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.

• 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.

Obviously, Oracle has the potential to be a titan in analytics. But it doesn’t have its act at all together yet.

And so I agree with a couple of comments on Stephen’s post, to the effect of “Well, gee, no wonder that Siebel’s BI tools look like they’ll be the surviving technology.”

EDIT: Mark Rittman offers a lot of screenshots of Oracle’s Siebel BI Suite. If you look at other posts on his blog, you’ll see Discoverer as well.

… Perhaps my difficulty is that there are several very different uses of planning … For example:

1. For almost any kind of planning exercise, it is useful to be able to import actual ratios, growth rates, etc. as seamlessly as possible.

2. For any kind of “control” or “compare against budget” operational process, it can be helpful to automatically compare actual (or requested) figures against plans. In some cases, instantly reworking the plan is good.

3. When managing employees by objective, full integration with planning would be very nice. SAP had some custom projects along these lines, but when I followed up recently trying to find out if they’d been productized, I didn’t get a favorable response.

4. Forecasting the expected results of a particular strategy, whether via predictive analytics or specialized production planning, can be usefully integrated with general planning and budgeting.

And each of those cases has lots of distinct subcases. And it’s probably not even complete as a top-level list.

Integration of BI and planning is a hot sales/marketing topic for BI companies. But technically it’s just barely begun to, in limited ways, be made to work.

On that webinar, I promised to post a link here to my whitepaper on third-generation analytic business processes. Done. (Scroll down to the bottom of the page.)

The webinar was pretty fast-moving, so I’d encourage you to replay it if you have a bit of time. But if you want to know just the tippy-topmost key points, the list is something like this:

• Portal technology can play a variety of different roles.
• Portals can be like an inhouse Yahoo, for static pages and knowledge management and self-service types of apps.
• Portals can be the best framework for “secondary” or “ad-hoc” operational apps and business processes, as an even lighter-weight technology than composite app development tools.
• Portals are an ideal base technology for dashboards.
• There should be much more BI-based collaboration going on, and portals are the obvious enabling technology for this.

• Classic BI tools have at various times fallen under the purview of the app dev tools group and the app server group.
• Data mining, stemming from a Thinking Machines, Inc. acquisition, is under a whole other group on the East Coast.
• That group is now collocated and somewhat integrated with the group that oversees the MOLAP database capability, which came in via a different Boston-area acquisition (IRI/Express).
• While Oracle brags of its integrated BI stack, enterprise reporting is an exception.
• Discoverer 1.0 (Oracle’s original BI tool), was one of the most impressive new products I ever saw. But then BI technology at Oracle almost stagnated. The reason seems to have been largely a series of platform ports – client/server, Java client, thin client, etc. Other BI vendors faced the same problems, however, and they now have products generally agreed to be ahead of Oracle’s.
• Oracle didn’t seem to have a coherent analytic apps strategy even before the Peoplesoft acquisition, which obviously just confused things further. (Of course, neither does SAP, really, Dennis Moore’s passionate insistence to the contrary notwithstanding.)
• ETL/data integration is of course a historical Oracle sore spot.

That’s even before getting to Oracle’s problems in data warehousing itself, where it can’t beat Teradata and DB2/mainframe at the very high end, and low-cost options like Netezza are a looming threat as well.

What’s particularly ironic is that some of Oracle’s core marketing pitches have a lot to do with analytics. The whole integrated stack story? Doesn’t make much sense when you’re only talking OLTP; only with analytics in the picture is it coherent. The whole scalability story? A few huge websites and the like aside, that’s mainly about data warehousing now.

Obviously, Oracle has the potential to be a titan in analytics. But it doesn’t have its act at all together yet.