Forecast Bleak for Bad UIs

The days of developing enterprise applications wherein the User Interface is an afterthought are rapidly coming to an end, and enterprise software companies had better adjust. Steve Jobs and Apple started the shift, and while Apple’s products were oriented toward the consumer, the business world is now driving the need for user friendly applications. (Open your eyes and you’ll see quite a few Vice Presidents of major corporations showing up for meetings with iPads, not PCs. )

Meanwhile, business users, for the most part, remain resigned (or sentenced) to the tedium of navigating the pathetically arcane and clunky UIs provided by the IBMs, Oracles and SAPs of the world. Some progress has been made, but sales people still struggle with their ERPs, business analysts still need three days to figure out how to create a purchase order and HR departments remain at risk for carpel tunnel syndrome on a massive scale as they scroll through dozens of screens just to add an employee into the system.

So what happens when the world of the friendly, intuitive, even fun user interface meets that of the overburdened, non-technical business user? More importantly, what happens when the non-technical, overburdened, business user is a CEO who makes multi-million dollar software buying decisions?

As an illustration, consider the Nest Thermostat from Nest Labs. Designed by Tony Fadell, who just happens to have been one of the design brains behind the iPod and iPhone, the Nest Thermostat does more than just combine aesthetics with functionality; it adds ease of use. Without thinking, without trying, without wrestling with an owner’s manual (90% of all programmable thermostats are so difficult to program, no one does it) the homeowner can optimize his or her energy usage, save money (and help save our environment), make a political statement (“No more foreign oil!”), and last but not least, always be warm in winter and cool in summer.
Image
So, what’s that got to do with SAP, IBM and Oracle? That’s easy. They are all great transactional systems of record, but when it comes to essential business use cases like BI, they are seriously deficient. In simple terms, only an impolite allusion to drinking out of a straw would describe their efficacy within the BI spectrum. And their efforts to mitigate this problem by purchasing edge applications (Cognos, Hyperion, Business Objects, etc.) have only made it worse. Companies using these traditional solutions often need armies of IT resources and consultants to take on issues like implementation, optimal use cases and change management.

So back to that CEO: after hours, days, weeks and months of strategy and implementation meetings; after wrangling over roadmaps and fighting over budgets, she goes home to an optimally cooled or warmed environment, and she sees that it’s all because of a beautifully-designed, technically-sophisticated and easy to use thermostat. And, she says to herself: “Why can’t the technology environment at my company be like this?” Little does she know, insofar as BI is concerned, it can.

Meet the new BI. Not the same as the old BI.

GoodData announced earlier today a $15M B-round of financing led by Andreessen Horowitz.   Mark, Ben and the rest of the team have managed to quickly build one of Sand Hill’s leading venture capital firms, but their influence is felt well beyond Silicon Valley.

Like a lot of entrepreneurs, I love reading Ben Horowitz’s blog, and since I’ve been selling to enterprise customers for over 20 years, I especially loved Ben’s post last November: Meet the New Enterprise Customer, He’s a Lot Like the Old Enterprise Customer

Ben argues that as much as we all like the consumerization of IT, real adoption in large enterprises will require a more traditional sales approach. We agree, and it’s why we are thrilled to be partnering with Andreessen Horowitz and adding general partner John O’Farrell to our board.

But disrupting the $25 billion BI and Data Warehousing space is going to take a little more than great enterprise selling. The BI vendor approval ratings are probably below those of the US Congress and most of the BI software ever sold ends up as a shelf-ware!

That’s why we’re going to balance traditional enterprise selling and a product-first customer experience. Unlike traditional BI, that still dump-trucks software onto the laps of unsuspecting enterprise buyers, we have to sell, deliver and delight our customers. We can’t just take the money and run; our SaaS business model keeps us honest.

So, how do we plan to overcome the challenges that Ben lists in his post? Let me focus on a few of his many killer quotes:

“Many companies literally do not know how to buy new technology products”

We see this clearly in the enterprises we are already selling. The BI technology stack is so convoluted that large companies often need to hire consultants simply to help them evaluate the BI products, organize the bake-offs and the selection process. No start-up is set up for success in a procurement process that is designed to avoid innovation, and that clearly advantages the incumbents.

To overcome this, we are not selling the traditional IT tools to IT audiences. GoodData is not another piece of the complex BI value chain. Our semi-official tagline is “GoodData equals BI minus BS”. Our economic buyer is the functional VP in a medium or large organization. It is the VP of Sales or Marketing rather than the head of the BI competency center. Our sales cycle usually takes less than six weeks rather than six months, and when we do sell directly to the IT department, they are usually in an S.O.L. situation.

Once deployed, enterprises develop great affection for the technology that runs their companies.

That’s the irony about BI. While technically deployed, it’s rarely actually used. Few organizations or practitioners have any affection for their BI vendor or their software sitting on the shelf. Unlike line-of-business, IT systems do not effectively run the business. There are few companies that can actually manage their business by the metrics, and few business users that can easily use a BI tool. And this is the problem we’re solving. We do this by making BI approachable and pervasive, through our pre-built analytics apps, and by integrating our dashboards into end-user applications and processes (such as Salesforce.com or Zendesk) as much as possible.

Enterprise users are concerned with getting home to see their 8 year old’s pee wee baseball game

This is why we run a fully managed service. My vision for GoodData has always been of an end-to-end product that includes not only the infrastructure components (ETL, Data Warehouse, analytical engine…) but also the technical operations, support and best practices, so that we can deliver a complete service to our customers. We even publish our Operational & Service Performance here: http://www.gooddata.com/trust.

I could go on all day quoting more from this and other AH blog posts, but the fact of the matter is that the new enterprise customer needs new BI, and GoodData is here to help.

The S.O.L. Moment for BI Hobbyists

When the PC industry was young we used to build computers from build-it-yourself computer kits. The best known kit was the Altair 8800 but another was actually called SOL (maybe due to the condition in which kit purchasers/PC hobbyists would find themselves…).

Nobody buys and builds computers from a kit anymore. We buy professional products that are built by people who design, assemble and test thousands of these every day. Yet the “build-it-yourself  BI kit” is still the dominant way IT teams today buy, assemble and deliver Business Intelligence. That’s insane!

Since BI projects are built by hobbyists from tens of building blocks, it’s no surprise that their requirements so closely match “hobbyist” PC requirements:

  • More Important Requirements: Performance, cost, expandability, upgradeability.
  • Less Important Requirements: Reliability, availability, service, ergonomics and usability.

In an ideal world, BI Hobbyists would realize that assembling and operating a diverse set of technologies and products that come usually from number of suppliers (or from a single supplier through multiple acquisitions) would be overwhelmingly complex. But the fact that the pieces come from a large company typically gives the buyer the illusion of completeness and unwarranted optimism about the chance of success.

And we don’t live in an ideal world anyway. That’s why BI projects are so often delivered without the adequate “reliability, availability, service, ergonomics and usability”. And quite often they’re not delivered at all. Clearly, some BI hobbyists find themselves in similar position as PC kit purchasers used to…

These are the “S.O.L. moments” when we get calls from prospects today (increasingly from Fortune 500 types). As much as I would love to see GoodData as a part of large-scale BI projects from the very beginning, I understand that we need to prove ourselves first. I am more than happy to come to the rescue.

But isn’t it obvious to the industry that business intelligence should no longer be built by hobbyists? BI buyers should focus on business value (metrics, dashboards…) and BI projects should be built by people who design, test and deliver at least hundreds of these every day…

BI at SaaS Speed

Winston Churchill once said that “difficulties mastered are opportunities won”. His quote is very applicable to the the effort of building BI in the cloud. GoodData announced earlier today that May 2011 was our biggest month ever, so it is good time to look at difficulties and opportunities of BI Cloud in greater detail.

Business Intelligence is a huge opportunity. Even in its current, dysfunctional, on-premise form it is $10B software industry. And on-premise BI is extensive and expensive IT initiative that involves building a complete chain of data integration, data-warehousing, dashboarding and visualizations. On top of the IT efforts comes tricky business part: what to measure, what are the right metrics, how to present them and to whom. And it all has to happen at the speed of business, not at the speed of IT.

This IT/business dichotomy leads to extremely low success rate of BI projects – as much as $7 billion annually is spent on BI undertakings that are considered failures. That’s right – $7 billion worth of BI software ends up sitting on the shelf every year!

On the contrary the SaaS model works best when the product is well defined, customer adoption is fast, satisfaction/loyalty is high and cost of servicing the customer is low (for more information on SaaS metrics please read “Top 10 Laws of Cloud Computing and SaaS” here). This means that the traditional, slow moving, complex and expensive BI will NEVER make it to the cloud. Numerous small and large companies have tried to host their traditional on-premise BI products in the cloud, but SaaS laws are called laws for a reason – these companies either failed already or will eventually fail.

So what is GoodData doing differently to master the difficulties of Cloud BI?

1. Product Definition/Customer Adoption – in order to make customer adoption as quick as possible, we are building a set of BI applications. These apps are templates that contain not only connectors to standard data sources (such as Salesforce, Zendesk and Facebook) but also complete dashboards and reports that incorporate best practices in the form of metrics. Our Sales Analytics app helps you measure predicted revenue. Our Helpdesk Analytics app measures your backlog and resolution times. Our Marketing Analytics app teaches you how to calculate campaign ROI. We’re adding these applications on a weekly basis. You can see the full list of our apps here: http://www.gooddata.com/apps

2. Customer Loyalty – We deliver a complete, managed service to our customers. Our developers, ops and support personnel are making sure that every single data load goes as planned, all reports are loaded correctly and that there are no performance issues. We even publish our Operational & Service Performance here: http://www.gooddata.com/trust

3. Cost of Service – We’ve architected a very different platform that allows us to host a large number customers at a relatively low cost. The platform is so different that we often have a hard time communicating it to the BI analyst community (concepts like REST APIs and stateless services are not part of normal BI nomenclature). And the flexibility built into the platform allows us to move at the pace of business and not the pace of IT: we deliver a new version of GoodData to our customers every two weeks and we make tons of changes to customer projects daily.

Even the fact that we know how many reports we served to our customers in May of 2011 (over 1,000,000) sets us apart. While the old BI industry can only guess the level of adoption and product usage (of shelfware) we actually know. But again, “difficulties mastered are opportunities won”!

In BI, APIs are the Cloud’s OEM

To put it simply, I am in the business of building platforms.

NetBeans was the first extensible Java IDE platform with plug-ins back in 1999. Systinet had a product that was actually called Web Application & Services Platform (WASP). But both NetBeans and Systinet were “only” what my investor Marc Andreessen calls Platform Level 2:

This is the kind of platform approach that historically has been used in end-user applications to let developers build new functions that can be injected, or “plug in”, to the core system and its user interface.

(Everyone should read Marc’s excellent article on different platform levels here.)

My goals for GoodData are different. I want to build a platform that would become a one stop shop for BI developers, architects and users. A BI platform that will provide a set of APIs where developers can define their own models, schemas, queries, metrics, reports and dashboards. The highest level of Marc’s platform taxonomy:  Platform Level 3.  But here I need to quote Marc again:

Level 3 platforms are much harder to build than Level 2 platforms.

Yes. Building GoodData – a multi-tenant, scalable and open BI platform – was not easy (and we are not finished yet) but the possibilities are endless. We opened the platform to developers only a few months ago and today we are announcing a number of partnerships that are only possible because of GoodData APIs. We call this program Powered by GoodData and it is available to all developers and architects who need have access to BI functionality.

Access to BI functionality… let’s stop here for one moment. Accessing BI functionality in the world of enterprise software usually meant a build-versus-buy/OEM decision (the third option – open source BI – is as complex as the build option and as expensive in the long term as the buy option). But now GoodData gives our partners a completely new option to access BI functionality: an API call.

Instead of building, managing and operating a datawarehousing and BI stack our partners rely on our cloud-based service to deliver that functionality for them. And we are delivering BI functionality to hundreds of companies and thousands of users via API calls. It is Tuesday afternoon and our BI platform served 1,218,689 REST API calls this week. Now that’s what I call a new way of access to BI functionality. And what Marc would call a Level 3 platform.

The Masses Against The Classes

“When you open BI to the masses, people get a taste of what they can do and start demanding more and more information and analytics” Dan Vesset, IDC analyst, Computerworld, June 21, 2004

“Let them eat cake”. Marie Antoinette, Versailles, 1789

I’ve always found the BI industry’s fascination with elitism a throwback to the old days of IT. It seems that most of the industry calls users with no access to their tools “the masses”. And it gets worse. Bloggers from Endeca call them half jokingly “the angry mobs” and SAP has BI for “the rest of us”. All these words describe a business user who doesn’t have the time or skills to operate a complex BI solution designed for electrical engineers (who go by the name of IT). BI has penetration rate of 10% and everybody else is “the rest of us”.

It’s not just BI. The telco industry thinks their customers reside in “the last mile” – as far away from what’s important (the core of the network) as you can get. Shouldn’t their customers be in “the first mile”? And now BI is adopting the same “last mile” language, and the intent is the same: “keep my business users as far away as possible so I can focus on the core of my BI system.”

Making BI accessible to the “angry mobs” is in contradiction with BI industry’s quest for ever more complexity and hype. Petabyte warehouses, data visualization, social media analytics, predictive clustering, corporate performance management are the current industry buzzwords. Press releases and PowerPoint charts are full of names like Pig, Hadoop and Hive. These trends and tools were designed for the selected few; not for the average business user.

My vision for GoodData was always very different. Our goal is to get rid of the convoluted BI value chain. We are using the economics of the cloud to offer a service that can be used by a business audience. I am on a personal mission to support “the masses against the classes” and to build BI that is not a dumbed down version of an expensive, complex and brittle enterprise solution. I’ve always believed that the enterprise data warehouse is the place where data goes to die, leaving the poor business users with Excel spreadmarts.

This is why we just announced a fully integrated and free service: GoodData for Zendesk. Every Zendesk Plus+ customer gets free analytics from us; and the setup time is less than 5 minutes. And why free? We actually believe in what Dan Vesset wrote back in 2004. That once our users get a taste of what they can do with it they will start demanding more and more information and analytics. GoodData is BI for the business user. Something the elitist industry will call BI for “the masses”, “angry mobs” or even “the rest of us”…

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

[tweetmeme]
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…

COSS BI: Open Source, Open Core or Openly Naked?

Peter Yared wrote recently a BusinessWeek guest blog post called “Failure of Commercial Open Source Software.” Not surprisingly his post caused a lot of angry replies from people who work for COSS companies. “The emperor is not naked” they argued.

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.

Please Don’t Let the Cloud Ruin SaaS

Back in the old good days of enterprise software, we did not need to worry about our customers. We delivered bits on DVDs – it was up to the customers to struggle with installation, integration, management, customization and other aspects of software operations. We collected all the cash upfront, took another 25% in annual maintenance. Throwing software over the wall … that’s how we did it. Sometimes almost literally…

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…

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.

%d bloggers like this: