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…

I believe that the COSS emperor is openly naked. And the discussion shouldn’t be whether COSS is a complete or a partial failure just because there are few successful exits that Peter neglected to mention. At the end of the day Peter’s comment that “selling software is miserable” is true. Every sales rep involved in selling COSS would agree (I’m interviewing many of them now). Selling COSS is no easier than selling any other form of software.

Any company using the word “open” should be able to explain the true cost of delivery (this is one of Peter’s points). And there is an obvious litmus test of openness of COSS companies: One that I would call “open pricing.” COSS companies should openly publish their price list and clearly mark what’s free and open and what’s paid and closed. Otherwise OSS is just a bait-and-switch to a familiar proprietary software tactic of customer lock-in. This is what OSS was supposed to get rid of in the first place.

Let’s take a look at some of COSS companies in the Business Intelligence space. The bait and switch is in a full swing here:

Jaspersoft: https://www.jaspersoft.com/jaspersoft-business-intelligence-suite-0 Let us prepare a custom quote for you.

Pentaho: http://www.pentaho.com/products/buy_bi_suite.php Request a Quote

Talend: http://www.talend.com/store/talend-store-inquiries.php A Talend account manager will be in touch shortly to provide information and/or a detailed quote.

We announced GoodData pricing earlier today and I would actually argue that we are a more open company than any of companies listed above. Our customers know exactly what service they get and how much it will cost.

We stick to our company motto: GoodData = BI – BS. And at there is a lot of BS going on in COSS space. It may actually be its biggest failure.

Full disclosure: I have been a big believer in open source since we opensourced NetBeans more than 10 years ago.

15% in 2 years – not exactly the exponential growth in the performance/price curve that I expected. And I started to wonder why. Here are my two explanations – I believe the second one is more likely:

1. AWS prices were set way too low to attract developers two years ago. Moore’s Law helped the price to catch up with the real cost of running the cloud.
2. AWS is a monopoly and Moore’s Law does not apply.

What? Cloud and monopoly? Isn’t utility computing a perfect example of fiercely competitive commodity where the price curve is shaped only by demand/supply? What would Nick Carr say? Unfortunately not. As much as we read about different cloud providers, AWS is the only real provider of “infrastructure as a service” in town. If you don’t want to be locked-in to proprietary Python or .Net libraries there is not that much choice.

Until we will see performance/price of AWS double every two years, we should still wonder about monopolistic pricing.

I now live in the SaaS world. My customers only pay us if we deliver a service level consistent with our SLAs. We are responsible for deployment, security, upgrades and so on. We operate software for our customers and we deliver it as service.

But there now seems to be a new way how to “throw software over the wall” again. Many software companies have repackaged their software as Amazon Machine Image (AMI) and relabeled them as SaaS or Cloud Computing. It’s so simple, it’s so clever: Dear customer, here is the image of our database, server, analytical engine, ETL tool, integration bus, dashboard etc. All you need it is go to AWS, get an account and start those AMIs. Scaling, integration, upgrades is your worry again. Welcome back to the world of enterprise software…

AMI is the new DVD and this approach to cloud computing is the worst thing that could happen to SaaS. And SaaS in my vocabulary is still Software as a Service…

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 rise of open source is clearly linked to the rise of the web. Buy a commodity piece of hardware, download source code of any of the thousands of open source projects and start to “scratch your own itch”. My Linux box will communicate with your Linux box as long as we stick to some minimal set of protocols. The web is loosely coupled and software can be developed independently in a bazaar style.

It’s not quite as straightforward in the cloud. Clouds are also composed of thousands of commodity PCs, but the cloud operator manages the overall architecture and deployment – power supply, cooling, hypervisors, security, networks and so on. We don’t rely on minimal set of protocols in the cloud. On the contrary the cloud is defined by fairly complex, high level APIs. Even though the actual cloud OS may come from the open source domain, the tightly coupled nature of the cloud prevents users from modifying the cloud software.

There’s a lot of talk today about setting up private clouds with an Open Source Cloud OS, but the idea of private clouds is simply a delusion. Since the owner of private cloud has to purchase all required HW upfront, private clouds don’t provide the main benefit of cloud computing: elasticity. Other people will claim that clouds are not compatible with the open source movement or call it outright ‘stupidity’.

I see two possible solutions to this problem:

Benevolent dictator: Leading cloud providers (Amazon, Google, MSFT) will open-source their complete stack. This means that they would let the community to inspect the code, fix bugs, suggest improvements and define a clear roadmap similar to the Linux roadmap. This will also require a role of benevolent dictator to manage the evolution of the cloud. Given the level of investment required to build and operate the cloud I don’t believe that this is likely scenario.
The new PC: The open source community accepts the cloud as the new HW/OS platform. Instead of building apps on top of x86 platforms (Wintel, Mac…), open source applications would be built on top of Amazon Web Services or Google AppEngine APIs. And these apps would handle the portability of data so that data doesn’t get locked in the cloud.

At the end of the day, cloud computing equals utility and utility creates stability. And a stable set of APIs, protocols and standards is a great place for open source to flourish. The best open source projects grew on top of stable standards: MySQL/SQL, Linux/x86, Firefox/http/HTML. I wonder what will be the most important OSS that will grow on top of the cloud…

There are some fundamental differences between first generation SaaS BI providers and cloud-based platforms like Good Data. Some of them are technological while others are simply common sense:

• Good Data is based on true cloud architecture
• We use Amazon Web Services to host our multitenant platform and so we have minimal fixed and very low variable costs.
• We are true believers in Steve Blank’s Four Steps to the Epiphany, and the idea of spending over $20M before validating our go-to-market strategy is foreign to us.
• Cookie-cutter pre-built analytics apps are should be the STARTING POINT for customers to try – not the conclusion of an enterprise sales process.
• LucidEra was probably too expensive for small companies and too limited for large ones. And this is why we offer plain-vanilla NetSuite analytics for free.

I am sure we will see the era of success of on-demand analytics. The most useful analogy here is the disruptive business model of low cost airlines – it did not disappear after the demise of People Express airlines either…

PS. Good Data Offers Safe Harbor to LucidEra Customers (link)

PS. This presentation was featured today as one of the Top Presentations of the Day by Slideshare…

Nobody argues with the need for more Business Intelligence; BI is one of the few remaining IT initiatives that can make companies more competitive. But only the largest companies can live with the costs or the high failure rates. BI is a luxury.

I believe that the bad economics of BI are rooted in the IT department/BI vendor duopoly on BI infrastructure. This post focuses on IT’s inability to deliver efficient BI projects; I will write about the BI industry in my next blog:

There are three fundamental reasons why IT departments in their current form fail to deliver economical BI solutions:

1) They don’t understand elastic scale

IT departments are good at scaling: adding more and more hardware and software but scaling makes sense for tasks that are highly predictable. Given the ad hoc nature of BI we not only need to increase the compute power when we need it for a complex queries but we also need to be able to decrease the compute power when it’s not needed to keep the costs down. Elastic is more important than scalable. And this precisely why internal BI solutions will always be either too expensive or too slow for complex queries…

2) They try to control BI with a single version of the truth

While the volatility of business environment is increasing the IT departments are trying to button up the business knowledge (data, metadata, processes) into a top-down, inflexible and lengthy process that should produce a single version of truth. The problem is that the underlying business is changing so rapidly that by the time this is done the resulting analysis and reports are not correct anymore and the BI project becomes shelfware.

3) They cannot measure success of BI

“If you can’t measure it, it’s not worth doing!” is one of the selling point of BI but it is difficult to measure the success of BI projects. IT delivers on initiatives that are quantifiable (throughput, response time, performance, data sizes) and since the data size is one of the few easily measured aspects of BI it is the only metric where IT can claim success. This is why we often read about terabyte and petabyte datawarehouses. But it is a small portion of the BI market (2%) and they happen to be places where data goes to die.