Big Data, Small Screens

Big Data and Mobile Apps Are Converging in the Enterprise

Yesterday, I nearly drowned in a sea of extraneous data. In just one hour during an important conference call, my laptop overflowed with 300 e-mails from an email thread I frankly didn’t care about. Imagine how much time I could have saved if my system knew I was unavailable, and sent me only the two notifications I truly needed: That the customer I was on the call with owed us an invoice, and that my next appointment was delayed by half an hour.

Clearly, enterprise users need an easy and intuitive way to parse all their data into a useful context. Just as clearly, they also need to have the right information delivered to them at the right time, on the right device. These days, that device is likely to be mobile — be it laptop, smartphone or tablet — as sales of desktop computers erode and enterprises increasingly accommodate tablets in the workplace..

I say it’s time for big data to play a starring role on the small screen — the small screen of mobile devices, that is. Businesses primarily view big data as collecting and storing zetabytes of data from diverse sources for eventual business analysis. But in today’s connected and mobile world, decision-makers can’t wait for “eventual.” They need big data apps that intelligently gather and analyze data as it comes in from other apps on their device (your calendar and sales management apps, for instance). Think of the ramifications: big data apps could suggest different ways to improve sales or — dare I say it — know not to send me thousands of emails on topics I don’t care about when my calendar shows I’m in a meeting.

Such contextual real-time analytics can be extended across any number of roles and tasks: A sales rep driving to one meeting could be alerted that a good prospect two blocks away wants to meet. A Chief Marketing Officer could see which social media campaigns deliver the best return on investment. Or an inventory manager could know which store just sold out of fashion’s “It” purse and needs immediate replenishment. These are just examples. The convergence of big data and enterprise mobile apps means that anyone, anywhere, can glean the insight she needs to make better, faster decisions.

Think Like a Consumer

The key is in the design. Developers building mobile apps for the enterprise need to combine the ease of use of consumer apps with enterprise-class security and data-collection technologies. And they need to optimize their apps for each device’s small screen.

Consider Intuit’s Mint, which organizes and analyzes consumers’ finances. The company’s desktop, tablet and smartphone apps are all designed to maximize both screen real estate and context. On the desktop app, you can manage and sort your finances in full detail. Mint’s tablet app is smaller and more limited, enabling you to see a list of accounts, but not interact with them in the same depth. Its smartphone app focuses on notifications. Imagine how much could get done if businesses designed their big data apps this way.

In healthcare, for example, doctors making bedside rounds could tap into mountains of clinical research to discover the optimum treatments for their patients — and they could see the results as instantly intuitive charts or as scrollable lists (similar to the iPhone’s email app) depending on whether they’re carrying tablets or smartphones.

In IT, big data apps could predict cyber-attacks and send alerts, show scenarios and recommend actions depending on which mobile device technologists are carrying.

These are just examples, but I firmly believe that big data will soon permeate every aspect of business. I also believe that the convergence of contextual, real-time, data with mobile devices makes everyone and everything smarter. When you look at it that way, the small screen becomes huge.

A Look Back at 2012: A Reminder to Cherish all Things Right and Good

As I look back at 2012, I’m reminded of Charles Dickens’ opening lines in A Tale of Two Cities: “It was the best of times, it was the worst of times…” In the year now passing, we have seen Sandy’s fury and the horrors of Newtown, and we also have witnessed amazing acts of discovery, wonder and acts of kindness. I prefer to remind myself of the good things that happened — in the world around us and in my company, GoodData. Which is why my look back is one of thanks.

And so, I give thanks for man’s curiosity and capacity to achieve great things. To me, the final flight of Space Shuttle Endeavour atop a jumbo jet epitomized our desire to constantly reach for the stars. And we saw that same yearning for the great and new after Curiosity traveled hundreds of millions of miles to land safely on Mars.

I also give thanks for technology’s impact on our everyday lives. This year, that impact is embodied by the combination of cloud computing, mobility and big data. This nexus of technologies changes everything.

With cloud computing, for example, we have the technology to pull in data from hundreds of sources — no matter if it’s social media, email, cloud-based apps, suppliers’ enterprise software or companies’ internal applications.

Mobile computing has reshaped how we expect to see that information, with users demanding fast access to visually intuitive apps and data.

And big data — specifically the emerging ability to make sense of vast sums of data — is changing how researchers, medical workers and business people find what’s important so they can make better decisions. These technologies have transformed how and where we work.

Taken together, cloud, mobile and big data have made 2012 a very good year for GoodData, allowing us to experience incredible momentum: We grew our annual revenue fivefold, tripled our customer base to 8,000 customers, doubled our staff to reach 220 employees, and tripled the number of our Powered By partners. And, we raised $25 million in a Series C round of funding, for almost $55 million to date.

This past year we’ve also honed our go-to market strategy, making it easier for our customers to harness and benefit from big data. The pillar of our new BizData Monetization strategy is Bashes — cloud-based, pre-built apps on our platform that enable anyone, in any size business, to turn mountains of disparate data into insight they can act on.

This year I’m also grateful for our amazing, new and existing customers, including mobile marketer Urban Airship, CourseSmart, who hosts the world’s largest library of eTextbooks and digital course materials, Time Warner and Pandora, which are all using GoodData to mine their data and uncover new sources of revenue and profit.

But GoodData is more than apps that present information in meaningful ways. It’s also a cloud-based platform that partners use to build apps for their specific needs. And I have to say: We have more than 100 exceptional partners including procurement software company Coupa, wine and spirits distributor GreatVines, customer-community provider Get Satisfaction, ShopLocal, a leading provider of online marketing retail solutions, Zendesk, a leading provider of Web-based customer-support software, and Zuora, the the global leader in Relationship Business Management solution

Finally, none of this would be able without our amazing employees, or what I affectionately call the GoodTeam. A big thank you to all of you, for making GoodData a great company, on our way to building a long-lasting legacy.

So thank you 2012 for an incredible year. I am profoundly grateful.

Here’s to an even more amazing New Year for us all.

Big Data Conundrum: Show me the money!

Inventory levels. Sales results. Negative comments on Facebook. Positive comments on Twitter. Shopping on Amazon. Listening to Pandora. Online search habits. No matter what you call it or what the information describes, it’s all data being collected about you.

Thanks to new technologies like Hadoop, once-unquantifiable data (like Facebook conversations and Tweets) can now be quantified. Now, because nearly everything is measurable, everything is measured. The result: companies are spending big dollars to collect, store and measure astronomical amounts of data.

Show me the data!

There’s a name for this movement: Big Data. Not only is it a name, it has been the “it, it” of 2012, possibly trumping “the cloud.”

IDC defines Big Data as projects collecting 100 terabytes of data (hence the name), comprising two or more data formats. Earlier this year, the research firm predicted the market for Big Data technology and services will reach $16.9 billion by 2015, from $3.2 billion in 2010. That’s an astounding 40 percent annual growth rate.

The interesting thing is that IDC expects most of this spending to focus on infrastructure — the plumbing that enables companies to download, collect and store vast amounts of data.

To me, this is a missed opportunity. Why? We need to focus on unlocking the real business benefits from all this data.

Companies have not yet grasped the business potential of all the data pouring in from hundreds of sources—think apps in the cloud, on-premise partner software and from their own enterprise. In effect, businesses haven’t figured out how to make money from this fire hose of disparate data sources.

My point-of-view is that Big Data’s only real value lies in businesses’ ability to transform data into insight they can act on.

This means enabling sales managers to quickly analyze sales reps’ results, view new contracts lost or signed, and react to how actual performance compares against the plan they set months earlier. Help-desk staff could see how individual customers affect sales and profit, showing them when to go above-and-beyond to retain certain customers while allowing low-flyers to churn. Or helping insurance agents to predict kinds and amounts of damage as hurricanes hurtle toward their region.

Steps to Monetize Big Data

To glean value from Big Data efforts, companies need to embrace the real-time value provided by the cloud. Viewing one’s data in real-time through the lens of cloud computing enables anyone, in any company, to make smart business decisions from the mammoth amounts of data, coming from all over the place.

Therefore, companies looking to monetize Big Data need to take these steps:

Use the cloud: These days businesses can tap into an enormous range of cloud services. They can subscribe to high-performance infrastructure services like Amazon Web Services, rent platforms as a service (comprising hardware, operating systems, storage and network capacity) from salesforce.com, store information in services like Box or automate billings with companies like Zuora. These are just examples.

Companies can also pick and choose from a long list of cloud-based apps to handle business tasks, from customer relationship management and marketing to human resources and financial management. In fact, I would argue that cloud services become the business application suite, eventually displacing behemoth on-premise packages from SAP or Oracle. Emphasis on “eventually,” since few enterprises are ready to jettison their million-dollar investments in Oracle and SAP.

For this reason, I advise companies to:

Start with what’s important: Forget about separate data sources. Data today spews in from hundreds sources, be it sales and customer data from salesforce.com, inventory levels from SAP, logistics information from your suppliers and employee data from Oracle. Companies run into trouble when they start off boiling the ocean, which is why I suggest companies begin with a few sources and then build up from there.

Fortunately, there is a way, thanks to a new generation of application programming interfaces (APIs) that allows more kinds of software, from different software makers, to communicate with each other, regardless of location. As a result, any company, regardless of size, can access the data it needs to make better decisions.

Which is why my next point is:

Make Big Data insight democratic: Five years ago, only executives at very large companies had access to business intelligence tools that culled patterns from data.

The cloud makes everything democratic — not just access to the data itself, but the insight as well, including best practices that don’t require the expertise of a SQL or a MapReduce programmer. The cloud enables anyone, anywhere, to recognize patterns from data and make smart decisions, faster. And that means any business professional, at any company should be able to monetize their Big Data.

When Big Data finally becomes useful to the rest of us, and not just IT wizards, it will take on an even larger role today and into tomorrow.

The Resurgence of the Enterprise: What’s Old is New Again

A few weeks ago Jim Goetz — a leading VC and partner of Sequoia Capital — said it was “shocking” that more entrepreneurs weren’t targeting the enterprise. Goetz’s point: Since 2002, twice as many enterprise startups had $1 billion IPOs as consumer companies.

While he’s right, I can understand the disconnect. I can count on one hand (and still have fingers left over) the number of venture capitalists investing in the enterprise space in the past 10 years. That’s because the consumer space was sexy and — thanks to innovations like Amazon Web Services — it was cheap. Using AWS, a handful of engineers working with $2 million or less could write apps like Instagram or Meebo, then sell their companies months later for a healthy profit for their investors.

Contrast that with the enterprise market. Startups in this space need an army of highly expert developers to create an enterprise-hardened platform and apps that meet stringent corporate demands. (At GoodData, for example, we have more than 100 engineers focused on delivering the capabilities and experience our enterprise customers expect.) And forget about courting customers with something that’s merely “cool,” like nifty effects from your mobile phone’s camera. In the enterprise space, finding the right product/market fit means knowing what enterprises already have and what they need — and understanding how they work. Until recently, that’s the sort of expertise that VCs have short-changed.

Marc Andreessen defined product/market fit as “being in a good market with a product that can satisfy that market.” Gartner Research recently projected companies worldwide will spend $3.6 trillion on technology, including $109 billion on public cloud services. By 2016, enterprise global cloud spending will reach $209 billion1.

Now comes the hard part in Marc’s formula, which I define as having a product that’s easy to sell, for a problem that needs to be solved, that can scale quickly and for which there’s strong enterprise demand. But in the enterprise space, “easy to sell” is relative. That’s because many of the dynamics that defined selling to the enterprise in 1999 still apply: New technologies and products have to work with what’s already installed. Large purchases have to be justified. And customers are leery of buying from companies that aren’t ready for primetime.

That’s why today’s enterprise-focused companies — start-ups included — still need dedicated sales reps who can demo a product, explain its value proposition, offer competitive positioning and traverse customers’ different purchasing processes. As proof, consider salesforce.com, which reported 4,700 new employees between January 2010 and August 2012, primarily in sales.

For these reasons, it takes about five years and more than $50 million in funding for a startup to succeed in the enterprise. It also requires a level of maturity and experience that venture capitalists haven’t actively sought out since the dot-com bubble burst.

Building a successful startup is never easy. That’s especially true in the enterprise market, given the upfront costs and tough going just to meet enterprise requirements. To paraphrase Seth Godin: “It takes three years to be an overnight success” in the enterprise space. That’s three years, lots of money, and the expertise to know what customers want. That might explain why there are so few startups gearing up for the enterprise.

1: “Gartner Says Worldwide IT Spending On Pace to Surpass $3.6 Trillion in 2012,” press release. July 9, 2012.
2: Transcripts of salesforce.com’s quarterly conference calls with analysts.

Filling in Big Data’s Missing Link: Making Big Data Pay for Itself

Say hello to Bashes — the first cloud-apps that enable companies to turn Big Data into new sources of revenue

I don’t want to sound overly dramatic, but today’s the day GoodData makes it possible for companies to finally monetize Big Data. That’s because today we unveil our first Bashes — cloud-based business mashups — on our platform that enable anyone, in any size business, to turn mountains of disparate data into insight that finds new sources of revenue, boosts profit and builds a competitive edge.

We call these new solutions Bashes because they combine the best elements of consumer apps with modern, enterprise-class technologies. That means consumer apps’ clean and intuitive user interface, ease of use and device independence, with cloud-based business technologies that collect and manage structured and unstructured data from hundreds of sources. With Bashes, businesses can discern meaning from all the data flooding in from emails, social media, enterprise software and cloud apps.

I firmly believe this is revolutionary. Bashes not only change the economics of the Big Data market, they also deliver on the promise that companies have struggled to fulfill for years.

Here’s what I mean: Today, everything is measurable, and everything is measured. As a result, Big Data is becoming big business. Market research firm IDC predicts the market for Big Data technology and services will reach $16.9 billion by 2015, from $3.2 billion in 2010, but much of this spending will focus on infrastructure — the plumbing that enables companies to download, collect and store vast amounts of structured and unstructured data. It’s as if companies are collecting data for a rainy day, with the hope that someone, somewhere, will help them figure out how to make money from all that data now that they have it.

In the immortal words of Yogi Berra, it’s like déjà vu all over again. After all, it’s why SAP paid $6.78 billion for Business Objects, Oracle acquired Hyperion Solutions for $3.3 billion and IBM spent $4.9 billion to buy Cognos — all makers of business intelligence infrastructure software designed to help IT and business analysts cull insight from data. It never worked as promised, primarily because of the expense, IT heavy lifting and technical expertise these infrastructure tools require.

Now, thanks to the cloud, everything has changed. Finally, we have the technology to pull in data from hundreds of sources — no matter if it’s social media, email, cloud-based apps, suppliers’ enterprise software or internal applications. At GoodData, we’re leveraging the cloud to offer pre-built apps for different functions — such as sales, marketing and customer engagement — and have created a platform that enables any company to add new apps for their specific needs.

Notice I said these are apps. That’s a key distinction because it allows each Bash to automatically present thought leadership by business function, with best practices that make it easy for business people to determine what really matters. Now contrast that to BI infrastructure tools from Oracle and others, where IT departments have spent years and millions of dollars trying to integrate products that were never meant to work together. Does that sound like a good use of time to you?

And because it’s the cloud, our apps and our platform are democratic. You don’t have to work in a large company, blow your budget or need a Ph.D. in computer science to recognize what all that data is telling you and make smart decisions, faster.

So when I say this is revolutionary, I mean it — literally. With our apps and our platform, we’re democratizing both data and business insight — so that any business person, in any company, on any device can turn Big Data into a competitive advantage. That’s power to the people, and I say let the revolution begin.

Is an Enterprise Twitter on the Horizon?

Twitter’s latest iteration of its site is great, but  it’s abundantly clear that the newly dumbed down design is aimed exclusively at the consumer.  But what about the enterprise?  Does the new Twitter design mean that ultimately, there will be a second conception of the beloved social networking tool, an enterprise edition?

Now, keep in mind, in this context “enterprise edition” does not mean a Twitter app built in ABAP that would require the user to navigate eleven screens in order to Tweet.  That would be the SAP version.  (They could call it “Sapper”, as in sapping the users’ energy and patience.)

No, an enterprise edition as it relates to Twitter would include the following, while maintaining its user-friendliness:

  1. Corporate policies and policy management.  Unlike regular Twitter, it wouldn’t do to have employees lobbing grenades at one another.
  2. Comprehensive data persistence and archiving. This would mainly be for auditing and compliance purposes.  And the company would want to make sure that everyone in the organization is keenly aware that if they Tweet “You looked really hot in the project planning session today” to a colleague, it’s stored and logged in the company’s database.
  3. Security (access controls, etc.) Users would only have access to the information that they would be permitted to see and/or act upon.
  4. Integration with anti-virus and content filtering tools. This one’s obvious.
  5. Application development (plug-ins, integration with productivity tools)
  6. Enterprise-class administration tools to support some of the above requirements.

The point would be to provide a simple and quick way (and just as importantly, a uniform and standardized format) for workers to communicate and share resources with each other.

Enterprise versions of social networking tools already exist, the most prominent being the very-successful startup, Yammer. (We use Yammer extensively at GoodData). There are numerous other players in the market place as well, including biggest Twitter competitor: Google+.

So, is Twitter poised to enter the fray?  It appears not.  As a matter of fact, the new design seems to fulfill one of Twitter’s main goals; to increase the simplicity of use.  And, at the recent Web 2.0 Summit in San Francisco, Twitter CEO Dick Costolo talked of many things, but an upcoming enterprise version of Twitter was notably not one of them.  With usage approaching 300 million tweets per day, for now, it appears, Twitter has other fish to fry.

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”!

Follow

Get every new post delivered to your Inbox.

Join 5,657 other followers

%d bloggers like this: