Category Archives: Entrepreneurship

The Domo S-1: Does the Emperor Have Clothes?

I preferred Silicon Valley [1] back in the day when companies raised modest amounts of capital (e.g., $30M) prior to an IPO that took 4-6 years from inception, where burn rates of $10M/year looked high, and where $100M raise was the IPO, not one or more rounds prior to it.  When cap tables had 1x, non-participating preferred and that all converted to a single class of common stock in the IPO. [2]

How quaint!

These days, companies increasingly raise $200M to $300M prior to an IPO that takes 10-12 years from inception, the burn might look more like $10M/quarter than $10M/year, the cap table loaded up with “structure” (e.g., ratcheting, multiple liquidation preferences).  And at IPO time you might end up with two classes common stock, one for the founder with super-voting rights, and one for everybody else.

I think these changes are in general bad:

  • Employees get more diluted, can end up alternative minimum tax (AMT) prisoners unable to leave jobs they may be unhappy doing, have options they are restricted from selling entirely or are sold into opaque secondary markets with high legal and transaction fees, and/or even face option expiration at 10 years. (I paid a $2,500 “administrative fee” plus thousands in legal fees to sell shares in one startup in a private transaction.)
  • John Q. Public is unable to buy technology companies at $30M in revenue and with a commission of $20/trade. Instead they either have to wait until $100 to $200M in revenue or buy in opaque secondary markets with limited information and high fees.
  • Governance can be weak, particularly in cases where a founder exercises directly (or via a nuclear option) total control over a company.

Moreover, the Silicon Valley game changes from “who’s smartest and does the best job serving customers” on relatively equivalent funding to “who can raise the most capital, generate the most hype, and buy the most customers.”  In the old game, the customers decide the winners; in the new one, Sand Hill Road tries to, picking them in a somewhat self-fulfilling prophecy.

The Hype Factor
In terms of hype, one metric I use is what I call the hype ratio = VC / ARR.  On the theory that SaaS startups input venture capital (VC) and output two things — annual recurring revenue (ARR) and hype — by analogy, heat and light, this is a good way to measure how efficiently they generate ARR.

The higher the ratio, the more light and the less heat.  For example, Adaptive Insights raised $175M and did $106M in revenue [3] in the most recent fiscal year, for a ratio of 1.6.  Zuora raised $250M to get $138M in ARR, for a ratio of 1.8.  Avalara raised $340M to $213M in revenue, for a ratio of 1.6.

By comparison, Domo’s hype ratio is 6.4.  Put the other way, Domo converts VC into ARR at a 15% rate.  The other 85% is, per my theory, hype.  You give them $1 and you get $0.15 of heat, and $0.85 of light.  It’s one of the most hyped companies I’ve ever seen.

As I often say, behind every “marketing genius” is a giant budget, and Domo is no exception [4].

Sometimes things go awry despite the most blue-blooded of investors and the greenest of venture money.  Even with funding from the likes of NEA and Lightspeed, Tintri ended up a down-round IPO of last resort and now appears to be singing its swan song.  In the EPM space, Tidemark was the poster child for more light than heat and was sold in what was rumored to be fire sale [5] after raising over $100M in venture capital and having turned that into what was supposedly less than $10M in ARR, an implied hype ratio of over 10.

The Top-Level View on Domo
Let’s come back and look at the company.  Roughly speaking [6], Domo:

  • Has nearly $700M in VC invested (plus nearly $100M in long-term debt).
  • Created a circa $100M business, growing at 45% (and decelerating).
  • Burns about $150M per year in operating cash flow.
  • Will have a two-class common stock system where class A shares have 40x the voting rights of class B, with class A totally controlled by the founder. That is, weak governance.

Oh, and we’ve got a highly unprofitable, venture-backed startup using a private jet for a bit less than $1M year [7].  Did I mention that it’s leased back from the founder?  Or the $300K in catering from a company owned by the founder and his brother.  (Can’t you order lunch from a non-related party?)

As one friend put it, “the Domo S-1 is everything that’s wrong with Silicon Valley in one place:  huge losses, weak governance, and now modest growth.”

Personally, I view Domo as the Kardashians of business intelligence – famous for being famous.  While the S-1 says they have 85 issued patents (and 45 applications in process), does anyone know what they actually do or what their technology advantage is?  I’ve worked in and around BI for nearly two decades – and I have no idea.

Maybe this picture will help.

domosolutionupdated

Uh, not so much.

The company itself admits the current financial situation is unsustainable.

If other equity or debt financing is not available by August 2018, management will then begin to implement plans to significantly reduce operating expenses. These plans primarily consist of significant reductions to marketing costs, including reducing the size and scope of our annual user conference, lowering hiring goals and reducing or eliminating certain discretionary spending as necessary

A Top-to-Bottom Skim of the S-1
So, with that as an introduction, let’s do a quick dig through the S-1, starting with the income statement:

domo income

Of note:

  • 45% YoY revenue growth, slow for the burn rate.
  • 58% blended gross margins, 63% subscription gross margins, low.
  • S&M expense of 121% of revenue, massive.
  • R&D expense of 72% of revenue, huge.
  • G&A expense of 29% of revenue, not even efficient there.
  • Operating margin of -162%, huge.

Other highlights:

  • $803M accumulated deficit.  Stop, read that number again and then continue.
  • Decelerating revenue growth, 45% year over year, but only 32% Q1 over Q1.
  • Cashflow from operations around -$150M/year for the past two years.  Stunning.
  • 38% of customers did multi-year contracts during FY18.  Up from prior year.
  • Don’t see any classical SaaS unit economics, though they do a 2016 cohort analysis arguing contribution margin from that cohort of -196%, 52%, 56% over the past 3 years.  Seems to imply a CAC ratio of nearly 4, twice what is normally considered on the high side.
  • Cumulative R&D investment from inception of $333.9M in the platform.
  • 82% revenues from USA in FY18.
  • 1,500 customers, with 385 having revenues of $1B+.
  • Believe they are <4% penetrated into existing customers, based on Domo users / total headcount of top 20 penetrated customers.
  • 14% of revenue from top 20 customers.
  • Three-year retention rate of 186% in enterprise customers (see below).  Very good.
  • Three-year retention rate of 59% in non-enterprise customers.  Horrific.  Pay a huge CAC to buy a melting ice cube.  (Only the 1-year cohort is more than 100%.)

As of January 31, 2018, for the cohort of enterprise customers that licensed our product in the fiscal year ended January 31, 2015, the current ACV is 186% of the original license value, compared to 129% and 160% for the cohorts of enterprise customers that subscribed to our platform in the fiscal years ended January 31, 2016 and 2017, respectively. For the cohort of non-enterprise customers that licensed our product in the fiscal year ended January 31, 2015, the current ACV as of January 31, 2018 was 59% of the original license value, compared to 86% and 111% for the cohorts of non-enterprise customers that subscribed to our platform in the fiscal years ended January 31, 2016 and 2017, respectively.

  • $12.4M in churn ARR in FY18 which strikes me as quite high coming off subscription revenues of $58.6M in the prior year (21%).  See below.

Our gross subscription dollars churned is equal to the amount of subscription revenue we lost in the current period from the cohort of customers who generated subscription revenue in the prior year period. In the fiscal year ended January 31, 2018, we lost $12.4 million of subscription revenue generated by the cohort in the prior year period, $5.0 million of which was lost from our cohort of enterprise customers and $7.4 million of which was lost from our cohort of non-enterprise customers.

  • What appears to be reasonable revenue retention rates in the 105% to 110% range overall.  Doesn’t seem to foot to the churn figure about.  See below:

For our enterprise customers, our quarterly subscription net revenue retention rate was 108%, 122%, 116%, 122% and 115% for each of the quarters during the fiscal year ended January 31, 2018 and the three months ended April 30, 2018, respectively. For our non-enterprise customers, our quarterly subscription net revenue retention rate was 95%, 95%, 99%, 102% and 98% for each of the quarters during the fiscal year ended January 31, 2018 and the three months ended April 30, 2018, respectively. For all customers, our quarterly subscription net revenue retention rate was 101%, 107%, 107%, 111% and 105% for each of the quarters during the fiscal year ended January 31, 2018 and the three months ended April 30, 2018, respectively.

  • Another fun quote and, well, they did take about the cash it takes to build seven startups.

Historically, given building Domo was like building seven start-ups in one, we had to make significant investments in research and development to build a platform that powers a business and provides enterprises with features and functionality that they require.

  • Most customers invoiced on annual basis.
  • Quarterly income statements, below.

domo qtr

  • $72M in cash as of 4/30/18, about 6 months worth at current burn.
  • $71M in “backlog,” multi-year contractual commitments, not prepaid and ergo not in deferred revenue.  Of that $41M not expected to be invoiced in FY19.
  • Business description, below.  Everything a VC could want in one paragraph.

Domo is an operating system that powers a business, enabling all employees to access real-time data and insights and take action from their smartphone. We believe digitally connected companies will increasingly be best positioned to manage their business by leveraging artificial intelligence, machine learning, correlations, alerts and indices. We bring massive amounts of data from all departments of a business together to empower employees with real-time data insights, accessible on any device, that invite action. Accordingly, Domo enables CEOs to manage their entire company from their phone, including one Fortune 50 CEO who logs into Domo almost every day and over 10 times on some days.

  • Let’s see if a computer could read it any better than I could.  Not really.

readability

  • They even have Mr. Roboto to help with data analysis.

Through Mr. Roboto, which leverages machine learning algorithms, artificial intelligence and predictive analytics, Domo creates alerts, detects anomalies, optimizes queries, and suggests areas of interest to help people focus on what matters most. We are also developing additional artificial intelligence capabilities to enable users to develop benchmarks and indexes based on data in the Domo platform, as well as automatic write back to other systems.

  • 796 employees as of 4/30/18, of which 698 are in the USA.
  • Cash comp of $525K for CEO, $450K for CFO, and $800K for chief product officer
  • Pre-offering it looks like founder Josh James owns 48.9M shares of class A and 8.9M shares of class B, or about 30% of the shares.  With the 40x voting rights, he has 91.7% of the voting power.

Does the Emperor Have Any Clothes?
One thing is clear.  Domo is not “hot” because they have some huge business blossoming out from underneath them.  They are “hot” because they have raised and spent an enormous amount of money to get on your radar.

Will they pull off they IPO?  There’s a lot not to like:  the huge losses, the relatively slow growth, the non-enterprise retention rates, the presumably high CAC, the $12M in FY18 churn, and the 40x voting rights, just for starters.

However, on the flip side, they’ve got a proven charismatic entrepreneur / founder in Josh James, an argument about their enterprise customer success, growth, and penetration (which I’ve not had time to crunch the numbers on), and an overall story that has worked very well with investors thus far.

While the Emperor’s definitely not fully dressed, he’s not quite naked either.  I’d say the Domo Emperor’s donning a Speedo — and will somehow probably pull off the IPO parade.

###

Notes

[1] Yes, I know they’re in Utah, but this is still about Silicon Valley culture and investors.

[2] For definitions and frequency of use of various VC terms, go to the Fenwick and West VC survey.

[3] I’ll use revenue rather than trying to get implied ARR to keep the math simple.  In a more perfect world, I’d use ARR itself and/or impute it.  I’d also correct for debt and a cash, but I don’t have any MBAs working for me to do that, so we’ll keep it back of the envelope.

[4] You can argue that part of the “genius” is allocating the budget, and it probably is.  Sometimes that money is well spent cultivating a great image of a company people want to buy from and work at (e.g., Salesforce).  Sometimes, it all goes up in smoke.

[5] Always somewhat truth-challenged, Tidemark couldn’t admit they were sold.  Instead, they announced funding from a control-oriented private equity firm, Marlin Equity Partners, as a growth investment only a year later be merged into existing Marlin platform investment Longview Solutions.

[6] I am not a financial analyst, I do not give buy/sell guidance, and I do not have a staff working with me to ensure I don’t make transcription or other errors in quickly analyzing a long and complex document.  Readers are encouraged to go the S-1 directly.  Like my wife, I assume that my conclusions are not always correct; readers are encouraged to draw their own conclusions.  See my FAQ for complete disclaimer.

[7] $900K, $700K, and $800K run-rate for FY17, FY18, and 1Q19 respectively.

The Two Engines of SaaS: QCRs and DEVs

I remember one day, years ago, when I was a VP at $10M startup and Larry, the head of sales, came in one day handing out t-shirts that said:

“Code, sell, or get out of the way.”

Neither I, nor the rest of marketing team, took this particularly well because the shirt obviously devalued the contributions of F&A, HR, and marketing.  But, ever seeking objectivity, I did concede that the shirt had a certain commonsense appeal.  If you could only hire one person at a startup, it would be someone to write the product.  And if you could only hire one more, it would be someone to sell it.

This became yet another event that reconfirmed my belief in my “marketing exists to make sales easier” mantra.  After all, if you’re not coding or selling, at least you can help someone who is.

Over time, Larry’s t-shirt morphed in my mind into a new mantra:

“A SaaS company is a two-engine plane.  The left engine is DEVs.  The right is QCRs.”

QCR meaning quota-carrying (sales) representative and DEV meaning developer (or, for symmetry and emphasis, storypoint-burning developer).  People who sell with truly incremental quota, and people who write code and burndown storypoints in the process.

It’s a much nicer way of saying “code, sell, or get out of the way,” but it’s basically the same idea.  And it’s true.  While Larry was coming from a largely incorrect “protest overhead and process” viewpoint, I’m coming from a different one:  hiring.

The two hardest lines in a company headcount plan to keep at-plan are guess which two?  QCRs and DEVs.  Forget other departments for a minute — I’m saying is the the hardest line for the VP of Engineering to stay fully staffed on is DEVs, and the hardest line for the VP of Sales to stay fully staffed on is QCRs.

Why is this?

  • They are two, critical highly in-demand positions, so the market is inherently tight.
  • Given their importance, the hiring VPs can be gun-shy about making mistakes and lose candidates due to hesitation or indecision.
  • Both come with a short-term tax and mid-term payoff because on-boarding new hires slows down the rest of the team, a possible source of passive resistance.
  • Sales managers dislike splitting territories because it makes them unpopular, which could drive more foot-dragging.
  • It’s just plain easier to find the associated support functions — (e.g,. program managers, QA engineers, techops, salesops, sales productivity, overlays, CSMs, managers in general) than it is find the QCRs and DEVs.

Let me be clear:  this is not to say that all the supporting functions within sales and engineering do not add value, nor is this to say that supporting corporate functions beyond sales and engineering do not add value — it is to say, however, that far too often companies take their eye off the ball and staff the support functions before, not after, those they are supporting.  That’s a mistake.

What happens if you manage this poorly?  On the sales side, for example, you end up with an organization that has 1 SVP of Sales, 1 VP of sales consulting, 4 sales consultants, 1 director of sales ops, 1 director of sales productivity, 1 manager of sales development reps (SDRs), 4 SDRs, an executive assistant, and 4 quota-carrying salespeople.  So only 22% of the people in your sales organization actually carry a quota.

“Uh, other than QCRs, we’re doing great on sales hiring,”  says the sales VP.  “Other than that, Mrs. Lincoln, how did you find the play?” thinks the board.

Because I’ve seen this happen so often, and because I’ve seen companies accused of it both rightfully and unjustly, I’d decided to create two new metrics:

  • QCR density = number of QCRs / total sales headcount
  • DEV density = numbers of DEVs / total engineering headcount

The bad news is I don’t have a lot of benchmark data to share here.  In my experience, both numbers want to run in the 40% range.

The good news is that if you run a ratio-driven staffing model (which you should do for both sales and engineering), you should be able to calculate what these densities should be when you are fully staffed.

Let’s conclude with a simple model that does just that on the sales side, producing a result in the 38% to 46% range.

qcr dens

Finally, let me add that having such a model helps you understand whether, for example, your QCR density is low due to slow QCR hiring (and/or bad retention) against a good model, or on-pace hiring against a “fat” model.  The former is an execution problem, the latter is a problem with your model.

A Look at the Tintri S-1

Every now and then I take a dive into an S-1 to see what clears the current, ever-changing bar for going public.  After a somewhat rocky IPO process, Tintri went public June 30 after cutting the IPO offering price and has traded flat thus far since then.

Let’s read an excerpt from this Business Insider story before taking a look at the numbers.

Before going public, Tintri had raised $260 million from venture investors and was valued at $800 million.

With the performance of this IPO, the company is now valued at about about $231 million, based on $7.50 a share and its roughly 31 million outstanding shares, (if the IPO’s bankers don’t buy their optional, additional roughly 1.3 million shares.)

In other words, this IPO killed a good $570 million of the company’s value.

In other words, Tintri looks like a “down-round IPO” (or an “IPO of last resort“) — something that frankly almost never happened before the recent mid/late stage private valuation bubble of the past 4 years.

Let’s look at some numbers.

tintri p+l

Of note:

  • $125M in FY2017 revenue.  (They have scale, but this is not a SaaS company so the revenue is mostly non-recurring, making it easier to get to grow quickly and making the revenue is worth less because only the support/maintenance component of it renews each year.)
  • 45% YoY total revenue growth.  (On the low side, especially given that they have a traditional license/maintenance model and recognize revenue on shipment.)
  • 65% gross margins  (Low, but they do seem to sell flash memory hardware as part of their storage solutions.)
  • 87% of revenue spent on S&M (High, again particularly for a non-SaaS company.)
  • 43% of revenue spent on R&D  (High, but usually seen as a good thing if you view the R&D money as well spent.)
  • -81% operating margins (Low, particularly for a non-SaaS company.)
  • -$70.4M in cashflow from operating activities in 2017 ($17M average quarterly cash burn from operations)
  • Incremental S&M / incremental product revenue = 73%, so they’re buying $1 worth of incremental (YoY) revenue for an incremental 73 cents in S&M.  Expensive but better than some.

Overall, my impression is of an on-premises (and to a lesser extent, hardware) company in SaaS clothing — i.e., Tintri’s metrics look like a SaaS company, but they aren’t so they should look better.  SaaS company metrics typically look worse than traditional software companies for two reasons:  (1) revenue growth is depressed by the need to amortize revenue over the course of the subscription and (2) subscriptions companies are willing to spend more on S&M to acquire a customer because of the recurring nature of a subscription.

Concretely, if you compare two 100-unit customers, the SaaS customer is worth twice the license/maintenance customer over 5 years.

saas compare

Moreover, even if Tintri were a SaaS company, it is quite out of compliance with the Rule of 40, that says growth rate + operating margin >= 40%.  In Tintri’s case, we get -35%, 45% growth plus -81% operating margin, so they’re 75 points off the rule.

Other Notes

  • 1250+ customers
  • 21 of the Fortune 100
  • 527 employees as of 1/31/17
  • CEO 2017 cash compensation $525K
  • CFO 2017 cash compensation $330K
  • Issued special retention stock grants in May 2017 that vest in the two years following an IPO
  • Did option repricing in May 2017 to $2.28/share down from weighted average exercise price of $4.05.
  • $260M in capital raised prior to IPO
  • Loans to CFO and CEO to exercise stock options at 1.6% to 1.9% interest in 2013
  • NEA 22.7% ownership prior to opening
  • Lightspeed 14.5% ownership
  • Insight Venture Partners 20.2% ownership
  • Silver Lake 20.4% ownership
  • CEO 3.8% ownership
  • CFO 0.7% ownership
  • $48.9M in long-term debt
  • $13.8M in 2017 stock-based compensation expense

Overall, and see my disclaimers, but this is one that I’ll be passing on.

 

The Strategy Compiler: How To Avoid the “Great” Strategy You Couldn’t Execute

Few phrases bother me more than this one:

“I know it didn’t work, but it was a great strategy.  We just didn’t have the resources to execute it.”

Huh.  Wait minute.  If you didn’t have the resources to execute it, then it wasn’t a great strategy.  Maybe it was a great strategy for some other company that could have applied the appropriate resources.  But it wasn’t a great strategy for you.  Ergo, it wasn’t a great strategy.  QED.

I learned my favorite definition of strategy at a Stanford executive program I attended a few years back.  Per Professor Robert Burgelman, author of Strategy is Destiny, strategy is simply “the plan to win.”  Which begets an important conversation about the definition of winning.  In my experience, defining winning is more important than making the plan, because if everyone is focused on taking different hills, any resultant strategy will be a mishmash of plans to support different objectives.

But, regardless of your company’s definition of winning, I can say that any strategy you can’t execute definitionally won’t succeed and is ergo a bad strategy.

It sounds obvious, but nevertheless a lot of companies fall into this trap.  Why?

  • A lack of focus.
  • A failure to “compile” strategy before executing it.

Focus:  Think Small to Grow Big

Big companies that compete in lots of broad markets almost invariably didn’t start out that way.

BusinessObjects started out focused on the Oracle financials installed base.  Facebook started out on Harvard students, then Ivy league students.  Amazon, it’s almost hard to remember at this point, started out in books.  Salesforce started out in SMB salesforce automation.  ServiceNow on IT ticket management.  This list goes on and on.

Despite the evidence and despite the fame Geoffrey Moore earned with Crossing the Chasm, focus just doesn’t come naturally to people.  The “if I could get 1% share of a $10B market, I’d be a $100M company” thought pattern is just far too common. (And investors often accidentally reinforce this.)

The fact is you will be more dominant, harder to dislodge, and probably more profitable if, as a $100M company, you control 30% of a $300M target as opposed to 1% of a $10B target.

So the first reason startups make strategies they can’t execute is because they forget to focus.  They aim too broadly. They sign up for too much.  The forget that strategy should be sequence of actions over time.  Let’s start with Harvard. Then go Ivy League.  Then go Universities in general.  Then go everyone.

Former big company executives often compound the problem.  They’re not used to working with scarce resources and are more accustomed to making “laundry list” strategies that check all the boxes than making focused strategies that achieve victory step by step.

A Failure to Compile Strategy Before Execution

The second reason companies make strategies they can’t execute is that they forget a critical step in the planning process that I call the strategy compiler.  Here’s what I think a good strategic planning process looks like.

  • Strategy offsite. The executive team spends a week offsite focused on situation assessment and strategy.  The output of this meeting should be (1) a list of strategic goals for the company for the following year and (2) a high-level financial model that concretizes what the team is trying to accomplish over the next three years.  (With an eye, at a startup, towards cash.)

 

  • First round budgeting. Finance issues top-down financial targets.  Executives who own the various objectives make strategic plans for how to attain them.  The output of this phase is (1) first-draft consolidated financials, (2) a set of written strategies along with proposed organizational structures and budgets for attaining each of the company’s ten strategic objectives.

 

  • Strategy compilation, resources. The team meets for a day to review the consolidated plans and financials. Invariably there are too many objectives, too much operating expense, and too many new hires. The right answer here is to start cutting strategic goals.  The wrong answer is to keep the original set of goals and slash the budget 20% across the board.  It’s better to do 100% of 8 strategic initiatives than do 80% of 10.

 

  • Strategy compilation, skills. The more subtle assessment that must happen is a sanity check on skills and talent.  Do your organization have the competencies and do your people have the skills to execute the strategic plans?  If a new engineering project requires the skills of 5 founder-level, Stanford computer science PHDs who each would want 5% of a company, you are simply not going to be able to hire that kind of talent as regular employees. (This is one reason companies do “acquihires”).  The output of this phase is a presumably-reduced set of strategic goals.

 

  • Second round budgeting. Executives to build new or revised plans to support the now-reduced set of strategic goals.

 

  • Strategy compilation. You run the strategy compiler again on the revised plan — and iterate until the strategic goals match the resources and the skills of the proposed organization.

 

  • Board socialization. As you start converging via the strategy compiler you need to start working with the board to socialize and eventually sell the proposed operating plan.  (This process could easily be the subject of another post.)

 

If you view strategy as the plan to win, then successful strategies include only those strategies that your organization can realistically execute from both a resources and skills perspective.  Instead of doing a single-pass process that moves from strategic objectives to budgets, use an iterative approach with a strategy compiler to ensure your strategic code compiles before you try to execute it.

If you do this, you’ll increase your odds of success and decrease the odds ending up in the crowded section of the corporate graveyard where the epitaphs all read:

Here Lies a Company that Had a “Great” Strategy  It Had No Chance of Executing

Blocking the End Run: Eleven Words to Reduce Politics in Your Organization

People are people.  Sometimes they’re conflict averse and just not comfortable saying certain things to their peers.  Sometimes they don’t like them and are actively trying to undermine them. Sometimes they’re in a completely functional relationship, but have been too darn busy to talk.

So when this happens, how do you — as a manager — respond?  What should you do?

“Hey Dave, I wanted to say that Sarah’s folks really messed up on the Acme call this morning.  They weren’t ready with the proposal and were completely not in line with my sales team.”

Do you pile on?

“Again?  Sarah’s folks are out of control, I’m going to go blast her.”  (The “Young Dave” response.)

Do you investigate?

“You know my friend Marcy always said there are three sides to every story:  yours, mine, and what actually happened.  So let me give Sarah a call and look into this.”

Do you defend?

“Well, that doesn’t sound like Sarah.  Her team’s usually buttoned up.”

In the first case, you’re going off half-cocked without sufficient information which, while emotionally satisfying in the short-term, often leads to a mess followed by several apologies in the mid-term.  In the second case, you’re being manipulated into investigating something when perhaps you were planning a better use of your time that day.  In the third case, you’re going off half-cocked again, but in the other direction.

In all three cases, you’re getting sucked into politics.  Politics?  Is it really politics?  Well, how do you think Sarah is going to feel in when you show up asking a dozen questions about the Acme call?  She’ll certainly consider it politics and, among other things, there’s about a 98% chance that she will say:

“Gosh, I wish Bill came and talked to me first.”

At which point, if you’re like me, you’re going to say:

“No, no, no.  I know what you’re thinking.  Don’t worry, this isn’t political.  It’s not like Bill was avoiding you on this one.  He just happened to be talking to me about another issue and he brought this up at the end.  It’s not political, no.”

But can you be sure?  Maybe it just did pop into Bill’s mind during the last minute of the other call.  Or maybe it didn’t.  Maybe the reason Bill called you was a masterfully political pretext.  Can you know the difference?

So what do you say to Bill when he drops the comment about Sarah’s team into your call?  The eleven words that reduce politics in any organization:

“What did Sarah say when you talked to her about this?”

[Mike Drop.]

# # #

(Props to Martin Cooke for teaching me the eleven words.)

Why has Standalone Cloud BI been such a Tough Slog?

I remember when I left Business Objects back in 2004 that it was early days in the cloud.  We were using Salesforce internally (and one of their larger customers at the time) so I was familiar with and a proponent of cloud-based applications, but never felt great about BI in the cloud.  Despite that, Business Objects and others were aggressively ramping on-demand offerings all of which amounted to pretty much nothing a few years later.

Startups were launched, too.  Specifically, I remember:

  • Birst, née Success Metrics, and founded in 2004 by Siebel BI veterans Brad Peters and Paul Staelin, which was originally supposed to be vertical industry analytic applications.
  • LucidEra, founded in 2005 by Salesforce and Siebel veteran Ken Rudin (et alia) whose original mission was to be to BI what Salesforce was to CRM.
  • PivotLink, which did their series A in 2007 (but was founded in 1998), positioned as on-demand BI and later moved into more vertically focused apps in retail.
  • GoodData, founded in 2007 by serial entrepreneur Roman Stanek, which early on focused on SaaS embedded BI and later moved to more of a high-end enterprise positioning.

These were great people — Brad, Ken, Roman, and others were brilliant, well educated veterans who knew the software business and their market space.

These were great investors — names like Andreessen Horowitz, Benchmark, Emergence, Matrix, Sequoia, StarVest, and Tenaya invested over $300M in those four companies alone.

This was theoretically a great, straightforward cloud-transformation play of a $10B+ market, a la Siebel to Salesforce.

But of the four companies named above only GoodData is doing well and still in the fight (with a high-end enterprise platform strategy that bears little resemblance to a straight cloud transformation play) and the three others all came to uneventful exits:

So, what the hell happened?

Meantime, recall that Tableau, founded in 2003, and armed in its early years with a measly $15M in venture capital, and with an exclusively on-premises business model, literally blew by all the cloud BI vendors, going public in May 2013 and despite the stock being cut by more than half since its July 2015 peak is still worth $4.2B today.

I can’t claim to have the definitive answer to the question I’ve posed in the title.  In the early days I thought it was related to technical issues like trust/security, trust/scale, and the complexities of cloud-based data integration.  But those aren’t issues today.  For a while back in the day I thought maybe the cloud was great for applications, but perhaps not for platforms or infrastructure.  While SaaS was the first cloud category to take off, we’ve obviously seen enormous success with both platforms (PaaS) and infrastructure (IaaS) in the cloud, so that can’t be it.

While some analysts lump EPM under BI, cloud-based EPM has not had similar troubles.  At Host, and our top competitors, we have never struggled with focus or positioning and we are all basically running slightly different variations on the standard cloud transformation play.  I’ve always believed that lumping EPM under BI is a mistake because while they use similar technologies, they are sold to different buyers (IT vs. finance) and the value proposition is totally different (tool vs. application).  While there’s plenty of technology in EPM, it is an applications play — you can’t sell it or implement it without domain knowledge in finance, sales, marketing or whatever domain for which you’re building the planning system.  So I’m not troubled to explain why cloud EPM hasn’t been a slog while cloud BI absolutely has been.

My latest belief is that the business model wasn’t the problem in BI.  The technology was.  Cloud transformation plays are all about business model transformation.  On-premises applications business models were badly broken:  the software cost $10s of millions to buy and $10s of millions more to implement (for large customers).  SMBs were often locked out of the market because they couldn’t afford the ante.  ERP and CRM were exposed because of this and the market wanted and needed a business model transformation.

With BI, I believe, the business model just wasn’t the problem.  By comparison to ERP and CRM, it was fraction of the cost to buy and implement.  A modest BusinessObjects license might have cost $150K and less than that to implement.  That problem was not that BI business model was broken, it was that the technology never delivered on the democratization promise that it made.  Despite shouting “BI for the masses” in 1995, BI never really made it beyond the analyst’s desk.

Just as RDBMS themselves failed to deliver information democracy with SQL (which, believe it or not, was part of the original pitch — end users could write SQL to answer their own queries!), BI tools — while they helped enable analysts — largely failed to help Joe User.  They weren’t easy enough to use.  They lacked information discovery.  They lacked, importantly, easy-yet-powerful visualization.

That’s why Tableau, and to a lesser extent Qlik, prospered while the cloud BI vendors struggled.  (It’s also why I find it profoundly ironic that Tableau is now in a massive rush to “go cloud” today.)  It’s also one reason why the world now needs companies like Alation — the information democracy brought by Tableau has turned into information anarchy and companies like Alation help rein that back in (see disclaimers).

So, I think that cloud BI proved to be such a slog because the cloud BI vendors solved the wrong problem. They fixed a business model that wasn’t fundamentally broken, all while missing the ease of use, data discovery, and visualization power that both required the horsepower of on-premises software and solved the real problems the users faced.

I suspect it’s simply another great, if simple, lesson is solving your customer’s problem.

Feel free to weigh in on this one as I know we have a lot of BI experts in the readership.

Do You Want to be Judged on Intentions or Results?

It was early in my career, maybe 8 years in, and I was director of product marketing at a startup.  One day, my peer, the directof of marketing programs hit me with this in an ops review meeting:

You want to be judged on intentions, not results.

I recall being dumbfounded at the time.  Holy cow, I thought.  Is he right?  Am I standing up arguing about mitigating factors and how things might have been when all the other people in the room were thinking only about black-and-white results?

It was one of those rare phrases that really stuck with me because, among other reasons, he was so right.  I wasn’t debating whether things happened or not.  I wasn’t making excuses or being defensive.  But I was very much judging our performance in the theoretical, hermetically sealed context of what might have been.

Kind of like sales saying a deal slipped instead of did not close.   Or marketing saying we got all the MQLs but didn’t get the requisite pipeline.  Or alliances saying that we signed up the 4 new partners, but didn’t get the new opportunities that were supposed to come with them.

Which phrase of the following sentence matters more — the first part or the second?

We did what we were supposed to, but it didn’t have the desired effect.

We would have gotten the 30 MQLS from the event if it hadn’t snowed in Boston.  But who decided to tempt fate by doing a live event in Boston in February?  People who want to be judged on intentions think about the snowstorm; people who want to be judged on results think about the MQLs.

People who want to judged on intentions build in what they see as “reasons” (which others typically see as “excuses”) for results not being achieved.

I’m six months late hiring the PR manager, but that’s because it’s hard to find great PR people right now.  (And you don’t want me to hire a bad one, do you?)

No, I don’t want you to hire a bad one.  I want you to hire a great one and I wanted you to hire them 6 months ago.  Do you think every other PR manager search in the valley took 6 months more than plan?  I don’t.

Fine lines exist here, no doubt.  Sometimes reasons are reasons and sometimes they are actually excuses.  The question isn’t about any one case.  It’s about, deep down, are you judging yourself by intentions or results?

You’d be surprised how many otherwise very solid people get this one thing wrong — and end up career-limited as a result.