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Don’t let instincts get in the way of doing the right thing

Don’t let instincts get in the way of doing the right thing

I like to believe that I am an objective being, actively in control of my decisions and actions, especially at work.

Yet time and again, I find myself less than proud of some of my past behaviors. I’ve had demographic biases toward people; I’ve opposed arguments without assessing their basis; and I’ve agreed to ideas that are against my personal values.

Fact is, I am often a slave of my subconscious, of my brain running on cruise control. And I’m starting to recognize that like animals, I have instincts that are challenging to overpower.

Why is this a problem? It’s a problem if our actions differ from our desired actions. If our brain on autopilot takes decisions that go against those we’d take consciously. It’s especially a problem if our instincts get in the way of creating a fair, transparent, and innovative workplace. The type that startup companies in this globalized world need.

So let’s take a moment to recognize our instincts. Allow me to share three instinctual behaviors that get in the way of…

… debating with the boss

I can recall numerous occasions where I’ve disagreed with my boss and yet didn’t try to voice or argue the matter. I’ve disagreed over the team’s compensation plan, our holiday policy, and even our company strategy. Yet on many of these issues, I’ve kept my thoughts to myself.

Why?

Well, to survive of course. Self-preservation is the need to keep myself alive and economically healthy. It is also the reason I will avoid arguing with my boss. Fact is, I see my boss as the hand that feeds me, so the last thing I want to do is to create conflict and paint myself as an enemy. I simply don’t want to get fired.

How can I become more vocal with my thoughts?

Everytime that I disagree with my boss nowadays, I first note it down in my journal to first avoid losing that thought. I then let it sit for 24 hours to ensure that my reactionary emotions are gone. If I still disagree after that, I will start to work on a way to introduce my disagreement, gathering evidence to support my thoughts, and planning for the right time to speak up. I also find it helpful to state my goals (why I’m of this different opinion), because they are often the same as my boss’s goals, so it helps us start the conversation from the same footing.

… being excited about changes

Most people I know react to a new proposed change with skepticism. Not many individuals react to a surprise change with a “Hooray!” Ok, maybe extreme sport athletes do. But for most of us commoners, we love a good old routine.

Why?

For the simple reason that we are creatures of habit and routine. As explored by NPR and Psychology Today, our habits and routines help us navigate our days with greater ease, greater comfort. As I’m typing this blog post, I am not actively thinking about which letter to press on my keyboard, my brain has made typing a habit, and I only have to think about what I want to say. There are dozens and dozens of tools that each of us depend on to do our work. To become more productive, we make a habit of using all these tools.

Yet when things change, our habits and routines have to be reset. We thus are naturally upset by change. If someone was to change the letter placement of my laptop keyboard, I’d be frustrated regardless of whether it’s better for my health or not. It simply takes me outside my comfort zone and I have to re-learn basics of typing again. We thus dislike it when people change the tools or processes we’ve grown accustomed to.

Being skeptical of change is in my opinion a good thing – it ensures that we take the time to properly review any proposed change’s potential impact, and take the necessary precautions. Yet this instinct can also backfire when people are stubbornly opposed to change without reason. According to some studies, 70% of change management initiatives fail. I’m willing to bet that people’s instinctive opposition to change has something to do with that.

How can a workplace assess changes objectively?

On our team, we first make sure that there are no surprises. No changes are made or even proposed before we first accurately pinpoint the problem at hand. We then work to ensure that all stakeholders agree on the problem. Only then do we start working to find a solution to the problem. Since all impacted parties are already involved and have agreed to participate in solving the problem, there is usually little to no opposition to any proposed changes. They architected it together.

… objectively judging people, especially individuals that are different

When I interview candidates, I often find myself asking more questions to people that did not come from a background (education, experience) similar to those of existing team members. In a way, we could call it playing it safe, but on another level, I’m simply judging people differently because they come from different walks of life.

As I consulted colleagues from other companies and startups on how they handled these situations, it became clear that this problem exists across industries, and in companies large and small. Age, gender, education, ethnicity, and even fashion discriminations were rampant. My colleagues and I both suffered such discriminations as well as contributed to them. We realized that most of the time, people were not even aware that they were discriminating. We’re talking about really smart, often Ivy league educated managers that would fight for feminist causes or march with Martin Luther King should he still be with us.

Why?

In my opinion, it comes down to the fact that we fear the unknown. We are afraid of things we are not familiar with: Foreign cultures, people, ideas. Here, foreign can take the form of a different neighborhood in the same city, not just another country. In its worst form, our fear morphs into Xenophobia, as witnessed in the recent Brexit. Day-to-day, we avoid certain parts of the city, sit with colleagues that are similar to us at the cafeteria, or ask some people more questions than others at interviews.

Again, why?

The question then begs… Why in our multicultural society (at least in much of the western world), are we still so afraid? Haven’t we been exposed to enough different people, cultures, and ideas that we can comfortably shed away our biases?

Well, fact is that even though there are multiple cultures found near each other geographically, there are limited interactions between them. Cultures are not mixing.

Simply glossing over a demographic map of the USA will expose the fact that most neighborhoods in cities are segmented demographically. African Americans, White Americans, Asian Americans, and Hispanic Americans can all be found living apart from each other, in different neighborhoods. How do we expect to really understand other cultures if we are never exposed to them? Do we really understand their differing values and cultures? The situation is even worse in rural areas and smaller cities.

So this leaves us popular culture to educate us on the values and lives of foreign cultures. Yet no luck there either. According to research from USC, 73% of actors in Hollywood are white, 13% black, 5% Asian, and 5% Hispanic. That means that we are all overwhelmingly educated on white American culture, but little else.

All these stats are further augmented by the fact that 75% of white Americans do not have non-white friends. White Americans thus have no clue about the values, culture, and ideologies of the ~70 million non-white neighbors they share their land with.

This problem persists in the startup ecosystem and Silicon Valley, where most people are White or Asian. It reflects the demographic of university populations.

So how can I avoid being biased toward foreign people / cultures?

Simply being aware that we feel safer around people like us, and less so around those that look and think differently is a good start. Acknowledging we need people who think differently for innovation may be the next step. Let’s not fear our differences, but embrace them. We are all different, not better or worse.

The next time that candidates are being interviewed, perhaps we should take cues from musical orchestras and do it behind a curtain with voices masked. I’m kidding. Let’s all start with being more aware of how our brain operates on cruise control.


Recommended exercise

The next time that someone proposes a change, at work or at home, on how we do things, take note of our initial reaction. Did we oppose it instinctively, or did we keep our mind open and curious?


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Top 6 business intelligence mistakes

Top 6 business intelligence mistakes

These days, companies large and small have an insane amount of data to help with decision making.

A small mom and pop restaurant with a cloud based reservation system can forecast how much ingredients to order for the week. Yet we all still make bad decisions. Why?

First of all, let’s not blame the data. By itself, data can’t do anything.

If there’s anyone to blame, it’s us. That’s right: the human beings behind the data.

We are the ones that decide what data to record, how to record it, how to analyze it, and how to look at it. Between the moment we have a question and the moment we make a decision, there are numerous chances of misusing data and arriving at the wrong conclusion. It’s like walking through a minefield.

Working in the analytics field, I’ve seen hundreds of data analyses go nowhere, wasting thousands of hours of effort. So I’m going to share five of the most prevalent mistakes I’ve seen.

“What’s the actual problem?”

I once helped an e-commerce company analyze their top 10 sources of new visitors. After seeing the results, they were ecstatic to find that both their paid campaigns and their blog were top sources of new visitors. These were channels that they could actively control and scale. So they did just that: They invested more money in their paid campaigns and kept their blog active.

Yet a few weeks in, they started to complain that their effort didn’t translate into higher revenue. A lot of new people were visiting the site, but not buying. Why is that?

The simple answer is that the analysis they wanted answered a specific question: Which sources brought the highest number of new visitors? It did not answer which sources brought the highest number of new paying customers, or high lifetime revenue customers, which would both have been more helpful to their actual problem of growing new revenue. So to avoid wasting time, effort, and money, let’s ask the right questions to begin with.

“Is the sample statistically significant?”

I once observed a sales team cancel a process change after 10 prospects failed to convert under a new process (they handled on average 200 prospects a month). By no means was that sample size significant enough to draw any conclusions yet, scientifically speaking. It was not a data-driven decision. It was an emotional decision.

I’ve also witnessed a case where a company made product decisions based on half-a-dozen phone interviews with select clients that they had good relationships with. This particular company had 500+ clients. Half-a-dozen people among a population of 500+ clients does not represent an accurate view of growth opportunities. In addition, the quality of the sample was also questionable. All clients interviewed had good relationships with the company, which indicates that the opinion of unhappy customers and churned customers were not acknowledged.

Sampling problems, including selection bias and lower than optimal sample size, abound in business intelligence. Startups are especially prone to taking shortcuts and use poor samples. Sometimes, it’s because there is simply not enough data… If a company just started acquiring customers, there may not be enough customers to make the analysis statistically significant. Other times, it’s because of pure impatience… Teams want to take decisions now, not in two weeks, so they often fail to wait for their experiments to fully complete.

The result is a decision based on poor data.

“Are the numbers relevant?

I’ve also witnessed many companies set future sales goals based on historical trends, but then change their entire sales process and expect the same goals to be hit. How can one expect the the same forecast when all input variables have changed?

It’s like expecting to fly from New York to Los Angeles in 6 hours, but then change our plane for a car and still expect to get there in 6 hours.

Let’s recognize that the analysis or forecast that we do is only good for the scenario that we considered. Should we decide to tweak or change our scenario, a new analysis needs to be performed.

“Are you sure the numbers are right?”

NASA once lost a $328 million satellite in space because one of its components failed to use the same measurement units as the rest of the machine. Target lost $5.4 billion in Canada partially because its inventory system had incorrect data.

Time and again, huge mistakes were made because the underlying data fueling these projects was bad to begin with.

So to make sure that my analysis is accurate, I often ask a second party to check the numbers. One should never review their own essay. The rule applies to analyses as well.

“What does this mean?”

Having access to information doesn’t mean that we know what to do with it. I’ve seen many people confused by data reports and unsure of what decision to take.

I once helped a B2B company evaluate which customer group to target for an advertising campaign. Their product was used by customers from three different industries, but they didn’t have the resources to tailor their sales processes and marketing content to all three groups yet.

So they began by looking at revenue generated by the three industries. Then they looked at revenue growth over time, profitability, and lifetime revenue. The results showed that 50% of their revenue came consistently from one industry, but that another industry was the fastest growing, going from 10% to 35% of their revenue over the past year. Both were potentially good choices to target and they didn’t know which one to pick.

I thus asked them to divide the total revenue by the number of clients/companies in each industry, effectively giving us the average revenue per client. My logic was that their sales and marketing efforts were going to be spent on a select number of prospects, so targeting prospects with higher individual revenue may yield a better ROI (e.g. between a $500/year client and a $5,000/year client, I’d advise to choose the $5,000/year client assuming that cost of support is similar). Based on the analysis, we saw that the fastest growing industry was also the one with the highest paying clients. This thus made the decision easier.

The point is that looking at the right information is important, not just information. This requires people that can interpret data, explain caveats, and tell a story. I thus highly recommend for all managers, data analysts, and data scientists to read Cole Nussbaumer’s Storytelling with Data book.

“We deleted what?

I once tried to help a SaaS company understand their user churn trends, only to discover that they delete customer account information 3 months after a user deactivates their account. This meant that there was only data on recently churned clients. The sample proved to be too small and biased to draw any useful conclusions.

Developers may delete data because they are running out of room on their hard disk, or because they think that a certain piece of data is unimportant. Regardless of what developers think, from an analytical perspective, we should never ever ever delete data.

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