From a review of the rather powerful Fair Isaac Blaze Advisor, which will surely be far less successful than its functionality deserves:
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.
1. SAP needed outside talent again. In March I wrote that Shai Agassi’s departure wasn’t as a big a deal as it seemed, because guys like Dennis Moore were still there. Well, by now Dennis Moore is NOT still there, and rumor had more of the good personnel acquisitions leaving as well. And unfortunately, my personal experience of some of those remaining is that they’re ethically unfit for their roles (and that’s putting it kindly).
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 product 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.
|Categories: Analytic technologies, Business intelligence, Business Objects, Enterprise applications, SAP||3 Comments|
I just finished another Monash Letter. It was a follow-up to a previous one that discussed various strategic positioning possibilities in business intelligence. In the prior piece, I pointed out that most leading vendors were pursuing similar strategies — BI as enterprise infrastructure play. In this piece — for Monash Advantage members only — I point out how that sameness allowed for disruption and revolution, and highlight a few trends that are pointing in those directions. Specifically, the trends I cited included:
- New(ish) trends in technology and the marketplace, especially:
- The return of load-and-go. (A major current trend.)
- UI diversity. (An accelerating trend.)
- Analytic business process support. (A huge opportunity for transactional application vendors that they haven’t yet seized.)
- 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.
The most recent Monash Letter – exclusively for Monash Advantage members — spells out some ideas on BI technology and vendor strategy. Specifically, it argues that there are at least four major ways to think about BI and other decision support technologies, namely as:
- 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.
- 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.
- 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.”
- 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.”
Shai Agassi is leaving SAP because, in essence, the old guard didn’t want to turn over the reins to him as fast as he would have liked.* Often, this kind of departure is a bad thing (e.g., Ray Lane at Oracle). But I suspect that SAP may actually be improved by Shai’s leaving.
*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:
- Automate business processes.
- Provide the technical infrastructure for automating business processes.
- 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.
“Decision support”, “information centers”, “business intelligence”, “analytic technology”, and “information services” have been around, in one form or other, for 35+ years. For most of that time, there have been two fundamental ways to sell, market, and position them:
- 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
- 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.
- 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 intelligence (BI) used to be characterized by speed and cost-effectiveness — short sales cycles, low-cost departmental purchases and deployments, evasion of IT departments’ strangleholds of data, and so on and so forth. That focus has blurred, as BI vendors have increasingly focused on analytic applications or enterprise-wide standardization sales. But increasingly I’m seeing signs that the pendulum has swung at least partway back. For example:
- 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.
|Categories: Analytic technologies, Business intelligence, Computing appliances, Data mining, DBMS vendors and technologies, Usability and UI||1 Comment|
It is becoming ever clearer that dashboards aren’t working out too well, any more than predecessor technologies like EIS (Executive Information Systems) did. The recurring problem with these technologies is that if they’re mind-numbingly simple, people don’t find them very useful; but if they’re not, people are overwhelmed and still don’t find them useful. This column by Sandra Gittlen does a good job of spelling the problem out.
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. Read more
Data mining is hugely important, but it does have issues with accessibility. The traditional model of data mining goes something like this:
- 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.
- 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.
- The statistical experts pound away at the data in their dungeons, torturing it until it reveals its secrets.
- The results are made available to business operating units, both as reports and in the form of executable models.
|Categories: Analytic technologies, Data mining, Software as a service, Usability and UI, Verix||8 Comments|
Data mining requires and justifies huge investments. The smallest part is the data mining software itself. A much bigger part is the investment in data warehouse technology, a subject about which I’ve been posting extensively recently on DBMS 2.com. But there’s yet another part to the picture, namely investing in actually gathering data for analysis, that I’ve written about, most recently in a blog I posted elsewhere and am now copying below.