Analysis of SAP AG, and most especially its memory-centric BI Accelerator technology. Also covered are SAP’s overall database, connectivity, and analytics strategies. Related subjects include:
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
|Categories: Business intelligence, Business Objects, Data mart outsourcing, Investment research and trading, Log analysis, Metamarkets and Druid, Oracle, SAP AG, SAS Institute, Web analytics, WibiData||16 Comments|
When I drafted a list of key analytics-sector issues in honor of look-ahead season, the first item was “execution of various big vendors’ ambitious initiatives”. By “execute” I mean mainly:
- “Deliver products that really meet customers’ desires and needs.”
- “Successfully convince them that you’re doing so …”
- “… at an attractive overall cost.”
Vendors mentioned here are Oracle, SAP, HP, and IBM. Anybody smaller got left out due to the length of this post. Among the bigger omissions were:
As a new year approaches, it’s the season for lists, forecasts and general look-ahead. Press interviews of that nature have already begun. And so I’m working on a trilogy of related posts, all based on an inquiry about hot analytic trends for 2012.
This post is a moderately edited form of an actual interview. Two other posts cover analytic trends to watch (planned) and analytic vendor execution challenges to watch (already up).
|Categories: Business intelligence, Cloud computing, Data warehouse appliances, Data warehousing, EMC, Greenplum, HP and Neoview, QlikTech and QlikView, SAP AG, Software as a Service (SaaS), Tableau Software, Vertica Systems||4 Comments|
It is widely rumored that there will be a leadership change at HP (Meg Whitman in, Leo Apotheker out). In connection with that, I found myself holding forth on points such as:
- HP needs to make outstanding enterprise systems again.
- They fell away from that target under Mark Hurd, but they surely can hit it again, based on the remnants of DEC (Digital Equipment Corporation), Tandem, the higher-end part of Compaq, and of course the original HP systems group.
- In particular:
- Rumors say that Oracle Exadata 1 boxes, made by HP, were much lower quality than Exadata 2 boxes made by Sun.
- HP Neoview was a waste of good engineering talent.
- I’d like to see a few excellent Vertica appliances.
- I hope the SAP HANA appliances go well, whenever HANA finally becomes a serious product.
- The general move from disk to solid-state memory should offer some opportunities.
In January, 2010, I posited that it might be helpful to view data as being divided into three categories:
- Human/Tabular data –i.e., human-generated data that fits well into relational tables or arrays.
- Human/Nontabular data — i.e., all other data generated by humans.
- Machine-Generated data.
I won’t now stand by every nuance in that post, which may differ slightly from those in my more recent posts about machine-generated data and poly-structured databases. But one general idea is hard to dispute:
Traditional database data — records of human transactional activity, referred to as “Human/Tabular data above” — will not grow as fast as Moore’s Law makes computer chips cheaper.
And that point has a straightforward corollary, namely:
It will become ever more affordable to put traditional database data entirely into RAM. Read more
|Categories: Analytic technologies, Cache, In-memory DBMS, memcached, Memory-centric data management, OLTP, Oracle, Oracle TimesTen, SAP AG, solidDB, Storage, Theory and architecture, VoltDB and H-Store||24 Comments|
Edit: This disclosure has been superseded by a March, 2012 version.
From time to time, I disclose our vendor client lists. Another iteration is below. To be clear:
- This is a list of Monash Advantage members.
- All our vendor clients are Monash Advantage members, unless …
- … we work with them primarily in their capacity as technology users. (A large fraction of our user clients happen to be SaaS vendors.)
- We do not usually disclose our user clients.
- We do not usually disclose our venture capital clients, nor those who invest in publicly-traded securities.
- Included in the list below are two expired Monash Advantage members who haven’t said they will renew, as mentioned in my recent post on analyst bias. (You can probably imagine a couple of reasons for that obfuscation.)
With that said, our vendor client disclosures at this time are:
- Aster Data
- SAND Technology
- Schooner Information Technology
The Forrester Wave: Enterprise Data Warehouse Platforms, Q1 2011 is now out,* hot on the heels of the Gartner Magic Quadrant. Unfortunately, this particular Forrester Wave is riddled with inaccuracy. Read more
|Categories: Analytic technologies, Columnar database management, Data warehousing, EMC, Exadata, Greenplum, Netezza, Oracle, Pricing, SAP AG, Sybase, Teradata, Vertica Systems||8 Comments|
In no particular order: Read more
|Categories: Business intelligence, EnterpriseDB and Postgres Plus, Fun stuff, Hadoop, Humor, In-memory DBMS, MapReduce, Memory-centric data management, Open source, Oracle, SAP AG||2 Comments|
As you might imagine, there are a lot of blog posts I’d like to write I never seem to get around to, or things I’d like to comment on that I don’t want to bother ever writing a full post about. In some cases I just tweet a comment or link and leave it at that.
And it’s not going to get any better. Next week = the oft-postponed elder care trip. Then I’m back for a short week. Then I’m off on my quarterly visit to the SF area. Soon thereafter I’ve have a lot to do in connection with Enzee Universe. And at that point another month will have gone by.
Anyhow: Read more
|Categories: Analytic technologies, Business intelligence, Data warehousing, Exadata, GIS and geospatial, Google, IBM and DB2, Netezza, Oracle, Parallelization, SAP AG, SAS Institute||3 Comments|
Raj Nathan of Sybase has been calling around to chat quickly about the SAP/Sybase deal and related matters. Talking with Raj didn’t change any of my initial reactions to SAP’s acquisition of Sybase. I also didn’t bother Raj with too many hard questions, as he was clearly in call-and-reassure mode, reaching out to customers and influencers alike.
That said, Read more
|Categories: Aleri and Coral8, Analytic technologies, Business intelligence, Columnar database management, Complex event processing (CEP), In-memory DBMS, Memory-centric data management, Mid-range, SAP AG, Sybase, Theory and architecture||13 Comments|