Category Archives: Management

What I learned working under Turing Award winner Jim Gray – 10 years since his disappearance

A few days ago, I sent out the following email remembering Jim to close friends and colleagues. I did not intend to share this broadly, but I received many positive replies encouraging me to post this publicly … so, here it is:

10 years ago this month, my mentor and idol Jim Gray disappeared at sea. I had the greatest fortune to work under him. We had published a paper together in the weeks leading up to his final sail.

I learned so much from Jim, and I think about him a lot. We even incorporated our company name after his saying “party on the data” (Party On Data, Inc.). To this day, I continue to unpack and internalize the lessons that I absorbed while working with him more than a decade ago.

I learned it’s important to make time for the unexpected. It felt like nearly everyone I knew in my circle had talked to Jim at some point – even people from very different fields of study. I am not sure how he was able to be so generous with his time given his position and stature, but if someone reached out to him with an interesting hook and was passionate about it, he made time. And, it wasn’t just a meet and greet – he truly listened. He would be engrossed in the conversation, and listen intently like you were the professor and he was the student. He made you feel special; that you had some unique insight about a very important problem area.

Jim’s projects were proof that making time and having an open mind for the unexpected – to converse and collaborate with people beyond your direct connections – can lead to breakthroughs in other disciplines. He made significant contributions to the SkyServer project, which helped astronomers federate multiple terabytes of images to serve as a virtual observatory (a world-wide telescope). He applied a similar approach to mapping data with the Virtual Earth project (the precursor to Google Maps – minus the AJAX).

In today’s world, with so many distractions and communication channels (many of which are being inundated with spam), it has become commonplace to ignore cold inbound requests. However, I learned from Jim that it’s crucial to make time for surprises, and to give back. No other Turing award winner responded to my emails and calls – only Jim did – and by doing so, he completely changed my life for the better. Jim instilled confidence in me that I mattered in this world, if someone important like him was willing to invest his precious time with me.

I learned from Jim that it’s important to tackle very good and crisp problems – and to work diligently on them (and to write everything down). Jim had a knack for identifying great problems. Comb through his website – it’s hard to find a dud in his resume or project list. I remember we were in talks with a major hospital about an ambitious project to improve the detection of diseases. The hospital group was willing to support this high profile project in any way we needed (thanks to Jim being a rock star), but Jim immediately knew we wouldn’t be able to develop crisp, tangible results within a year. He wanted more control, and craved a project with more short-term wins.

When Jim did identify a crisp problem to work on, he went all-in. His work ethic was second to none. We were once at a baseball game together, and I could tell from his demeanor that he was itching to get back to the office to continue our work. If I was working late in the office, he would work late too. He remained technical (writing code right next to me) and deep in the weeds despite his senior management role. He was responsive. Late night emails to emails at 5 AM (he liked waking up with the birds). He pushed me to work harder – not by asking for it, but by leading by example.

With any project, but especially database projects, there are so many low-level, unsexy problems (like data cleaning) that have to be addressed before you can “party on the data.” “99% perspiration and 1% inspiration,” he would always say, like it was a constant, inevitable force of nature that we have to equip ourselves for. He prepared me for that, which taught me how to stay focused and work harder.

I learned that it’s important to learn about key inflection points from previous products and projects – to know your history in order to make better decisions. Jim was a master story teller – constantly reciting history. I still remember his story about how Sybase was outgunned in the database market, but their innovation with stored procedures gave them the differentiation they needed to fight the fight with DB2 and Oracle. And, by the way, he was very laudatory of key features coming from competitors. He would never dismiss them – he loved the innovation, no matter where it came from. He wanted the truth for how to best solve a particular problem.

He loved to teach his lessons too. I recall one time I asked him a technical question, and an important call came through to his desk phone. He immediately hung up the call and took me to the whiteboard to teach me what he knew about the topic in question. Who does that? You’d be lucky to meet with your thesis advisor or manager once a week for 30 minutes, but Jim was present for me like this almost every day.

Jim set the highest management bar imaginable for me. He showed me why I should optimize 100% for mentorship throughout my career – not company brand – and to do this every time.

I sometimes wish he could see me now, as I feel like I wasn’t able to show him everything that I could do then, as I was still in the infancy of my career. I know better now where I excel (and where I don’t). At the time, I wanted to learn and do it all, like there was no tomorrow. He encouraged me to follow my passions – even if they were outside his comfort zone. Jim had no ego – he would loop in another mentor who knew more about a particular subject area. He gave me rope to learn, fail and rebuild. I tried to savor every minute I had with Jim, and am thankful that I did.

Despite his amazing technical accomplishments, I honestly do not remember many of the technical concepts that he had taught me. What I remember is how he made me feel. That’s what lives on and matters most. He gave me confidence, by just responding to me, and of course, working side by side with me. He rewarded my proactive outreach (which certainly encouraged me to send many more cold emails thereafter), and most importantly, taught me how to approach and solve big problems.

Jim truly inspires me, and I am forever grateful for what he did for me and my career. I sincerely hope that one day, I too, can have such a profound positive influence on so many people’s lives.

To being tenacious like Jim.

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Filed under Computer Science, Databases, Education, Job Stuff, Management, Microsoft, Publications

Amazon, Google and Apple vs the Big 5 Unicorns on Hiring and Churn

I’ve received multiple requests to analyze employee churn and new hiring rates for big companies and unicorns with the approach I took earlier for studying engineering and sales retention rates. I figured I’d give it a shot – and combine all of the key metrics in one chart …

amazon_google_apple_unicorns_retention_hiring

How to read this:

Blue bar represents the number of expected new hires that particular company will make in a 30 day (one month) period. Black bars (negative values) indicate how many employees will churn in a one month period. The orange line (the top most numerical labels correspond to the orange line plot) represent the net change in hires per month (new hires less churn). The companies are ranked by churn from left to right in descending order (so highest churn on the left).

As you can see in the chart, the big three companies included in this analysis are Amazon, Apple and Google. The unicorns are Uber, Lyft, Airbnb, Pinterest and Snapchat. “Big 5” combines these unicorns together as if they were one whole company. Also note, this is looking at employees worldwide with any job title.

Key Insights:

  1. Apple is not hiring enough new heads when compared with Amazon and Google. In fact, the Big 5 unicorns combined will hire more net heads than Apple with almost 50% less employee churn.
  2. Amazon’s churn is the highest – losing a little over 10 people a day. However, this is not bad relatively speaking – Google loses 8-9 people a day, and Apple is a tad over 9 a day (and Amazon has 36% more employees than Google). Given the recent press bashing Amazon’s culture and the periodic press envying Google’s great benefits, their retention rates tell a different a story – that it’s closer to a wash. Big tech companies with great talent churn people at pretty similar high rates regardless it seems (have some more thoughts on this but will save those for another post).
  3. At these current rates, all of the companies here (collectively) will increase their employee size by 20K (19, 414 to be precise) heads by year end (this is new hires less churn). That’s a measly 5% increase in their current collective employee size – and this is across the Big 3 Tech Companies and Big 5 Unicorns.
  4. Let’s compare Amazon to the Big 5 Unicorns. The Big 5 will hire 79% as many incremental heads as Amazon in a month, even though their collective employee size is 24% that of Amazon’s. Amazon has been in business for much longer (2-3x days since incorporation), and the Big 5’s churn is 43% of Amazon’s figure – both factors contributing to the closeness in the incremental head rate between the two.

Want more details?

How did I calculate these figures? Take a look at my previous post on engineering retention for more details. Same caveats listed there apply here, and then some (such as how this depends on the participation rates of LinkedIn which may differ considerably internationally compared to the US market which my previous posts exclusively focused on). Feel free to connect or email me if you have any questions or feedback.

 

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Filed under Data Mining, Economics, Entrepreneurship, Google, LinkedIn, Management, Non-Technical-Read, Research, Startups, Statistics, Trends

Top Tech Companies Ranked By Engineering Retention

(TL;DR) Here’s the ranking going from top to bottom (so higher / longer the better):

eng_ret

How did you measure this?

By running advanced Linkedin searches and counting up the hits. Specifically, for each company, at their headquarters location only, I searched for profiles that were or are software engineers, and had at least 1+ years of experience. Then I filtered these results in two ways:

1) Counting how many of those profiles used to work at the company in question (and not currently). Call this result Past Not Current Count.

2) Separately (not applying the above filter), filtering to those who are currently working at the company for at least 1+ years. Call this Current Count.

I also computed the number of days since incorporation for each respective company to be able to compute Churn Per Day – which is simply dividing Past Not Current Count by the number of days since incorporation.

Then I took this rate, and computed how long in years it would take for each company to churn through all of their Current Count or current heads who were or are software engineers and who’ve been with the company for at least 1 year (those who possess the most tribal wisdom and arguably deserve more retention benefits). Call this the Wipeout Period (in years) figure. This is what’s plotted in the chart above and is represented by the size of the bars – so longer the better for a company.

What does the color hue indicate?

The Churn Per Day (described in the previous answer). The darker the color the higher the churn rate.

Who’s safe and who’s at risk?

I would think under a 10 year wipeout period (esp. if you’re a larger and mature company) would be very scary.

In general (disclaimer – subjective – would like to run this over more comps) greater than 20 years feels safe, but if you’re dark green (and hence experience more churn per day) then in order to keep your wipeout period long you need to be hiring many new engineering heads constantly (but you may not always be hot in tech to be able to maintain such a hiring pace!).

What are the caveats with this analysis?

There are several, but to mention a few:

Past Not Current Count biases against older companies – for ex. Microsoft has had more churn than # of present heads because they’ve been in business for a long time.

I needed more precise filtering options than what was available from Linkedin to be able to properly remove software internships (although could argue that’s still valid churn – means that the company wasn’t able to pipeline them into another internship or full-time position) as well as ensure that the Past Not Current Count factored only software engineers at the time that they were working at that company. So, given the lack of these filters, a better description for the above chart would be Ranking Retention of Folks with Software Experience.

Also, this analysis assumes the Churn Per Day figure is the same for all folks currently 1+ years at their respective company, even though it’s likely that the churn rate is different depending the # of years you’re at the company (I’m essentially assuming it’s a wash – that the distributions of the historical Past Not Current vs Current are similar).

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Filed under Blog Stuff, Computer Science, Data Mining, Entrepreneurship, Job Stuff, LinkedIn, Management, Non-Technical-Read, Research, Statistics, Trends, VC

Want to compete with Salesforce? Buy Marketo

An op-ed I wrote for TechCrunch:

There are several enterprise players that want a share of Salesforce’s business, but just aren’t making headway by knuckling up against the company’s dominant, entrenched SaaS CRM offerings. Rather than competing head on, a smarter approach for these businesses is to “front door” Salesforce, instead.

By acquiring Marketo, a competitor could get into Salesforce’s accounts, then, over time, work themselves down the funnel and leverage better integrations with Marketo in order to eventually displace Salesforce. Marketo’s strategic foothold in the enterprise and its current market value relative to potential acquirers like IBM, Microsoft, Oracle, SAP and even Salesforce make this a great time to buy the leading marketing automation vendor.

Many industry watchers overlook the mission-critical role Marketo plays in its customers’ go-to-market operations. The majority of Marketo’s 4,000 customers also use Salesforce, but the marketing automation system has access to more data about the funnel than its CRM counterpart. Marketo can sync bi-directionally with Salesforce, capturing all the data stored there, while also holding top-of-the-funnel lead behavior data that doesn’t get stored in CRM. Hence, it has access to an invaluable superset of data about a company’s potential and existing customers.

Read More …

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Filed under Enterprise, Entrepreneurship, Management, Non-Technical-Read, Startups, VC

Why Most Product Managers Suck

A piece I wrote for The Next Web:

The first product manager (PM) is a crucial unicorn hire that no startup should compromise on. The reason is simple – your PM is responsible for managing your team’s most precious resource: time.

Unfortunately, nearly everyone seems to think they’d make a great PM (engineers, consultants, you name it), but the reality is that most folks just can’t hack it. I’ve worked with countless PMs at huge companies like Yahoo and Google, and over the past two months have interviewed over twenty PM candidates.

Out of all these folks, I’ve only encountered two PMs who actually do the job well … (read more).

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Filed under Blog Stuff, Entrepreneurship, Management