With friends like Forrester and Gartner, IBM and SAP don’t need enemies…

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The Innovator’s Dilemma by Clayton M. Christensen is my favorite business book – its main idea (disruptive technologies serve new customer groups and “low-end” markets first) was the guiding principle of all my startups. The best part is that even though everybody can read about the power of disruptive technologies, there is no defense against them. Vendors can’t help themselves. They study The Innovator’s Dilemma, pay Christensen to speak to their managers, but their existing customer base and “brand promise” prevent them from releasing products that are limited, incomplete or outright “crappy.” That’s what makes them disruptive. And industry analysts seem to be the only hi-tech constituency that has either never read Christensen, or is still in absolute denial about it. It makes sense: a book claiming that “technology supply may not equal market demand” is heresy for people who spend their lives focused primarily on the technology supply side.

Christensen argues that vendors no longer develop features to satisfy their users, but just to maintain the price points and maintenance charges (can you name a new Excel feature?). But in many cases the vendor decisions are driven more by industry analysts and their longer and longer feature-list questionnaires. The criteria for inclusion into the Gartner Magic Quadrants and Forrester Waves seem to be copied straight from Christensen’s chapter: “Performance oversupply and the evolution of product competition”. Analysts are the best supporters that startups can have: they are being paid by the incumbents to keep them on a path of “performance oversupply”, making them so vulnerable to young vendors “not approved” by the same analysts!

Forester BI analyst Boris Evelson gives us a great example of this point in his blog about “Bottom Up And Top Down Approaches To Estimating Costs For A Single BI Report”. While Boris is a super smart BI analyst, he somehow failed to observe that his price point of $2,000 to $20,000 per report opens a huge space for economic disruption of the BI market. Anybody interested in power of disruptive technology in BI should listen to a recent GoodData webinar with Tina Babbi (VP of Sales and Services Operations at TriNet). Tina described how the economics of Cloud BI enabled her to shift TriNet’s sales organization “from anecdotal to analytical”. This would not be possible in the luxury-good version of BI, where each report costs thousands. Fortunately, Tina is paying less for a year for a “sales pipeline analytics” service delivered by GoodData than the established vendors would charge for a single report.

I hope Boris’ blog post will appear in one of the future editions of The Innovators Dilemma as a textbook example of how leading analysts failed to recognize that established products are being pushed aside by newer and cheaper products that, over time, get better and become a serious threat. And with friends like Forrester and Gartner, the incumbents don’t really need young and nimble enemies…

Bad economics are difficult to shake off

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.

The New Chasm

I believe that readers of my blog are familiar with the chart below. It is the classical “Crossing the chasm” diagram from Geoffrey A. Moore’s book Crossing the Chasm: Marketing and Selling High-Tech Products to Mainstream Customers. This book was published back in 1991 and it is still number two on “Twelve Business Books in One Hour for the Busy CEO” list. The main argument that Geoffrey Moore makes here is that the “early adopters” have no ability to influence “early majority” and it leads to a chasm that is very difficult for startups (or any company with disruptive technology) to cross:

Adoption Cycle

This book was written in the days when startups typically sold to electrical engineers (a.k.a. IT) and Brad Burnham described it well in his recent post:

In the old days, electrical engineers focused on getting computers to work not on getting people to engage with the systems built on top of those computers. The folks that built enterprise software were vaguely aware that their systems had to be accessible to the humans that used them but they had a huge advantage. The people who used them did so as part of their job, they were trained to use them and fired if they could not figure them out.

This is why even Wikipedia uses the following example to describe the end-user:

The end-user or consumer may differ from the person who purchases the product. For instance, a zookeeper, the customer, might purchase elephant food for an end-user: the elephant.

But virtually no startup gets funded today if it sells directly to electrical engineers. Innovation happens in the consumer space anyway and so the assumption is that any disruptive technology (social networks, SaaS, web2.0…) gets adopted first by the end-users and then it is picked up by the IT. But here comes the new chasm: low ability of end-users to influence IT:

Reversed Adoption Cycle

This chasm is the new manifestation of the classical “Business-IT” gap but this time is the innovation flow reversed: business leads and IT follows. And this new flow doesn’t make it any easier to cross the new chasm