But employing a usability expert when designing the tools and observing how users interact with them would go a long way toward improving their usefulness.

My mind utterly boggles each time I discover that a large software vendor still doesn’t seem to have realized this. Or maybe Fair Isaac did do usability engineering, but entrusted it to a blithering incompetent. That frankly would be more reassuring than them not having tried at all.

2. The NetWeaver strategy has been failing. Does anybody care about NetWeaver any more? The whole thing includes some great ideas, but implementation has been lacking.

3. The Business Objects guys are proven successes at integrating disparate BI products suites. The Crystal Reports acquisition proved that.

Before writing more, I should check the extremely one-sided consulting contract I had with SAP, specifically for the expiration date of the NDA. How one-sided? Well, I naively agreed to a clause that I couldn’t sue them under the contract, expecting their concern about their reputation to keep them in line. Since then, they’ve cheated me out of considerable amounts of money that they owed. Arggh. Live and learn.

• New(ish) trends in technology and the marketplace, especially:

1. The return of load-and-go. (A major current trend.)
2. UI diversity. (An accelerating trend.)
3. Analytic business process support. (A huge opportunity for transactional application vendors that they haven’t yet seized.)
4. Expansion from relational/tabular/structured to text/unstructured data. (The biggest opportunity of all, although it’s still in the very early stages.)

• A whole lot of analytics-oriented startups.
• A whole lot of industry consolidation.

Relevant links include:

• My old white paper on analytic business processes.
• A post about BI product segmentation.
• A more recent post on the same subject, with a substantial link list of its own.
• A couple of posts on QlikTech.

My list of potentially major disruptors starts with Endeca, QlikTech, and the open source movement.

1. A specialized application development technology. That’s what BI is, after all. Selling app dev runtimes isn’t a bad business. Selling analytic apps hasn’t gone so well, however.
2. An infrastructure upgrade. That’s what the BI vendors have been pushing for some years, as they try to win enterprise vendor-consolidation decisions. To a first approximation, it’s been a good move for them, but it also has helped defocus them from other things they need to be doing.
3. A transparent window on information. As Google, Bloomberg, and Lexis/Westlaw all demonstrate, users want access to “all” the possible information. BI vendors and management theorists alike have erred hugely in crippling enterprise dashboards via dogmas such as “balanced scorecards” and “seven plus-or-minus two.”
4. A communication and collaboration tool. Communication/collaboration is as big a benefit of reporting as the numbers themselves are. I learned this in the 1980s, and it’s never changed. But BI vendors have whiffed repeatedly at enhancing this benefit.

The Letter then goes on to suggest two areas of technical need and opportunity in BI, which may be summarized as:

• “Play very nicely with portals.”
• “Do a much better job of managing personal metrics customization.”

Good launching points for my other research on these subjects are this recent post on analytic technology marketing strategies and two high-concept white papers available here.

*His other stated reasons include two very good and highly admirable ones – working on energy technologies and improving matters in Israel.

SAP’s technical strategy has three core elements:

1. Automate business processes.
2. Provide the technical infrastructure for automating business processes.
3. Encapsulate process and data at the object/process level.

This strategy has been heavily developed and refined on Shai’s watch, with major contributions from lots of other folks. The issue isn’t vision any more. What SAP needs to do better is execute on the vision.

The problem hasn’t exactly been technology implementation. Rather, it’s been technology implementation anybody cares about. SAP has a huge potential advantage in analytics due to its ability to integrate operational and analytical business processes. But has that gotten past the “potential” stage? Dennis Moore’s’ composite apps strategy is brilliant. But has it much affected anybody’s life? Meanwhile, application functionality is so mature that added development may have diminishing practical returns. So I have to ask: Just where exactly does SAP still have a significant product advantage?

SAP is doing plenty of things well enough to mine its massive installed base. But preserving and increasing the size of the base? For that, vision needs to be turned into more substance. And of senior SAP execs I’ve talked with and listened to — in Shai’s case mainly the latter — Shai wasn’t necessarily at the top of the substance list. Indeed, he’s the only one who routinely left me shaking my head about a gap between rhetoric and actual technological fact.

Bottom line: SAP may do better getting out from underneath Shai’s overarching decision-making.

Links:

• The official press release. (March, 2007)
• Some coverage of the Shai Agassi news. (March, 2007)
• My two-part take on SAP’s excellent architectural vision. (December, 2005)
• My overall view of SAP’s consistent strategic focus. (April, 2006)
• A white paper illustrating SAP’s erstwhile potential to dominate analytics. (November, 2004)
• The dark side of SAP. (January, 2007)
• My own take on future energy technology. (April, 2006)

• Access to information
• Application software

More recently – especially the past five years – there’s been a third way:

• Infrastructure upgrade

as early-generation implementations get replaced by newer ones.

At the 50,000 foot level, here’s some of what I see going on:

• Classical BI marketing is floundering. BI vendors don’t know whether they’re in the business of quick/easy information access, analytic apps, or better-enterprise-system-software.
• A few areas of analytic application are being packaged and marketed well, with solid business-process stories and good customer acceptance of same. The biggies are budgeting/planning and CRM analytics. On the whole, however, analytic apps are floundering, or else are little more than reporting front-ends on operational systems (e.g., in network management).
• Data warehouse software startups are on a roll. Especially at the high end, this is a pure infrastructure-upgrade business. There’s plenty of room still for improvement, but multiple vendors each are doing good jobs of marketing on the basis of:
  • Speeds and feeds
  • Ease of deployment
  • Ease of administration
  • Price
  • Credibility
• Data integration is mainly an infrastructure improvement play. After all, that integration COULD be hand-coded. Automating the process is usually a better-infrastructure story.
• Text search is still an information-access story. There are multiple niches where search is booming. But in all cases the story is information access. Evidently the technology and/or market aren’t mature enough yet for strong infrastructure stories. And in the limited cases where text search gets integrated into general application software packages, it’s usually just for information access rather than a real business process.
• Data mining and predictive analytics are mainly information access plays. Yes, the information being accessed is calculated rather than raw. Yes, I believe that the heart of the data mining market is continuous process improvement. Even so, what users buy from the vendors is usually little more than information toolkits.
• Text analytics is mainly an information access play. Text mining and information extraction have two main uses right now. Either they resemble – and indeed often feed into — data mining, or else they are used to enhance search and search-like document access.
• Information services have always been an information access play. When you think about it, the financial-quote-machine business is a huge part of the whole decision support market. Lexis/Nexis is no slouch either. And they’re all about providing information access.

Related links

• This three-headed taxonomy of strategies is similar to one I previously postulated for Microsoft, SAP, and IBMOracle.
• I covered analytic business processes at length in a November, 2004 white paper. Unfortunately, industry progress since then has been relatively slow.
• I’ve written voluminously about data warehouse software startups on DBMS2.
• One example of infrastructure focus is the ease-of-deployment trend.
• Web search and generic enterprise search aren’t the only search areas to focus on information access. (And yes, they’re most definitely separate areas.) Even customer-facing structured search does; the information is just tailored according to different criteria.

• Business Objects and Netezza have announced a mid-range BI appliance.
• Ingres is headed in the same direction.
• QlikTech is enjoying great growth for its fast-deploying BI technology.
• KXEN and Verix offer “easy” data mining technology.
• Search-based BI is trying to circumvent the data warehouse deployment process.

It’s about time.

I think there are lots of problems like that in BI, and what we need to do is step back and consider all the different kinds of BI that enterprises value and need. More precisely, let’s consider the major kinds of use of BI, because it seems that each calls for different kinds of technological support. Here’s one possible list:

• Early warning of situations that require action.
• Communication of company results.
• Deep analysis and decision support.
• Operational analytics.

Here’s what I mean by each category.

Early warning of situations that require action. This is the classic image of BI. People get reports or graphs on paper or on screen, see that some numbers are out of whack, and react accordingly. Dashboards can be a prettier version of the same thing, or they can be more focused on alarms and alerts. Nowadays, alarms and alerts can also arrive by IM, email, text message, pager, and so on.

On the one hand, a large fraction of the economic value in the history of BI has been generated in this area. On the other, technology for doing so is continually perceived as inadequate. I continue to think that KPI management, alerting technology, and so on are advancing much more slowly than they should be. Even the buzz around Business Activity Monitoring doesn’t seem to have accelerated things much.

Nor does this change when the warnings are the product of text or data mining. For example, despite a very interesting approach to generating alerts, at this point in its development Verix delivers them in uninspired ways.

Mainly, the supporting technology here is the standard query/reporting stack, including new dashboard/scorecard/alerting tools. It’s also the main place where data mining and text mining should be more integrated into standard BI than they are – i.e., to define and populate metrics and KPIs.

Communication of company results. Back in the 1980s, the conventional wisdom was that half the benefit of reporting tools was for actual analytics, while half was just for communicating among enterprise employees. That’s probably still valid.

BI vendors had the good idea a few years ago to build out their collaboration capabilities, but generally didn’t follow through on it, SAP somewhat excepted. Bad choice, in my opinion. The use of text search to get at BI results is something of a plausibility argument for my views in this area.

Again, the supporting technology here is largely the standard reporting stack. Portal/collaboration tools should be more involved than they are.

Deep analysis and decision support. Routine, scheduling reporting was covered in my first two categories. But this third one is where the bulk of ad hoc query and data mining fall. Generally, it’s where lots of specialized and/or calculation-intensive analytic technology comes into play. It’s also where the drilldown aspect of standard reporting shows up. Also, this is the area that is driving much of the recent transformation and disruption in the data warehouse market, because different kinds of BI need different kinds of data warehousing technology.

Operational analytics. In operational analytics, a small amount of analysis is done real-time or near-real-time, in connection with an operational business process. The most technically demanding examples are probably the customer-facing ones, such as call centers or personalized e-commerce sites. This is where the buzz around “active/enterprise data warehousing” is concentrated. There may also be interesting messaging aspects. And data mining scoring may be a consideration.

1. Data is assembled in a data warehouse from transactional information, with all the effort and expense that requires. Maybe more data is even deliberately gathered. Or maybe the data is in large part acquired, at moderate cost, from third-party providers like credit bureaus.
2. The database experts fire up long-running, expensive data extraction processes to select data for analysis. Often, special data warehousing technology is used just for that purpose.
3. The statistical experts pound away at the data in their dungeons, torturing it until it reveals its secrets.
4. The results are made available to business operating units, both as reports and in the form of executable models.

Each in its own way, KXEN and Verix (the imminent new name of the company now called Business Events) want to change all that.

KXEN believes they have found a one-size-fits-all set of data mining models and algorithms. This is not an SVM (Support Vector Machine), which they actually don’t offer any more, but rather something else from the fertile brain of SVM co-inventor Vladimir Vapnik, called Structured Risk Minimization. While the details have been published, they asked me not to write about them anyway for some kind of security-through-obscurity competitive reason. So let’s just say that these are not just the linear models they previously were or seemed to be stuck with. (For a small company with limited footprint, there sure is a lot of false information out there about how the whole thing works.)

A limited set of models lets one design a fairly simple user interface, especially when the models are good at helping one zoom through what otherwise can be annoying steps (like variable reduction, in which you choose which 80-90%+ of the data columns to disregard). Based on that relative simplicity, KXEN wants to let business users data mine directly, without being dependent on statistical specialists and their machinery. They position this as providing better results, because it allows rapid-cycle-time data exploration.

They also have a pickier statistical point to make, which is that their model-building process is streamlined and automated enough that it’s realistic to build lots of parallel “local” models, e.g. for each store or region in a retail chain. By way of contrast, in traditional data mining one would normally have one model used for all localities, but perhaps with additional variables indicating which locality the model was currently referring to. KXEN confidently believes that its way is superior, but in a recent discussion didn’t actually provide me with much beyond hand-waving to back that claim up.

I don’t actually have a good feel for how well these pitches are being received by the market. KXEN’s biggest sales successes seem to be via partnerships with various other analytics players, and it’s tough to judge whether that’s due more to price or to embeddability or to the fundamental merits of their overall case.

Business Events, imminently to be renamed Verix, is a raw start-up with a story even more extreme than KXEN’s: Sophisticated analytic results just delivered on a SaaS basis, with no thinking required by the customer at all. Obviously, this can only make sense if the universe of possible results is rather limited, and indeed it is.

Verix’s approach assumes a classical star set-up: A single measure/fact table and a complexly hierarchical set of dimensions. Verix looks exhaustively at time series on the facts, pulls out all series that are showing anomalies in two or more dimensions at once, and isolates exactly the point in the dimension network where the anomaly is occurring. If sales of frobalizing widgets in Houston are off plan, it identifies whether this is really a Houston issue or a Texas one, and whether it’s a problem just for frobalizers or – gulp – for the entire widget category.

The company claims that some insights you get this way just wouldn’t have been found by conventional BI. E.g., if frobalizers are down in Houston but up in Dallas, and the analysis stopped at Texas, nobody (not even the Houston district manager?) would ever know of the great Houstonian frobalizer downturn.

The company sounds like they’re working on all the right things to generalize this model. Initial interest in what they have seems to be concentrated in the pharmaceutical and CPG (Consumer Packaged Goods) industry, although there are a couple of paying telecom customers as well. One thing pharma and CPG have in common is that a lot of your raw data comes from third parties, such as IMS, and so your sales data are visible to your competitors anyway. Given that, it’s easy to believe that the SaaS nature of the service isn’t causing a lot of customer discomfort.

And by the way, IT departments aren’t involved in the Verix buying process whatsoever.

Analytic business processes — or the areas of overlap between analytics and business process — are poorly understood. Business Activity Monitoring and Operational BI? Great buzzwords, but there’s way too little thought put into figuring out exactly which metrics are most useful for making which kinds of business decisions. Continuous planning/budgeting? The surface has only been scratched. A numerate, “one-truth” enterprise culture? Hah. When we identify an enterprise that truly has a pervasive numbers-oriented culture, it usually is one that winds up pathologically managing to a purely short-term set of goals. (But some exceptions to that rule are among the great corporations of the world.)

One area that really needs more consideration is data capture. You can’t analyze data you don’t have. Certain industries have indeed recognized this. E.g., travel and gaming have been hugely successful with loyalty cards; indeed, casino giant Harrah’s probably gets over 100% of its profits via targeted marketing based on the mining of its loyalty card data. Credit transaction data and the like is of course also heavily exploited. I made this whole case in a Computerworld column a year ago, and if you missed it I suggest still checking that column out today.

But that’s all transactional data. The story for text data is much worse. Indeed, survey forms typically try to force people away from just saying what they think, instead giving them endless checklists that bring back unhappy memories of SATs and #2 pencils. Yet text mining technology now exists that makes it possible to glean crucial information from free-form text. If you haven’t already checked it out, you should.

Particularly interesting, I think, are some examples in the area of text data and analytics.