Comparing Demand Generation Systems

Now that I have that long post about analytical databases out of the way, I can get back to thinking about demand generation systems. Research on the new Guide is proceeding nicely (thanks for asking), and should be wrapped up by the end of next week. This means I have to nail down how I’ll present the results. In my last post on the topic, I was thinking in terms of defining user types. But, as I think I wrote in a comment since then, I now believe the best approach is to define several applications and score the vendors in terms of their suitability for each. This is a pretty common method for serious technology evaluations.

Ah, but what applications? I’ve tentatively come up with the following list. Please let me know if you would suggest any changes.

- Outbound campaigns: generate mass emails to internal or imported lists and manage responses. Key functions include list segmentation, landing pages, response tracking, and response handling. May include channels such as direct mail, call center, and online chat.

- Automated followup: automatically respond to inquiries. Key functions include landing pages, data capture surveys, and trigger-based personalized email.

- Lead nurturing: execute repeated contacts with leads over time. Key functions include multi-step campaigns, offer selection, email and newsletters, landing pages, and response tracking.

- Score and distribute leads: assess leads and distribute them to sales when appropriate. Key functions including lead scoring, surveys, data enhancement, lead assignment, and CRM integration.

- Localized marketing: coordinate efforts by marketing groups for different product lines or geographic regions. Key functions include shared marketing contents, templates, version control, campaign calendars, asset usage reports, and fine-grained security.

- Performance tracking: assess the value of different marketing programs, including those managed outside the demand generation system. Key functions include response capture, data imports including revenue from CRM, response attribution, cross-channel customer data integration, and program cost capture.

- Event management: execute marketing events such as Webinars. Key functions include reservations and reminder notices.

There are also some common issues such as ease of use, scalability, cost, implementation, support, and vendor stability. Most of these would be evaluated apart from the specific applications. The one possible exception Is ease of use. The challenge here is the same one I keep running into: systems that are easy to use for simple applications may be hard to use for complex forms of the same application. Maybe I’ll just create separate scores for those two situations—that is, “simple application ease of use” and “advanced application ease of use”. I’ll give this more thought, and look for any comments from anyone else.

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Looking for Differences in MPP Analytical Databases

“You gotta get a gimmick if you wanna get ahead” sing the strippers in the classic musical Gypsy. The same rule seems to apply to analytical databases: each vendor has its own little twist that makes it unique, if not necessarily better than the competition. This applies even, or maybe especially, to the non-columnar systems that use a massively parallel (“shared-nothing”) architecture to handle very large volumes.

You’ll note I didn’t refer to these systems as “appliances”. Most indeed follow the appliance path pioneered by industry leader Netezza, I’ve been contacted by Aster Data, Microsoft), Dataupia, and Kognitio. A review of my notes shows that no two are quite alike.

Let’s start with Dataupia. CEO and founder Foster Hinshaw was also a founder at Netezza, which he left in 2005. Hinshaw still considers Netezza the “killer machine” for large analytical workloads, but positions Dataupia as a more flexible product that can handle conventional reporting in addition to ad hoc analytics. “A data warehouse for the rest of us” is how he puts it.

As it happens, all the vendors in this group stress their ability to handle “mixed workloads”. It’s not clear they mean the same thing, although the phrase may indicate that data can be stored in structures other than only star/snowflake schemas. In any event, the overlap is large enough that I don’t think we can classify Dataupia as unique on that particular dimension. What does set the system apart is its ability to manage “dynamic aggregation” of inputs into the data cubes required by many business intelligence and reporting applications. Cube building is notoriously time-consuming for conventional databases, and although any MPP database can presumably maintain cubes, it appears that Dataupia is especially good at it. This would indeed support Dataupia’s position as more reporting-oriented than its competitors.

The other apparently unique feature of Dataupia is its ability to connect with applications through common relational databases such as Oracle and DB2. None of the other vendors made a similar claim, but I say this is “apparently” unique because Hinshaw said the connection is made via the federation layer built into the common databases, and I don’t know whether other systems could also connect in the same way. In any case, Hinshaw said this approach makes Dataupia look to Oracle like nothing more than some additional table space. So integration with existing applications can’t get much simpler.

One final point about Dataupia is pricing. A 2 terabyte blade costs $19,500, which includes both hardware and software. (Dataupia is a true appliance.) This is a much lower cost than any competitor.

The other true appliance in this group is DATAllegro. When we spoke in April, it was building its nodes with a combination of EMC storage, Cisco networking, Dell servers, Ingres database and the Linux operating system. Presumably the Microsoft acquisition will change those last two. DATAllegro’s contribution was the software to distribute data across and within the hardware nodes and to manage queries against that data. In my world, this falls under the heading of intelligent partitioning, which is not itself unique: in fact, three of the four vendors listed here do it. Of course, the details vary and DATAllegro’s version no doubt has some features that no one else shares. DATAllegro was also unique in requiring a large (12 terabyte) initial configuration, for close to $500,000. This will also probably change under Microsoft management.

Aster Data lets users select and assemble their own hardware rather than providing an appliance. Otherwise, it generally resembles the Dataupia and DATAllegro appliances in that it uses intelligent partitioning to distribute its data. Aster assigns separate nodes to the tasks of data loading, query management, and data storage/query execution. The vendor says this makes it easy to support different types of workloads by adding the appropriate types of nodes. But DATAllegro also has separate loader nodes, and I’m not sure about the other systems. So I’m not going to call that one unique. Aster pricing starts at $100,000 for the first terabyte.

Kognitio resembles Aster in its ability to use any type of hardware: in fact, a single network can combine dissimilar nodes. A more intriguing difference is that Kogitio is the only one of these systems that distributes incoming data in a round-robin fashion, instead of attempting to put related data on the same node. It can do this without creating excessive inter-node traffic because it loads data into memory during query execution—another unique feature among this group. (The trick is that related data is sent to the same node when it's loaded into memory. See the comments on this post for details.)

Kognitio also wins the prize for the oldest (or, as they probably prefer, most mature) technology in this group, tracing its WX2 product to the WhiteCross analytical database of the early 1980’s. (WX2…WhiteCross…get it?) It also has by far the highest list price, of $180,000 per terabyte. But this is clearly negotiable, especially in the U.S. market, which Kognitio entered just this year. (Note: after this post was originally published, Kognitio called to remind me that a. they will build an appliance for you with commodity hardware if you wish and b. they also offer a hosted solution they call Data as a Service. They also note that the price per terabyte drops when you buy more than one.)

Whew. I should probably offer a prize for anybody who can correctly infer which vendors have which features from the above. But I’ll make it easy for you (with apologies that I still haven’t figured out how to do a proper table within Blogger).

______________Dataupia___DATAllegro___Aster Data___Kognitio
Mixed Workload_____Yes________Yes________Yes________Yes
Intelligent Partition___Yes________Yes________Yes________no
Appliance__________Yes________Yes________no________no
Dynamic Aggregation__Yes________no_________no________no
Federated Access_____Yes________no_________no________no
In-Memory Execution__no________no_________no________Yes
Entry Cost per TB___$10K(1)___~$40K(2)______$100K______$180K

(1) $19.5K for 2TB
(2) under $500K for 12TB; pre-acquisition pricing

As I noted earlier, some of these differences may not really matter in general or for your application in particular. In other cases, the real impact depends on the implementation details not captured in such a simplistic list. So don’t take this list for anything more than it is: an interesting overview of the different choices made by analytical database developers.

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More on QlikView - Curt Monash Blog

I somehow ended up posting some comments on QlikView technology on Curt Monash's DBMS2 blog. This is actually a more detailed description than I've ever posted here about how I think QlikView works. If you're interested in that sort of thing, do take a look.

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More on Vertica

I finally had a conversation with columnar database developer Vertica last week. They have done such an excellent job explaining their system in white papers and other published materials that most of my questions had already been answered. But it’s always good to hear things straight from the source.

The briefing pretty much confirmed what I already knew and have written here and elsewhere. Specifically, the two big differentiators of Vertica are its use of sorted data and of shared-nothing (MPP) hardware. Loading the data in a sorted order allows certain queries to run quickly because the system need not scan the entire column to find the desired data. Of course, if a query involves data from more than one column, all those columns be stored in the same sequence or must be joined on a common ID. Vertica supports both approaches. Each has its cost. Shared-nothing hardware allows scalability and allows redundant data storage which simplifies recovery.

Our conversation did highlight a few limits that I hadn’t seen clearly before. It turns out that the original releases of Vertica could only support star and snowflake schema databases. I knew Vertical was star schema friendly but didn’t realize the design was required. If I understood correctly, even the new release will not fully support queries across multiple fact tables sharing a dimension table, a fairly common data warehouse design. Vertica’s position is that everybody really should use star/snowflake designs. The other approaches were compromises imposed the limits of traditional row-oriented database engines, which Vertica makes unnecessary. I suspect there are other reasons people might want to use different designs, if only to save the trouble of transforming their source data.

On a somewhat related note, Vertica also clarified that their automated database designer—a major strength of the system—works by analyzing a set of training queries. This is fine so long as workloads are stable, but not so good if they change. A version that monitors actual queries and automatically adjusts the system to new requirements is planned for later this year. Remember that database design is very important to Vertica, since performance depends in part on having the right sorted columns in place. Note also that the automated design will become trickier as the system supports more than start/snowflake schemas. I wouldn’t be surprised to see some limits on the automated designs as a result.

The other bit of hard fact that emerged from the call is that the largest current production database for Vertica is 10 terabytes. The company says new, bigger installations are added all the time, so I’m sure that number will grow. They added that they’ve tested up to 50 TB and are confident the system will scale much higher. I don’t doubt it, since scalability is one of the key benefits of the shared-nothing approach. Vertica also argues that the amount of data is not a terribly relevant measure of scalability—you have to consider response time and workload as well. True enough. I’d certainly consider Vertica for databases much larger than 10 TB. But I’d also do some serious testing at scale before making a purchase.

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Still More on Assessing Demand Generation Systems

I had a very productive conversation on Friday with Fred Yee, president of ActiveConversion, a demand generation system aimed primarily at small business. As you might have guessed from my recent posts, I was especially interested in his perceptions of the purchase process. In fact, this was so interesting that I didn’t look very closely at the ActiveConversion system. This is no reflection on the product, which seems to be well designed, is very reasonably priced, and has a particularly interesting integration with the Jigsaw online business directory to enhance lead information. I don't know when or whether I'll have time to do a proper analysis of ActiveConversion, but if you're in the market, be sure to take a look.

Anyway, back to our talk. If I had to sum up Fred’s observations in a sentence, it would be that knowledgeable buyers look for a system that delivers the desired value with the least amount of user effort. Those buyers still compare features when they look at products, but they choose the features to compare based on the value they are seeking to achieve. This is significantly different from a simple feature comparison, in which the product with the most features wins, regardless of whether those features are important. It differs still further from a deep technical evaluation, which companies sometimes perform when they don’t have a clear idea of how they will actually use the system.

This view is largely consistent with my own thoughts, which of course is why I liked hearing it. I’ll admit that I tend to start with requirements, which are the second step in the chain that runs from value to requirements to features. But it’s always been implied that requirements are driven by value, so it’s no big change for me to explicitly start with value instead.

Similarly, user effort has also been part of my own analysis, but perhaps not as prominent as Fred would make it. He tells me they have purposely left many features out of ActiveConversion to keep it easy. Few vendors would say that—the more common line is that advanced features are present but hidden from people who don’t need them.

Along those lines, I think it’s worth noting that Fred spoke in terms of minimizing the work performed by users, not of making the system simple or easy to use. Although he didn’t make a distinction, I see a meaningful difference: minimizing work implies a providing the minimum functionality needed to deliver value, while simplicity or ease of use implies minimizing user effort across all levels of functionality.

Of course, every vendor tries to make their system as easy as possible, but complicated functions inevitably take more effort. The real issue, I think, is that there are trade-offs: making complicated things easy may make simple things hard. So it's important to assess ease of use in the context of a specific set of functions. That said, some systems are certainly better designed than others, so it's possible to be easier to use for all functions across the board.

Looking back, the original question that kicked off this series of posts was how to classify vendors based on their suitability for different buyers. I’m beginning to think that was the wrong question—you need to measure each vendor against each buyer type, not assign each vendor to a single buyer type. In this case, the two relevant dimensions would be buyer types (=requirements, or possibly values received) on one axis, and suitability on the other. Suitability would include both features and ease of use. The utility of this approach depends on the quality of the suitability scores and, more subtly, on the ability to define useful buyer types. This involves a fair amount of work beyond gathering information about the vendors themselves, but I suppose that’s what it takes to deliver something useful.

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