Terry Pratchett once wrote that “Gravity is a habit that is hard to shake off”. We could make a similar comment about the financials of SaaS BI companies. As much as startups in this field would like to shake off their bad economics, reality always catches up. We’re seeing one after another SaaS BI startup to go out of business. Back in June it was LucidEra and earlier this week Blink Logic ceased operations. But anybody who only briefly looked at Blink Logic’s finances (it was a public company) shouldn’t be surprised by this event.
Why do so many of the attempts to marry BI and SaaS fail? The problem is that Saas BI sounds simple … simple enough to take an existing BI asset (integration engine, open source analytical engine, columnar database, dashboarding, even domain expertise & consulting) and just host it! All it takes is VMware or an AWS account, web server and Flash or JavaScript. Some people call this a paradigm shift, I call it window dressing. LucidEra was essentially restarted Broadbase, BlinkLogic was once called DataJungle, PivotLink recently changed their name from SeaTab, Cloud9 Analytics has a secret history as Certive, Success Metrics morphed into Birst. I could go on…
Why do SaaS BI companies have bad economics? It’s an attractive market – one of the last few open spaces in software. BI requires dealing with lots of data, lots of compute power and many users. SaaS + BI seems obvious. But truthfully, it’s such a difficult opportunity that it requires a new approach, yet everybody is taking shortcuts. SaaS BI isn’t just hosted BI just as email is not just better faxing, wikis are not just simplified Microsoft Word. Some time ago I wrote a case study on how my former company, NetBeans, was able to successfully compete against giants like Symantec, Borland or IBM, this case study is very relevant to our SaaS BI discussion.
The SaaS BI paradigm shift needs to be truly transformational in order to be successful – something that will get BI above the 9% adoption flatline it’s been at for years. Not everybody gets this. One of the best analysts in this space Boris Evelson wrote a blog post earlier this week where he focuses on differentiation of SaaS BI startups. His first question is: VC backing. Is the firm backed by a VC with good track record in information management space? But LucidEra was very well funded by leading VCs. The correct question that Boris should have asked is: Are the backers of the company funding innovation? Do they understand that it takes three years to become an overnight success?
At the end of the day, it’s about economics. At Good Data, our economics are simple – cloud computing, multitenancy and adherence to customer development. We’ve spent two years investing in innovation. That is what I tell my investors every day. And that is how we are going to avoid the startup death spiral.
IMHO, being backed by a “leading VC” (whatever that means anymore) who is “well funded” (again, not sure what that means anymore) in the BI space and specifically in the SaaS arena is far from a sign of potential success. I would refer your readers to this article as well: http://bit.ly/jIuVK
Fact is, most VCs don’t understand BI/database technology – much less SaaS. Heck, a lot of CEOs of such companies don’t even understand it themselves!
What we have in SaaS (and cloud in general) are a lot of worried VCs playing the sheep game and investing in that space simply because they follow each other typically much like lemmings off a mountain range. I’m not saying they’re all empty suits, but there are a lot of clothless emperors in this game out there.
In all fairness, there are also several who really DO get it (namely Evangelos at Trident, for example) and those are the people you want backing up a SaaS play, for example.
So I don’t think it’s so much the “bad economics” we need to shake off as much as the bad investors IMHO. Yes there IS such a thing as bad money
Just my 2 cents.
Oops, I clicked too fast and posted as Anonymous by mistake — Hate then that happens
I never post anonymous ever.
Sorry.
Roman
When I started reading about your comment around window dressing, I thought you were onto something. After that you disappointed me.
Each of the companies that you mentioned LucidEra, SuccessMetrics, Cloud9 and PivotLink – all of them funded innovation. Although each had a different take on what was the most important problem to solve, as a first problem. I can say the same for GoodData – Your company isn’t solving all the BI problems – which is a wise business decision. You have a take on what is important to address first and are directing your innovation efforts there. Time will tell if this was enough. None of these companies – as far as I know – went the VMWare/AWS route. So, I am not sure that you have the right perspective here.
LucidEra did not fail because it was a Broadbase restart. It failed because it tried to build all the technology itself with a mostly US based engineering team that burned through $23MM quickly – and decided to limit itself to Sales Analytics – a small market that got commoditized by Cloud9.
I don’t think that having a history as a different name, equates to a VMWare/AWS backend and hence a window dressing. Secondly, all of the companies above have been in business much longer than GoodData has so please wait until your company has accumulated some BI experience.
Dobrý den,
Rád bych se Vás zeptal na pár otázek ohledně Vašeho podnikání. Mohl byste mi poslat mail, eventuálně telefonní spojení.
Děkuji předem,
Jiri Zatloukal
Časopis TÝDEN
@Ajay: “with a mostly US based engineering team that burned through $23MM quickly” – I have to jump in here. You’re implying that their [Lucidera] burn rate was caused mostly by their using a US-based engineering team as oppose, I presume, to an outsourced one to build bits from scratch? And if so you are claiming they ripped through $23M in doing so?
Just trying to make sure I understand here.
Thanks.
@Roman. You are mostly right. What I imply by mostly US team is the following: They were not offshore (I personally don’t like outsourced) – in other words the cost could have been much lower. They off shored quite late and only for a small portion. Secondly they tried to build everything from scratch, including the database – the team (including me) took the building everything from scratch a bit too far – DB, ETL, Connectors, Reporting UI, Dashboards, Charts, Administration, Grid Deployment, multi-tenancy and more.. – you name it. Imagine doing all of this in the US and add to that the support and service staff – also in the US. Not that there is anything wrong with it per-se but when you juxtapose this against the meager revenue stream from focusing just on Sales Analytics it created a burn rate that was too risky. Like most things in life there is some gray here. The downturn created a tight funding environment and it precipitated in the company’s shutdown. Hard to single out one particular thing – but I would say, wrong technical approach (wrong because it was too costly, too slow) combined with bad funding environment. Absent either one, the company would have been around.
Roman et al.,
It’s really difficult to pin down the failure of a business to one area. There’s many factors that coalesce into a “behind the woodshed” moment. I would argue this space will see plenty more dead dogs before a true best of breed emerges.
Remember that SFDC needed somewhere in the neighbourhood of $70M to fuel its ascent if I am to mix metaphors momentarily. I’m not saying throwing money at the problem will solve the problem; I’m saying that I doubt, based on the rounds of financing gained, that they planned on needing as much lead time.
My hope is that the decomposing bones of these corpse companies will serve as a warning and a lesson to the next generation. That is all.