Category Archives: BI

My Appearance on DisrupTV Episode 100

Last week I sat down with interviewers Doug Henschen, Vala Afshar, and a bit of Ray Wang (live from a 777 taxiing en route to Tokyo) to participate in Episode 100 of DisrupTV along with fellow guests DataStax CEO Billy Bosworth and big data / science recruiter Virginia Backaitis.

We covered a full gamut of topics, including:

  • The impact of artificial intelligence (AI) and machine learning (ML) on the enterprise performance management (EPM) market.
  • Why I joined Host Analytics some 5 years ago.
  • What it’s like competing with Oracle … for basically your entire career.
  • What it’s like selling enterprise software both upwind and downwind.
  • How I ended up on the board of Alation and what I like about data catalogs.
  • What I learned working at Salesforce (hint:  shoshin)
  • Other lessons from BusinessObjects, MarkLogic, and even Ingres.

DisrupTV Episode 100, Featuring Dave Kellogg, Billy Bosworth, Virginia Backaitis from Constellation Research on Vimeo.

 

Kellblog Predictions for 2018

In continuing my tradition of offering predictions every year, let’s start with a review of my hits and misses on my 2017 predictions.

  1. The United States will see a level of divisiveness and social discord not seen since the 1960s.  HIT.
  2. Social media companies finally step up and do something about fake news. MISS, but ethical issues are starting to catch up with them.
  3. Gut feel makes a comeback. HIT, while I didn’t articulate it as such, I see this as the war on facts and expertise (e.g., it’s cold today ergo global warming isn’t real despite what “experts” say).
  4. Under a volatile leader, we can expect sharp reactions and knee-jerk decisions that rattle markets, drive a high rate of staff turnover in the Executive branch, and fuel an ongoing war with the media.  HIT.
  5. With the new administration’s promises of $1T in infrastructure spending, you can expect interest rates to raise and inflation to accelerate. MISS, turns out this program was never classical government investment in infrastructure, but a massive privatization plan that never happened.
  6. Huge emphasis on security and privacy. PARTIAL HIT, security remained a hot topic and despite numerous major breaches it’s still not really hit center stage.
  7. In 2017, we will see more bots for both good uses (e.g., customer service) and bad (e.g., trolling social media).  HIT.
  8. Artificial intelligence hits the peak of inflated expectations. HIT.
  9. The IPO market comes back. MISS, though according to some it “sucked less.”
  10. Megavendors mix up EPM and ERP or BI. PARTIAL HIT.  This prediction was really about Workday and was correct to the extent that they’ve seemingly not made much progress in EPM.

Kellblog’s Predictions for 2018

1.  We will again continue to see a level of divisiveness and social discord not seen since the 1960s. We have evolved from a state of having different opinions about policies based on common facts to a dangerous state based on different facts, even on easily disprovable claims, e.g., the White House nativity scene.  The media is advancing, not reducing, this divide.

2.  The war on facts and expertise will continue to escalate. Read The Death of Expertise for more.   This will extend to a war on college. While an attempted opening salvo on graduate student tuition waivers didn’t fire, in an environment where the President’s son says, “we’ll take $200,000 of your money; in exchange we’ll train your children to hate our country,” you can expect ongoing attacks on post-secondary education.  This spells trouble for Silicon Valley, where a large number of founders and entrepreneurs are former grad students as well as immigrants (which is a whole different area of potential trouble).

3.  Leading technology and social media companies finally step up to face ethical challenges. This means paying more attention to their own culture (e.g., sexual harassment, brogrammers).  This means taking responsibility for policing trolls, spreading fake news, building addictive content, and enabling foreign intelligence operations.  Thus far, they have tended to argue they are simply keepers of the town square, and not responsible for the content shared there.  This abdication of responsibility should start to stop in 2018, if only because people start to tune-out the services.  This leads to one of my favorite tweets of the year:

Capture

4.  AI will move from hype to action, meaning bigger budgets, more projects, and some high visibility failures. It will also mean more emphasis on voice and more conversational chatbots.  For finance departments, this means more of what Ventana’s Rob Kugel calls the age of robotic finance, which unites AI and machine learning, robotic process automation (RPA), natural language bots, and blockchain-based distributed ledgers.

5. AI will continue to generate lots of controversy about job displacement. While some remain optimistic, the consensus viewpoint seems to be that AI will suppress employment, most likely widening the wealth inequality gap.  A collapsing educational system combined with AI-driven pressure on low-skilled work seems a recipe for trouble.

6.  The bitcoin bubble bursts. As a reminder, at one point during the peak of tulip mania, the Dutch East India Company was worth more, on an inflated-adjusted basis, than twenty of today’s technology giants combined.

tulips

7.  The Internet of Things (IoT) will continue to build momentum.  IoT won’t hit in a massive horizontal way, instead B2B adoption will be lead by certain verticals such as healthcare, retail, and supply chain.

8.  The freelance / gig economy continues to gain momentum with freelance workers poised to pass traditional employees by 2027. While the gig economy brings advantages to high-skilled knowledge workers (e.g., freedom of location, freedom of work projects), this same trend threatens low-skilled workers via the continual decomposition of full-time jobs in a series of temp shifts.  This means someone working 60 hours a week across three 20-hour shifts wouldn’t be considered to be a full-time employee and thus not eligible for full-time benefits, further increasing wealth inequality.

freelancers

9.  M&A heats up due to repatriation of overseas cash. Apple alone, for example, has $252B in overseas cash.  With the new tax rate dropping from 35% to 15.5%, it will now be ~$50B less expensive for Apple to repatriate that cash.  Overall, US companies hold trillions of dollars overseas and making it cheaper for them to repatriate that cash suggests that they will be flush with dollars to invest in many areas, including M&A

10.  2018 will be a good year for cloud EPM vendors. The dynamic macro environment, the opportunities posed by cash repatriation, and the strong fundamentals in the economy will increase demand for EPM software that helps companies explore how to best exploit the right set of opportunities facing them.  Oracle will fail in pushing PBCS into the NetSuite base, creating a nice third-party opportunity.  SAP, Microsoft, and IBM will continue to put resources into other strategic investment areas (e.g., IBM and Watson, SAP and Hana) leaving fallow the EPM market adjacent to ERP.  And the greenfield opportunity to replace Excel for financial planning, budgeting, and even consolidations will continue drive strong growth.

Let me wish everyone, particularly the customers, partners, and employees of Host Analytics, a Happy New Year in 2018.

# # #

Disclaimer:  these predictions are offered in the spirit of fun.  See my FAQ for more on this and other usage terms.

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.

Kellblog’s 2017 Predictions  

New Year’s means three things in my world:  (1) time to thank our customers and team at Host Analytics for another great year, (2) time to finish up all the 2017 planning items and approvals that we need to get done before the sales kickoff (including the one most important thing to do before kickoff), and time to make some predictions for the coming year.

Before looking at 2017, let’s see how I did with my 2016 predictions.

2016 Predictions Review

  1. The great reckoning begins. Correct/nailed.  As predicted, since most of the bubble was tied up in private companies owned by private funds, the unwind would happen in slow motion.  But it’s happening.
  2. Silicon Valley cools off a bit. Partial.  While IPOs were down, you couldn’t see the cooling in anecdotal data, like my favorite metric, traffic on highway101.
  3. Porter’s five forces analysis makes a comeback. Partial.  So-called “momentum investing” did cool off, implying more rational situation analysis, but you didn’t hear people talking about Porter per se.
  4. Cyber-cash makes a rise. CorrectBitcoin more doubled on the year (and Ethereum was up 8x) which perversely reinforced my view that these crypto-currencies are too volatile — people want the anonymity of cash without a highly variable exchange rate.  The underlying technology for Bitcoin, blockchain, took off big time.
  5. Internet of Things goes into trough of disillusionment. Partial.  I think I may have been a little early on this one.  Seems like it’s still hovering at the peak of inflated expectations.
  6. Data science rises as profession. Correct/easy.  This continues inexorably.
  7. SAP realizes they are a complex enterprise application company. Incorrect.  They’re still “running simple” and talking too much about enabling technology.  The stock was up 9% on the year in line with revenues up around 8% thus far.
  8. Oracle’s cloud strategy gets revealed – “we’ll sell you any deployment model you want as long as your annual bill goes up.”  Partial.  I should have said “we’ll sell you any deployment model you want as long as we can call it cloud to Wall St.”
  9. Accounting irregularities discovered at one or more unicorns. Correct/nailed.  During these bubbles the pattern always repeats itself – some people always start breaking the rules in order to stand out, get famous, or get rich.  Fortune just ran an amazing story that talks about the “fake it till you make it” culture of some diseased startups.
  10. Startup workers get disappointed on exits. Partial.  I’m not aware of any lawsuits here but workers at many high flyers have been disappointed and there is a new awareness that the “unicorn party” may be a good thing for founders and VCs, but maybe not such a good thing for rank-and-file employees (and executive management).
  11. The first cloud EPM S-1 gets filed. Incorrect.  Not yet, at least.  While it’s always possible someone did the private filing process with the SEC, I’m guessing that didn’t happen either.
  12. 2016 will be a great year for Host Analytics. Correct.  We had a strong finish to the year and emerged stronger than we started with over 600 great customers, great partners, and a great team.

Now, let’s move on to my predictions for 2017 which – as a sign of the times – will include more macro and political content than usual.

  1. The United States will see a level of divisiveness and social discord not seen since the 1960s. Social media echo chambers will reinforce divisions.  To combat this, I encourage everyone to sign up for two publications/blogs they agree with and two they don’t lest they never again hear both sides of an issue. (See map below, coutesy of Ninja Economics, for help in choosing.)  On an optimistic note, per UCSD professor Lane Kenworthy people aren’t getting more polarized, political parties are.

news

  1. Social media companies finally step up and do something about fake news. While per a former Facebook designer, “it turns out that bullshit is highly engaging,” these sites will need to do something to filter, rate, or classify fake news (let alone stopping to recommend it).  Otherwise they will both lose credibility and readership – as well as fail to act in a responsible way commensurate with their information dissemination power.
  1. Gut feel makes a comeback. After a decade of Google-inspired heavily data-driven and A/B-tested management, the new US administration will increasingly be less data-driven and more gut-feel-driven in making decisions.  Riding against both common sense and the big data / analytics / data science trends, people will be increasingly skeptical of purely data-driven decisions and anti-data people will publicize data-driven failures to popularize their arguments.  This “war on data” will build during the year, fueled by Trump, and some of it will spill over into business.  Morale in the Intelligence Community will plummet.
  1. Under a volatile leader, who seems to exhibit all nine of the symptoms of narcissistic personality disorder, we can expect sharp reactions and knee-jerk decisions that rattle markets, drive a high rate of staff turnover in the Executive branch, and fuel an ongoing war with the media.  Whether you like his policies or not, Trump will bring a high level of volatility the country, to business, and to the markets.
  1. With the new administration’s promises of $1T in infrastructure spending, you can expect interest rates to raise and inflation to accelerate. Providing such a stimulus to already strong economy might well overheat it.  One smart move could be buying a house to lock in historic low interest rates for the next 30 years.  (See my FAQ for disclaimers, including that I am not a financial advisor.)
  1. Huge emphasis on security and privacy. Election-related hacking, including the spearfishing attack on John Podesta’s email, will serve as a major wake-up call to both government and the private sector to get their security act together.  Leaks will fuel major concerns about privacy.  Two-factor authentication using verification codes (e.g., Google Authenticator) will continue to take off as will encrypted communications.  Fear of leaks will also change how people use email and other written electronic communications; more people will follow the sage advice in this quip:

Dance like no one’s watching; E-mail like it will be read in a deposition

  1. In 2015, if you were flirting on Ashley Madison you were more likely talking to a fembot than a person.  In 2016, the same could be said of troll bots.  Bots are now capable of passing the Turing Test.  In 2017, we will see more bots for both good uses (e.g., customer service) and bad (e.g., trolling social media).  Left unchecked by the social media powerhouses, bots could damage social media usage.
  1. Artificial intelligence hits the peak of inflated expectations. If you view Salesforce as the bellwether for hyped enterprise technology (e.g., cloud, social), then the next few years are going to be dominated by artificial intelligence.  I’ve always believed that advanced analytics is not a standalone category, but instead fodder that vendors will build into smart applications.  They key is typically not the technology, but the problem to which to apply it.  As Infer founder Vik Singh said of Jim Gray, “he was really good at finding great problems,” the key is figuring out the best problems to solve with a given technology or modeling engine.  Application by application we will see people searching for the best problems to solve using AI technology.
  1. The IPO market comes back. After a year in which we saw only 13 VC-backed technology IPOs, I believe the window will open and 2017 will be a strong year for technology IPOs.  The usual big-name suspects include firms like Snap, Uber, AirBnB, and SpotifyCB Insights has identified 369 companies as strong 2017 IPO prospects.
  1. Megavendors mix up EPM and ERP or BI. Workday, which has had a confused history when it comes to planning, acquired struggling big data analytics vendor Platfora in July 2016, and seems to have combined analytics and EPM/planning into a single unit.  This is a mistake for several reasons:  (1) EPM and BI are sold to different buyers with different value propositions, (2) EPM is an applications sale, BI is a platform sale, and (3) Platfora’s technology stack, while appropriate for big data applications is not ideal for EPM/planning (ask Tidemark).  Combining the two together puts planning at risk.  Oracle combined their EPM and ERP go-to-market organizations and lost focus on EPM as a result.  While they will argue that they now have more EPM feet on the street, those feet know much less about EPM, leaving them exposed to specialist vendors who maintain a focus on EPM.  ERP is sold to the backward-looking part of finance; EPM is sold to the forward-looking part.  EPM is about 1/10th the market size of ERP.  ERP and EPM have different buyers and use different technologies.  In combining them, expect EPM to lose out.

And, as usual, I must add the bonus prediction that 2017 proves to be a strong year for Host Analytics.  We are entering the year with positive momentum, the category is strong, cloud adoption in finance continues to increase, and the megavendors generally lack sufficient focus on the category.  We continue to be the most customer-focused vendor in EPM, our new Modeling product gained strong momentum in 2016, and our strategy has worked very well for both our company and the customers who have chosen to put their faith in us.

I thank our customers, our partners, and our team and wish everyone a great 2017.

# # #

 

Introducing a New SaaS Metric: The Hype Factor

I said in yesterday’s post, entitled Too Much Money Makes You Stupid, that while I don’t have much of a beef with Domo, that I did want to observe in today’s fund-to-excess environment that any idea — including making a series of Alec Baldwin would-be viral videos — can sound like a good one.

While I credited Domo with creating a huge hype bubble through secrecy and mystery, big events, and raising tremendous amounts of money (yet again today) at unicorn valuations — I also questioned how much (as Gertrude Stein said of Oakland) “there there” Domo has when it comes to the company and its products.

Specifically, I began to wonder how to quantify the hype around a company.  Let’s say that, as organisms, SaaS companies convert venture capital into two things:  annual recurring revenue (ARR) and hype.  ARR has direct value as every year it turns into GAAP revenue.  Hype has value to the extent it creates halo effects that drive interest in the company that ultimately increase ARR. [1]

Hype Factor = Capital Raised / Annual Recurring Revenue

Now, unlike some bloggers, I don’t have any freshly minted MBAs doing my legwork, so I’m going to need to do some very back of the envelop analysis here.

  • Looking at some recent JMP research, I can see that the average SaaS company goes public at around $25M/quarter in revenue, a $100M annual run-rate, and which also suggests an ARR base of around $100M.
  • Looking at this post by Tomasz Tunguz, I can see that the average SaaS company has raised about $100M if you include everyone or $68M if you exclude companies that I don’t really consider enterprise software.

So, back of the envelope, this suggests that 1.5 (=100/68) is a typical capital-to-ARR ratio on the eve of an IPO.  Let’s look at some specific companies for more (all figures are approx as I’m eye-balling off charts in some cases and looking at S-1s in others) [2]:

  • NetSuite:  raised $125M, run-rate at IPO $92M  –> 1.3
  • Cornerstone:  raised $41M, run-rate $44M –> 1.0
  • Box:  raised $430M, run-rate $228M –> 1.8
  • Xactly:  raised $83M, run-rate $50M –> 1.7
  • Workday:  raised $200M, run-rate $168M –> 1.2

There are numerous limitations to this analysis.

  • I do not make any effort to take into account either how much VC was left over on the eve of the IPO or how much debt the company had raised.
  • Capital consumption per category may vary as a function of the category as a CFO friend of mine reminded me today.
  • Some companies don’t break out subscription and services revenue and the ARR run-rate calculations should only apply to subscription.

Since private companies raise capital and burn it down until an IPO, you should expect that the above values represent minima from a lifecycle perspective. (In theory, you’d arrive on IPO day broke, having raised no more cash than you needed to get there.)

So I’m going to rather subjectively assign some buckets based on this data and my own estimates about earlier stages.

  • A hype factor of 1-2 is target
  • A hype factor of 2-3 is good, particularly well before an IPO
  • A hype factor of 3-5 is not good, too much hype and too little ARR
  • A hype factor of 5+ suggests there is very little “there there” at all.

I know of at least one analytics company where I suspect the hype factor is around 10.   If I had to take a swag at Domo’s hype factor based on the comments in this interview:

  • Quote from the article:  “contracted revenue is $100M.”  Hopefully this means ARR and not TCV.
  • Capital raised:  $613M per Crunchbase, including today’s round.

This suggests Domo’s hype factor is 6.1 including today’s capital and 4.8 excluding it.  So if you’ve heard of Domo, think they are cool, are wowed by the speakers and rappers at Domopalooza, you should be.  As I like to say:  behind every marketing genius, there is usually a massive budget. [3]

Domo’s spending heavily, that’s for sure.  How efficient they are at converting that spending to ARR remains to be seen.  My instinct, and this rough math, says they are more efficient at generating hype than revenue. [4]

Time will tell.  Gosh, life was simpler (if less interesting) when companies went public at $30M.

# # #

Notes

[1] In a sense, I’m arguing that hype takes two forms:  good hype that drives ARR and wasted hype that simply makes the company, like the Kardashiansfamous for being famous.

[2] And having some trouble making the different data sources foot.  For example, the SFSF S-1 indicates $45M in convertible preferred stock, but the Tunguz post suggests $70M.  Where’s my freshly minted MBA to help?

[3] You can argue that the first step in marketing genius is committing to spend large amounts of money and I won’t debate you.  But I do think many people completely overlook the massive spend behind many marketing geniuses and, from a hype factor perspective, forget that the purpose of all that genius is not to impress TechCrunch and turn B2B brands into household words, but to win customers and drive ARR.

[4] Note that Domo says they have $200M in the bank unspent which, if true, both skews this analysis and prompts the question:  why raise more money at a flat valuation in smaller quantity when you don’t need it?  While my formula deliberately does not take cash or debt into account (because it’s hard enough to just triangulate on ARR at private companies), if you want to factor that claim into the math, I think you’d end up with a hype factor of 3-4.  (You can’t exclude all the cash because every startup keeps cash on hand to fund them through to their next round.)

Too Much Money Makes You Stupid — Let’s Make an Alec Baldwin Viral Video

There are two sayings I like when it comes to the unicorn bubble:

  • “Too much money makes you stupid”
  • “Any idea’s a good one when you’ve got $100M burning a hole in your pocket.”

Startups are supposed to be focused.  Startups are supposed to need to prioritize ideas and opportunities.  Just as startups weren’t supposed to buy Superbowl ads, startups aren’t supposed to have hundreds of millions of dollars to plow through in the name of creating brand mystique either via huge-budget events like Domo’s Domopalooza or would-be viral videos, like the one below.

But wait, you protest, didn’t Salesforce always do aggressive marketing and wasn’t that risk-taking part of their greatness?  Well, yes and no.  A good part of their early marketing was guerrilla PR done on the cheap.  Yes, they also ran big events, but they mostly found a way to pay for them — Salesforce raised $53M in VC before going public.  Domo has raised nearly 10x that.

Now, I have no particular beef with Domo. Other than being next-generation BI, I must admit to always having had some trouble figuring out what they do — in part due to the abnormal secrecy they had in their early days.  I know they don’t compete with Host Analytics so I have no beef there.  I also know they have sexed-up the BI category a bit, and they’ve certainly done a great job of positioning themselves as a cool company and have created a lot of buzz in the market.

But at what cost?

Domo has raised $483M.  It does cause one to wonder about their capital-to-ARR ratio, which is a great overall capital efficiency metric and one that no ever seems to talk about.

  • While I don’t know in Domo’s case, I’d guess for many unicorns that this ratio is 10 to 20x — where the company is running a kind of perpetual motion machine strategy where you generate the Halo Effects hoping to drive the sales that justify the valuation that you got on your last financing.  This strategy, as many will discover, works well until it doesn’t.  If the epitaph of Bubble 1.0 was about Network Effects, that of Bubble 2.0 will be about Halo Effects.  Remember Warren Buffet’s famous quote:  “only when the tide goes out can you see who’s swimming naked.”
  • I know for a reasonably capital-efficient SaaS business the capital-to-ARR ratio might be 2-3x.  Perhaps an order of magnitude difference.

Back to our core topic — what’s an example of something that looks like a good idea when you have $483M burning a hole in your pocket that, well, might not look like such a good idea if you were forced to lead a more frugal marketing existence?

How about  a YouTube mini-series with Alec Baldwin?  That’s exactly what Domo did.

Here’s episode 1 about “rancid data” which, among several issues, breaks the fundamental rules about how to make a successful viral video.

SAP Cloud for Analytics: Tilting at Windmills

Back in the early 2000s when I was running marketing at Business Objects, Gartner’s then-lead BI analyst, Howard Dresner (known as the father of BI and the person who named the category) started pushing a notion called enterprise performance management (EPM).  Back then, EPM meant the unification of BI and planning/budgeting.

The argument in favor of EPM made sense and was actually kind of cool:  with BI you could ask any question, but BI never knew the correct answer.  What did that mean?

It meant that BI tools were primarily tied to operational systems and could tell you the value of sales/salesrep for any quarter in any region.  The problem was that BI didn’t know what the answer was supposed to be.  BI knew the cost of everything and the value of nothing.

The solution was tie to BI to financial systems, which were full of targets and thus could allow us not just to know the value of any given metric, but what the value of that metric should be.

It sounded great and I bought in.  More importantly, so did the category:

Then what happened?  In my opinion, pretty much nothing.  Sure Hyperion reps could increase deal sizes by trying to drop Brio licenses across the whole financial department, as opposed to just FP&A.  Cognos could cross-sell Adaytum, with the help of an overlay sales force.

But did integration happen?  No.  BI and financial planning/budgeting  consolidated, but they never converged.  This is interesting because it’s rare.  For example, by contrast, CRM really happened.  SFA vendors didn’t just acquire customer service vendors and marketing vendors — the three applications came together to create one category.

That didn’t happen with EPM.  You could always ask someone who worked at Hyperion my favorite question, “which side did you work on?” and you always heard either, “oh, the BI side,” or “oh, the finance side.”  You never, ever got asked to clarify the question.

Over time, EPM came to mean financial planning, budgeting, and consolidation (along with associated reporting/analytics) — and not the unification of BI and financial planning.

What did this prove?   You can put the two categories under one roof via consolidation, but the actual markets are oil-and-water and don’t mix together well.  Why?  Two reasons:

  • BI is a platform sale, EPM is an applications sale
  • BI is sold to IT, EPM is sold to the finance department

So other than selling to a completely different buyer with a completely different value proposition, they make excellent candidates for integration!  Put concretely, if you can’t talk about inter-company eliminations, AVB reports, AOPs, topside journal entries, long-range models, FX rate handling, and legal entities, then you can’t even start to sell EPM.  I marketed BI for 9 years and we talked a totally different language:  aggregate awareness, multi-pass SQL, slow-changing dimensions, and star schemas.  The two languages are not totally unrelated.  They are nevertheless different.

Despite this history, many vendors still seem hell bent on mixing EPM water with BI oil.  One cloud EPM vendor positioned themselves for years as a leader in “BI and CPM” somehow thinking the rock-bottom acquisition of a cheap scorecarding tool made them a player in the $15B BI market.

To be clear, I view EPM and BI as cousins.  Yes, in EPM we make scorecards, dashboards, and reports.  Yes, in EPM we do multi-dimensional modeling and analysis.  No doubt.  But we do it for finance departments, we tie our planning/budgeting systems to the general ledger and we are focused on both financial outcomes and financial reports.  Yes, we also care about integrating models across the organization — sales, marketing, services, and operations.  But we are not trying to sell generic infrastructure for making reports and visualizations across the enterprise.

Put simply, in EPM we use BI technologies to build financial applications that tie together the enterprise on behalf of the finance department.

Surprisingly, SAP didn’t get the consolidation-not-convergence memo.  This is somewhat amazing given that SAP is a strong player in both BI and EPM, but somehow hasn’t seemed to notice not only that the two markets never converged but also that there is a very good reason for that.  They are still tilting at windmills fighting to integrate two categories not destined for integration with a vintage-2002 message.

Here’s the press release:

SAP Redefines Analytics in the Cloud

WALLDORF — SAP SE (NYSE: SAP) today unveils the SAP Cloud for Analytics solution, a planned software as a service (SaaS) offering that aims to bring all analytics capabilities into one solution for an unparalleled user experience (UX).

Built natively on SAP HANA Cloud Platform, this high-performing, real-time solution plans to be embedded with existing SAP solutions and intends to connect to cloud and on-premise data to deliver planning, predictive and business intelligence (BI) capabilities in one analytics experience. The intent is for organizations to use this one solution to enable their employees to track performance, analyze trends, predict and collaborate to make informed decisions and improve business outcomes.

Note, that in addition to my strategic concerns, I have a few tactical ones as well:

  • This is a futures announcement without a date.  The service “planned.”  The “planned benefits” are stated.  The only thing I can’t find in the plan is an availability date.
  • Pricing hasn’t been announced either.  So other than knowing what it costs and when it will be available, it was an informative announcement.
  • While SAP is claiming that it’s previously announced SAP Cloud for Planning is included in the new offering, I have heard rumors on the street that SAP Cloud for Planning is actually being discontinued and customers will be moved to the new offering.  At this point, I’m not sure which is the case.

In the end, I’m not trying to beat on SAP in general.  I don’t love the Hana branding strategy, that’s true, but Hana itself (i.e., columnar, in-memory database) is a good idea.  I have no problems with SAP BI’s products — heck, my fingerprints still remain lightly on a few of them.  In EPM, we compete with SAP, so my agenda there is obvious.

But the thing I object to, the tilting at windmills, is that they are still banging the unify EPM and BI drum.  SAP’s new analytics may eventually end up a reasonable or good BI solution.  But if they’re betting serious chips on unifying BI and EPM it’s misguided.