscience. This dilutes the concept of testing. A better approach is gradual deployment and post-deployment monitoring.

2. Not comparing apples to apples

Sometimes, especially in organizations new to A/B testing, the “B” variant is deployed using a workaround or a hack. In one case I witnessed during my freelancing career, the “B” variant was created by injecting pieces of JavaScript into the frontend, causing it to malfunction on several browsers. In another case, the test algorithm was deployed on an old and slow server. In both cases, the diminished performance of the “B” variant wasn’t because it was inherently worse but because of these implementation issues.

3. Not defining proper metrics

What do you want to measure? Conversion, lifetime value, user satisfaction? Do you assign the same weight to an improvement in one metric compared to a decrease in another? How sensitive are you to the risk of adopting the “B” variant when it’s actually worse? What about the opposite scenario? Answering these questions is critical. It’s the data person’s responsibility to ask them and demand answers. It’s the leadership’s responsibility to provide meaningful and thoughtful responses.

4. Not committing beforehand

Before starting an A/B test, discuss all possible outcomes and commit to accepting them. If you ignore some types of outcomes, you don’t need to perform the test at all (see point #1).

5. The peeking problem

I’ve seen many test owners examine the results of ongoing tests and decide whether to continue based on what they see. This is called peeking. Depending on the statistical approach you use, peeking ranges from being frowned upon to a huge no-no. If you can’t resist peeking, I advise using Bayesian methods for analysis which are considered less prone to errors from peeking.

6. Relying too much on statistical tests

You might recall power analysis from your introductory stats classes. We use power analysis to define the experiment size. Too small a sample size, and you won’t detect meaningful differences; too large, and you may waste resources. But sometimes, sample size isn’t the whole story. Some sites and applications have such high traffic that you can reach the required sample size in hours or days. However, if you do so, you might miss intrinsic variations in audience behavior: your nighttime users might differ from your daytime users, and weekday interactions may differ from weekend ones. Ignoring these aspects in your test planning can lead to unpleasant surprises.

A Likert scale study is a type of survey that measures respondents’ attitudes or opinions across a range of agreement levels.

Unfortunately, many visualizations for Likert scale data are poorly designed and fail to effectively convey the results.

To address this, I wrote a Python function that generates decent visualizations for Likert scale data. It also handles text in both Hebrew and Arabic, which is not a trivial task in Matplotlib.

Check out the code here: https://gist.github.com/bgbgbg-gmail/9aeced5372c3974eab25fa3103064f17

This post shares my recent experiences witnessing the resilience and innovation of Israelis. From mobilizing workers and students to volunteer efforts and professional development, we continually advance under challenging conditions.

In October 2023, Israel was in the midst of a political crisis. Then, we were attacked with unprecedented brutality, halting the entire economy. With nothing to do, I joined a volunteer team. We utilized our data expertise to build an OSINT analysis tools for the war effort. Initially, none of us knew each other or who know what. Yet, two weeks later, dozens of volunteer analysts were using our tools in three shifts, providing crucial support.

As a freelance data researcher, I was surprised to see how my clients, despite the difficulties, continued to work. They managed to meet deadlines even when some had to deal with employees who were killed, wounded, or enlisted into the reserves, and others who were present but absent due to friends or family being killed, injured, or enlisted.

I saw freelancers like myself joining startups at subsidized rates in place of missing employees to help meet deadlines and advance projects. This partnership not only demonstrated innovation but also showed the great solidarity that is so characteristic of Israeli society.

Additionally, a few weeks ago, I volunteered for reserve duty from which I’m exempt due to my age. I saw reservists of different ages, men and women, engaged in routine security tasks for the Israeli Defense Forces, with many continuing to work in their spare time—some on their work tasks, some on their studies, and others sitting in a corner discussing new business ideas.

I also lecture at a college. The entire college—the lecturers, the dean, the secretariat—everyone is mobilized to help students affected by the war progress towards their degrees as much as possible. This academic year is anything but usual, and yet, the second semester is over, and it looks like most of the students managed to overcome the challenges.

Everything I have seen reinforces the belief that Israel faces a bright future. Our innovation continues to improve. The recent influx of Jews, driven by waves of anti-Semitism in the West, adds even more strength and innovation. Before the war, Israel ranked among the top countries in wealth and innovation. I am confident that even after the war, we will maintain our high position and continue to strengthen our innovation and wealth.

https://worldhappiness.report/

https://en.wikipedia.org/wiki/Global_Innovation_Index

For the past three and a half years, I’ve hosted the Hebrew podcast This Week in the Middle East Podcast . Despite not being a Middle Eastern studies expert and knowing little Arabic, my passion and curiosity have led me on a remarkable journey of sharing knowledge and public speaking.

Each week, I’ve engaged with experts on various topics, providing insights into the Middle East and highlighting the importance of diverse voices. This experience has reinforced the value of knowledge sharing and open dialogue.

Recently, a major channel invited me to discuss Ramadan, despite my lacking of of formal credentials.

This illustrates that expertise extends beyond degrees to include passion, learning, and effective communication.

I am already perceived as a #datavisualization expert, and now, people start asking for my opinion in a completely different field. How did this happen?

It starts with you talking about something, then fearing to sound foolish, you learn about the subject to avoid embarrassment, and indeed, you become an expert

As we progress in our careers and lives, the significance of voicing our thoughts, exchanging ideas, and embracing various viewpoints becomes clear. Through such engagements, we evolve, learn, and foster meaningful discussions. Let’s keep breaking barriers through conversation.

In data visualization, much like in any form of communication, it’s vital to keep the main point front and center. That’s precisely why I’m a proponent of a clean, minimalistic approach to crafting data visuals, coupled with the inclusion of descriptive titles for each graph. These titles aren’t just fluff; they serve as a psychological lever, aiding in persuading your audience of your argument. Moreover, the act of titling forces a second look at the graph to ensure it accurately represents your intended message.

During a recent practical data visualization workshop I led, we tackled creating a graph that illustrated the income inequality in Israel in comparison to OECD countries. In the “before” version of the graph, displayed below on the left, there’s a noticeable redundancy between the title and the Y-axis label. Both essentially echoed each other, added no real value, and worst of all, were obscure to anyone not versed in the jargon of the “Gini Index”.

Our strategy for improvement was straightforward but effective: we swapped the title for the overarching conclusion. This modification was the kickoff for a cascade of enhancements. Yet, we hit a snag with the Gini Index itself—our focal point. Our solution? We underscored the fact that this index is a measure of inequality, clarified its scale (“Higher - more unequal”), and kept the term for those already in the know.

Wrapping up, the derision towards explaining the seemingly obvious, sparked by the “mansplaining” trend, has bled into all areas of communication. However, in the realm of data visualization, clarity and comprehensibility must reign supreme. By making our visual presentations both accessible and elucidatory, we widen the doorway for a more extensive audience to connect with and grasp complex information.

Or, How to Extract Value from Failed Projects

Typically, a professional post should begin with an introductory paragraph that provides some background and engages the reader. Let’s pretend that such a paragraph has been written and proceed directly to the story at hand. This story doesn’t end happily, nor does it end sadly. It simply begins and ends, and that’s all. Nonetheless, it’s worth your time.

The Story

I have a client who implements a variety of smart algorithms to assist individuals with money and innovative ideas in making a positive impact on society and the environment. They requested my help with modeling, and given my extensive experience as a data scientist and my track record of building numerous predictive models, I was keen to assist.

After working our modeling magic, we ended up with a predictive model that was “statistically significant” but practically unusable. What do I mean by that? Typically, we evaluate the value of a predictive model by comparing the predicted values with the known (“observed”) ones. Standard comparison procedures involve a correlation metric (or R-squared, which is NOT a correlation metric) and, for those who want to sound intellectual, a p-value. Both these metrics were excellent in our model. We also generated plots for a more comprehensive analysis. Below is a representation of our data using a completely fictitious dataset (rest assured, I would never share a client’s data).

Statistically Significant but Practically Useless

The graph indeed looks promising: the correlation coefficient was over 0.95, and the p-value (I can’t believe I’m resorting to this!) was 0.00000001, which is considered “excellent.”

However, there’s the rub: echoing an old Russian proverb, “you can’t spread the p-value on your bread”, or to quote a less old Hebrew saying, “you can’t pay with a correlation coefficient at the grocery store.” Statistical tests demonstrate the existence of a connection between your model and reality. Yet, this connection isn’t sufficient for making informed decisions due to the excessively high spread in our case.

For our model to be of practical use to my client’s clients, the typical deviation from real life should be within an order of magnitude of 0.5 (whatever the units may be). If the deviation is higher, my client’s clients would be wasting time and money. Despite our diligent efforts and the extensive work with the client’s team, the typical deviation was significantly larger, rendering the model practically useless.

… or is it?

An old Yiddish adage offers wisdom: [yeah, I don’t have anything relevant, but I’m sure there is one]. Consider our situation: we’ve spent considerable time and effort building a model. Does the model’s prediction bear any relation to reality? Yes. Are the deviations too high? Again, yes. What does this mean? It indicates that many instances we’re trying to model don’t behave as anticipated based on our data. Herein lies an opportunity.

In this project, we’re attempting to forecast a key business metric. If an entity’s metrics are notably worse than expected, this identifies a significant opportunity for improvement—a low-hanging fruit. Consequently, my client or my client’s clients could approach the entity and offer assistance.

However, there’s another side to this. What hasn’t been mentioned is that the “observed” data comes from self-reports. This implies that some reports may be manipulated to portray a more optimistic picture than reality. Therefore, the same model can be used to identify potential “mistakes” 😉 in self-reports, which is a valuable exercise in its own right.

A happy ending?

The typical “war story” of a freelance consultant generally concludes with the client accepting the consultant’s insights, raking in substantial profits, and treating the consultant to a swanky race car. Let’s pretend that this is what happened, despite the reality: my client listened to my take and decided to invest their resources in procuring more high-quality data.

Of course, if you ever need help with your modeling, feel free to reach out to me. And if a model doesn’t turn out as productive as you’d hoped, we can always attempt to make a rewarding ‘modelade’ from it. I’m always reachable at boris@gorelik.net.

[copied from my Substack newsletter]

The subject of this post primarily revolves around creators of digital solutions, such as programmers, designers, analysts, and data scientists. Regardless of whether you identify as one or manage one, I assure you there’s a valuable takeaway for all within this read.

We often encounter “lone wolves,” individuals who are the sole professional in their field within their organization. This situation typically arises when the company lacks the resources to employ more than one programmer, designer, or analyst. Such circumstances can pose significant risks and necessitate proper risk management.

Now, you may say, “What about C-level managers? They too are often the sole professionals in their field, and working alone is the norm.” I’ll try to address the scenario of C-levels later, but let’s concentrate on everyone else.

What’s the big deal?

So, what’s the big deal?

To put it succinctly, we’re discussing knowledge workers: individuals who translate their brain prowess into value. To maximize this value creation, the process needs to be as efficient and honed as possible. The Talmud tells us about rabbi Hama bar Hanina who said, “Just as a knife is sharpened only by the steel of its mate, so too, a scholar [ knowledge worker, in our context] is sharpened only by his fellow.” When knowledge workers lose that sharpness, the quality of their work suffers. The output becomes suboptimal due to a lack of adversarial oversight, the curse of knowledge, and the bus factor. Let’s delve into each of these aspects.

Lack of adversarial oversight

When I operate within a team alongside peers, I understand that my work is continually subject to review. This can, and should, be a formal review process, such as code review in programming. But it can also take the form of informal exchanges of ideas during daily communication. A healthy organization fosters a culture of review and constructive debates. In such an environment, everyone is expected to receive and offer feedback, everyone anticipates being challenged and to challenge others. This potential for critique and the ongoing drive to critique others maintain a state of alertness and motivation for continuous improvement.

The Talmud, which I mentioned earlier, is full of records of scholars disputing and challenging one another. That’s how these scholars ensured their constant intellectual growth. The renowned philosopher Karl Popper presented the concept of risky predictions, suggesting that any hypothesis—or piece of code, for that matter—should be bold enough to potentially be proven incorrect. If these bold assertions undergo testing and remain unrefuted, they are deemed accurate and, I would add, their author is deemed credible.

Now, consider our Lone Wolf. There’s no one to criticize them, no one to review their work, and no one requiring their review for their own tasks. The Lone Wolf’s colleagues hold deep respect for them because they’re the only ones in the team who know how to program, design a logo, or perform p-hacking. They admire the Lone Wolf, they appreciate the Lone Wolf, but they fail to sharpen the Lone Wolf’s skills.

The curse of knowledge

Rarely do you know what I don’t know. What may seem trivial to you could be completely enigmatic to me, and you might not even realize it. This phenomenon is known as the “curse of knowledge.”

One issue associated with the curse of knowledge relates to the first point of this post, the absence of oversight. When certain pieces of information seem obvious, you start treating your hypotheses and assumptions as facts and behave accordingly. Another risk is that the curse of knowledge can lead to inadequate planning and documentation.

When working solo on a small or moderately-sized project, you know exactly what’s happening within it. To onlookers, you resemble the archetypal chef in the kitchen, effortlessly grabbing the sharpest knife from the drawer without a second glance and instinctively knowing where every spice jar is located.

But this seamless workflow can falter when one of two scenarios occurs: either your project becomes too large for you to manage all its details mentally, or a new member joins your team. In both cases, you start losing time trying to remember which function performs the preprocessing, which directory houses the client’s mockups, and which file contains the up-to-date data versus which one is merely a backup. Consequently, your code becomes less clean, you design against incorrect mockups, and your analysis is flawed. Worse still, you produce these substandard results in quadruple the time it would have taken had you properly planned and documented.

The risk of such chaos is significantly reduced when two or more colleagues collaborate in a team. Like dance partners, they must be careful not to step on each other’s toes, which encourages them to dedicate time to planning and documentation. As a team, they have more strength to resist the constant pressure to sacrifice quality, planning, and documentation for speed.

The bus factor

The bus factor refers to the risk associated with information and capabilities not being shared among team members, a concept that draws from the hypothetical scenario of ‘what if they were hit by a bus.’ Of course, we don’t need to be so morbid. Team members can leave their roles for various reasons: they might become parents, win the lottery, choose a monastic life, or any other myriad of joyful reasons. When a team member departs, there should exist some level of redundancy to compensate for the expertise lost. The issue with a lone professional leaving the organization extends beyond having no one to perform their tasks; there’s also no one who knows how to perform their tasks.

Onboarding a replacement for a team member is always a challenge. However, since the Lone Wolf wasn’t subjected to constant reviews (lack of oversight) and didn’t allocate enough time for planning and documentation (remember the curse of knowledge?), what is a challenge for a multi-member team escalates into a nightmare that can halt operations for weeks or even months. I personally witnessed first-hand situations like this. I personally saw thousands of lines of code rewritten from scratch because nobody knew how it worked.

This resulting chaos not only disrupts workflow but also creates undue stress on the remaining team members and the new recruit, who must scramble to fill a knowledge gap without a clear roadmap. It’s a stark reminder of the importance of collaborative processes and shared understanding within a team.

Is there a solution?

The old saying goes, “It’s better to be young, healthy, and wealthy than old, sick, and poor.” Similarly, it’s obviously preferable to hire at least two professionals for each role. However, this isn’t always feasible. Even if budget isn’t a constraint, you might not require two designers, analysts, or programmers on your team. Having a bored knowledge worker is an issue in itself, warranting a separate discussion.

So, what alternatives are there? One approach to mitigating this issue is to introduce a part-time colleague, a co-pilot, or a proverbial sidekick—either as a hired employee or a freelancer. This additional team member’s role would be to serve as a sharpening tool for your Lone Wolf, their sparring partner, someone with whom they can exchange ideas, ensure nothing is taken for granted, and verify that the correct processes are adhered to. For this arrangement to be effective, the co-pilot should not be a one-time visitor but rather a regular contributor. They need to understand your company’s business and culture and become a part of its institutional memory. In this way, you not only ensure a proper workflow but also safeguard against embarrassing bugs and unforeseen departures.

One of the services I provide aligns exactly with this solution, and you’re encouraged to reach out if you’re seeking a collaborator for your Lone Wolf.

Copied from Substack newsletter

Let’s go pseudo-intellectual, shall we?

Feedback: The Essential Ingredient

One of the advantages of being a freelance consultant, as opposed to a traditional employee, is the opportunity for more frequent feedback. Each piece of feedback is precious, steering your career path. Positive feedback? Even better - it can truly make your day.

Freelancers typically receive more feedback. But this doesn’t mean that traditional employees should settle for less. If you’re in a management position, remember to provide feedback regularly. And don’t shy away from seeking feedback from your own managers.

If I were going for a poetic analogy, giving feedback could be compared to watering a plant: it needs to be done regularly and in the right amounts, or the plant either withers or becomes waterlogged.

To keep things straightforward, here’s the key takeaway: Feedback is essential.

Now, speaking of feedback, let’s discuss the feedback strategy that should disappear from the face of this world - the ‘shit sandwich.’

Against the ‘Shit Sandwich’

Fact check No. 1: The sandwich, as a concept, was savored by Hillel, a Talmudic scholar from the 1st Century BC, centuries before it was “invented” by a notorious British gambler of the same name.

Fact check No. 2: Sabich ([saˈbiχ]), a pita bread sandwich filled with fried eggplants, hard-boiled eggs, chopped salad, parsley, amba, and tahini sauce, is the best street food dish globally. That’s it. Full stop. Period. The debate ends here.

Sabich. By the Wikipedia user Gilabrand under the Creative Commons Attribution-Share Alike 3.0 Unported license

Fact check No. 3: The ‘shit sandwich’ is a feedback strategy conceived by corporate America that basically involves sandwiching negative feedback between two layers of positive feedback. In theory, this approach aims to deliver bad news in a way that doesn’t hurt someone’s feelings. In practice, however, I’ve yet to meet someone who reacted positively to this method. Instead, I’ve heard numerous stories of people being called for a pre-dismissal hearing (a “performance review,” as they call it) without realizing it because the “bad news” was sugar-coated with fake positivity.

I suspect the real motive behind the ‘shit sandwich’ is to make the delivery of feedback more comfortable for the giver rather than the receiver. Giving harsh feedback is challenging, but the person on the receiving end deserves the dignity of hearing your honest opinion. Make the effort - they’re worth it.

Copied from my Substack newsletter

I want to share an experience I had with a CEO-entrepreneur that might offer some valuable insights for other managers and business owners who struggle delivering projects. I wish this were a success story post, but I see this case as a personal failure.

Before we continue, it’s a good time to remind you to share this newsletter with your colleagues.

This CEO approached me with concerns about the security of her custom-tailored questionnaire website, which couldn’t be created on standard platforms like Typeform or Crowdsignal. A small development company had built her site using WordPress, but an “expert” had warned her about potential security risks. Not knowing what to do, she spent a month searching for help before turning to me for advice on securing her site.

Here’s what I told her

You don’t necessarily need a fortress for a website

Securing a site is like securing an office or a house. There’s no limit to how secure they can be, but there is a limit to how much time and effort you should spend on it. Most likely, your house doesn’t have armed personnel patrolling its perimeter, but strategic infrastructure buildings, such as an electric company, do.

Understanding your website’s potential vulnerabilities, the consequences of these vulnerabilities, and the resources you are willing to invest to mitigate them is vital. The best approach is to compile a list of potential security events and their potential impacts on your business, your customers, and the general public. Then, estimate the likelihood of each event. With this list in hand, you can engage a skilled consultant to formulate a plan.

Premature optimization as a form of procrastination

There is a saying in the software engineering industry, “premature optimization is the root of

There’s a saying in the software industry, “Premature optimization is the root of all evil.” Of course, you should strive to deliver the best product you can. In an ideal world, you would build a perfect product, produce bug-free code, and write flawless documents. Realistically, however, you need to balance costs and benefits, allocate limited resources, and manage uncertainty. Thus, “good enough” is not always a sign of laziness but can be the most practical approach.

The urge to optimize often stems from a lack of understanding or could be a form of perfectionism that masks procrastination. So, how can you ensure you’re on the right track? Seek advice from a trusted friend, colleague, or consultant. Ask open, non-leading questions, and be genuinely willing to consider perspectives other than your own.

(Yes, you can reach out to me at: boris@gorelik.net)

I failed

Back to the CEO-entrepreneur. During our conversation, I learned about her business and potential customers and concluded that her venture didn’t require the same security measures as a bank or a utility company. I researched the development company that built her site and found them reputable. So, I suggested launching the service, starting to work, acquiring new customers, making money, and allocating a portion of the income for future security investments.

Unfortunately, she disagreed with me. It has been five months since our conversation, and she’s still searching for the right security expert to rewrite her entire site from scratch. Two days ago, I asked her about her site. “It’s almost done,” she told me.

Are her customers waiting for her? I’m not sure.

To sum up

Recognizing the balance between perfect and practical can prevent unnecessary delays in your business ventures. Don’t hesitate to contact me if you’re looking for advice on how to navigate these waters.

“Climbing Invisible Ladders and Falling into Deep Holes: A Discourse in Five Parts” is a witty, engaging, and profoundly insightful exploration of corporate dynamics and career progression.”

Climbing Invisible Ladders and Falling Deep Holes: A Discourse in Five Parts

DRAMATIS PERSONAE

BORIS: A seasoned data scientist, middle-aged but ridiculously good-looking. An ex-Soviet Israeli, he adds an extra layer of cynicism to his character, complemented by a mysterious Russian-Israeli accent.

LAURA: The epitome of kindness, Laura is an American HR manager, and potentially the nicest person you’ll ever meet. She wears a constant, sincere smile.

DAPHNE: As a junior software developer, Daphne is smart and ambitious, constantly seeking opportunities to grow and evolve in her career.

Part 0. Prologue

FADE IN:

INT. HOTEL BAR - NIGHT

BORIS, LAURA, and DAPHNE sit around a table, each wearing a company name tag. A thought bubble appears above BORIS, reading, “It’s bullshit.” Boris shakes his head, dispelling the bubble.

LAURA

(Thoughtfully)

I hear you, Daphne. It’s great that you’re considering a promotion just three months into your first job. However, you should understand that management is more of a lateral move rather than a vertical one.

BORIS

(Shakes head, dispelling the “bullshit” thought bubble again, speaks sternly)

Laura, I strongly disagree.

A thought bubble appears above LAURA, reading, “Not him again.”

LAURA

(Smiling)

Interesting, Boris. Why do you think so?

BORIS

(Sighs)

Let’s discuss the term “promotion.” What do we seek when we aim for a promotion? More money, more autonomy, and a higher social status, wouldn’t you agree?

LAURA

(Nods)

Absolutely! And that’s exactly why transitioning to leadership roles doesn’t necessarily mean more money. We compensate employees based on their impact, not their position in the organizational chart. We also value and celebrate developers as much, if not more than managers, so their social status is already at its peak. All that managers do is facilitate developers in performing their jobs.

Part 1. Social status

BORIS

Here’s where I beg to differ. Even the terminology we employ suggests a higher social status. I have a “manager,” a “team leader,” or even a “boss.” Regardless of how much you’d like me to believe that a manager’s role is to assist me, they’re still referred to as a manager, not an assistant. Moreover, my manager has a direct influence on my evaluation, an influence I don’t hold over them.

LAURA

(Amused)

Boris, you couldn’t be more mistaken! Have you forgotten the annual engagement survey that you complete each year? We specifically ask for your thoughts on your team lead.

BORIS

Yes, but you ask both me and all my teammates, so my individual voice is diluted. Moreover, my team leader’s superior —

(Cynically, with air quotes)

“l e a d e r,” not assistant —

(Continues)

provides their direct feedback.

(Sips from a glass of cheap gin, longing for it to be Arak)

And that’s just one aspect. Our vacation policy is indeed generous, but it explicitly states that I need my team lead’s approval before taking time off. My team lead doesn’t require my consent for their time off; they consult their own superior. So yes, a manager does hold a higher social status than an individual contributor.

Part 2. Autonomy

LAURA

You know what? I’ll give you that. But when it comes to professional autonomy, an experienced individual contributor has the full power to decide how they solve the problem they work on.

(Daphne smiles)

BORIS

(takes another sip from the gin glass)

Oh, this is not true either. Take me as an example. I’m not a manager. It is true that I have the autonomy to decide how to solve a problem, but I often don’t get to decide what problem to solve. I can have some influence on this matter, but when my opinion collides with the opinion of my manager or their managers, my opinion is put aside.

DAPHNE

(interrupts)

Right, the other day…

LAURA

(irritated)

You can always take initiative and start working on something that really interests you.

(adds pathos to her voice)

In our company, you can write your own history. Identify a problem, start working on a solution in your spare time, and one day you may convince the management that the solution is worth adopting and expanding.

DAPHNE

(sarcastically)

Free time? You must be kidding.

BORIS

For once, I agree with Laura. We have some free time, and moderate switching between projects might be a good form of rest. Not only that, but a technical hands-on person might also have more tools to solve a technical problem. But… a manager usually has better knowledge of company needs and, more importantly, company politics. That is why a manager’s pet project has a higher chance of being accepted by the company than the one initiated by an IC.

LAURA

(doubtful)

Hmm… I don’t know… Well, at least in terms of money, management isn’t promotion.

Part 3. Money

BORIS

(Chuckles)

Ah, money. Who doesn’t love money? However, I’m afraid I must disagree with you on this one.

LAURA

(Joyfully)

Well, as the head of HR, not you, I understand how compensation is calculated. You are all compensated based on your impact on our business, nothing more. I know several managers who earn less than the individuals they manage.

(Makes a dramatic pause)

It’s all about the impact!

BORIS

(Points a finger)

Correct. I presume that when you talk about these managers, you’re primarily referring to team leads. Am I right?

LAURA

(Pauses, then nods)

Actually, yes.

BORIS

(Chuckles)

You see, a team lead may earn less than a developer, researcher, or designer they manage, especially if they oversee senior and experienced professionals. But who can bring a greater impact to the business: a senior programmer or a senior manager?

DAPHNE

(Looks puzzled)

What do you mean?

BORIS

(Turns to Daphne)

Let’s take me as an example. I’m an outstanding data scientist, one of the best in the field.

(LAURA and DAPHNE nod in agreement)

Nevertheless, my brain operates optimally only 8-9 hours a day. On the other hand, David, the head of the Modelling division, is also a top-tier professional, and he too works 8-9 hours a day. But since he is at the helm of a division, his work is amplified by the ten people who work under him.

LAURA

There are thirteen now; we’ve hired two additional scientists.

BORIS

(Turns to Daphne)

See what I mean? David’s impact is over a dozen times larger than mine. Therefore, it would only make sense for his salary to exceed mine.

Everyone falls silent. Boris signals for a refill. Daphne appears dejected.

Part 4. Don’t lose your sleep over this

BORIS

(Looks at Daphne)

Don’t lose sleep over this.

Boris takes a salt shaker, opens it, pours the salt onto the table, and draws two partially overlapping bell curves.

BORIS

Here’s an analogy. Consider men and women. On average, men are stronger than women, right?

Laura and Daphne nod their heads. Laura looks concerned, anticipating that Boris might say something stupidly inappropriate.

BORIS

(Points to the salt)

In this graph, the X-axis represents strength, and the two curves represent men and women. Now, what does this mean? Does it mean that all men are stronger than all women? Certainly not! There are many strong women and many weak men! You can see this by looking at the overlapping part of these curves here.

He points to the intersecting area of the two curves drawn in the salt.

BORIS

(Continues)

Now, let’s return to our original discussion. Let’s say that the X-axis now stands for “promotion” – a vague amalgamation of social status, power, and money. I hope I’ve convinced you that management is a form of promotion, but consider these curves. As in the men versus women case, there will be many individual contributors positioned higher on the promotion axis than some managers.

Laura looks relieved. Daphne is deep in thought.

DAPHNE

That makes sense. I could focus on improving my development skills… Conversely, I could invest the same energy into enhancing my management skills and transition to the better curve.

The atmosphere in the room becomes dense with contemplation.

Part 5. You have to like your job

BORIS

(pensively)

You’re right but also somewhat wrong. Becoming good at your work is hard. It becomes even harder if you don’t enjoy it. If you like managing people, go for it. Enjoy the process, grow your skills, and plan the mansion you want to buy when you’re a big-shot CEO.

He takes a sip of his drink.

BORIS

(continuing)

But if you enjoy writing good code more than dealing with people, you might become miserable during your quest for a management career. Being miserable won’t leave enough energy to improve your skills, and you might end up as a mediocre, bitter, mid-level manager who’s jealous of her younger self.

Laura smiles in agreement.

BORIS

(sincerely)

I fully agree with that. In the past, in some companies, such a move would be perceived as a demotion, but now and not here. Nowadays, many companies, small and big, recognize that management is a separate profession. The atmosphere in our company is kind enough to accept that people need to search for their path in life. Take me for example I “stepped down” from a management position twice. I don’t regret taking those positions. Neither do I regret stepping down from them.

Daphne looks thoughtful, contemplating the information she’s received.

DAPHNE

(tentatively)

I think I need to explore more about myself. I need to see what suits me best. But I guess I won’t know until I try.

Laura and Boris share a look of approval.

Boris raises his glass for a toast.

BORIS

(smiling)

To exploration and finding what truly makes us happy!

Everyone raises their glasses, and the scene ends on a positive note of camaraderie and mutual respect.

FADE OUT.

FADE IN.

INT. HOTEL BAR TABLE – NIGHT

The conversation has come to a natural end. Boris stays at the table. Daphne and Laura are leaving the bar, their faces illuminated by the soft lights of the lobby.

LAURA

(sincerely)

Remember, Daphne. You have a whole community here that believes in you. Reach out anytime.

(checks to make sure nobody’s listening)

And remember, don’t take Boris too seriously. He’s… well… he’s Boris.

FADE OUT.

THE END

So, I started a substack newsletter called “Direction Matters” (I hope you like the word play).

https://directionmatters.substack.com

It doesn’t matter how hard you push if you’re pushing in the wrong direction.

Direction Matters is a newsletter that focuses on teamwork, communication, and data, delivered with a blend of candid honesty and just the right amount of cynicism.

People managers will find value in the fresh perspectives, real-life case studies, and insightful advice on how to lead their colleagues effectively and with empathy.

For byte managers—an inventive term for individual contributors—I offer an opportunity to enhance communication skills, broaden their perspective, and learn strategies for making a more significant impact within their teams and organizations.

Join me on this exciting journey as we delve into the intricacies of teamwork, communication, and data-driven decision-making. Let’s find the right direction together.

Do you recall when data scientists were the talk of the town? Dubbed the sexiest job of the 21st century, they boasted a unique blend of knowledge and skills. I still remember the excitement I felt when I realized that the work I did had a name, and the warm feeling I got when I saw those cool Venn diagrams showing just how awesome data scientists were. Well, it’s time for data scientists to step aside and make way for the new heroes in town: the Prompt Engineers!

The demand for prompt engineers is soaring, and it seems like everyone is trying to become one. But what exactly is a prompt engineer, and what are my thoughts on this new profession?

Let’s take a step back in time: we started with assembly languages, and then a language called Formula Translator (better known as Fortran), which significantly lowered the barrier of entry into the field. I’m sure back then, people rolled their eyes and said that with the emergence of high-level programming languages, anyone could now take any formula and get an output, without understanding how semiconductors worked.

Fast forward to today. What do prompt engineers do? They essentially translate their domain knowledge, language understanding, and AI algorithm expertise into computer output (sounds like “ForTran,” right?). Prompt engineering is, in essence, a super-high-level programming language. Over time, I believe we’ll see dedicated tools and established standards emerge. But for now, it’s a wild, untamed frontier.

In 2017, I wrote a blog post titled “Don’t study data science as a career move; you’ll waste your time!”. Until today, this is the most read post in my blog. Now, it’s time for a new warning: “Don’t study prompt engineering as a career move; you’ll waste your time!”

Meanwhile, here’s a nice Venn diagram for you :-)

Read this to learn about well-rounded teams that can effectively collaborate and communicate. As an experienced team leader and builder, contact me to learn more about my services and how I can help you achieve better outcomes.

As a freelancer and a manager, I have worked with many companies and teams. Recently, I talked to a CEO who built a data science team that consisted of several “wonder kids” who obtained University degrees before graduating high school. The CEO was very proud of them. However, he complained that they don’t deliver as expected. This made me realize that having only superstars is not a feature but a bug.

The fact is that most of us are average, even geniuses are average in most aspects. Richard Feynman, the Nobel laureate physicist, was also a painter, musician, and an excellent teacher, but he is unique. I, for example, tend to think of myself as an excellent generalizer, leader, and communicator. However, I need help with attention to detail and deep domain-specific knowledge. To work well, I need to have pedantic specialists in my team. Why? Because, on average, I’m average.

Most “geniuses” are extremely talented in one field but still need help in others. Many tend to be individual workers, meaning their team communication is often suboptimal. Additionally, the fact that the entire team is very young also means they need more expertise in project management, inter-team communication, business orientation, or even enough real-life experience. The result: a disaster. That company got a team of solo players who don’t communicate within the team, don’t communicate with other teams, and don’t deliver on time.

What do I suggest? They say that “A’s hire A’s”. However, this doesn’t mean that each “A person” must ace the same field. A good team needs an A generalizer, an A specialist, an A communicator, and an A business expert. If you only hire “A++ specialists,” you risk ending up with a group of individuals who are “C-“ communicators.

As another CEO I consulted once told me, “genius developers can do 10x job. They also tend to enter rabbit holes, and if unattended, they can do 10x damage.” If you build a team, you cannot afford to have unbalanced expertise sets.

The bottom line is to ensure your team is diverse in its capabilities. Hiring only superstars may seem like a good idea, but it can result in a lack of collaboration, communication, and the necessary skills to succeed as a team. A diverse team with various skills and expertise is essential for achieving better outcomes.

In conclusion, avoid falling into the trap of thinking that only superstars can make a great team. Instead, focus on creating a diverse team with various skills, and you’ll be surprised at how much your team can achieve.

Read this if you are a data scientist (or another professional) worried about your career.

So many people, including me, write about how fields such as copywriting, drawing, or data science change from being accessible to a niche of highly professional individuals to a mere commodity. I claim it’s a good thing, not only for humankind but for the individual professional. Since I know nothing about drawing, I’ll talk about data science.

I started working as a data scientist a long time ago, even before the term data science was coined. Back then, my data science job included:

• writing code that implements this optimization algorithm or the other
• writing code that implements this statistical analysis or the other
• writing code that implements this machine learning technique of the other
• writing code that implements this quality metric or the other
• writing code that handles named columns
• writing code that deals with parallelization, caching, fetching data from the internet

Back then, exactly when the term data scientist was coined, I used to say “data is data”. I claimed that it didn’t matter whether you write a model that detects cancer or detects online fraud, a model that simulates two molecules in a solution or a model that simulates players in the electric appliances market. Data was data, and my job, as a data scientist was to crunch it.

Time passed by. Suddenly, I discovered one cool library, the other, and a third one … Suddenly, my job was to connect these libraries, which allowed me to be more expressive in what I could achieve. It also allowed me to concentrate better on “business logic.” Business logic is the term I use to describe all the knowledge required for the organization that pays your salary to keep doing so. If you work for a gaming company, “business logic” is the gaming psychology, competitor landscape, growth methods, and network effect. If you work for a biotech company, “business logic” is the deep understanding of disease mechanisms, biochemistry, genetics, or whatever is needed to perform the breakthrough. The fact that I don’t need to deal with “low-level coding” made me obsolete and drove me to a state where I became more specialized.

These days, we are facing a new era in knowledge commoditization. This commoditization makes our skills obsolete but also makes us more efficient in tasks that we were slow at and lets us develop new skills.

In 2017, Gartner predicted that more than 40% of data science tasks would be obsolete by 2020. Today, in 2023, I can safely say that they were right. I can also say that today, despite the recent layouts, there are much more busy data scientists than there were in 2017 or 2020.

The bottom line. Stop worrying.

Let me cite myself from 2017:

Data scientists won’t disappear as an occupation. They will be more specialized.

I’m not saying that data scientists will disappear in the way coachmen disappeared from the labor market. My claim is that data scientists will cease to be perceived as a panacea by the typical CEO/CTO/CFO. Many tasks that are now performed by the data scientists will shift to business developers, programmers, accountants and other domain owners who will learn another skill — operating with numbers using ready to use tools. An accountant can use Excel to balance a budget, identify business strengths, and visualize trends. There is no reason he or she cannot use a reasonably simple black box to forecast sales, identify anomalies, or predict churn.

This is another piece of career advice. I have more of them in my blog

Read this if you are considering hiring data scientists

I already wrote about how data science becomes a commodity.

If you read this, I guess data science is not the core part of your business. If this is the case, consider the following before you hire data scientists.

Data engineers

Your data scientists can be as good as the data you provide them. You must collect the correct data, validate it, store it well, and be able to access it easily. I have hours of “war stories” about how each component of the last message went wrong, and the company burned tons of money because of that. Data piping is a serious challenge. So, before you hire a data scientist, ask yourself whether your data engineering needs are covered.

Data analysts

Data Analysts mainly focus on the organization and interpretation of data. Unlike data scientists, Analysts don’t build predictive models or create unique algorithms. However, they identify trends and insights and present their findings clearly and understandably. Not being required to build novel models and algorithms allow them to better connect with stakeholders’ business needs and practical questions. A good data analyst will take the business problem, translate it into a data-based question, will know its potential value, and in many cases, will be able to answer it.

Boxed Solutions

Data Science as a Service is a term for boxed solutions that are constantly becoming more versatile, flexible, and affordable. I was a freelancer for a company that built its data-based product on an open-source implementation of a single optimization algorithm. They managed to run a successful company without a single data scientist for more than five years, and they started thinking of better solutions when they squeezed everything they could from their MRE. At this point, they had their data storage pipelines (data engineering), a better picture of their business (data analysts), and paying customers to finance the development of new algorithms.

How to work with data scientists?
I’ll write separate posts on this topic, but the gist is: to make sure they know your business needs. Ensure you communicate your needs and problems to them and make sure they share their efforts with you. I have seen many failed data science projects in my life. Most failed due to a lack of alignment, communication, or both.

This was another career advice post. Read more of them here.

Read this if you’re a data scientist or consider becoming one.

Almost six years ago, when Data Scientist was named the “sexiest job of the 21st century”, I wrote a blog post telling young professionals not to learn data science as a career move. My claim was that the data science field fill gets commoditized, and if you don’t possess deep (I mean DEEP) knowledge of either algorithms or the business you are working at, you will end up a mediocre coder.

Look what happened. Data science has indeed become commoditized in many fields. Many data-intence businesses work just fine without data scientists. Even I, a very experienced data scientist, got laid off because I couldn’t bring the company value that would justify my salary. People like Matthew Yglesias from https://www.slowboring.com suggest that data scientists learn how to roll a burrito or mine lithium.

Why did this happen? Well, I was right. Data science has become a commodity. Each self-respecting platform offers AI tools (I hate the term AI, by the way) such as keyword extraction, insights, predictions, anomaly detection, recommendations, and many more. Tableau, PowerBI, and even Google Sheets or Excel offer tools that were once only available through custom data and code fiddling. The Data-Science-As-A-Service niche is full of products such as https://www.pecan.ai and https://www.anodot.com. And we haven’t even started talking about the new word of the day: the GPT.

Being an experienced data scientist, people often ask for my advice and help. In the past, when this happened, I used to discuss possible custom-tailored solutions. Now, I find myself suggesting the person looking at product X or Y will solve their problems in a fraction of the time and cost.

So, what do we have? What does all that mean?

Data science has become a commodity. In the past, to get a nice salary and a sexy title, it was enough to know what training, testing, and cross-validation were. Today, you absolutely have to know the theory and be a fast and good coder. But most of all, you must hone your communication skills and learn the business of the company where you work. Only this way will you be able to ensure your efforts are always aligned with the stakeholders and that you can consistently deliver value.

This is a career advice post. Check out the career tag and the Career Advice category of this blog.

I think that I’m in love with Midjourney. Look how easy it is to be creative when you have AI at your disposal!

Tishrei is the seventh month of the Hebrew calendar that starts with Rosh-HaShana — the Hebrew New Year. It is a 30 days month that usually occurs in September-October. One interesting feature of Tishrei is the fact that it is full of holidays: Rosh-HaShana (New Year), Yom Kippur (Day of Atonement), first and last days of Sukkot (Feast of Tabernacles) **. All these days are rest days in Israel. Every holiday eve is also a *de facto rest day in many industries (high tech included). So now we have 8 resting days that add to the usual Friday/Saturday pairs, resulting in very sparse work weeks. But that’s not all: the period between the first and the last Sukkot days are mostly considered as half working days. Also, the children are at home since all the schools and kindergartens are on vacation so we will treat those days as half working days in the following analysis.

I have counted the number of business days during this 31-day period (one day before the New Year plus the entire month of Tishrei) between for a perios of several years.

Overall, this period consists of between 14 to 17 working days in a single month (31 days, mind you). This year, we only have 14 working days during the Tishrei holiday period. This is how the working/not-working time during this month looks like:

Now, having some vacation is nice, but this month is absolutely crazy. There is not a single full working week during this month. It is very similar to the constantly interrupt work day, but at a different scale.

So, next time you wonder why your Israeli colleague, customer or partner barely works during September-October, recall this post.

(*) New Year starts in the seventh’s month? I know this is confusing. That’s because we number Nissan – the month of the Exodus from Egypt as the first month.

(**)If you are an observing Jew, you should add to this list Fast of Gedalia, but we will omit it from this discussion

TL;DR Own your wins, own your failures, stay calm and make decisions. Read it. 5/5

“Extreme ownership” is a book about leadership in business written by two ex-SEAL fighters. This book is full of war stories, as in actual stories from a real war. I read this book by the recommendation (an instruction, really) of the serial entrepreneur Danny Lieberman. After three years in the Israeli Border Police and after a cumulative year-and-a-half in active IDF reserve over almost twenty years, I learned to dislike war stories strongly. Had Danny not told me, “you have to read this book,” I would have ditched it after the first couple of pages. The war stories are self-bragging, and the business case studies are oversimplified and always have a happy ending. Moreover, the connection between a war story and a business case is sometimes very artificial.

Nevertheless, I’m glad that I read this book. It has several powerful messages and shows leadership aspects that I haven’t managed to formalize in my head before.

Key points

The best leaders don’t just take responsibility for their job. They take Extreme Ownership of everything that impacts their mission. When subordinates aren’t doing what they should, leaders that exercise Extreme Ownership cannot blame the subordinates. They must first look in the mirror at themselves.

• It’s not what you preach; it’s what you tolerate

• “Relax, look around, make a call.”

This point takes me back to my days as the chief combat medic in an IDF infantry battalion (here we come, more war stories!). One day, an instructor, a very experienced paramedic, told me that the first thing a medic should do when they arrive at a scene is to take a pulse, not the pulse of the victims, but your own pulse, to make sure you’re calm and take the right decisions.

• Prioritize your problems and take care of them one at a time, the highest priority first.
• Leadership doesn’t just flow down the chain of command, but up as well.

This is a super valuable and insightful message.

The bottom line: Read it 5/5

I will skip the usual “I’m thrilled and excited…”. I’ll just say it.
As of today, I am the CTO of wizer.me, a platform for teachers and educators to create and share interactive worksheets.

On a scale of 1 to 10, how thrilled am I? 10
On a scale of 1 to 10, how terrified am I? 10
On a scale of 1 to 10, how confident am I that wizer.me will become the “next big thing” and the most significant chapter in my career? You won’t believe me, but also 10.

Today, I gave my first in-person presentation since the pandemic. It was awesome! I was talking about the study I performed with Nabeel Sulieman about data visualization in environments that use right-to-left writing systems.

I wrote about this study in the past [one, two]. Today, you may find the results of our study at http://direction-matters.com/. I hope to be able to publish the video recording of this presentation really soon.

An example of a very bad graph

Nature Medicine is a peer-reviewed journal that belongs to the very prestigious Nature group. Today, I was reading a paper that included THIS GEM.

These two graphs are so bad. It looks as if the authors had a target to squeeze as many data visualization mistakes as possible in a single piece of graphics.

Let’s take a look at the problems.

• Double Y axes. Don’t! Double axes are bad in 99% of cases (exceptions do exist, but they are rare).
• Two subgraphs that are meant to work together have different category orders and different Y-axis scales. These differences make the comparison much harder.
• Inverted Y scale in a bar chart. Wow! This is very strange. Bizarre! It took me a while to spot this. First, I tried to understand why the line of P<0.05 (the magic value of statistics) is above 0.1. Then, I realized that the right Y-axis is reversed. At first, I thought, “WTF?!” but then I understood why the authors made this decision. You see, according to the widespread statistical ritual, the lower the “P-value” is, the more significant it is considered. The value of 1 is deemed to be non-significant at all, and the value of 0 is considered “as significant as one can have.” So, in theory, the authors could have renamed the axis to “Significance” and reversed the numbers. Still, the result would not be a real “significance,” nor would the name be intuitive to anyone familiar with statistical analysis. On the other hand, they really wanted more “significant” values to be bigger than less significant ones. So, what the heck? Let’s invert the scale! Well, no, this is not a good idea
• Slanted category labels. This might be a matter of taste, but I dislike rotated and slanted labels. Turning the graph solves the need for label rotation, thus making it more readable and having zero drawbacks.

What can be done?

I don’t like criticism without improvement suggestions. Let’s see what I would have done with this graph. To make this decision, I first need to decide what I want to show. According to my understanding of the paper, the authors wish to show that the two data sets are very different in determining a specific outcome. To show that, we don’t need to depict both the P-value and variance (mainly since these two values are very much correlated). Thus, I will depict only show one metric. I will stick with the P-value.

I will keep the category order the same between the two subgraphs. Doing so will create a “table lens” effect; it will show the individual values while demonstrating the lack of correlations between the two groups. Finally, I will convert the bars into points, primarily to reduce the data-ink ratio. Two additional arguments against bar charts, in this case, are the facts that the P-values of a statistical test cannot possibly be zero and that bar charts don’t allow log-scale, in case we’ll want to use it.

The result should look like this sketch.