Business Objects
Analysis of business intelligence pioneer Business Objects, now a division of SAP AG. Related subjects include:
- SAP
- Business intelligence
- (in Text Technologies) Business Objects in the text analytics market
- (in The Monash Report) Notes on SAP’s acquisition of Business Objects
BI and quasi-DBMS
I’m on two overlapping posting kicks, namely “lessons from the past” and “stuff I keep saying so might as well also write down”. My recent piece on Oracle as the new IBM is an example of both themes. In this post, another example, I’d like to memorialize some points I keep making about business intelligence and other analytics. In particular:
- BI relies on strong data access capabilities. This is always true. Duh.
- Therefore, BI and other analytics vendors commonly reinvent the data management wheel. This trend ebbs and flows with technology cycles.
Similarly, BI has often been tied to data integration/ETL (Extract/Transform/Load) functionality.* But I won’t address that subject further at this time.
*In the Hadoop/Spark era, that’s even truer of other analytics than it is of BI.
My top historical examples include:
- The 1970s analytic fourth-generation languages (RAMIS, NOMAD, FOCUS, et al.) commonly combined reporting and data management.
- The best BI visualization technology of the 1980s, Executive Information Systems (EIS), was generally unsuccessful. The core reason was a lack of what we’d now call drilldown. Not coincidentally, EIS vendors — notably leader Comshare — didn’t do well at DBMS-like technology.
- Business Objects, one of the pioneers of the modern BI product category, rose in large part on the strength of its “semantic layer” technology. (If you don’t know what that is, you can imagine it as a kind of virtual data warehouse modest enough in its ambitions to actually be workable.)
- Cognos, the other pioneer of modern BI, depending on capabilities for which it needed a bundled MOLAP (Multidimensional OnLine Analytic Processing) engine.
- But Cognos later stopped needing that engine, which underscores my point about technology ebbing and flowing.
A new logical data layer?
I’m skeptical of data federation. I’m skeptical of all-things-to-all-people claims about logical data layers, and in particular of Gartner’s years-premature “Logical Data Warehouse” buzzphrase. Still, a reasonable number of my clients are stealthily trying to do some kind of data layer middleware, as are other vendors more openly, and I don’t think they’re all crazy.
Here are some thoughts as to why, and also as to challenges that need to be overcome.
There are many things a logical data layer might be trying to facilitate — writing, querying, batch data integration, real-time data integration and more. That said:
- When you’re writing data, you want it to be banged into a sufficiently-durable-to-acknowledge condition fast. If acknowledgements are slow, performance nightmares can ensue. So writing is the last place you want an extra layer, perhaps unless you’re content with the durability provided by an in-memory data grid.
- Queries are important. Also, they formally are present in other tasks, such as data transformation and movement. That’s why data manipulation packages (originally Pig, now Hive and fuller SQL) are so central to Hadoop.
The two sides of BI
As is the case for most important categories of technology, discussions of BI can get confused. I’ve remarked in the past that there are numerous kinds of BI, and that the very origin of the term “business intelligence” can’t even be pinned down to the nearest century. But the most fundamental confusion of all is that business intelligence technology really is two different things, which in simplest terms may be categorized as user interface (UI) and platform* technology. And so:
- The UI aspect is why BI tends to be sold to business departments; the platform aspect is why it also makes sense to sell BI to IT shops attempting to establish enterprise standards.
- The UI aspect is why it makes sense to sell and market BI much as one would applications; the platform aspect is why it makes sense to sell and market BI much as one would database technology.
- The UI aspect is why vendors want to integrate BI with transaction-processing applications; the platform aspect is, I suppose, why they have so much trouble making the integration work.
- The UI aspect is why BI is judged on … well, on snazzy UIs and demos. The platform aspect is a big reason why the snazziest UI doesn’t always win.
*I wanted to say “server” or “server-side” instead of “platform”, as I dislike the latter word. But it’s too inaccurate, for example in the case of the original Cognos PowerPlay, and also in various thin-client scenarios.
Key aspects of BI platform technology can include:
- Query and data management. That’s the area I most commonly write about, for example in the cases of Platfora, QlikView, or Metamarkets. It goes back to the 1990s — notably the Business Objects semantic layer and Cognos PowerPlay MOLAP (MultiDimensional OnLine Analytic Processing) engine — and indeed before that to the report writers and fourth-generation languages of the 1970s. This overlaps somewhat with …
- … data integration and metadata management. Business Objects, Qlik, and other BI vendors have bought data integration vendors. Arguably, there was a period when Information Builders’ main business was data connectivity and integration. And sometimes the main value proposition for a BI deal is “We need some way to get at all that data and bring it together.”
- Security and access control — authentication, authorization, and all the additional As.
- Scheduling and delivery. When 10s of 1000s of desktops are being served, these aren’t entirely trivial. Ditto when dealing with occasionally-connected mobile devices.
SAP HANA today
SAP HANA has gotten much attention, mainly for its potential. I finally got briefed on HANA a few weeks ago. While we didn’t have time for all that much detail, it still might be interesting to talk about where SAP HANA stands today.
The HANA section of SAP’s website is a confusing and sometimes inaccurate mess. But an IBM whitepaper on SAP HANA gives some helpful background.
SAP HANA is positioned as an “appliance”. So far as I can tell, that really means it’s a software product for which there are a variety of emphatically-recommended hardware configurations — Intel-only, from what right now are eight usual-suspect hardware partners. Anyhow, the core of SAP HANA is an in-memory DBMS. Particulars include:
- Mainly, HANA is an in-memory columnar DBMS, based on SAP’s confusingly-renamed BI Accelerator/BW Accelerator. Analytics and most OLTP (OnLine Transaction Processing) go against the columnar part of HANA.
- The HANA DBMS also has an in-memory row storage option, used to store metadata, small tables, and so on.
- SAP HANA talks both SQL and MDX.
- The HANA DBMS is shared-nothing across blades or rack servers. I imagine that within an individual blade it’s shared everything. The usual-suspect data distribution or partitioning strategies are available — hash, range, round-robin.
- SAP HANA has what sounds like a natural disk-based persistence strategy — logs, snapshots, and so on. SAP says that this is synchronous enough to give ACID compliance. For some hardware partners, those “disks” are actually Fusion I/O cards.
- HANA is fault-tolerant “across servers”.
- Text support is “coming soon”, which makes sense, given that BI Accelerator was based on the TREX search engine in the first place. Inxight is also in the HANA text mix.
- You can put data into SAP HANA in a variety of obvious ways:
- Writing it directly.
- Trigger-based replication (perhaps from the DBMS that runs your SAP apps).
- Log-based replication (based on Sybase Replication Server).
- SAP Business Objects’ ETL tool.
SAP says that the row-store part is based both on P*Time, an acquisition from Korea some time ago, and also on SAP’s own MaxDB. The IBM white paper mentions only the MaxDB aspect. (Edit: Actually, see the comment thread below.) Based on a variety of clues, I conjecture that this was an aspect of SAP HANA development that did not go entirely smoothly.
Other SAP HANA components include: Read more
Third-party analytics
This is one of a series of posts on business intelligence and related analytic technology subjects, keying off the 2011/2012 version of the Gartner Magic Quadrant for Business Intelligence Platforms. The four posts in the series cover:
- Overview comments about the 2011/2012 Gartner Magic Quadrant for Business Intelligence Platforms, as well as a link to the actual document.
- Business intelligence industry trends — some of Gartner’s thoughts but mainly my own.
- Company-by-company comments based on the 2011/2012 Gartner Magic Quadrant for Business Intelligence Platforms.
- (This post) Third-party analytics, pulling together and expanding on some points I made in the first three posts.
I’ve written a lot this weekend about various areas of business intelligence and related analytics. A recurring theme has been what we might call third-party analytics — i.e., anything other than buying analytic technology and deploying it in your own enterprise. Four main areas include:
- Business intelligence software OEMed to packaged operational application vendors.
- Business intelligence software OEMed to SaaS (Software as a Service) application vendors.
- Business intelligence software bundled into information-selling businesses.
- Stakeholder-facing analytics, which usually is just BI allowing customers (or suppliers, investors, citizens, etc.) to look into one of your databases.
| Categories: Business intelligence, Business Objects, Information Builders, Intersystems and Cache', Jaspersoft, Pentaho, Software as a Service (SaaS) | 1 Comment |
The 2011/2012 Gartner Magic Quadrant for Business Intelligence Platforms — company-by-company comments
This is one of a series of posts on business intelligence and related analytic technology subjects, keying off the 2011/2012 version of the Gartner Magic Quadrant for Business Intelligence Platforms. The four posts in the series cover:
- Overview comments about the 2011/2012 Gartner Magic Quadrant for Business Intelligence Platforms, as well as a link to the actual document.
- Business intelligence industry trends — some of Gartner’s thoughts but mainly my own.
- (This post) Company-by-company comments based on the 2011/2012 Gartner Magic Quadrant for Business Intelligence Platforms.
- Third-party analytics, pulling together and expanding on some points I made in the first three posts.
The heart of Gartner Group’s 2011/2012 Magic Quadrant for Business Intelligence Platforms was the company comments. I shall expound upon some, roughly in declining order of Gartner’s “Completeness of Vision” scores, dubious though those rankings may be. Read more
Business intelligence industry trends
This is one of a series of posts on business intelligence and related analytic technology subjects, keying off the 2011/2012 version of the Gartner Magic Quadrant for Business Intelligence Platforms. The four posts in the series cover:
- Overview comments about the 2011/2012 Gartner Magic Quadrant for Business Intelligence Platforms, as well as a link to the actual document.
- (This post) Business intelligence industry trends — some of Gartner’s thoughts but mainly my own.
- Company-by-company comments based on the 2011/2012 Gartner Magic Quadrant for Business Intelligence Platforms.
- Third-party analytics, pulling together and expanding on some points I made in the first three posts.
Besides company-specific comments, the 2011/2012 Gartner Magic Quadrant for Business Intelligence (BI) Platforms offered observations on overall BI trends in a “Market Overview” section. I have mixed feelings about Gartner’s list. In particular:
- Not inconsistently with my comments on departmental analytics, Gartner sees actual BI business users as favoring ease of getting the job done, while IT departments are more concerned about full feature sets, integration, corporate standards, and license costs.
- However, Gartner says as a separate point that all kinds of users want to relieve some of the complexity of BI, and really of analytics in general. I agree, but don’t think Gartner did a great job in outlining how this complexity reduction could really work.
- Gartner is bullish on mobile business intelligence, but doesn’t really contradict my more skeptical take. Even as it confesses that mobile BI use cases are somewhat thin (my word, not Gartner’s, and no pun intended), it sees mobile BI rapidly becoming mainstream technology.
- Gartner makes a distinction between “data discovery” tools and “enterprise BI” platforms. By “data discovery” I think Gartner means what I’d call the “pattern discovery” focus of investigative analytics. Anyhow, it seems that Gartner:
- Sees users as being confused about how the traditional pattern-monitoring kinds of BI fit with the newer emphasis on investigative analytics, and …
- … shares that confusion itself.
- Gartner observes that “Most BI platforms are deployed as systems of performance measurement, not for decision support.” It evidently sees this as a bad tendency, which is thankfully changing. Automated decisioning is part of the fix Gartner sees, along with collaboration. While I agree on both counts, Gartner oddly doesn’t also connect this to the general rise of investigative analytics.
- Gartner also had a catch-all trend of “new use cases”, listing some examples, but also sort of confessing it wasn’t doing a great job of articulating the point. I think that part of the difficulty is contortions as to what is or isn’t BI; Gartner seems to run into expositional difficulties whenever it touches on the core point that analytics isn’t all about performance-monitoring BI. Another problem is that Gartner doesn’t seem to have really thought through what does and doesn’t work in the area of analytic applications.
Here’s the forest that I suspect Gartner is missing for the trees:
- Even though all-in-one enterprise BI platforms are great at getting data to a multitude of endpoints …
- … and even though the number of endpoints for data are increasing (more users, more devices) …
- … all-in-one enterprise BI platforms fall short in helping the data be used once it arrives …
- … and all-in-one enterprise BI platform vendors will find it hard to catch up with other vendors’ data-use capabilities.
| Categories: Business intelligence, Business Objects, IBM and DB2, Microsoft and SQL*Server, MicroStrategy, Oracle, SAP AG | 11 Comments |
Applications of an analytic kind
The most straightforward approach to the applications business is:
- Take general-purpose technology and think through how to apply it to a specific application domain.
- Produce packaged application software accordingly.
However, this strategy is not as successful in analytics as in the transactional world, for two main reasons:
- Analytic applications of that kind are rarely complete.
- Incomplete applications rarely sell well.
I first realized all this about a decade ago, after Henry Morris coined the term analytic applications and business intelligence companies thought it was their future. In particular, when Dave Kellogg ran marketing for Business Objects, he rattled off an argument to the effect that Business Objects had generated more analytic app revenue over the lifetime of the company than Cognos had. I retorted, with only mild hyperbole, that the lifetime numbers he was citing amounted to “a bad week for SAP”. Somewhat hoist by his own petard, Dave quickly conceded that he agreed with my skepticism, and we changed the subject accordingly.
Reasons that analytic applications are commonly less complete than the transactional kind include: Read more
Some issues in business intelligence
In November I wrote two parts of a planned multi-post series on issues in analytic technology. Then I got caught up in year-end things and didn’t blog for a month. Well … Happy New Year! I’m back. Let’s survey a few BI-related topics.
Mobile business intelligence — real business value or just a snazzy demo?
I discussed some mobile BI use cases in July 2010, but I’m still not convinced the whole area is a legitimate big deal. BI has a long history of snazzy, senior-exec-pleasing demos that have little to do with substantive business value. For now, I think mobile BI is another of those; few people will gain deep analytic insights staring into their iPhones. I don’t see anything coming that’s going to change the situation soon.
BI-centric collaboration — real business value or just a snazzy demo?
I’m more optimistic about collaborative business intelligence. QlikView’s direct sharing of dashboards will, I think, be a feature competitors must and will imitate. Social media BI collaboration is still in the “mainly a demo” phase, but I think it meets a broader and deeper need than does mobile BI. Over the next few years, I expect numerous enterprises to establish strong cultures of analytic chatter (and then give frequent talks about same at industry conferences). Read more
| Categories: Business intelligence, Business Objects, Gooddata, PivotLink, Software as a Service (SaaS) | 10 Comments |
Quick reactions to SAP acquiring Sybase
SAP is acquiring Sybase. On the conference call SAP said Sybase would be run as a separate division of SAP (no surprise). Most of the focus was on Sybase’s mobile technology, which is forecast at >$400 million in 2010 revenues (which would be 30%ish of the total). My quick reactions include: Read more