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Kellblog on SaaS Metrics, A Comprehensive Introduction Podcast

I’m pleased to announce that I was recently featured in a six-part SaaS podcast mini-series on SaaShimi hosted by Aznaur Midov, VP at PNC Technology Finance Group, a debt provider who works primarily with private equity (PE) firms for SaaS buyouts, growth capital, and recapitalizations.

Let’s talk first about the mini-series.  It’s quite a line-up:

  • A Brief History of SaaS with Phil Wainewright, co-founder of Diginomica and recognized authority on cloud computing.
  • Key SaaS Metrics with me.
  • Building a Sales Org with Jacco van der Kooij, founder and CEO of Winning by Design
  • Building a Marketing Org with my old friend Tracy Eiler, CMO at InsideView and author of Aligned to Achieve, a book on aligning sales and marketing.
  • Building a Customer Success Org with Ed Daly, SVP of Customer Success and Growth at Okta.
  • Raising Capital with my friend Bruce Cleveland, partner at Wildcat Ventures and former operational executive at Oracle and Siebel.

The series is available on RedCircle, Apple podcasts, and Spotify.

Now, let’s talk about my episode.  The first thing you’ll notice is Aznaur did the interviews live, with a high-quality rig, and you can hear it in the audio which is much higher quality than the typical podcast.

In terms of the content, Aznaur did his homework, came prepared with a great set of questions in a logical order, and you can hear that in the podcast.  His goal was to do an interview that effectively functioned as a “SaaS Metrics 101” class and I think he succeeded.

Here is a rough outline of the metrics we touched on in the 38-minute episode:

  • ARR vs. ACV (annual recurring revenue vs. annual contract value)
  • ARR vs. MRR (ARR vs. monthly recurring revenue)
  • TCV (total contract value)
  • RPO (remaining performance obligation)
  • Bookings
  • Average contract duration (ACD)
  • Customer acquisition cost
  • Customer acquisition cost (CAC) ratio
  • CAC Payback Period
  • Renewal and churn rates
  • ARR- vs. ATR-based churn rates (ATR = available to renew)
  • Compound vs. standalone metrics
  • Net dollar expansion rate (NDER)
  • Survivor bias in churn rates
  • The problem with long customer lifetimes (due to low churn rates)
  • LTV/CAC (LTV = lifetime value)
  • Net promoter score (NPS)
  • The loose correlation between NPS and renewals
  • Intent to renew
  • Billings
  • Services gross margin
  • Cash burn rate
  • The investor vs. the operator view on metrics

Appearance on the AI and the Future of Work Podcast with Dan Turchin

ai and future of workI’m happy to announce that I was recently interviewed on the AI and the Future of Work podcast, hosted by Dan Turchin, Founder and CEO of PeopleReign, and formerly of Astound.ai, Big Panda, ServiceNow, and Aeroprise.  Dan’s a great technologist, entrepreneur, and visionary so I was happy to sit down with him for this wide-ranging, twenty-five minute chat.

On the podcast, we discuss:

  • A bit of my career history and background
  • How COVID-19 will change work in Silicon Valley
  • Innovation beyond Silicon Valley (one of my favorite topics, given my five years in Europe.)
  • The one most important SaaS metric.  (Hint:  LTV/CAC.)
  • The most misunderstood SaaS metric.  (I can’t remember what I said, but I should have said CAC Payback Period.)
  • A prediction about a workplace activity that is outrageous today but could be commonplace in the future.  (I said salary transparency after struggling a bit.  I suppose face masks and elbow bumps would have been an easier answer.)
  • Thoughts on the best software cultures.  (Keyword:  winning.)
  • My advice to my younger self.  (“Put your hands in the air and step away from the keyboard,” in reference to the various troubles I’ve caused myself over email when I should have either said nothing or called.)

The link to the podcast episode is here.  I hope you get a chance to listen to it and enjoy it if you do.  Thanks for having me Dan.

The First Three Slides of a SaaS Board Deck, with Company Key Metrics

I’m a SaaS metrics nut and I go to a lot of SaaS board meetings, so I’m constantly thinking about (among other things) how to produce a minimal set of metrics that holistically describe a SaaS company.  In a prior post, I made a nice one-slide metrics summary for an investor deck.  Here, I’m changing to board mode and suggesting what I view as a great set of three slides for starting a (post-quarter) board meeting, two of which are loaded with carefully-chosen metrics.

Slide 1:  The Good, The Bad, and the Ugly
The first slide (after you’ve reviewed the agenda) should be a high-level summary of the good and the bad  — with an equal number of each [1] — and should be used both to address issues in real-time and tee-up subsequent discussions of items slated to be covered later in the meeting.  I’d often have the e-staff owner of the relevant bullet provide a thirty- to sixty-second update rather than present everything myself.

slide 0The next slide should be a table of metrics.  While you may think this is an “eye chart,” I’ve never met a venture capitalist (or a CFO) who’s afraid of a table of numbers.  Most visualizations (e.g., Excel charts) have far less information density than a good table of numbers and while sometimes a picture is worth a thousand words, I recommend saving the pictures for the specific cases where they are needed [2].  By default, give me numbers.

Present in Trailing 9 Quarter Format
I always recommend presenting numbers with context, which is the thing that’s almost always missing or in short supply.  What do I mean by context? If you say we did $3,350K (see below) in new ARR in 1Q20, I don’t necessarily know if that’s good or bad.  Independent board members might sit on three to six boards, venture capitalists (VCs) might sit on a dozen.  Good with numbers or not, it’s hard to memorize 12 companies’ quarterly operating plans and historical results across one or two dozen metrics.

With a trailing nine quarter (T9Q) format, I get plenty of context.  I know we came up short of the new ARR plan because the plan % column shows we’re at 96%.  I can look back to 1Q19 and see $2,250K, so we’ve grown new ARR, nearly 50% YoY.  I can look across the row and see  a nice general progression, with only a slight down-dip from 4Q19 to 1Q20, pretty good in enterprise software. Or, I can look at the bottom of the block and see ending ARR and its growth — the two best numbers for valuing a SaaS company — are $32.6M and 42% respectively.  This format gives me two full years to compare so I can look at both sequential and year-over-year (YoY) trends, which is critical because enterprise software is a seasonal business.

What’s more, if you distribute (or keep handy during the meeting) the underlying spreadsheet, you’ll see that I did everyone the courtesy of hiding a fair bit of next-level detail with grouped rows — so we get a clean summary here, but are one-click away from answering obvious next-level questions, like how did new ARR split between new logos and upsell?

Slide 2:  Key Operating Metrics

Since annual recurring revenue (ARR) is everything in a SaaS company, this slide starts with the SaaS leaky bucket, starting ARR + new ARR – churn ARR = ending ARR.

After that, I show net new ARR, an interesting metric for a financial investor (e.g., your VCs), but somewhat less interesting as an operator.  Financially, I want to know how much the company spent on S&M to increase the “water level” in the leaky bucket by what amount [3].  As an operator, I don’t like net new ARR because it’s a compound metric that’s great for telling me there is a problem somewhere (e.g., it didn’t go up enough) but provides no value in telling me why [4].

After that, I show upsell ARR as a percent of new ARR, so we can see how much we’re selling to new vs. existing customers in a single row.  Then, I do the math for the reader on new ARR YoY growth [5].  Ultimately, we want to judge sales by how fast they are increasing the water they dump into the bucket — new ARR growth (and not net new ARR growth which mixes in how effective customer success is at preventing leakage).

The next block shows the CAC ratio, the amount the company pays in sales & marketing cost for $1 of new ARR.  Then we show the churn rate, in its toughest form — gross churn ARR divided not by the entire starting ARR pool, but only by that part which is available-to-renew (ATR) in the current period. No smoothing or anything that could hide fluctuations — after all, it’s the fluctuations we’re primarily interested in [6] [7].  We finish this customer-centric block with the number of customers and the net promoter score (NPS) of your primary buyer persona [8].

Moving to the next block we start by showing the ending period quota-carrying sales reps (QCRs) and code-writing developers (DEVs).  These are critical numbers because they are, in a sense, the two engines of the SaaS airplane and they’re often the two areas where you fall furthest behind in your hiring.  Finally, we keep track of total employees, an area where high-growth companies often fall way behind, and employee satisfaction either via NPS or an engagement score. [9]

Slide 3:  P&L and Cash Metrics

slide 3 newYour next (and final [10]) key metrics slide should include metrics from the P&L and about cash.

We start with revenue split by license vs. professional services and do the math for the reader on the mix — I think a typical enterprise SaaS company should run between 10% and 20% services revenue.  We then show gross margins on both lines of business, so we can see if our subscription margins are normal (70% to 80%) and to see if we’re losing money in services and to what extent [11].

We then show the three major opex lines as a percent of revenue, so we can see the trend and how it’s converging.  These are commonly benchmarked numbers so I’m showing them in % of revenue form in the summary, but in the underlying sheet you can ungroup to find actual dollars.

Moving to the final block, we show cashflow from operations (i.e., burn rate) as well as ending cash which, depending on your favorite metaphor is either the altimeter of the SaaS plane or the amount of oxygen left in the scuba tank.  We then show Rule of 40 Score a popular measure of balancing growth vs. profitability [12].  We conclude with CAC Payback Period, a popular compound measure among VCs, that I could have put on the operating metrics but put here because you need several P&L metrics to build it.

I encourage you to take these three slides as a starting point and make them your own, aligning with your strategy — but keeping the key ideas of what and how to present them to your board.

You can download the spreadsheet here.

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Notes

[1] I do believe showing a balance is important to avoid getting labeled as having a half-empty or hall-full perspective.

[2] I am certainly not anti-visualization or anti-chart.  However, most people don’t make good ones so I’d take a table numbers over almost any chart I’ve ever seen in a board meeting.  Yes, there is a time and a place for powerful visualizations but, e.g., presenting single numbers as dials wastes space without adding value.

[3] Kind of a more demanding CAC ratio, calculated on net new ARR as opposed simply to new ARR.  For public companies you have to calculate that way because you don’t know new and churn ARR.  For private ones, I like staying pure and keeping CAC the measure of what it costs to add a $1 of ARR to the bucket, regardless of whether it stays in for a long time or quickly leaks out.

[4] Did sales have a bad quarter getting new logos, did account management fail at expansion ARR, or did customer success let too much churn leak out in the form of failed or shrinking renewals?  You can’t tell from this one number.

[5] There are a lot of judgement calls here in what math you for the reader vs. bloating the spreadsheet.  For things that split in two and add to 100% I often present only one (e.g., % upsell) because the other is trivial to calculate.  I chose to do the math on new ARR YoY growth because I think that’s the best single measure of sales effectiveness.  (Plan performance would be second, but is subject to negotiation and gaming.  Raw growth is a purer measure of performance in some sense.)

[6] Plus, if I want to smooth something, I can select sections in the underlying spreadsheet using the status bar to get averages and/or do my own calculations.  Smoothing something is way easier than un-smoothing it.

[7] Problems are hard to hide in this format anyway because churn ARR is clearly listed in the first block.

[8] Time your quarterly NPS survey so that fresh data arrives in time for your post-quarter ops reviews (aka, QBRs) and the typically-ensuing post-quarter board meeting.

[9] Taking a sort of balanced scorecard of financial, customer, and employee measures.

[10] Before handing off to the team for select departmental review, where your execs will present their own metrics.

[11] Some SaaS companies have heavily negative services gross margins, to the point where investors may want to move those expenses to another department, such as sales (ergo increasing the CAC) or subscription COGS (ergo depressing subscription margins), depending on what the services team is doing.

[12] With the underlying measures (revenue growth, free cashflow margin) available in the sheet as grouped data that’s collapsed in this view.

Will Your CEO Search Produce the Best Candidate or the Least Objectionable?

I was talking to a founder friend of mine the other day, and she made a comment about her startup’s search for its first non-founder (aka, “professional”) CEO.  She said the following about the nearly one-year recruiting process:

“Because every person in the search process had veto power, the process was inadvertently designed to go slowly and not produce the best candidate.  We passed on plenty of candidates superior to the person we eventually hired because someone had a problem with them and we assumed we’d find someone better in the future.  Eventually, the combination of search fatigue with dwindling cash compelled us to act so we locked in to the best person we had in-process at the time.  In effect, the process wasn’t designed to hire the most qualified candidate, but the least objectionable one.”

Character Ceo Talking From Tribune Set Vector

In this case the failed process was catastrophic.  The candidate they selected took the company in a different direction and my friend the founder was pushed out a few months later.

Here are some thoughts on how to create a CEO search process that produces the best, as opposed to the least objectionable, candidate:

  • Set up a search committee that does not include the whole board, so you are not creating a process with a large number of people who have veto power.
  • Write down what you want in a candidate in the form of a must-have, nice-to-have list.  Don’t delegate writing the core of the job spec for your new CEO to an associate at your search firm, cutting and pasting from the last spec.
  • Be mindful about the sequencing and timing of candidates.  Ask to see calibration candidates first to get people warmed up.  Try to cluster candidates.  Try to have sure candidates see the search committee in a different orders.  Slow down highly-qualified early candidates and speed up highly-qualified late entrants.  Like it or not, timing matters enormously.
  • Check some references before passing candidates beyond the committee.  Do some blind reference checking before moving candidates to the next step in the process.  There’s no point in having the group falling in love with a candidate only to discover they have poor reputation or dubious claims on their resume.
  • Let candidates ask for additional interviews beyond a relatively small core team  instead of defining a process where every candidate automatically sees every board member and executive staffer.  You can learn a ton about candidates by who they ask, and don’t ask, to see.
  • Ask candidates to present their plans for the company.  While all of them should include 90 days of learning and assessment (think:  “seek first to understand”) before taking action, virtually any qualified and engaged candidate has an 80% developed plan in their mind, so ask them to share it with you.

On the Perils of Taking Advice from Successful Business People

One of the hardest things about running a startup is you’re never sure who to listen to.

Your board members own big stakes in the company, but that doesn’t automatically align them with you.  Your late-stage investors want low multiples on big numbers.  Your early-stage investors want big multiples on small numbers.  And they have their own specific needs driven by their funds and their partnerships.  Your rank-and-file employees own relatively small stakes which, ceteris paribus, should make them want you to swing for the fences — but, in these days of decade-to-liquidity, you may have employees so jaded on equity compensation that they’d just like to keep their well-paying jobs.

Your executive team wants to hit their targets, earn their bonuses, and maybe some of them are deeply motivated by winning in the market, but maybe not.  With a 0.5% to 1% share, a $500M exit can mean a $2.5M to $5.0M pop.  Maybe some would prefer to take the early exit, upgrade the house in Menlo Park, and go do it again somewhere else, as opposed to riding it out for the long term.

The idea that giving everyone some equity is a good one, but as I wrote nearly ten years ago, it’s quaint to think that doing so aligns everyone.

So, if you can’t really look inside the company, what then?  Well, if you’re like many, you look outside.  You might read books, subscribe to blogs, or listen to podcasts.  You might seek out advisors or create an advisory board.

In all such cases, you’ll be taking advice from business people who have gone before you, have had anywhere from some to considerable success, and interested in sharing their learnings with others.  You know, people like me [1].

Look, I’m not going to argue that getting advice from successful people is a bad idea — it certainly seems preferable to the alternative — but I am going to point out a few caveats, most of which aren’t obvious in my estimation:

  • Successful people don’t actually know what made them successful.  They know what they did.  They know it worked.  They have hunches and beliefs.  Causality, not so much.  Some of them can be quick to forget that, so you shouldn’t be [2].  There was no control group.  If Marc Benioff carried a rabbit’s foot, would you?
  • Too many successful people are rinse/repeat [3].  I’m frankly surprised by how many successful people are chomping at the bit to do exactly what worked for them at their last company with total disregard for whether it applies to yours.  Beware these folks.  Interview question:  so could you tell me about a situation where you wouldn’t do that?  It’s not foolproof because most will catch the hint, so this is really something you need to listen for before asking.  Do they diagnose-then-prescribe or prescribe without diagnosing?
  • Their situation was likely different from yours.  In fact, in the land of disruption, as Kelly Wright points out in this podcast, it almost certainly was.  Are you creating a new category without competition?  Are you in an over-funded next-big-thing category?  Are you competing against a big company transitioning product lines?  Are you trying to get people to buy something they don’t believe they need or pick among alternatives when they know they do?  Are you disrupting technology, business model, or both?  Are you filling a need that is in the midst of being created the rise of another category?

Should you listen to these people?  I think yes [4].  But try to find ones who have seen both success and failure, seen success in many situations (not just one), and who are thoughtful about a company’s specific situation, and approach the advisory process and their own prior success with humility.

# # #

[1] While I’d characterize my own success as towards the left of that spectrum, I am advising and/or have advised over 20 startups, some of them stunningly successful.

[2] One of my favorite quotes of this ilk is from former Harvard marketing professor, Theodore LevittNothing in business is so remarkable as the conflicting variety of success formulas offered by its numerous practitioners and professors.  And if, in the case of practitioners they’re not exactly “formulas,” they are explanations of “how we did it” implying with firm control over any fleeting tendencies toward modesty that “that’s how you ought to do it.”  Practitioners filled with pride and money turn themselves into prescriptive philosophers, filled mostly with hot air.

[3] By the way, “I made $1B doing it this way” is one of the more difficult arguments you’re probably wise not to take on.

[4] “Duh.”

On Recruiting: The Must-Have / Nice-to-Have List

I’m amazed by the number of times I see companies performing searches, even for key positions, without a clear idea of what they’re looking for.  Rephrasing Lewis Carroll, “if you don’t know what you’re recruiting for, any candidate looks great.”

lewis

I liken executive recruiters to Realtors.  If you don’t give a Realtor specific guidance on what you want to see, they’ll show you whatever’s on the market.  Moreover, even if you do tell a Realtor that you want a 4-bedroom on a cul de sac with great schools, you are likely to end up visiting a 3-bedroom “charmer” on a main thoroughfare that they just had to show you because it has a certain “je ne sais quoi.”  That know-not-what, by the way, is that it’s for sale.

This is a moment of truth for your relationship with your Realtor because if you do not say “if you show me another house that doesn’t meet my must-have criteria I’ll be working with another Realtor,” then three years hence you’ll be wondering, to the sound of passing traffic, why you live in a 3-bedroom and the kids are in private school.

Let’s stick with the house metaphor.  It’s actually fairly easy to make a list of criteria.  Make a two-column list, with one column titled “Must Have” and the other “Nice to Have.”  (One way things go wrong is when you mix up the two.)

Must Have Nice to Have
4 bedrooms Hot tub
3 baths Ranch (one level)
Quarter-acre lot Half-acre lot
Great schools (K-12) Less than 20 years old
No swimming pool Walk to downtown
$1.0 to $1.5M price

This process has a number of advantages:

  • It forces you and your spouse to discuss what you really want.  What’s truly a must-have vs. a nice-to-have criteria?  You might be surprised.
  • It provides a crystal-clear basis of communication with your Realtor.
  • If you provide the list before engaging with Realtor, they have the chance to refuse the business if they think your criteria are unrealistic, e.g., given your price point.
  • Once engaged, it gives you the basis for holding the Realtor accountable for showing you only what you want to see.

Let’s switch to executive recruiting.  What do we typically find in an executive job specification?  This is excerpted from a real CEO spec:

The ideal candidate will be or have:

  • A track record in building and leading high-performance teams
  • Confidence to interact with and inspire belief from present and future investors
  • The ability to articulate and define relevant methodology
  • An excellent communicator, effective in front of Customers, Employees, Analysts
  • Sound judgment and maturity
  • A leader who recognizes and respects talent outside of his/her own and recruits that talent to work close to and complement him/her within the company
  • Unquestionable integrity
  • Organizational tolerance:  ability to work with fluidity and ambiguity

That ambiguity tolerance starts right with this spec.  Think for a minute:

  • Are these as clear as our house spec?  A track record for how long, two quarters or ten years?
  • Are they measurable in any way?   How do I know if they respect talent outside their own or have sound judgement?
  • Are they well thought out?  (“There, I just questioned your integrity. We’re done.”)
  • Are they specific?  Which relevant methodology should they be able to define?

Compared to our house criteria, this is a mess.  And there are 17 more bullets.

How does this happen?  It’s just a tradition in executive recruiting; these sorts of specs get created. These bullets were probably selectively copied and pasted from other specs by an associate at the search firm.  While the selection was likely based a conversation with the company about what they want, it’s clear that nobody did any hard thinking about what they really needed.

A big clue that they have no must-have criteria is this “ideal candidate” nonsense.  Our house spec didn’t say “the ideal house will have” and then describe some fantasy house we can never afford.  We decided what the house must have, and then added some things that would be nice to have as well.

Let’s make an example of what an must-have / nice-to-have list could look like for an EVP of Sales at $50M startup.  This list makes a lot of assumptions about company needs and is far from perfect.  But it’s a heck of a lot better than the bullets above.

Must Have Nice to Have
Previously led all sales at an enterprise SaaS startup as it grew from $50M to $100M in ARR Knowledge of the CRM space
Has previously established detailed operational metrics and processes to run a velocity sales model Ability to quickly recruit a strong VP of salesops
A network including top reps and regional managers that can be  immediately recruited Prior experience creating and growing a sales enablement  function with onboarding and certification
At least 3 years’ experience managing international sales Prior experience selling or managing outside of North America
At least 5 years’ experience managing a three-level sales organization with at least 50 sellers Early-career experience in a technical or pre-sales role
Demonstrated compatibility with the organization’s culture and values Technical undergraduate degree plus MBA

What’s most important is that the process of making this list — writing it down, talking to peers about it, sharing it with the board, discussing it with prospective search firms — will clarify your own thinking and help you build consensus around precisely who is needed to do the job.

Otherwise, you’ll just get an “athlete” that the recruiter had in inventory.

Why I’m Advising Cyral

When I sign up to advise a company, I’ll often do a post to let readers know and discuss the reasons why I like the company.  This post is about Cyral, a cloud data security company I’m advising that I’ve been talking with for over a year.

Earlier this year, Cyral announced an $11M series A led by Redpoint, Costanoa, and others.  That was on top of a $4.1M in angel seed financing, bringing the total invested capital to $15.1M.

Cyral_logo_for_web.e28367f0

Cyral does cloud data security.  I indirectly referred to the company in my 2020 Predictions post, where I talked about a new, data-layer approach to security.  Cyral acts as a database proxy on top of every data endpoint in your data layer, watching all the traffic, figuring out (via machine-learning) what is normal, detecting what is not, and either alerting or stopping threats in real-time as they occur.

I remember when I first met co-founder Manav Mital at Peet’s Coffee to discuss the company.  He was surprised that I actually understood a thing or two about databases [1], which was fun. During the meeting a light-bulb went off in my head:  why were data breaches always measured megarows or terarows (hundreds of millions to billions of rows) as opposed say rows or kilorows?  Can’t we stop these things while they’re going down?

I initially viewed Cyral as a next-generation data loss prevention (DLP) company because I thought DLP was about stopping security problems in real-time.  But DLP was more about content than data, more about classification than anomaly detection, and more about business rules than machine learning.  DLP could do things like detect email attachments that contained source code and intercept an outbound email with such an attachment.  It had nothing to do with monitoring traffic to the data endpoints in a company’s both on-premise and (increasingly) cloud data layer, providing visibility into activity, fine-grained data access control, and real-time protection against data exfiltration.  That’s Cyral.

Here are some of the reasons I decided to work with the company.

  • Manav is not only a great guy and (a fellow) member of the illustrious Aster Data mafia [2], he is a second-time entrepreneur, having co-founded Instart Logic, which raised $140M from a top set of investors and built a strong business before eventually hitting hard times in the highly competitive CDN space, ultimately being acquired by Akamai.  It’s great to work with Manav because he has the wisdom from both his successes and his failures on his nearly decade-long journey at Instart.

 

  • I think security is a race without a finish line and thus a great and growing market space.  In addition to data-layer anomaly detection, Cyral provides fine-grained access control in a world where too many applications defeat security using shared data-layer logins.  Cyral can distinguish different users even if they’re coming into the database through the same username/password.  What’s more, Cyral provides more than just security, it provides insight by giving you visibility into who’s doing what.

 

  • New cloud data endpoints from Snowflake to Redshift to Kafka introduce complexity that breaks traditional approaches to security.  The old approach to security was largely about building a strong perimeter.  In a hybrid cloud world, that mixes traditional and cloud data sources, there is no perimeter to defend.  The perimeter is dead, long live data-layer security!

 

When talent meets opportunity, great things can happen.

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Notes

[1] Having worked in technical support at Ingres (RDBMS), as VP of Marketing at Versant (ODBMS), as CMO at BusinessObjects (a BI tool, but with an embedded micro-multidimensional DBMS), as CEO at MarkLogic (XML DBMS), as board member at Aster Data (SQL/MapReduce DBMS), advisor to MongoDB (document-oriented DBMS),  and as CEO of Host Analytics (which included a multidimensional modeling engine) well, heck, you think I might have picked something up.

[2] Aster Data was an amazing well of entrepreneurship and the success of its mafia is an untold story in Silicon Valley.  A large number of companies, some of them amazingly successful, were founded by Aster Data alumni including:  ActionIQ, Arcadia Data, ClearStory, Cohesity, DataHero, Imanis Data, Instart Logic, Level-Up Analytics, Moveworks, Nutanix, The Data Team, ThoughtSpot, and WorkSpan.