This is part three in a four-part series on cloud computing for BI professionals.
There are no shortcuts in business intelligence (BI). And Software-as-a-Service (SaaS) BI vendors and some of their Cloud-based customers are finding this out the hard way.
I'm a firm believer that most computing will eventually move to the Cloud but I've been surprised that the adoption of SaaS BI services has been slower than expected. Most pureplay SaaS BI vendors today are small and struggling, and leading BI vendors no longer market their SaaS BI solutions to a significant degree (if at all.) So the question is "Why?"
Red Herrings. The two most commonly cited obstacles to SaaS BI adoption are security and data transfer rates. The security issue is mostly a red herring, in my opinion, except at organizations with strict compliance regulations. Data can be safer in the Cloud than in many corporate data centers. In terms of data transfer rates, a majority of organizations simply don't generate enough daily data to overwhelm a reasonable internet connection. And internet speeds are getting faster and cheaper all the time. Another red herring.
The Missing Link
I believe there is something deeper going on. There is a fundamental flaw in the SaaS BI equation. And I think I've found it.
But first, it's important to recognize that there is a lot to like about the Cloud. There are numerous benefits to running your applications as a service rather than on premise. There is no hardware and software to buy, install, tune, and upgrade. Consequently, there are no IT people to hire, pay, and manage. As a result, software services drive down costs and speed delivery. What's not to like?
Preparing Data. Unfortunately, this equation doesn't add up in the BI space. That's because the hard part about delivering BI applications is not what users see--the graphical report or dashboard--it's collecting, cleaning, normalizing, integrating, and aggregating data from various systems so it can be viewed in a clear, coherent way by business users.
Preparing data is hard, tedious work, but it's the foundation of BI. Do it right, and you can ice your cake with sweet-tasting frosting. Do it wrong or not at all, and there is no cake to ice! Too many SaaS BI vendors have been peddling the icing and downplaying the need to bake the cake, and now they're suffering. The same thing is happening with visual analysis tools, such as QlikView. They are great at handling simple data sets, but give them dirty data from complex operational systems and they fall apart. Someone, somewhere has to do (and pay for) the dirty work of preparing data or else everyone goes hungry.
Software Services or Professional Services?
Let me take another slight digression: What's the difference between a SaaS BI vendor and a BI consultancy? Not much.
Custom Data Marts. On one hand, you can argue that pureplay SaaS BI vendors, such as GoodData, Indicee, Birst, and PivotLink, offer software, which consultancies don't, and that the best offer true multi-tenant BI services that run in a virtualized environment. But on the other hand, SaaS BI vendors, just like BI consultancies, provide professional services to build custom data marts for their customers. Like consultants, they need to gather requirements, build a data model, extract and map source data, and build reports. This is a lot of work. If you peel back the covers on many SaaS BI deployments, they are really custom consulting jobs masquerading as a software service. But that's not the end of it.
Operational Management. Once the development work is done, BI consultancies go home or move on to the next job, but SaaS BI vendors have to stick around and run the BI environment, just like an inhouse IT staff would. They have to schedule and execute jobs to extract and clean data and then transform and load it into the data mart. They have to manage change control and error processes, troubleshoot problems, and staff a help desk to answer any questions customers might have. And before they can upgrade their software, they need to test every customization that they've built for every customer (which happens to undermine one of the major benefits of Cloud-based services, which is rapid delivery of software upgrades.)
Fixed Costs. Adding insult to injury, before SaaS BI vendors can begin collecting money, they have build out and staff a highly secure and scalable data center that offers full backup/recovery, failover, and disaster recovery services. Customers have been trained to demand the highest level of IT platform and administration services possible from a Cloud or hosting vendor even though many would not pay for the same level of services in their own data centers.
Subscription Pricing. Obviously, all of this involves a lot of work and is very expensive. So you would think that SaaS BI vendors command premium prices, right? Well, not really. In fact, mostly the opposite. Customers pay only for what they use on a monthly or annual basis and they can cancel their subscription at any time (although there may be exit fees.) Compared to on-premise software where vendors get all their money upfront, SaaS BI vendors have to wait several years before they accrue a comparable sum. But, in the meantime, they have to finance an expensive technical and organizational infrastructure that requires large upfront capital outlays and ongoing expenditures. In short, the business model for SaaS BI just doesn't work.
Wrong Audience? SaaS BI vendors have backed themselves into this corner by touting their services as low cost, easy to use, and fast to deploy. They've had a receptive audience among the unwashed masses of small- and medium-sized businesses that have no or minimal IT budget and people and little knowledge of BI. They've also done well selling to department heads at large companies which have clamped down hard on IT budgets. So, SaaS BI vendors have done a good job of selling an information-rich vision to data-hungry business people who have few capital dollars, tight budgets, and minimal understanding of BI.
Unfortunately, unlike on premises software vendors, SaaS BI vendors have to back up their claims. They can't sell a promise and then vacate the premises. They have to live daily with the expectations that they've created among their customers who demand low-cost, high-speed delivery of robust BI services. So SaaS BI vendors are stuck between a rock and a hard place: it costs more money to deliver SaaS BI solutions than customers seem to be willing to pay for them.
Market Strategies - The Way Forward
As I see it, SaaS BI vendors have five options to extricate themselves from this pickle:
1) Consult. If SaaS BI vendors want to deliver a complete BI solution that solves real business problems, they should shift from selling software services to professional services, and compete head-on with BI consultancies. SaaS BI vendors would have several advantages here:
-- SaaS BI vendors can not only develop custom solutions, they can run them. And they can do so in a cost-effective (but not inexpensive) way due to the economies of scale of a virtualized, hosted infrastructure.
-- They can also develop solutions faster than BI consultancies because they can leverage prebuilt software, models, and metrics built for other customers (although veteran consultancies will also have at least prebuilt models and metrics to contribute to a project.)
I haven't come across any SaaS BI vendor that is taking this approach overtly, although many are doing so in practice. Perhaps the closest is SAP BusinessObjects OnDemand.
2) Simplify and Shift. Another approach is for SaaS BI vendors to strip out all the custom work from the equation by making the application as simple as possible, shifting the burden of uploading, modeling, and mapping data to the customer. In other words, the SaaS BI vendor does the easy stuff and the customer does the hard stuff.
The challenge is here making the modeling and mapping tools both easy to use and suitably sophisticated. This is a devilish tradeoff and, in most cases, a SaaS BI vendor will side with simplicity rather than power and flexibility. This means that their customers will likely hit the wall with such tools once they want to do something complex. And if the application is really simple, then it is probably more cost effective to build it in Excel than in the Cloud.
Of all the SaaS BI vendors, Indicee seems to be following this path most closely.
Package and Configure. Another way to minimize the amount of custom development is to deliver packaged analytic applications that come with canned but configurable data mappings, data models, metrics, and reports. The mappings extract, transform, and load data from a specific source application (e.g., Salesforce.com) to a target data model with predefined dimensions, hierarchies, and metrics. Packaged analytic applications streamline development and accelerate deployment.
The challenge with packaged analytic applications is that they only work if the customer has the same source application that the package supports and they can live with the canned reports, dashboards, and metrics with some modification. Packages typically fall apart when customers want to customize rather than configure the application or they want to extract data from more than one source application to feed the canned data models and reports. And then the implementation becomes a custom consulting engagement. The key to making the packaged approach work is for vendors is to build out a sizable portfolio of applications that meet the majority of customers's needs out of the box. This obviously takes time and long-term investment.
PivotLink and GoodData seem to be following this approach, although GoodData claims it only packages back-end mappings to various Cloud-based applications, such as Salesforce.com, Microsoft Dynamics CRM Online, and SugarCRM. (And most of its packages only source data from a single Cloud-based application.) GoodData reportedly leaves the front-end fully customizable although they offer rich templates that embed metrics and reports for each source application. In essence, GoodData delivers a series of packaged operational reports for various Cloud-based applications.
Go On Premise. Another option is to abandon the Cloud, either in part or in full, and deliver software as an on premise solution. Here, the SaaS BI vendor gets its money upfront and leaves the customer with the responsibility of managing its data and delivering a BI solution. However, if the vendor also maintains a SaaS BI service, it can use the cost differential between its on premise and Cloud-based service to educate customers about the true expense of building and maintain a BI solution. This might push customers to purchase the SaaS BI service if they don't want the hassle of building a solution themselves .
The challenge here is that the vendor needs to offer both Cloud services and on-premises software, which is a mixed business model that might be hard to sustain. The vendor still has to maintain a large-scale data center operation while it also has to provide maintenance and support for on-premise software. The vendor will need patient investors to achieve economies of scale to support both models.
There is a chance that Birst might follow this course so it can better compete head on with what it considers its chief rival, QlikTech.
Offer A Real Software Service. Another approach is to offer a software service, not a solution service, which is what most SaaS BI vendors deliver today. A software service takes a component of a BI solution and makes it aailable as a service via the Cloud or a hosted environment. We have already seen database, ETL, and data quality vendors put their software in the Cloud and provide subscription based access to it. This includes companies such as Kognitio (database), SnapLogic (ETL), and Melissa Data (data quality.) These vendors don't purport to deliver a complete BI solution, only a piece of a larger puzzle.
ConclusionThe only way to make money in the Cloud is to have a lot of customers. The only way to get a lot of customers quickly is to give everyone the same configurable application and avoid custom development work. (A configurable application lets users customize the GUI, create unique workflows, and extend the data model.) In the Cloud, economies of scale are everything. But BI is largely a custom development effort. Unfortunately, most business customers don't realize this and most SaaS BI vendors have done little to disabuse them of the notion. In addition, most SaaS BI vendors have underestimated the challenge of delivering robust BI services that address real business needs and are now struggling to find a sustainable business model that will deliver real profitability.
Ultimately, the industry will figure out a way to make SaaS BI work for everyone involved. We may have to ratchet down our expectations on both sides of the equation. But there is too much value in running applications remotely in a virtualized environment for SaaS BI not to succeed in the long run.