Learning Lessons from the Apollo program

I’m fascinated by the Apollo program. It’s a triumph of human endeavour that we sent twenty-four men to the moon, and twelve to the surface between 1969 and 1972. It was a program built with reliable 1950s-designed equipment, and computers far less sophisticated than a cheap modern wristwatch. Achieving Kennedy’s audacious vision was made possible through teamwork, planning, hard work, and ingenuity.

Recently, I decided to learn more about the early Apollo missions to understand what work went into getting us to the moon. It’s an incredibly story, and perhaps more interesting than several later missions.

An incremental approach

Apollo 7 was the first manned Apollo mission to fly in space. It was a confidence-builder, and the first time three people had flown together in space around the Earth. After eleven days in 1968, the crew returned having tested the command module and successfully made a live TV appearance.

The Apollo 7 crew during the first live broadcast from space

The Apollo 7 crew during the first live broadcast from space

The critical pieces of Apollo 7 had flown earlier. The earlier unmanned Apollo 4 and 6 had tested launching a similar unmanned command module and returning it to earth, while Apollo 5 had also tested the same Saturn IB rocket that was used to launch Apollo 7. Apollo 7 was focused on testing putting three astronauts in space in what was a now-trusted setup.

Apollo 8 was a shorter, six day mission. Three men flew to the moon, orbited it a few times, and returned to earth. They were the first to fly atop the Saturn V rocket — the smaller Saturn IB rocket used in Apollo 7 wasn’t sufficient to reach the moon. The Saturn V had been tested in Apollo 4 and 6. This was an audacious mission — testing both a manned Saturn V mission, and visiting the moon. Behinds the scenes, it was a tough sell to management — they didn’t like being that aggressive. But it made sense as a mission: the lunar module was behind in development and wasn’t ready to be tested, and there was fear the Russians might get Cosmonauts around the moon in 1968.

The far side of the moon as seen from Apollo 8. The Apollo 8 crew was the first to ever see the dark side of the moon

The far side of the moon as seen from Apollo 8. The Apollo 8 crew was the first to ever see the dark side of the moon

Apollo 9 was a return to an incremental approach. The mission orbited the earth, tested the lunar module in earth orbit (that would later be used to land on the moon), and included a space walk to test the spacesuits. It also included a rendezvous, which was necessary with the command and lunar modules being separated.

Testing the Apollo 9 lunar module Spider in earth orbit

Testing the Apollo 9 lunar module Spider in earth orbit

Apollo 10 was the full dress rehearsal for landing on the moon. It was time to repeat the test of the lunar module, but this time in moon orbit. The lunar module detached from the command module, and the crew descended to within ten miles of the moon’s surface. They then returned to rendezvous with the command module, and journeyed back to earth. In total, the crew spent eight days in space, and the mission was a huge success — so successful that Apollo 11 was the mission that met Kennedy’s goal in July 1969.

The Apollo 10 lunar module Snoopy returns from almost landing on the moon

The Apollo 10 lunar module Snoopy returns from almost landing on the moon

Interestingly, many people at NASA thought early in the Apollo program that Apollo 12 was likely to be the mission that landed first on the moon. Perhaps if Apollo 9 had happened before Apollo 8 (as was originally planned), there might have been two separate missions to test the manned Saturn V and then a manned Saturn V to the moon. Certainly, if something substantial had gone wrong between Apollo 7 and 10, Apollo 11 would have been repeating validation of the space craft, space suits, and processes.

The tortoise beats the hare

The Soviet Union went all-in with Soyuz 1. It was the first flight of the new Soyuz spacecraft and Soyuz rocket, and was planned to be a rendezvous with the three-manned Soyuz 2. The mission had problems from the start — a solar panel failed to deploy, and this delayed the launch of Soyuz 2. The weather turned bad, and Soyuz 2 didn’t launch. This was fortunate, as the parachute on Soyuz 1 didn’t deploy due to a design fault, and the single Cosmonaut died on reentry. If Soyuz 2 had launched, the crew wouldn’t have survived.

Vladimir Komarov, the cosmonaut who died in the ill-fated Soyuz 1

Vladimir Komarov, the cosmonaut who died in the ill-fated Soyuz 1

This was 1967, a year before Apollo 7. The Soviets went for broke, testing rockets, capsules, rendezvous, and more in one mission. On paper, it looked like they were ahead. The result was failure — and an 18-month delay in the program, and ultimately failure to get to the moon before the Americans. Indeed, Soyuz 4 and 5 in early 1969 eventually completed the mission aims of Soyuz 1 and 2.

The tortoise beat the hare. It was a pretty fast tortoise, but you see the point. The pragmatic approach of trying one complex new component in each mission ultimately made the Apollo program successful. Doing everything at once didn’t work. There’s something in that for all of us. See you next time.

Music everywhere with Sonos

I’ve embraced Sonos as the way to enjoy music and radio in my house.

What’s Sonos?

I was late to the game too, so don’t worry if you haven’t heard of Sonos or don’t quite know what it does. Sonos is a company, and they make several powered speakers, that is, nice little units that contain an amplifier and speakers. They also make a product that allows you to connect your existing amplifier to the Sonos system.

The Sonos family of powered speakers and integration products. At the rear left is their subwoofer. The Play:3, Play:5, and Play:1 are grouped in the middle rear. At the front is Playbar for home theater. At the rear right are the integration products.

The Sonos family of powered speakers and integration products. At the rear left is their subwoofer. The Play:3, Play:5, and Play:1 are grouped in the middle rear. At the front is Playbar for home theater. At the rear right are the integration products.

One thing that’s cool about Sonos is that the powered speakers don’t need to be wired to a system. You put them where you want, and they connect wirelessly to a base station that’s plugged into your home wireless Internet router. Alternately, you can wire them to a standard Ethernet socket if you’ve wired your house. Sonos call their base station a bridge, and right now one of those comes free with any of Sonos’s speakers.

What makes a Sonos system cool, though, isn’t just that it’s portable and unwired. It’s that it sounds pretty darn good, and it integrates reasonably nicely with popular music services such as slacker and tunein radio. That means you can pay a few bucks a month and play a large library of music, and you can listen to a vast array of radio stations. You control this experience using your smartphone, tablet, or PC.

Playing music

It’s pretty simple to play music. You select the room you want to play — the available rooms are shown on the left in the image below. Then you select a source you want to play — you can choose from your own music library, or one of the streaming services, or a line-in input into one of the devices.

The Sonos Mac OS X application. Very similar to the Sonos iPad app. On the left are rooms, on the right are sounds sources.

The Sonos Mac OS X application. Very similar to the Sonos iPad app. On the left are rooms, on the right are sounds sources.

You can group rooms together to create a zone, and have the same source playing throughout part or all of your house. For example, I often put on the radio, and group together my bedroom, main living areas, garage gym, and outside patio so that I can listen to them as I move around the house.

I’ve got a turntable, and I’ve connected that to one of Sonos’s larger Play:5 systems; the smaller Play:1 and Play:3 don’t have a line-in input. I needed a pre-amp between the turntable and the Play:5, and picked up a reasonable one at an online store. With this setup, I can listen to vinyl throughout the house in the same way as I can listen to the rest of my music.

I sometimes plug other sources into another line-in socket in another Play:5. For example, when I want to listen to Major League Baseball, I fire up my MLB At:Bat app on my iPhone, and connect the iPhone to the Play:5. Then, I select the Line-in as a source in the Sonos app, and we’ve got baseball in the house. (Go Mariners!) The drawback is that if I want to adjust volume or settings, I have to walk to the Play:5 and fiddle with the iPhone.

What’s Great

Here’s the top five things I love about Sonos:

  1. Sounds good to great. I can’t get over how much sound is in the Play:1 for the size and price. The thing is about as big as a coffee tin, and it has nice bass response and looks good. The bigger Play:5 is a serious unit, and has five amplifiers and five speakers — when you pair two together to create a stereo system, and add a subwoofer, you’ve got a serious sound system (and it’s priced like one too — you’re talking US$1500)
  2. Music and radio everywhere. Buy a few units, put them around the house, and your life will be better. You’ll be better connected to the world through radio, and you’ll enjoy your music even more
  3. Easy to set up. When you buy a new speaker, you can use any Sonos app on any device to register the unit. It takes about two minutes to add the unit to your house
  4. Range. I can put speakers anywhere in my house — in locations where I don’t get wifi on my laptop or phone — and it works just fine. I can take one of them out in the yard, and all is well
  5. It’s an alarm clock. It’s easy to set up an alarm on any Sonos device, and choose a source. I wake up to KQED radio, and it gently fades in. It turns off after an hour (that’s configurable). The rest of my family uses this feature too

What Needs Work

Here’s where there’s room for improvement:

  1. It’s expensive. The Play:1 is the first sub $200 offering from Sonos, the Play:3 is $299, and it’s upward from there. The Play:1 is great value, but fitting out your house is an investment. Be warned: these things multiply, you’ll buy one or two, and you’ll be back for more
  2. The service integration is a bit clunky. I really like Slacker’s iPhone app — but you only get a fraction of the features when you use the Sonos app to stream the Slacker service. The Sonos folks use the APIs that these streaming companies provide, rather than the streaming companies integrating Sonos capabilities natively into their apps. You can also tell Sonos has no relationship with Apple — the music library integration is pretty clunky, it’s at the file system level
  3. The apps need a little bit of a rethink and redesign, they lack the beauty and simplicity of the hardware. The app paradigm is you select a room, then you select music. That isn’t always how you think — sometimes you want to dive into the music, and then select the room. You can do it, but it’s a little clunky (and sometimes you’ll surprise someone in your house with a blast of music). Still, I’ve seen tweens using it easily enough
  4. The apps or the network or something can be sluggish. I find that my iPhone is a little frustrating as the interface to Sonos — my iPad and Mac are much better. It sometimes takes a while for the iPhone app to find my Sonos system, and the app can be unresponsive to interactions sometimes. It’s also not a reliable device for streaming my music library
  5. It needs power. The Play:1 looks portable, but you need an electrical outlet

All up?

Pretty awesome. A game changer at my house. The hardware is amazing — and that’s what’s actually important. Software and music service integrations can be fixed, and they’re improving with every version.

See you again soon.

What’s Big Data anyway? Part Two

Last week, I shared a few ways in which big data adds value. This week, I share a few more.

Predictions

You can predict the future using data. Google gets publicity from predicting flu outbreaks.

I did something similar, years earlier, that is thematically similar and illustrates the idea of using big data to predict the future. I was interested in what queries users typed before and after the query stomach ache (and a few synonymous queries). Google and Bing both give you examples of what users type next, including: diarrhea, nausea, constipation, peptic ulcer, and stomach acid symptoms. Why was I interested? I wanted to see if I could figure out which drugs had side effects that included stomach upsets.

Talking about eBay's use of big data at the 2012 PHP UK conference

Talking about eBay’s use of big data at the 2012 PHP UK conference

I collected all the queries that users typed before and after stomach ache (and its synonyms) over the period of two or so years. I then threw away all queries that contained only English dictionary words, leaving queries that contained one or more non-dictionary words. What’s left? Drug names, and a ton of other junk (places, people, websites, misspellings, foreign words, and so on). What I found was that users were typing the names of drugs they were taking, learning about them, and then searching for information on stomach problems (and vice-versa). I could also see how frequently each drug was associated with a stomach ache.

I looked up some of the drugs on various websites, and learnt about the side effects. Guess what? More than half of the drugs I checked had a side effect of a stomach ache. Less than half didn’t — but I suspect that probably isn’t right. If you have enough users, you can learn about the future — and I know that at least a couple of the drug side effects have been updated to include rare incidences of stomach aches. See: you can predict the future!

The world of big data has many companies built on predicting the future using vast amounts of historical data. One of my favorites is The Climate Corporation (who recently were purchased by Monsanto) — they invested their time in doing a better job of predicting the weather than existing weather providers, and commercializing the insights through selling insurance against weather events.

Relative Performance

Every major website is running A/B tests. The idea is pretty simple: show one set of users “experience A” and show another set of users “experience B”. You do this for a while, and then compare various metrics between the populations. You might learn, for example, that customers prefer a blue button over a grey button, or that customers buy more products if you show them better product imagery. I’ve written about this topic previously.

Why’s this related to big data? Well, you have to collect and process an enormous amount of data to derive insights. To find statistically significant differences between the behaviors of populations of users, you typically need tens of thousands of users in each test and a reasonable time period of tracking all of their behaviors. If you multiply this by the number of tests you’re concurrently running, you plan to keep the data forever, and you want to produce many different insights, you will have petabytes of data on your hands.

Creating Feature Ideas

My third ever blog post was about inventing infinite scroll on the Web. It’s a good example of how you can use data to understand customers, and then create intuitive insights based on that understanding. In that example, we saw that users of image search paginated a ton, and we created a future without pagination — what’s now known as “infinite scroll”. You need lots of data, you need to keep that data, and you need to be able to create insights from that data to have these kinds of feature ideas.

Afterword

I don’t intend this to be a taxonomy of big data themes. There’s much more you can do with data — this is a stream of consciousness of themes I’ve seen in action. In my world, very little happens without big data: you’re using data to understand users and systems, you’re creating new ideas with that data, and you’re iterating on those ideas by measuring them at scale. Even the big leaps — like infinite scroll — aren’t ideas that are created in the absence of data.

See you next time.

What’s Big Data anyway?

I spoke recently at SMX East on Leveraging Big Data in Search Marketing. I was the opening speaker, and I started by defining Big Data. I thought I’d share some of what I said.

First, I believe that Big Data itself isn’t valuable, it’s what you do with it that is. The name

I just bought the t-shirt. Grab yourself one too.

I just bought the t-shirt. Grab yourself one too.

implies only that you have a large amount of data — more than you can process in Microsoft Excel — and that you’re investing to store it. It implicitly implies that you want to store the data in one common infrastructure, so that you can organize, process, and extract value from the data. This is a large topic in itself — it is hard to get data into one infrastructure, get it cleansed and organized, and to create order and structure around how its processed — and I’ll save that for another time.

In this post, I’m going to focus on examples of creating business and customer value using big data. It’s the first of two posts on the topic — stay tuned next week for the conclusion.

Discovering Patterns

I wrote early in 2012 on the topic of query alterations. They’re a great example of extracting customer value from big data — in this case, discovering patterns and using those to improve the experience of your users. Suppose you work at a search engine company. You decide to process vast amounts of data to discover examples where users have typed a query into a search engine, haven’t found what they wanted, and refined their query to improve the results. By processing hundreds or thousands of millions of such query patterns, you learn how to improve queries automatically. For example, you learn that users who misspelt ryhthm [sic] refine their query to rhythm, and so you learn that you can automatically do this with high confidence (as Google does today).

Finding Anomalies and Outliers

I’ve been lucky enough to run very large, distributed computing infrastructures at eBay and Microsoft. They’re incredibly complex — thousands of machines carrying out hundreds of different functions in several data centers, and all orchestrated to work together as a complex system. The vast majority of the time, it works almost perfectly — but there’s always some anomaly or quirky behavior at the margin. For example, users of a particular version of Internet Explorer 8 might be having a problem with one page on the site when they carry out four rare actions in a specific order; we might hear about this from a customer service representative who’d been speaking to a customer.

The customer probably simply stated that they’re having a specific issue on a specific page. That is, we’d typically learn about the symptoms, but not much about the problem itself. Here’s where big data comes along to help: we might look for a specific error message in our logs, and collect all the steps and information about all customer experiences that lead up to that error message. From there, we might discover that the common thread is the Internet Explorer 8 browser, and the four rare actions in a specific order. That gives us clues, and then it’s down to the engineering team to diagnose the problem — say, it’s some subtle issue where data isn’t synced across data centers because of a race condition — and to prepare a fix for the site. Splunk has built a successful business around mining system diagnostic big data.

Summarizing and Generalizing

On eBay, a cell phone is sold every five seconds. That’s amazing, and also a good example of how big data helps you summarize what’s happening in terms that people can understand and discuss. Similar examples include sharing that eBay has over 124 million users, that top rated sellers contribute 46% of US GMV, or that fixed price listings were 71% of global GMV.

You need big data to create these kinds of insights. Let’s take the top rated seller fact. First, you need to find all purchases in the relevant time period and sum the total dollar value of the purchases — I don’t know what the time period was, but let’s say for argument’s sake it was the past year. Then, you need to sum the total purchases of the top rated sellers, by joining together the purchases and seller information to ensure you’re only counting the dollars sold from the top rated sellers. From there, it’s simple division to get the 46% answer. The bottom line is you need a year of purchase data and your complete user information to find the answer — in eBay’s case, that’s 124 million active users and (a guess) at least 3,000 billion transactions that need to be processed.

In the follow-up post, I talk about three more examples of creating value using big data: predictions, relative performance, and creating new ideas with data.

Writing a Book

One day I’ll write another book. Perhaps a sports book about people and their stories, or the story of search engines and the people that build them.

I wrote my first book in 2001 with David Lane, and we rewrote it in 2003 for the second edition. I wrote another book with Saied Tahaghoghi in 2004 – the truth is I started it, and he picked up the pieces when I changed careers and countries; he’s a good man. The first book sold over 100,000 copies over the two editions (I still get a royalty check quarterly) and the second modestly (Saied and I earned our advance back). They’re both dated, old books now.

Web Database Applications with PHP and MySQL. My first book in its second English edition.

Web Database Applications with PHP and MySQL. My first book in its second English edition.

It’s thousands of hours of work to write a book. I spent at least 20 hours per week for 18 months on the first edition of the first book – that’s 1500 hours at least. I got out of bed at 5:30am and I did a few hours of writing before work. I’d also squeeze in a little more after work (typically some proof reading), and a longer period of writing on the weekend (where I’d still get out of bed at 5:30am).

Did I get rich? No. Typically, the authors get less than 10% of the wholesale book price — a couple of dollars per book sold at most. I got more than the minimum wage for the first book (roughly dividing the royalties by the number of hours by the number of authors to get an answer). The second book didn’t pay its way.

The longer I worked in a single sitting, the more productive I was. It takes a certain fixed amount of startup time to begin writing – you reread what you’ve written, edit it a little, get the context back, think about the structure of what you want to say next, and then start writing anew. But I can’t write for an extended period – it’s tiring, and I need to stop and take time away to think about what I want to do next. Three or four hour stints are the most productive for me.

When I wrote the first book, I’d count how many words I wrote in a session, and use that as a measure of success. I’d decide that I was going to write 1000 words before I took a break. It turns out, that doesn’t work for me: I’ve learnt that what’s important is sustained output, averaged over a month or so. Some days, I’ve got writer’s block. But I’ve learnt that that’s when I am doing valuable thinking – I’m working through a larger problem, or thinking through structure, or solving something that’s been bugging me for a while; sometimes, this is a subconscious activity. Other days, I’m a machine: I write as fast as I can type, and thousands of words flow. A whole chapter has been known to flow after a writer’s block.

My second book, Learning MySQL in its one-and-only edition.

My second book, Learning MySQL in its one-and-only edition.

Writing slows down as the book takes shape. I’ll be in the middle of a new section, and I’ll want to reference something else I wrote using something like “as you learned in Chapter <x>, the <something>”. Then I have to figure out what chapter it was, and what exactly was that <something> – that takes time. And, as the book gets longer, you repeat yourself – at least, my memory isn’t amazing enough to make sure I only say the same thing once. I’ll find myself waxing lyrical about some great idea, only to discover that it is somewhere else in the book too. Then it’s a case of figuring out where it should be – which is going to lead to editing a was-finished chapter elsewhere in the book or rethinking what I’m writing today.

I worked with a major publisher, O’Reilly Media Inc., and the wonderful editorial skills of Andy Oram helped me on both books. Editors are awesome – they push, prod, ask questions, push for clarity, and say things that make your book better. But they create a ton of work – you’ll get feedback such as “Perhaps you could merge those two chapters?” or “Chapter 4 needs to be broken into two chapters, and you need to really go into much more depth”. That often happens after you think you’re finished. You’ll get asked for extra chapters, rewrites, more or less content, and complete changes in style. It makes your book better. Between the first edition and second edition of my first book, Andy helped me change my style from a formal computer science style to a more conversational, chatty style – the kind that I use in this blog.

Writing a book isn’t a social experience. You need to enjoy being alone with your thoughts. Prepare for one hour of inspiration and conversation to turn into a hundred hours of hard labor and iteration. If it takes a couple of thousand hours to write a book, ten of them are the inspirational ones where you create the fundamental ideas. I wrote much of the second book in my parent’s Winnebago – parked on the grass, a good distance from the house, deliberately without wifi, and with nothing inside to distract me.

So, why do it? I enjoy writing – there’s rewarding impact in sharing knowledge with thousands of people. If you’re lucky, someone’s success or their impact will be because of what you shared. Or maybe you change how someone thinks or sees the world. Or you make their life better. It’s also cool to see your name on the cover, and to feel it in your hands – it’s even cooler when it’s translated into a language you don’t understand. I wonder what joy there is in knowing people are reading a digital copy? The digital sales on the royalty statement have never quite inspired me the same way.

Have fun. See you soon.

More Sweatember Action

It’s getting late in Sweatember, and I’ve only shared one workout. It’s time to take my Sweatember motivation to the blog and share more ideas.

My buddies at I Choose Awesome are a tough, energetic bunch of Australians. They have great ideas for challenging workouts, and I took one for a test drive this week. Give it a try.

Half An Hour of Power

Write down these six exercises on a piece of paper, and head to the gym. If you don’t know the exercises, the links have short videos:

Try the Half An Hour of Power workout

Try the Half An Hour of Power workout

Start your stopwatch. Do ten of the first exercise, then move onto the next one. When you’re done with all six, that’s one round. Start again from the top, and see how many rounds you can do in thirty minutes. (For what it’s worth, I managed 7 and a bit yesterday — couldn’t quite get to 8.)

Too Hard?

It’s ok if you can’t do a pull up, or you’re worried about a kettle bell clean. Substitute something easier until you’re ready for the full Half An Hour of Power.

For an easier time than a pull up, try a row. If you don’t like the sound of a kettle bell clean, pick up an object (such as a medicine ball) and put it down again. You can always do your push ups on your knees to make them easier. If you don’t like the sound of any exercise, replace it with another one; for example, if you don’t like the burpee, replace it with a abdominal crunch.

Have fun.

I’m not an exercise professional. Do this at your own risk. Talk to a professional before beginning an exercise program.

Delivering a Tough Message

A colleague of mine was recently disillusioned with a significant change that had affected them. They’d had a 1:1 meeting with their manager, chatted amicably about work for 25 minutes, and then the manager had dropped the bombshell in the last 5 minutes of the meeting. There wasn’t enough time to discuss the change, the person felt betrayed, and the meeting ended on time and with many questions unanswered.

Many people avoid the tough topics, dislike conflict, and don’t want to deliver tough messages. Unfortunately, it’s part of professional life — and here’s what I’ve learnt about how to do it.

Open with the Facts

If you’ve got something important to share, start by sharing it. Anything else you say before it will be ignored, seem trivial in hindsight, and you may even look rude for not getting started with the important topic. Take a deep breath, say “I’ve got something important to share with you”, and launch into the punchline: outline the conclusion or outcome you want to share.

Be very clear about the facts. For example, “Thanks for meeting me today, Bob. Unfortunately, you were not successful in getting the manager role: I’ve decided to promote Jenny into the role of team manager, I’ve let her know, and we will be announcing it tomorrow”. Here’s another example: “Sammy, we will not be launching your team’s Wizzle product. As a leadership team, we have decided to cancel the project, and reassign you and your team to the Zazzle initiative”.

Don’t get interrupted during the initial discussion. Politely tell the person you’re talking to that you’d like to finish. You owe it to them to share the complete outcome before they get a chance to have a conversation about it. If you want to explain how you got to the conclusion, do it after you’ve shared the conclusion — it’s a huge mistake to walk through the blow-by-blow account of the decision making process while holding back what decision you’ve made until later.

Minimize the Surprises

If you can, don’t surprise people. Lay the foundations for an important conversation by discussing what you’re thinking in the weeks or months that lead up to the decision. If you’re canceling the Wizzle product, hopefully you’ve spent weeks with the team talking about how it isn’t going well, sharing your concerns, and being clear that it isn’t meeting expectations. It’ll then be less of a shock when you make a change.

Sometimes, you have to surprise people. If you’re telling your boss you’re leaving the company, you probably haven’t been talking about it to them for months. That’s ok.

Don’t hide behind others

If you made the decision, own it. Don’t say “we” when it’s actually “I”. Don’t blame others, and don’t bring others unnecessarily into the conversation. Have the courage to own what you decided — you might not be loved for what you’ve decided, but you’ll be respected for having the courage and conviction to own your decisions.

Managers often have problems owning performance discussions. I’ve heard the story many times of a manager saying to an employee “I wanted to give you a 4.0 but my boss decided to give you a 3.0, I’m really sorry”. In 95% of cases, the manager really did drive the outcome — they didn’t put the person at the top of their list, they didn’t unreservedly advocate for the employee, and they were honest about one or more performance issues. So, own it: “When I got together with the leadership team and discussed your performance, I decided your performance was what was expected of someone at your grade and I’ve given you a 3.0. We have an amazing team, and you’ll need to work on three things to be a 4.0 at the next review”.

Conclude clearly

Make sure the conversation has been clearly understood. If you can get the person to play it back to you, you’ll be sure that it’s been understood. If you’re worried that it hasn’t been understood, you should follow it up with a written communication (an email is perfect) soon after the meeting. Indeed, this is often a good idea — I do this when I’m worried there’s room for misinterpretation, or that the decision or actions won’t stick how I want them to.

Above all, be courageous

You’re got past the initial discussion, you’ve conveyed the tough information you decided to share. Don’t change your mind. Don’t be intimidated. Don’t lose your cool. Nothing good ever comes of reversing a tough message in the heat of the moment.

Be calm. Courage doesn’t imply sternness or (worse still) yelling or anger. Be serious, but be rational, empathetic, and fair. Make sure you listen and be respectful. Do unto others what you would have them do unto you.

Good luck. See you next week.