Category Archives: M&A

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.

Bottom-Fishing Acquisitions and Catching Falling Knives

As mentioned in my recent Curse of the Megaround post, some companies that find themselves flush with cash and under heavy pressure to grow, decide to embark on dubious acquisitions to help shore up the growth story.

As one reader it put it, you can summarize your megaround post with the simple phrase “much money makes you stupid.”  And it can.  Thus, as the old saw goes, fools and their money are soon parted.

What separates good from bad acquisitions in this context?  As a general rule, I’d say that when high-growth venture-backed companies acquire firms that would otherwise best be acquired by private equity, it’s a bad thing.  Why?

Firms destined to be acquired by private equity follow a typical pattern.

  • They are old, typically 10+ years
  • They have tried multiple iterations on a strategy and none has worked
  • They have a deep stack of technology built over the years but most of which could be quickly replaced with modern, often open source, standard components
  • They tend to get strategically inverted — starting out with “what we have” as opposed to “what the market wants”
  • They have gone through several generations of management teams
  • Basically, they’re turnarounds

So private equity funds bottom-fish these opportunities, buy companies for a fraction of the total invested venture capital, scrap most of the original dream and either [1] double down on one core piece that’s working or [2] roll the company up with N adjacent companies all selling to the same buyer.

This is hard work.  This is dirty work.  This is “wet work” involving lots of headcount changes.  And private equity is good at it.   In one sense (and excluding private equity growth funds), it’s what they do.

High-flying VC backed startups are simply the wrong types of buyers to contemplate these acquisitions.  In the core business, it’s all about grow, grow, and grow.  In the acquired business, it’s all about cut, cut, cut and focus, focus, focus.  These are two very different mentalities to hold in your head at one time and the typical fail pattern is apply the grow-grow-grow mentality to the broken startup that repeatedly hasn’t-hasn’t-hasn’t.

The other failure pattern is what I call the worst-of-breed suite.  This happens when a player in space X acquires a two-bit player in space Y, hoping to “get a deal” on a cheap technology they can then sell to their customers.   The vendor is thinking “I can sell more stuff through my existing channel.”  However, the customer is thinking “I don’t want to use a worst-of-breed product just because you decided to acquire one on the cheap.”  Moreover, with easy of integration of cloud services, there is typically no real integration advantage between the cheaply acquired product and a third-party best-of-breed one.

On Wall Street, they say that bottom-fishing falling stocks is like catching falling knives.  For high-growth startups, trying to bottom-fish failed startups is pretty much the same thing.