0 BI Vendor QlikTech Reveals QlikView Pricing: I Modestly Help to Clarify

Business Intelligence software vendor QlikTech* officially published its price list last month, after years of keeping it a not-very-closely-held secret. I was personally pleased, since people occasionally ask me what QlikView costs.  But then I looked more closely at the list and realized I wasn’t quite certain what it meant. Happily, it didn’t take long to set up a briefing and clarify matters. Just in case anyone else is also confused, here’s what they told me:

- The lowest entry price for a fully functional version is $1,350. Although this is called a “Named User License”, it does NOT require connection to a server—the specific point I wasn’t sure of. What distinguishes this from the free Personal Edition is that the Named User License can read QlikView files created on other machines, while the Personal Edition cannot. Thus, a company could buy two Named User Licenses for $2,700 and those systems could share files back and forth.  Let me state clearly that as a confirmed QlikView fan, I think this is a terrifically low entry price.

- Companies with many infrequent users can purchase a “Concurrent License” that allows one user at a time for $15,000. This figure is so high that I thought it might be a typographic error, but QlikTech assures me it’s correct. In fact, they say it’s a bargain because they’ve found many clients can share more than 11 users on one license. These would be salespeople or other non-analysts who want to occasionally view a report. It seems to me that Murphy’s Law would ensure they all access the system during the same five minutes – presumably the evening before their monthly reports are all due – but I’ll take QlikTech’s word that this isn’t the case. After all, they're the analytical experts, eh?

- Companies who don’t want to spend $1,350 for casual users have some other options. These include a $350 “Document License” for one user for a single document, a $70,000 “Information Access Server” allowing unlimited users to access a single document (usually over a public Web site), and a $3,000 “Extranet Server Concurrent License” that allows one external user at a time to read documents on an $18,000 “Extranet Server”.

There are various other licenses for larger systems and special purposes. The descriptions are more or less self-explanatory, but of course you’d want to talk to QlikTech itself for detailed explanations.

One thing you definitely won’t find is a free or low-cost  “reader” licence that lets users view but not change a QlikView document. This is a disappointment to me personally, since we at Left Brain DGA use similar readers to send reports to our clients today. I can’t see those clients paying for Named User Licenses or Concurrent Licenses. But QlikTech is philosophically opposed to the idea of a limited-function reader, which it argues “goes against the current trend toward the democratization of software — in which line of business users can become as adept with an analytics tool as any business analyst or developer.” I can’t say I agree: QlikView takes considerable effort to learn, and many business users don’t have the time, need, or inclination to bother. They would be perfectly happy to consume existing reports without drilling any deeper, but are unlikely to pay $1,350 for the privilege.

I can’t judge how much business, other than mine, the lack of reader is costing QlikTech. Surely some people end up buying the full Named User License and using it as a reader, which makes up for some of the people who don’t buy at all. QlikView also has a strong argument that their total cost of ownership is lower than competitors, even at current pricing levels. The company grew 40% year-on-year as of its last financial report, so they’re clearly doing just fine with their existing approach.

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* QlikTech is the company, QlikView is the product

0 Case Study: Using a Scenario to Select Business Intelligence Software

Summary: Testing products against a scenario is critical to making a sound selection. But the scenario has to reflect your own requirements. While this post shows results from one test, rankings could be very different for someone else.

I’m forever telling people that the only reliable way to select software is to devise scenarios and test the candidate products against them. I recently went through that process for a client and thought I’d share the results.

1. Define Requirements. In this particular case, the requirements were quite clear: the client had a number of workers who needed a data visualization tool to improve their presentations. These were smart but not particularly technical people and they only did a couple of presentations each month. This mean the tool had to be extremely easy to use, because the workers wouldn’t find time for extensive training and, being just occasional users, would quickly forget most of they had learned. They also wanted to do some light ad hoc analysis within the tool, but just on small, summary data sets since the serious analytics are done by other users earlier in the process. And, oh, by the way, if the same tool could provide live, updatable dashboards for clients to access directly, that would be nice too. (In a classic case of scope creep, the client later added mapping capabilities to the list, merging this with a project that had been running separately.)

During our initial discussions, I also mentioned that Crystal Xcelsius (now SAP Crystal Dashboard Design) has the very neat ability to embed live charts within Powerpoint documents. This became a requirement too. (Unfortunately, I couldn’t find a way to embed one of those images directly within this post, but you can click here to see a sample embedded in a pdf. Click on the radio buttons to see the different variables. How fun is that?)

2. Identify Options. Based on my own knowledge and a little background research, I built a list of candidate systems. Again, the main criteria were visualization, ease of use and – it nearly goes without saying – low cost. A few were eliminated immediately due to complexity or other reasons. This left:

• Xcelsius


• Advizor Solutions


• TIBCO Spotfire


• QlikView


• Lyzasoft


• Tableau Software

3. Define the Scenario. I defined a typical analysis for the client: a bar chart comparing index values for four variables across seven customer segments. The simplest bar chart showed all segment values for one variable. Another shows all variables for all segments, sorted first by variable and then by segment, with the segments ranked according to response rate (one of the five variables). This would show how the different variables related to response rate. It looked like this:


The tasks to execute the scenario were:

• connect to a simple Excel spreadsheet (seven segments x four variables.)*


• create a bar chart showing data for all segments for a single variable.


• create a bar chart showing data for all segments for all variables, clustered by variable and sorted by the value of one variable (response index).


• provide users with an option to select or highlight individual variables and segments.

Because my requirements assumed users would have little or no training, I specified that the scenarios be performed without taking time to learn each system. This made the testing easy, but I should stress it's an unusual situation: in most cases, systems are run by people who use them often enough to become experts. For situations like that, you should have an experienced user (often a vendor sales rep or engineer) execute the scenario for you. In fact, one of the most common errors we see is people judging a system by how easily they can run it without training – something that favors systems which are easy to use for simple tasks, but lack the functional depth clients will eventually need to do their real jobs.

4. Results. I was able to download free or trial versions of each system. I installed these and then timed how long it took to complete the scenario, or at least to get as far as I could before reaching the frustration level where a typical end-user would stop.

I did my best to approach each system as if I’d never seen it before, although in fact, I’ve done at least some testing on every product except SpotFire, and have worked extensively with Xcelsius and QlikView. As a bit of a double-check, I dragooned one of my kids into testing one system when he was innocently visiting home over Thanksgiving: his time was actually quicker than mine. I took that as a proof I'd tested fairly.

Notes from the tests are below.

• Xcelsius (SAP Crystal Dashboard Design): 3 hours to set up bar chart with one variable and allowing selection of individual variables. Did not attempt to create chart showing multiple variables. (Note: most of the time was spent figuring out how Xcelsius did the variable selection, which is highly unintuitive. I finally had to cheat and use the help functions, and even then it took at least another half hour. Remember that Xcelsius is a system I’d used extensively in the past, so I already had some idea of what I was looking for. On the other hand, I reproduced that chart in just a few minutes when I was creating the pdf for this post. Xcelsius would work very well for a frequent user, but it’s not for people who use it only occasionally.)
  

• Advizor: 3/4 hour to set up bar chart. Able to show multiple variables on same chart but not to group or sort by variable. Not obvious how to make changes (must click on a pull down menu to expose row of icons).

• Spotfire: 1/2 hour to set up bar chart. Needed to read Help to put multiple lines or bars on same chart. Could not find way to sort or group by variable.
  

• QlikView: 1/4 hour to set up bar chart (using default wizard). Able to add multiple variables and sort segments by response index, but could not cluster by variable or expose menu to add/remove variables. Not obvious how to make changes (must right-click to open properties box – I wouldn’t have known this without my prior QlikView experience).

• Lyzasoft: 1/4 hour to set up bar chart with multiple variables. Able to select individual variables, cluster by variable and sort by response index, but couldn’t easily assign different colors to different variables (required for legibility). Annoying lag each time chart is redrawn.

• Tableau: 1/4 hour to set up bar chart with multiple variables. Able to select individual variables, cluster by variable, and sort by variable. Only system to complete the full scenario.

Let me stress again that these results apply only to this particular scenario. Specifically, the ability to cluster the bars by segments within variables turned out to be critical in this test but doesn't come up very often in the real world. Other requirements, such as advanced collaboration, sophisticated dashboards or specialized types of graphics, would have yielded very different ranks.

5. Final Assessment. Although the scenario nicely addressed ease of use, there were other considerations that played into the final decision. These required a bit more research and some trade-offs, particularly regarding the Xcelsius-style ability to embed interactive charts within a Powerpoint slide. No one else on the list could do this without either loading additional software (often a problem when end-user PCs are locked down by corporate IT) or accessing an external server (a problem for mobile users and with license costs).

The following table shows my results:


6. Next Steps. The result of this project wasn’t a final selection, but a recommendation of a couple of products to explore in depth. There were still plenty of details to research and confirm. However, starting with a scenario greatly sped up the work, narrowed the field, and ensured that the final choice would meet operational requirements. That was well worth the effort. I strongly suggest you do the same.


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* The actual data looked like this. Here's a link if you want to download it:

0 Tableau Software Adds In-Memory Database Engine

Summary: Tableau has added a large-scale in-memory database engine to its data analysis and visualization software. This makes it a lot more powerful.

Hard to believe, but it's more than three years since my review of Tableau Software’s data analysis system. Tableau has managed quite well without my attention: sales have doubled every year and should exceed $40 million in 2010; they have 5,500 clients, 60,000 users and 185 employees; and they plan to add 100 more employees next year. Ah, I knew them when.

What really matters from a user perspective is that the product itself has matured. Back in 2007, my main complaint was that Tableau lacked a data engine. The system either issued SQL queries against an external database or imported a small data set into memory. This meant response time depended on the speed of the external system and that users were constrained by the external files' data structure.

Tableau’s most recent release (6.0, launched on November 10) finally changes this by adding a built-in data engine. Note that I said “changes” rather than “fixes”, since Tableau has obviously been successful without this feature. Instead, the vendor has built connectors for high-speed analytical databases and appliances including Hyperion Essbase, Greenplum, Netezza, PostgreSQL, Microsoft PowerPivot, ParAccel, Sybase IQ, Teradata, and Vertica. These provide good performance on any size database, but they still leave the Tableau user tethered to an external system. An internal database allows much more independence and offers high performance when no external analytical engine is present. This is a big advantage since such engines are still relatively rare and, even if a company has one, it might not contain all the right data or be accessible to Tableau users.

Of course, this assumes that Tableau's internal database is itself a high-speed analytical engine. That’s apparently the case: the engine is home-grown but it passes the buzzword test (in-memory, columnar, compressed) and – at least in an online demo – offered near-immediate response to queries against a 7 million row file. It also supports multi-table data structures and in-memory “blending” of disparate data sources, further freeing users from the constraints of their corporate environment. The system is also designed to work with data sets that are too large to fit into memory: it will use as much memory as possible and then access the remaining data from disk storage.

Tableau has added some nice end-user enhancements too. These include:

- new types of combination charts;
- ability to display the same data at different aggregation levels on the same chart (e.g., average as a line and individual observations as points);
- more powerful calculations including multi-pass formulas that can calculate against a calculated value
- user-entered parameters to allow what-if calculations

The Tableau interface hasn’t changed much since 2007. But that's okay since I liked it then and still like it now. In fact, it won a little test we conducted recently to see how far totally untrained users could get with a moderately complex task. (I'll give more details in a future post.)

Tableau can run either as traditional software installed on the user's PC or on a server accessed over the Internet. Pricing for a single user desktop system is still $999 for a version that can connect to Excel, Access or text files, and has risen slightly to $1,999 for one that can connect to other databases. These are perpetual license fees; annual maintenance is 20%.

There’s also a free reader that lets unlimited users download and read workbooks created in the desktop system. The server version allows multiple users to access workbooks on a central server. Pricing for this starts at $10,000 for ten users and you still need at least one desktop license to create the workbooks. Large server installations can avoid per-user fees by purchasing CPU-based licenses, which are priced north of $100,000.

Although the server configuration makes Tableau a candidate for some enterprise reporting tasks, it can't easily limit different users to different data, which is a typical reporting requirement. So Tableau is still primarily a self-service tool for business and data analysts. The new database, calculation and data blending features add considerably to their power.

0 QlikView's New Release Focuses on Enterprise Deployment

I haven’t written much about QlikView recently, partly because my own work hasn’t required using it and partly because it’s now well enough known that other people cover it in depth. But it remains my personal go-to tool for data analysis and I do keep an eye on it. The company released QlikView 10 in October and Senior Director of Product Marketing Erica Driver briefed me on it in a couple of weeks ago. Here’s what’s up.

- Business is good. If you follow the industry at all, you already know that QlikView had a successful initial public stock offering in July. Driver said the purpose was less to raise money than to gain the credibility that comes from being a public company. (The share price has nearly doubled since launch, incidentally.) The company has continued its rapid growth, exceeding 15,000 clients and showing 40% higher revenue vs. the prior year in its most recent quarter. Total revenues will easily exceed $200 million for 2010. Most clients are still mid-sized businesses, which is QlikView’s traditional stronghold. But more big enterprises are signing on as well.

- Features are stable. Driver walked me through the major changes in QlikView 10. From an end-user perspective, none were especially exciting -- which simply confirms that QlikView already had pretty much all the features it needed.

Even the most intriguing user-facing improvements are pretty subtle. For example, there’s now an “associative search” feature that means I can enter client names in a sales rep selection box and the system will find the reps who serve those clients. Very clever and quite useful if you think about it, but I’m guessing you didn’t fall off you chair when you heard the news.

The other big enhancement was a “mekko” chart, which is bar chart where the width of the bar reflects a data dimension. So, you could have a bar chart where the height represents revenue and the width represents profitability. Again, kinda neat but not earth-shattering.

Let me stress again that I’m not complaining: QlikView didn’t need a lot of new end-user features because the existing set was already terrific.

- Development is focused on integration and enterprise support. With features under control, developers have been spending their time on improving performance, integration and scalability. This involves geeky things aimed at like a documented data format for faster loads, simpler embedding of QlikView as an app within external Web sites, faster repainting of pages in the AJAX client, more multithreading, centralized user management and section access controls, better audit logging, and prebuilt connectors for products including SAP and Salesforce.com.

There’s also a new API that lets external objects to display data from QlikView charts. That means a developer can, say, put QlikView data in a Gantt chart even though QlikView itself doesn’t support Gantt charts. The company has also made it easier to merge QlikView with other systems like Google Maps and SharePoint.

These open up some great opportunities for QlikView deployments, but they depend on sophisticated developers to take advantage of them. In other words, they are not capabilities that a business analyst -- even a power user who's mastered QlikView scripts -- will be able to handle. They mark the extension of QlikView from stand-alone dashboards to a system that is managed by an IT department and integrated with the rest of the corporate infrastructure.

This is exactly the "pervasive business intelligence" that industry gurus currently tout as the future of BI. QlikView has correctly figured out that it must move in this direction to continue growing, and in particular to compete against traditional BI vendors at large enterprises. That said, I think QlikView still has plenty of room to grow within the traditional business intelligence market as well.

- Mobile interface. This actually came out in April and it’s just not that important in the grand scheme of things. But if you’re as superficial as I am, you’ll think it’s the most exciting news of all. Yes, you can access QlikView reports on iPad, Android and Blackberry smartphones, including those touchscreen features you’ve wanted since seeing Minority Report. The iPad version will even use the embedded GPS to automatically select localized information. How cool is that?

0 Beautiful BABI: SiSense PrismCubed Offers Business Intelligence for Business Analysts

Summary: SiSense PrismCubed offers a reasonable option for a business-analyst business intelligence system. It’s probably a little harder to use than some competitors, but gives a bit more power and flexibility in return.

SiSense PrismCubed, officially launched this past August, is another member of the growing set of business intelligence systems aimed at empowering business analysts to build their own applications. I’ve also written about QlikView and Lyza, and think there are others.

What distinguishes these tools from other business intelligence systems is they let non-technical users manipulate source data in more sophisticated ways than a spreadsheet or report writer. Specifically, data from several sources can be merged on a common key, filtered, aggregated and processed through complex formulas.

This sort of manipulation has traditionally required SQL programmers, OLAP cube designers, or similar technical experts. Allowing business analysts to do it without having to learn deep technical skills is precisely what lets them build applications with minimal external assistance. (I say "minimal" because technical staff must still handle connections to the source data.)

These systems also provide report creation and distribution. But unlike business-analyst data manipulation, those capabilities are also found in other business intelligence products.

You’ll note that my definition does NOT specify a particular database technology, such as in-memory or columnar, that the data is updated in real time, that the system is targeted at mid-sized businesses, or that results are distributed pervasively through the organization. Those have all been proposed as ways to classify business intelligence systems, and several of the products in my business-analyst business intelligence (BABI--how cute!) category fall into one or another such group. But I think it’s a mistake to focus on those other features because they don’t get at real value provided by these tools, which is the flowering of applications made possible when business analysts can create them independently.

Now that I've defined a new type of application, complete with the all-important acronym, the next step is defining an evaluation framework to help compare the competitors. I’d like to claim I do this through deep research and brilliant insights into user needs, but, in fact, I generally start with the features in the existing systems. This runs the risk of missing some critical requirement that no vendor has yet uncovered, but it saves a ton of work. And I can still argue that I’m piggybacking on the vendors’ own deep research and insights as embodied in their products.

In any event, a starter set of review criteria for BABI systems (sorry, but I find the acronym irresistible) would include:

- combine data from multiple, heterogeneous sources (relational databases, CSV files, Excel tables, etc.)

- allow non-technical users to define processing flows to manipulate the data (merge, filter, aggregate, calculate)

- present the manipulated data in a structure that’s suitable for reporting and visualization

- allow non-technical users to create applications including reports, visualizations, and (optionally) additional functions such as data refresh and export

- allow other users to view (and optionally interact with) the applications

- meet reasonable performance standards for data load, storage, response time and scalability

- use appropriate technology (actually, I don’t care if the thing is powered by hamsters. But understanding the underlying technology helps to predict where problems might arise.)

- affordable pricing (not exactly a criterion, but important nevertheless)

Obviously these criteria could be much more detailed, and no doubt they will grow over time. But for now, they provide a useful way to look at PrismCubed.

1. Combine data from multiple sources: PrismCubed provides a wizard to connect with different data sources, including SQL Server, Oracle, CSV files, Excel and Amazon S3 logs (which earns them extra coolness points). The system can read the database schemas directly, saving users the need to define basic data structures. Users have the option modify structures if they desire. A connection can be live (i.e., the source is requeried each time a report is run) or reloaded on demand from within a completed application. This provides real-time data access, which isn’t always available in business intelligence systems. The system can also reload data automatically on a user-specified schedule.

2. Allow non-technical users to manipulate source data: PrismCubed does a particularly good job here. At a basic level, users can write complex formulas to add derived fields to a table during the import process. More important, a drag-and-drop interface lets them build complex visual processing flows from standard icons including data definition, filtering, inclusion or exclusion, unions, and top or bottom selects. These flows can combine multiple data sources and include branches that generate separate output sets that are all available to use in applications.

3. Present the manipulated data for reporting: the system automatically classifies input data as dimensions (text, dates, etc.) and measures (numbers which can be aggregated). Users can override the system’s assignments and can add new dimension fields during the data load. They can create derived measures at any time. Once the load is complete, the system presents the dimensions and measures in an “ElastiCube” available for reports and other applications.

4. Create reports and other applications: the system provides a remarkably rich development environment. Users build applications by dropping different types of objects (which the vendor calls widgets) onto dashboard pages. Widgets can make selections; display data in pivot tables, charts, calendars, and images; and execute actions including refresh data, jump to different pages, query external data sources, edit data, and export to Excel. A dashboard can have multiple pages.

The primary reporting widget is the pivot table, which itself is built by dragging dimensions into rows and columns, and the measures into cell values. Users can apply filters to widgets, such as selecting the top 10 values for a dimension. These filters can be static (a fixed list) or dynamic (reselected each time the dashboard is updated). PrismCubed also provides special features for time series calculations such as period-to-period growth and differences. That's a nice touch, because those can be quite difficult to define with conventional reporting systems.

Reporting widgets can be connected to the ElastiCube dimensions and measures or directly to SQL data sources. Users can also specify whether selections made in one widget affect the data displayed in other widgets. There are actually three options here, including complete independence, direct links from one widget to another, and global impact on other widgets. This gives more flexibility than systems that automatically apply global selections, but does force users to do more work in specifying which approach they want.

Widgets, filters and other components can be stored in a central repository and reused across applications.

5. Share applications: Users can export dashboard contents to Excel tables or can copy an entire dashboard as a static PDF. Applications, including underlying ElastiCubes, can be copied and run on another user’s PC. In addition, a Web server due for release this month (October) will let dashboard creators publish their dashboards to a central server, where other users will be able to access and modify them. The server will provide fine-grained control over what different users are allowed to change.

6. Scalability and Performance: SiSense has tested the PrismCubed engine on multiple terabytes of data. It cited one client who loaded 30 million telephone call detail records in 30 to 90 minutes. Loaded data usually takes somewhat less disk space than the original source. The system currently requires a complete reload to add new data to an existing ElastiCube, although the vendor plans to add incremental appends by the end of November. Once the data is loaded, reports within applications usually update in seconds.

7. Technology: PrismCubed stores data in a columnar data structure. It also stores a dimension map for each column, but doesn’t preaggregate the data along the dimensions. As with other columnar databases, this avoids the need for specialized data structures to handle particular queries. When data has not been preloaded into the system, PrismCubed can also run the same query across multiple external data sources.

Although PrismCubed stores the entire ElastiCube on disk, it only loads into memory the columns required for a particular query. This lets it can handle larger data sets than purely in-memory systems without massive hardware. There might be some problems if the selected columns for a query exceeded the system’s available memory.

PrismCubed runs on Windows PCs with the .NET framework installed. On 64 bit systems, this means the amount of potential memory is virtually unlimited. Although PrismCubed itself is new, a previous version of the product using the ElastiCube database engine was launched in September 2008 and has more than 6,000 users.

8. Pricing: PrismCubed is priced on an annual subscription basis, which is unusual for this type of product but common among hosted BI vendors. SisSense offers several versions of PrismCubed, ranging from a free Viewer that can only access dashboards created elsewhere, to a $1,500 per year Professional edition that allows full creation of dashboards and ElastiCubes. There are also a free version (limited to 2,000 rows of data), a $300 per year Personal edition (which can create dashboards but not share them), and a $700 per year Analyzer that can build and share dashboards but not ElastiCubes. Server pricing wasn’t quite set when I spoke with SiSense but will probably be around $3,500 per year per server.

These prices are quite reasonable compared with similar vendors, even considering the recurring annual subscription fees, particularly because the end-user Viewer is free. Price details are published on the vendor’s Web site.

0 Youcalc: On-Demand Analytics Without Stored Data

Summary: Youcalc is an on-demand analytics vendor with 130 prepackaged applications primarily for sales and marketing reporting. Unlike its competitors, youcalc it reads data directly from other Software-as-a-Service systems rather than loading it into its own database. This saves money and simplifies installation but has some drawbacks too. Still, it's an intriguing alternative to the standard approach.

Youcalc is fundamentally different from other on-demand analytics vendors like Birst, Cloud9 Analytics, Gooddata and Pivotlink: while those vendors all query data stored in their system, youcalc queries the source data directly. That is, youcalc provides analytical applications that read from an existing system, typically a Software-as-a-Service vendor like Salesforce.com or Google AdWords.

Although this sounds like a subtle difference, the implications are huge. It means that youcalc doesn’t need the infrastructure to build and store client databases, thereby reducing its costs dramatically.

It also means that youcalc can to give each new client immediate access to standard applications, since there is no need to adjust for differences in their data. Although this is possible with prebuilt applications at other on-demand analytics vendors, youcalc has made it more central to their business model. In fact, youcalc extends this to related community concepts such as user-contributed enhancements, forums, tagging and rating of popular applications.

I’m intrigued by the youcalc approach but do see some disadvantages. One is that data integration capabilities are limited: the current version of the system can only combine data sources that already share common keys or are linked with an existing cross reference table. I suppose it’s technically possible to allow more sophisticated data matching, but any processing will still be limited by the need to repeat it each time the data is read from its sources and loaded into memory.

A second, more fundamental limitation is that the system can’t access historical data, such as point-in-time snapshots of information which is not retained in operational systems. At best, youcalc could point to an externally-built data warehouse as a source – but now you’re back doing all the database development that youcalc is supposed to avoid. No free lunch here, folks.

Still, there are those cost savings. Youcalc is priced at an astonishingly low $19.95 per user per month, which gives access to 130+ prebuilt applications for products including Salesforce.com, SugarCRM, Google AdWords, Google Analytics, MailChimp and 37Signals’ BaseCamp (project management) and Highrise (contact management). There’s also a free version that is excludes some of the more powerful applications. The full set is available for a 30 day free trial.

Unfortunately, these prices may not last. CEO Rasmus Madsen told me the company plans eventually to charge higher fees for applications linked to higher priced source systems.

None of this would matter if the youcalc applications and underlying technology weren't worth having. But I found them quite impressive.

Applications can contain multiple objects such as charts and lists. They can also contain drop-down selection boxes to filter components and select alternative chart dimensions. A single application can have multiple pages linked by menus. Users can embed images, text notes and external URLs, and have control over style details such as type fonts and background colors. Although the presentation is nowhere near as advanced as products like Tableau or TIBCO Spotfire, it is competitive with other on-demand analytics systems.

Most current youcalc applications display a single chart from a single data source, such as “Time-Day Distribution for Google Analyzer”. But users can change the contents by selecting different dimensions (e.g., date range) and metrics (e.g. visits, new visitors, bounces, etc.). Some applications combine multiple data sources, such as the “AdWords Campaign ROI Overview for Salesforce.com” that compares cost from Google AdWords with revenue from Salesforce.com.

Users can modify these applications or create their own from scratch (although all the existing applications were built by youcalc). Development is done with Java-based desktop software that runs on Windows, Mac or Linux PCs. The interface involves dragging different components onto a whiteboard and then configuring and connecting them. There are two different whiteboards, one to show the actual application and another to display the flows used to construct each object. These flows begin with connection to an external data source and then send the data through functions to apply formulas, convert formats, create summaries, and perform other tasks. Parameters of each function can be edited during the set-up or connected to objects like drop-down menus for end-user interaction. A completed application can be saved as a stand-alone Web page, a mobile phone Web page, embedded within an external page, or deployed as a widget on an iGoogle home page.

None of this requires actual programming, and basic tasks should be easy enough for a skilled spreadsheet jockey. More demanding activities, such as connecting to an in-house data source, take considerable technical understanding. (The system doesn’t query in-house resources directly; rather, it sends a message to a “listener” on the in-house system, which runs the specified query and transmits the results back as an XML data stream.) Connections for standard sources such as Salesforce.com are very simple since they’re prebuilt: users just enter their log-in credentials and the system does the rest.

If youcalc has an Achilles heel, it will turn out to be data volume. The system accesses standard sources (Salesforce.com, AdWords, etc.) through their APIs, which often limit the number of records that can be pulled at once. Youcalc connectors can submit new calls until all the data has been read, but this is still awkward and will probably be slow for large volumes.

In addition, the data must be loaded into system memory during each user session. This also imposes some practical limits—we’re probably talking in the multi-gigabyte range—even though youcalc runs in the Amazon data cloud, which gives it access to very large servers. Madsen says the largest current installation works with data for 150 Salesforce.com users.

Youcalc was launched in its current form at the end of 2008, although the company has been working on its core technologies since 2003. Madsen said that 4,000 accounts were created in the first six months since launch, and there are currently more than 7,000 application sessions per week. Most are from small businesses, which makes sense for any number of reasons including price, functionality and ease of deployment. The company hopes eventually to attract larger firms as well.

0 Lyzasoft White Paper Looks at Coordinating Business Analysts and IT

Summary: a new white paper says business analysts gather data with little help from IT. I'm not so sure, but agree that collaboration tools like Lyza Commons can help both groups cooperate.

Analytical software vendor Lyzasoft has just published a white paper by data warehouse guru Dr. Barry Devlin on how business analysts and IT can work together. (Click to download Collaborative Analytics: Sharing and Harvesting Analytic Insights across the Business.) Since I’ve spent much time pondering this very issue, I was quite curious to see his perspective.

The paper describes a fundamental contrast between a “center out” model of data usage favored by IT (carefully and centrally controlled) and an “edge based” model favored by business analysts, who act as independent data “hunter-gatherers” to combine and use data in ways that the central resources are not designed to support. Devlin's term for this is “emergent prototyping”, a trial-and-error process of reworking an analysis until it produces something useful.

He also suggests that analysts work first by themselves, and then, if they find something interesting, share it with other analysts. Only later, when something seems really important and reusable, will they try to get corporate IT to add it to the central systems.

My own mental model is slightly different. I see analysts as spending very little time gathering data. In practice, most of what they need resides in corporate systems, so analysts are largely at the mercy of IT to provide extracts of required sources. Although waiting for those extracts is probably the biggest constraint on what analysts can accomplish, they don't spend that time twiddling their thumbs. Most of their work day (apart from meetings, etc.) is spent manipulating and interpreting data, and, as Devlin suggests, discussing results with other analysts.

This difference in perspective has some impact on judging what matters in a business analysis tool. If data gathering is really important, then features for extraction and consolidation are critical. If manipulation and interpretation matter most, then features for processing and visualization are at the top of the list.

As I recall, Lyza doesn’t offer particularly advanced extraction or consolidation features (e.g. fuzzy matching), so this isn’t necessarily a topic they should stress. Lyzasoft might disagree – and I’ll gladly concede that the system allows basic joins and filters that are well beyond what you can do in Excel. Still, to my mind, the real strength of Lyza is the ability to create data process flows, which save analysts from trying to do similar work by manually modifying Excel spreadsheets. (Click to read my Lyza review.)

Either way, though, features to document and share analytical processes still matter. Those are really the focus of this white paper, which is written to support the "Lyza Commons” product. Commons lets analysts share their work, trace the origins of each shared item, and use one analysis as input to another. As the paper points out, this both fosters cooperation among analysts and makes it easier for IT to add their activities to the company’s core business intelligence systems. Both benefits should free up analysts’ time for new projects, letting them foxus on what they do best.

0 LucidEra's Failure: More Evidence that Marketers Won't Pay for Measurement

I’m just catching up with what happened while I was on vacation these past two weeks. One piece of news is the demise of LucidEra, which this blog profiled almost exactly one year ago. According to SearchDataManagement.com, the company said it shut down because it couldn’t raise new funds or find a buyer.

There has been some learned discussion of the causes of LucidEra’s collapse on Timo Elliot’s BI Questions Blog. Much seems to focus on the apparent operating costs. These must have been substantial, since the company raised $15.6 million in 2007 and, presumably, has since spent it all.

Still, I think the fundamental problem was a lack of customers. When I spoke with LucidEra in June 2008 they said they had about 40 paying clients. When I spoke with them again in October 2008, the number was 50 and it was still at 50 when we spoke in April 2009. In other words, LucidEra was making very few sales or, even worse, was able to make new sales but couldn’t retain its customers. [For more insight based on comments by LucidEra managers, see this post on the Datadoodle blog.]

With the benefit of 20/20 hindsight, LucidEra’s strategic decision to focus on building sales analysis applications primarily for Salesforce.com was a mistake. Bear in mind that there are about 60,000 Salesforce.com customers – selling to 50 of them is less than 0.1% penetration.

I suspect LucidEra’s price point, around $3,000 per month depending on the details, was too rich for many of its prospective clients. Not that they couldn’t actually afford it – but they didn’t want to spend that much money on sales analysis.

This is not surprising. I reluctantly concluded some time ago that marketers (and presumably sales managers) are not willing to spend money on measurement systems even though they consistently say in surveys that better measurement is a high priority. For recent evidence along these lines, see the 2009 Marketing ROI and Measurements Study published by Lenskold Group and sponsored by MarketSphere, which found that “6 in 10 firms (59%) indicate having an increased demand for marketing measurements, analysis and reporting in 2009 without the budget necessary for those measurement efforts.”

Many analysts and other on-demand business intelligence vendors have been quick to assert that LucidEra’s failure does not reflect a problem with the notion of on-demand BI in general. I agree, since I see the key to LucidEra's demise as its uniquely narrow focus on sales analysis. Indeed, competitors including Birst and GoodData have leapt to offer a new home to orphaned LucidEra clients.

Still, the apparently high costs to sustain a small client base suggests the economics of this business are not as attractive as they seem. LucidEra's Darren Cunningham did tell me that their costs were particularly high because they were not a multi-tenant solution and had to manage the entire BI stack to support a single application. Presumably other on-demand BI vendors can run more cheaply. Still there does seem to be a little more reason for caution in approaching on-demand BI vendors, even though there is not (yet) any cause for alarm.

0 Cloud-Based QlikView Still Isn't Available as a Service

Summary: Pay-as-you-go pricing would make QlikView easier to buy, but the company doesn't offer this option. To make a stronger business case for the purchase, include the value of shifting work from IT to business users, and of producing results faster.

Last week’s post about QlikView 9.0 prompted an inquiry from a manager who has been trying for a year to convince his company to consider the product. Having run into this issue many times, I easily felt his pain and we speculated a bit on what might help things along.

One obvious tactic would be to purchase QlikView on a pay-as-you-go basis, presumably cloud-based. But a quick check with QlikView confirmed that they don’t allow this and have no plans to change.

The closest they come is to let their partners offer QlikView-based applications as a service. For example, they pointed me to SportsDataHub, which lets users analyze football statistics for $40 per year. But the key point about this and similar QlikView services is that you can only access data loaded by the partner. You can't define and load your own data sources directly. At best, you might be able to create your own reports based on the loaded data. (See QlikTech Marketing SVP Anthony Deighton's comment on this post for a little more on the subject.)

I don’t understand QliiView’s reluctance to adopt a Software-as-a-Service model. It has proven viable for many other software companies, including other business intelligence vendors. To me, it seems a natural extension of the company’s “seeing is believing” sales approach as well as a good way to sidestep the barriers raised by corporate IT.

In fact, QlikView’s tremendous ease-of-use makes it an excellent fit for the SaaS model, because business users can deploy it for themselves with minimal technical support. In our conversation last week, QlikTech's Deighton said the majority of clients already implement the system without purchasing any external services. If there was ever a piece of software suited to SaaS, this is it.

Be that as it may. The lack of a proper SaaS offering left my correspondent with several avenues to pursue:

- find a QlikView partner who would build an appropriate application and sell it to him on a services basis. This doesn’t seem very plausible because he probably won’t be able to commit enough funding to make the project worthwhile for the partner. I mean, if he had that much money, he could just buy the software outright in the first place.

- use an alternative system that costs less. Yes, QlikView is unique and wonderful, but products from ADVIZOR Solutions, Lyzasoft, Tableau Software and TIBCO Spotfire offer some of the same advantages at a much lower entry price. Again, this is far from ideal, and it might not work at all because I didn’t explore precisely which aspect of QlikView my correspondent found attractive. Still, it’s better than nothing.

(Vaguely related aside: today, people often cite author Jim Collins’ phrase “good is the enemy of great” as a reason to avoid compromise. Previously, I was more likely to see Voltaire’s “the best is the enemy of the good,” which means that compromise is better than nothing. I’m sure this reversal says something important about our society, although I can’t say what. You're welcome.)

- Find a way to sell QlikView internally. Of course, my correspondent had already been trying, so his question was whether I had any new ideas for how. This actually prompted some very deep thinking over the weekend, which will show up in my Information Management magazine column over the next several months. To summarize four pages in 100 words, there are two approaches to consider:

- do a cost of ownership analysis showing the savings from letting business users perform tasks currently done by IT. Traditional cost analysis compares the time it takes IT to do the work with one tool vs. another. This hides rather than highlights the advantages of QlikView and similar products.

- do a “time to result” analysis that measures the time spent waiting for IT to deliver solutions through multiple iterations. This applies to many analytical databases, not just QlikView, because their flexibility reduces the time spent building conventional BI structures like star schemas and data cubes.

Perhaps one of these will work. I hope so, because we could all benefit from finding ways to take advantage of what new technologies like QlikView have to offer.

0 QlikView 9.0 Reaches for Broader Business Intelligence Market

QlikTech released version 9 of its QlikView business intelligence software today. The product has been in public beta for several months, so the general features are well known to people who care about such things.

Probably the item that attracted the most advance attention is an iPhone version that supports interactive analysis; this also works for other Java Mobile clients like Blackberry. It's cool (or ‘qool’, if you must) but not so important in the grand scheme of things. More significant changes include:

- availability through the Amazon Elastic Compute Cloud (EC2), which lets companies order up a QlikView-equipped server in minutes. (Of course, they still have to purchase a QlikView license.) Users can also expand or reduce the number of servers to match fluctuating needs. Advantages including avoiding the wait for new hardware, no need to physically install a server, and the ability to meet peak demands without making a fixed investment.

- API for real-time updates of in-memory data. This is an extension of previous changes that allowed incremental batch updates and manual data entry. But it still marks a major step towards letting QlikView run time-critical applications such as stock trade analysis, pricing and inventory management. No one will be processing orders on QlikView (hmm, never say never), but the line between analytical and transaction databases just got that much thinner.

- enhanced support for enterprise-level deployments. This includes centralized control panels for multiple servers; load balancing and fail-over; better thin-client support; multi-billion-row data sets; and more efficient calculations. These are critical as QlikView moves from being a departmental solution run primarily by business analysts to a mission-critical system backed by corporate IT.

- free Personal Edition with full development capabilities. The main limit vs. the licensed version is that Personal Edition cannot read QlikView files developed on any other copy of the software, and no one else can read files that Personal Editon generates. The goal is to make it easier for users to try the system on their own – a continuation of the company's long-standing "seeing is believing" strategy.

- functional enhancements including improved visualization, search and automation functions. These are nice but none seemed especially exciting. Changes in previous recent releases, such as set analysis (simultaneously comparing two sets of selected records) were more fundamental. Remember, we're talking about version 9: the system is already quite polished.

Of all these items, the one I found most thought-provoking was the free Personal Edition, which replaces a 15-day free trial. Removing the time limit let users build QlikView into their regular work. The strategy makes sense, but it doesn’t lower the $30,000 - $50,000 investment required for the smallest licensed QlikView installation. Few analysts, who are the most likely users for Personal Edition, have the clout to sponsor so large an investment. Competing analyst tools such as LyzaSoft, ADVIZOR Solutions and Tableau can generally provide a 5-10 user departmental deployment for under $10,000. Although QlikView is vastly more powerful than the others, the lower cost will give them an initial advantage. And once they’re in place, it’s hard to get a company to switch.

On the other hand, maybe QlikView is really moving to compete with traditional business intelligence tools like Cognos, Business Objects and MicroStrategy. QikView’s entry cost is vastly lower than those products, especially once you consider the savings in labor. But most enterprises have a BI tool already in place, so it’s not a matter of comparing entry costs. Rather, the choice is entry cost for QlikView vs. incremental deployment cost on the incumbent. The labor savings with QlikView are so great that it will still be cheaper for many projects. But QlikView will remain be a tough sell because IT departments are reluctant to invest in the staff training needed to support an additional tool.

QlikView will never fully replace the traditional data warehouse and BI tools because its in-memory approach limits the size of its databases. With 64 bit systems, the product can easily handle dozens of gigabytes of data. This is quite a lot, but even the smallest enterprise data warehouses now hold multiple terabytes. QlikView works with such systems by executing SQL queries against them, pulling down limited data sets, loading these into memory, and analyzing them. That’s an excellent and perfectly viable approach, but it does rely on the warehouse being there in the first place.

None of this is to suggest that QlikView has anything but a very bright future. When I first spoke with the company in 2005, it had just reached 2,000 clients; at last count, it had over 11,000. Revenue for 2008 was $120 million and had risen 50% from the previous year. The product has finally attracted attention from analyst firms like Gartner and Aberdeen and is very well rated in Nigel Pendse’s latest BI Survey. My brief fling as a VAR ended two years ago, but I still use it personally for any non-trivial data analysis work and remain absurdly pleased with the results. I won’t say QlikView is better than sex, but its pleasures are equally difficult to describe to the uninitiated. Anyone interested in BI software who hasn’t given it a try (QlikView, not sex) should download a copy and see what they’ve been missing.

0 Lyzasoft: Independence for Analysts and Maybe Some Light on Shadow IT

Long-time readers of this blog know that I have a deep fondness for QlikView as a tool that lets business analysts do work that would otherwise require IT support. QlikView has a very fast, scalable database and excellent tools to create reports and graphs. But quite a few other systems offer at least one of these.*

What really sets QlikView apart is its scripting language, which lets analysts build processing streams to combine and transform multiple data sources. Although QlikView is far from comparable with enterprise-class data integration tools like Informatica, its scripts allow sophisticated data preparation that is vastly too complex to repeat regularly in Excel. (See my post What Makes QlikTech So Good for more on this.)

Lyzasoft Lyza is the first product I’ve seen that might give QlikView a serious run for its money. Lyza doesn’t have scripts, but users can achieve similar goals by building step-by-step process flows to merge and transform multiple data sources. The flows support different kinds of joins and Excel-style formulas, including if statements and comparisons to adjacent rows. This gives Lyza enough power to do most of the manipulations an analyst would want in cleaning and extending a data set.

Lyza also has the unique and important advantage of letting users view the actual data at every step in the flow, the way they’d see rows on a spreadsheet. This makes it vastly easier to build a flow that does what you want. The flows can also produce reports, including tables and different kinds of graphs, which would typically be the final result of an analysis project.

All of that is quite impressive and makes for a beautiful demonstration. But plenty of systems can do cool things on small volumes of data – basically, they throw the data into memory and go nuts. Everything about Lyza, from its cartoonish logo to its desktop-only deployment to the online store selling at a sub-$1,000 price point, led me to expect the same. I figured this would be another nice tool for little data sets – which to me means 50,000 to 100,000 rows – and nothing more.

But it seems that’s not the case. Lyzasoft CEO Scott Davis tells me the system regularly runs data sets with tens of millions of rows and the biggest he’s used is 591 million rows and around 7.5-8 GB.

A good part of the trick is that Lyza is NOT an in-memory database. This means it’s not bound by the workstation’s memory limits. Instead, Lyza uses a columnar structure with indexes on non-numeric fields. This lets it read required data from the disk very quickly. Davis also said that in practice most users either summarize or sample very large data sets early in their data flows to get down to more manageable volumes.

Summarizing the data seems a lot like cheating when you’re talking about scalability, so that didn’t leave me very convinced. But you can download a free 30 day trial of Lyza, which let me test it myself.

Bottom line: my embarrassingly ancient desktop (2.8 GHz CPU, 2 GB RAM, Windows XP) loaded a 400 MB CSV file with about 430,000 rows in just over 6 minutes. That’s somewhat painful, but it does suggest you could load 4 GB in an hour – a practical if not exactly desirable period. The real issue is that each subsequent step could take similar amounts of time: copying my 400 MB set to a second step took a little over 2 minutes and, more worrisome, subsequent filters took the same 2 minutes even though they reduced the record count to 85,000 then 7,000 then 50. This means a complete processing flow on a large data set could run for hours.

Still, a typical real-world scenario would be to do development work on small samples, and then only run a really big flow once you knew you had it right. So even the load time for subsequent steps is not necessarily a show-stopper.

Better news is that rerunning an existing filter with slightly different criteria took just a few seconds, and even rerunning the existing flow from the start was much faster than the first time through. Users can also rerun all steps after a given point in the flow. This works because Lyza saves the intermediate data sets. It means that analysts can efficiently explore changes or extend an existing project without waiting for the entire flow to re-execute. It’s not as nice as running everything on a lightning-fast data server, but most analysts would find it gives them all the power they need.

As a point of comparison, loading that same 400 MB CSV file took almost 11 minutes with QlikView. I had forgotten how slowly QlikView loads text files, particularly on my limited CPU. On the other hand, loading a 100 MB Excel spreadsheet took about 90 seconds for Lyza vs. 13 seconds in QlikView. QlikView also compressed the 400 MB to 22 MB on disk and about 50 MB in memory, whereas Lyza more than doubled data to 960 MB of disk, due mostly to indexes. Memory consumption in Lyza rose only about 10 MB.

Of course, compression ratios for both QlikView and Lyza depend greatly on the nature of the data. This particular set had lots of blanks and Y/N fields. The result was much more compression than I usually see in QlikView and, I suspect, more expansion than usual in Lyza. In general, Lyza seems to make little use of data compression, which is usually a key advantage of columnar databases. Although this seems like a problem today, it also means there's an obvious opportunity for improvement as the system finds itself dealing with larger data sets.

What I think this boils down to is that Lyza can effectively handle multi-gigabyte data volumes on a desktop system. The only reason I’m not being more definite is I did see a lot of pauses, most accompanied by 100% CPU utilization, and occasional spikes in memory usage that I could only resolve by closing the software and, once or twice, by rebooting. This happened when I was working with small files as well as the large ones. It might have been the auto-save function, my old hardware, crowded disk drives, or Windows XP. On the other hand, Lyza is a young product (released September 2008) with only a dozen or so clients, so bugs would not be surprising. I'm certainly not ready to say Lyza doesn't have them.

Tracking down bugs will be harder because Lyza also runs on Linux and Mac systems. In fact, judging by the Mac-like interface, I suspect it wasn't developed on a Windows platform. According to Davis, performance isn’t very sensitive to adding memory beyond 1 GB, but high speed disk drives do help once you get past 10 million rows or so. The absolute limit on a 32 bit system is about 2 billion rows, a constraint related to addressable memory space (2^31 = about 2 billion) rather than anything peculiar to Lyza. Lyza can also run on 64 bit servers and is certified on Intel multi-core systems.

Enough about scalability. I haven’t done justice to Lyza’s interface, which is quite good. Most actions involve dragging objects into place, whether to add a new step to a process flow, move a field from one flow stage to the next, or drop measures and dimensions onto a report layout. Being able to see the data and reports instantly is tremendously helpful when building a complex processing flow, particularly if you’re exploring the data or trying to understand a problem at the same time. This is exactly how most analysts work.

Lyza also provides basic statistical functions including descriptive statistics, correlation and Z-test scores, a mean vs. standard deviation plot, and stepwise regression. This is nothing for SAS or SPSS to worry about; in fact, even Excel has more options. But it’s enough for most purposes. Similarly, data visualization is limited compared to a Tableau or ADVIZOR, but allows some interactive analysis and is more than adequate for day-to-day purposes.

Users can combine several reports onto a single dashboard, adding titles and effects similar to a Powerpoint slide. The report remains connected to the original workflow but doesn’t update automatically when the flow is rerun.

Intriguingly, Lyza can also display the lineage of a table or chart value. It traces the data from its source through all subsequent workflow steps, listing any transformations or selections applied along the way. Davis sees this as quickly answering the ever-popular question, “Where did that number come from?” Presumably this will leave more time to discuss American Idol.


Users can also link one workflow to another by simply dragging an object onto a new worksheet. This is a very powerful feature, since it lets users break big workflows into pieces and lets one workflow feed data into several others. The company has just taken this one step further by adding a collaboration server, Lyza Commons, that lets different users share workflows and reports. Reports show which users send and receive data from other users, as well as which data sets send and receive information from other data sets.

Those reports are more than just neat: they're documenting data flows that are otherwise lost in the “shadow IT” which exists outside of formal systems in most organizations. Combined with lineage tracing, this is where IT departments and auditors should start to find Lyza really interesting.

A future version of Commons will also let non-Lyza users view Lyza reports over the Web – further extending Lyza beyond the analyst’s personal desktop to be an enterprise resource. Add in the 64-bit capability, an API to call Lyza from other systems, and some other tricks the company isn’t ready to discuss in public, and there’s potential here to be much more than a productivity tool for analysts.

This brings us back to pricing. If you were reading closely, you noticed that little comment about Lyza being priced under $1,000. Actually there are two versions: a $199 Lyza Lite that only loads from Microsoft Excel, Access and text files, and the $899 regular version that can also connect to standard relational databases and other ODBC sources and includes the API.

This isn’t quite as cheap as it sounds because these are one year subscriptions. But even so, it is an entry cost well below the several tens of thousands of dollars you’d pay to get started with full versions of QlikView or ADVIZOR, and even a little cheaper than Tableau. The strategy of using analysts’ desktop as a beachhead is obvious, but that doesn’t make it any less effective.

So, should my friends at QlikView be worried? Not right away – QlikView is a vastly more mature product with many features and capabilities that Lyza doesn’t match, and probably can’t unless it switches to an in-memory database. But analysts are QlikView’s beachhead too, and there’s probably not enough room on their desktops for both systems. With a much lower entry price and enough scalability, data manipulation and analysis features to meet analysts’ basic needs, Lyza could be the easier one to pick. And that would make QlikView's growth much harder.

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*ADVIZOR Solutions and Tableau Software have excellent visualization with an in-memory database, although they’re not so scalable. PivotLink, Birst and LucidEra are on-demand systems that are highly scalable, although their visualization is less sophisticated. Here are links to my reviews: ADVIZOR , Tableau, PivotLink, Birst and LucidEra.

0 Good Look at QlikView from a Microstrategy Consultant's Viewpoint

I noticed some visitors this morning from the blog of Microstrategy consultancy Aellament, and found they have published a nice look at QlikView on their blog. It's worth a read, and quite interesting in their appreciation of the advantages that QlikView offers over the product they know best. They fairly point out some disadvantages too, of which I think lack of a unified metadata view is probably most significant.

What really resonated was Aellament's sense (in an earlier blog post, which contains the link back to this blog) that QlikView is empowering departmental users to do work that otherwise takes support from technical specialists. That's exactly what I've seen as QlikView's advantage and I think it's fundamentally reshaping the industry.

As I see the future, BI specialists will still be needed to assemble source data into usable forms (i.e., build the data warehouses). This has always been the heavy lifting. But the huge army of people who then essentially reformat that data into cubes, reports, dashboards, etc. will dwindle as business analysts do that for themselves using tools like QlikView. Bad news for Microstrategy consultants (presumably why Aellament is hedging its bets with QlikView training) but good news for business users.

0 QlikView Is Champion In Aberdeen AXIS (But Is This Graph Necessary?)

I occasionally do some development work in QlikView and have made no secret that I think it's a great product. So part of me was pleased to learn that it had been listed as a—indeed, the only—“champion” in an Aberdeen Group AXIS report on BI/Performance Management software. Although QlikView has received plenty of recognition recently, it deserves every bit of it. (If you’ve been living in a cave, or, much worse, not reading this blog: QlikView is business intelligence software that combines an in-memory database, special “associative” data model, data transformation scripts, and a powerful presentation layer. My own experience has shown it lets business analysts build in days what would take months of work by heavyweight IT experts with conventional BI tools. For details, see What Makes QlikTech So Good?--by far the most popular post I've ever written.)

Here is the AXIS itself, with QlikView alone at the top:

And therein lies the problem. Fond as I am of QlikView, I can’t imagine any measure by which it would be a dominant business intelligence tool, let alone rank so far above its competitors. So I was more than a little perplexed to see the Aberdeen ranking, especially because I wasn’t aware that Aberdeen produced something similar to the Forrester “waves” and Gartner “magic quadrants”.

Indeed, it turns out that the AXIS program is brand new. The BI report is the second to be issued but will be followed by several per quarter. Aberdeen’s materials describe its reports as “unlike other comparative products that primarily focus on feature and functionality”, but to my eye it looks pretty similar. Apparently what makes it “the market’s first technology solution software provider assessment tool that is truly customer-centric” is that one of its two dimensions, “value delivered”, is based on Aberdeen’s surveys that identify best-in-class companies.

Exactly how this relates to the vendor rankings is unclear, although I could find out if I paid $895 for the report. Maybe a higher “value delivered” ranking means the product is used by more best-in-class companies, although I rather doubt it given QlikView's still-limited market penetration. More likely, the higher ranked products are viewed by users as delivering greater value, perhaps with some weighting towards the relative performance of the companies doing the ranking. I’ve no doubt that QlikView users are more enthusiastic than any other vendors’, both because it truly is a great product but also because it’s still in the relatively early adapter stage where users tend to be highly motivated and vocal. Perhaps Aberdeen looks at the features desired by best-in-class companies and compares those to the features delivered by the different products, although this too seems unlikely.

The second dimension of “market readiness” is described by Aberdeen as based on “evaluation of responses to a standardized vendor questionnaire, analyst briefings, public records and customer interviews.” This sure sounds like a conventional feature-and-function assessment to me.

As someone who has been evaluating software for many years, I fully appreciate the appeal of these sorts of matrices. Vendors love them because, if they’re ranked near the top, it gives them something to crow about. Buyers love them because they can save work by considering only the top-ranked alternatives (even though vendors piously warn this is inappropriate). The analyst firms love them because they get lots of publicity, both directly because the press loves a horse race and from the winning vendors who promote them.

I have always avoided producing such rankings, even when people ask for them, precisely because they make it too easy for buyers to avoid the essential work of assessing products against their own needs. Still, the commercial advantages of the rankings are so great that I may yet feel impelled to produce them. That being the case, I can’t really criticize Aberdeen for rolling out their own. But I do hope they make a serious effort at educating people on what the "AXIS" means and how it should and shouldn’t be used.

0 ADVIZOR's In-Memory Database Supports Powerful Visualization

Back when I was writing a great deal about QlikView, I proposed that its fundamental value came from empowering business analysts to do work for themselves that would otherwise require IT support. (See, for example, this post, which has the virtue of pretty graphics.) This same notion of considering which users do which work has permiated my ideas of usability measurement for demand generation systems and usability in general. But to get back specifically to business intelligence systems, I think there is a particularly large gap between the capabilities available to business analysts and those available to IT. That is, even though the business intelligence systems like Cognos and Business Objects give analysts many ways to slice and present prepared data, they do not let analysts add new data or restructure existing data to meet new needs. This still requires the IT staff to design new data cubes and loading processes.

This gap is partly filled by analytical technologies such as columnar systems and database appliances, which can give good performance without schemas tailored for each task. But those systems are purchased and managed by the IT department, so they still leave analysts largely reliant on IT’s tender mercies.

A much larger portion of the gap is filled by products like QlikView, which the analysts can largely control for themselves. These can be divided into two subcategories: database engines like QlikView and illuminate, and visualization tools like Tableau and TIBCO Spotfire. The first group lets analysts do complex data manipulation and queries without extensive data modeling, while the latter group lets them do complex data exploration and presentation without formal programming. This distinction is not absolute: the database tools offer some presentation functions, and the visualization tools support some data manipulation. Both capabilities must be available for the analysts the work independently.

This brings us to ADVIZOR from ADVIZOR Solutions. ADVIZOR features an in-memory database and some data manipulation, but its primary strength is visualization. This includes at least fifteen chart types, including some with delightfully cool names like Multiscape, Parabox and Data Constellations. Analysts can easily configure these by selecting a few menu options. The charts are also somewhat interactive, allowing users to select records by clicking on a shape or drawing a box around data points. Some settings can be changed within the chart, such as selecting a measure to report on. Others, such as specifying the dimensions, require modifying the chart setup. The distinction won’t matter much to business analysts, who will have the tools build and modify the charts. But final consumers of the analyses typically run a viewer that does not permit changes to the underlying graph configuration.

On the other hand, that in-memory database can link several charts within a dashboard so selections made on one chart are immediately reflected in all others. This is arguably the greatest strength of the system, since it lets users slice data across many dimensions without writing complex queries. Colors are also consistent from one chart to the next, so that, for example, the colors assigned to different customer groups in a bar chart determine the color of the dot assigned to each customer in a scatter plot. Selecting a single group by clicking on its bar would turn the dots of all the other customers to gray. Keeping the excluded records visible in this fashion may yield more insight than simply removing them, although the system could also do that. These adjustments appear almost instantly even where millions of records are involved.

Dashboards can easily be shared with end-users through either a zero-footprint Web client or a downloadable object. Both use the Microsoft .NET platform, so Mac and Linux users need not apply. Images of ADVIZOR dashboards can easily be exported to Office documents, and can actually be manipulated from within Powerpoint if they are connected to the underlying dashboard. It’s also easy to export results such as lists of selected records.

Circling back to that database: it employs technology developed at Bell Labs during the 1990’s to support interactive visualization. The data model itself is a fairly standard one of tables linked by keys. Users can import data from text files, relational databases, Excel, Access, text files, Business Objects or Salesforce.com. They can map the table relationships and add some transformations and calculated fields during or after the import process. Although the mapping and transformations are executed interactively, the system records the sequence so the user can later edit it or repeat it automatically.

The import is fairly quick: the vendor said that an extract of three to four gigabytes across thirty tables runs in about twenty minutes, of which about five minutes is the build itself. The stored data is highly compressed but expands substantially when loaded into RAM: in the previous example, the three to four GB are saved as a 70 MB project file, but need 1.4 GB of RAM. The current version of ADVIZOR runs on 32 bit systems which limits it to 2-4 GB of RAM, although a 64 bit version is on track for release in January 2009. This will allow much larger implementations.

Pricing of ADVIZOR starts at $499 for a desktop version limited to Excel, Access or Salesforce.com source data and without table linking. (A 30-day trial version of this costs $49.) The full version starts at around $10,000, with additional charges for different types of user seats and professional services. Few clients pay less than $20,000 and a typical purchase is $50,000 to $60,000. Most buyers are business analysts or managers with limited technical skills, so the company usually helps set up their initial data loads and applications. ADVIZOR was introduced in 2004 and has several thousand end users, with a particular concentration in fund-raising for higher education. The bulk of ADVIZOR sales come through vendors who have embedded it within their own products.

0 LucidEra and Birst Blaze New Trails for On-Demand BI

I spent a few minutes last week on the Web sites of about eight or nine on-demand business intelligence vendors, and within a few days received emails from two of them ostensibly asking about much earlier visits where I must have registered with my email address. Given my current obsession with demand generation systems, I’m pretty sure this was no coincidence: they had deposited a cookie linked to my email address during the earlier visit, and used this address to react when I returned. I suppose I should admire this as good marketing, although the disingenuousness of the messages was a bit disturbing. I suppose they felt that was better than the creepy feeling I might get if they said they knew I had visited. [Postscript: I later spoke with one of the salespeople, who sadly assured me it was a total coincidence. She only wished her firm could react so effectively.]

(I was about to coin the word “disingenuity” to mean something that is ingeniously disingenuous [i.e., cleverly deceptive], but see that the dictionary already lists it as a synonym for disingenuousness. Pity. )

Whatever. The reason I was looking at the on-demand BI sites was I’d spoken recently with two vendors in the field and wanted to get some context. One of the two was LucidEra , which was giving me an update since my post about them in July.

They’re doing quite well, thanks, and most excited about a new services offering they call a “pipeline healthcheck”. This is a standardized analysis of a company’s sales pipeline to find actionable insights. LucidEra says it has been tremendously successful in demonstrating the value of their system and thus closing sales. Apparently, many marketers never learned how to analyze the information buried within their sales automation systems, simply because it wasn’t available back when they were being trained. So doing it for them, and helping them learn to do it for themselves, adds great value.

This reinforced one of my few really profound insights into the software business, which is that the marketing software vendors who succeed have been the ones who provide extensive services to help their clients gain value from their systems. (Well, I think it's profound.) Interestingly, when I told LucidEra I have recently been applying this insight to demand generation vendors, they said they had recently switched to a new demand generation vendor and—this is the interesting part—found the new system was so much simpler to use that very little vendor support was necessary. That’s an interesting tidbit, although it doesn’t necessary confirm my service-is-essential thesis. Perhaps it needs a corollary of some sort when the applications are obvious or the users are already trained. Facts can be so pesky.

The other vendor on my mind was Birst. I actually spoke to them back in early September, but their product announcement was under embargo until September 30 and in any case I’ve been focused since then on the demand generation guide (have I mentioned http://www.raabguide.com/ yet today?) I’m glad to get back to Birst, though, because I was quite intrigued by what they showed me. Basically they claim to have fully automated the entire business intelligence implementation process: loading the data, designing the warehouse, identifying interesting information, and creating dashboards to display the results.

I’ll admit to being skeptical of how well they can do this, but the company’s managers have some excellent credentials and Birst itself is a project of a Success Metrics, which has been providing Web-based opportunity discovery to insurance and pharmaceutical sales forces since 2006. They offered me an online workspace to play with the tool, but I haven’t had time to take them up on it. (I think their Web site makes that same offer to anyone.)

I did spend a few minutes playing with a prebuilt demo on the Web site: it’s a reasonable user interface for ad hoc analysis and building dashboard reports. There was a lag of up to five seconds between each click when I was working with the data, which would quickly get annoying if I were trying to do real work. Part of the lag may be caused by the underlying technology, which generates relational OLAP cubes on the fly in response to user queries. But it also appears the system uses a traditional Web interface, which redraws the screen after each click, rather than AJAX and similar technologies which provide a smoother, faster user experience.

I don’t want to dwell on the Birst user interface, partly because I haven’t tested it thoroughly and partly because you can judge it for yourself, but mostly because their more important claim is the automated implementation. As I said last March, I think the labor involved with building the system is the biggest obstacle to on-demand BI, so Birst’s claim to have solved this is the real news.

It would take some serious testing to assess how good a job Birst’s automated systems can really do. Still, the system can be useful even if it’s not perfect and it will presumably improve over time. So if you’re thinking about on-demand business intelligence, either for a specific purpose or just to better understand what’s possible, Birst is certainly worth a look.

Incidentally, my quick scan of other on-demand business intelligence vendors (Autometrics, BlinkLogic, Good Data, oco, OnDemandIQ, and PivotLink) showed that only oco made a similar claim about having automated the implementation process.

On the other hand, Good Data, PivotLink LucidEra and possibly oco are using in-memory or columnar databases (PivotLink’s is in-memory and columnar: they win). In theory these should give quicker response than Birst’s on-the-fly OLAP cubes, although actual performance depends on the implementation details. (Speaking of experience, Birst’s database technology has been running at Success Metrics for several years, and has scaled to the terabyte range. I don’t know what scales the other vendors have reached.) It also seems to me that in-memory and columnar databases should be particularly compatible with automated implementation because their simpler structures and greater efficiency make them more forgiving than conventional databases if the automated design is less than optimal. But no one in this particular group of vendors seems to have put the two together.

I don’t know when I’ll have time to give all these other vendors the attention they deserve. But based on what I’ve heard from LucidEra and Birst, and seen on the other vendors’ Web sites, I’m more optimistic about the potential of on-demand business intelligence than I was back in March.

0 LucidEra Takes a Shot at On-Demand Analytics

Back in March, I wrote a fairly dismissive post about on-demand business intelligence systems. My basic objection was that the hardest part of building a business intelligence system is integrating the source data, and being on-demand doesn’t make that any easier. I still think that’s the case, but did revisit the topic recently in a conversation with Ken Rudin, CEO of on-demand business analytics vendor Lucid Era.

Rudin, who has plenty of experience with both on-demand and analytics from working at Salesforce.com, Siebel, and Oracle, saw not one but two obstacles to business intelligence: integration and customization. He described LucidEra’s approach as not so much solving those problems as side-stepping them.

The key to this approach is (drum roll…) applications. Although LucidEra has built a platform that supports generic on-demand business intelligence, it doesn’t sell the platform. Rather, it sells preconfigured applications that use the platform for specific purposes including sales pipeline analysis, order analysis, and (just released) sales lead analysis. These are supported by standard connectors to Salesforce.com, NetSuite (where Rudin was an advisory board member) and Oracle Order Management.

Problem(s) solved, eh? Standard applications meet customer needs without custom development (at least initially). Standard connectors integrate source data without any effort at all. Add the quick deployment and scalability inherent in the on-demand approach, and, presto, instant business value.

There’s really nothing to argue with here, except to point out that applications based on ‘integrating’ data from a single source system can easily be replaced by improvements to the source system itself. LucidEra fully recognizes this risk, and has actually built its platform to import and consolidate data from multiple sources. In fact, the preconfigured applications are just a stepping stone. The company’s long-term strategy is to expose its platform so that other people can build their own applications with it. This would certainly give it a more defensible business position. Of course, it also resurrects the customization and integration issues that the application-based strategy was intended to avoid.

LucidEra would probably argue that its technology makes this customization and integration easier than with alternative solutions. My inner database geek was excited to learn that the company uses a version of the columnar database originally developed by Broadbase (later merged with Kana), which is now open source LucidDB. An open source columnar database—how cool is that?

LucidEra also uses the open source Mondrian OLAP server (part of Pentaho) and a powerful matching engine for identity resolution. These all run on a Linux grid. There is also some technology—which Rudin said was patented, although I couldn’t find any details—that allows applications to incorporate new data without customization, through propagation of metadata changes. I don’t have much of an inner metadata geek, but if I did, he would probably find that exciting too.

This all sounds technically most excellent and highly economical. Whether it significantly reduces the cost of customization and integration is another question. If it allows non-IT people to do the work, it just might. Otherwise, it’s the same old development cycle, which is no fun at all.

So, as I said at the start of all this, I’m still skeptical of on-demand business intelligence. But LucidEra itself does seem to offer good value.

My discussion with LucidEra also touched on a couple of other topics that have been on my mind for some time. I might as well put them into writing so I can freely enjoy the weekend.

- Standard vs. custom selection of marketing metrics. The question here is simply whether standard metrics make sense. Maybe it’s not a question at all: every application presents them, and every marketer asks for them, usually in terms of “best practices”. It’s only an issue because when I think about this as a consultant, and when I listen to other consultants, the answer that comes back is that metrics should be tailored to the business situation. Consider, for example, choosing Key Performance Indicators on a Balanced Scorecard. But vox populi, vox dei (irony alert!), so I suppose I’ll have to start defining a standard set of my own.

- Campaign analysis in demand generation systems. This came up in last week’s post and the subsequent comments, which I highly recommend that you read. (There may be a quiz.) The question here is whether most demand generation systems (Eloqua, Vtrenz, Marketo, Market2Lead, Manticore, etc.) import sales results from CRM systems to measure campaign effectiveness. My impression was they did, but Rudin said that LucidEra created its lead analysis system precisely because they did not. I’ve now carefully reviewed my notes on this topic, and can tell you that Marketo and Market2Lead currently have this capability, while the other vendors I’ve listed should have it before the end of the year. So things are not quite as rosy as I thought but will soon be just fine.