Business intelligence
Analysis of companies, products, and user strategies in the area of business intelligence. Related subjects include:
- Data warehousing
- Business Objects
- Cognos
- QlikTech
- (in Text Technologies) Text mining
- (in Text Technologies) Text analytics/business intelligence integration
- (in The Monash Report) Strategic issues in business intelligence
- (in Software Memories) Historical notes on business intelligence
Some big-vendor execution questions, and why they matter
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:
- salesforce.com (multiple subjects).
- SAS HPA.
- The evolution of Hadoop.
Analytic trends in 2012: Q&A
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).
QlikView 11 and the rise of collaborative BI
QlikView 11 came out last month. Let me start by pointing out:
- As one might expect, QlikView 11 contains fairly leading-edge stuff, but also some “better late than never” features.
- The leading-edge stuff is concentrated in the general area of “collaboration”.
- Additionally, QlikTech is always pushing the QlikView user interface ahead in various ways.
- The “Well, it’s about time!” feature list starts with the ability to load QlikView via third-party ETL tools (Informatica now, others coming).
- QlikTech is generally good at putting up pretty pictures of its product. You can find some in the “What’s New in QlikView 11” document via a general QlikView resource page.*
- Stephen Swoyer wrote a good article summarizing QlikView 11.
*One confusing aspect to that paper: non-standard uses of the terms “analytic app” and “document”.
As QlikTech tells it, QlikView 11 adds two kinds of collaboration features:
- Integration with social media, which QlikTech calls “asynchronous integration.”
- Direct sharing of the QlikView UI, which QlikTech calls “synchronous integration.”
I’d add a third kind, because QlikView 11 also takes some baby steps toward what I regard as a key aspect of BI collaboration — the ability to define and track your own metrics. It’s way, way short of what I called for in metric flexibility in a post last year, but at least it’s a small start.
StreamBase LiveView — push-based real-time BI
My clients at StreamBase are coming out with a new product line called LiveView, and I agreed they could launch it via this blog. Key points about StreamBase LiveView Version 1.0 include:
- LiveView is a business intelligence and alerting suite built on/in the rest of StreamBase’s technology, meant to operate on streaming data.
- LiveView is positioned by StreamBase as having a true push event-driven architecture rather than pull/poll.
- StreamBase LiveView is designed to query in-memory data and then have the results change in real time as the data set changes.
- The LiveView user interface is a rapidly changing work in progress.
- LiveView has other Version 1 limitations as well
- LiveView is targeted squarely at StreamBase’s financial trading core market until some of the Version 1 limitations are lifted.
The basic StreamBase LiveView pipeline goes something like: Read more
| Categories: Business intelligence, Data warehousing, Memory-centric data management, StreamBase, Streaming and complex event processing (CEP) | 2 Comments |
Very brief CEP/streaming catchup
When I agreed to launch the StreamBase LiveView product via DBMS 2, I planned to catch up on the whole CEP/streaming area first. Due to the power and internet outages last week, that didn’t entirely happen. So I’ll do a bit of that now, albeit more cryptically than I hoped and intended.
- The upshot of my what to call CEP thread in August was that “streaming” and “event processing” are not the same concept, but it so happens that they have the most traction where they intersect. That said, I both observe and endorse an apparent shift from “event” to “stream” as the core of the terminology, in a reversal of my opinion of several years ago.
- IBM continues to throw a lot of resources at its System S/ InfoSphere Streams product, but I haven’t heard yet of much marketplace success. That said, I believe IBM is still pretty serious about Streams, as one would expect from an effort whose code name so cheekily references System R. In particular, Streams shows up prominently on IBM’s top-level analytic architecture slide.
- Sybase recently released its ESP (Event Stream Processor) 5.0, which it says is the full merger of the Aleri and Coral8 predecessors. You can still get Sybase ESP without buying into the full Sybase RAP stack, and Sybase has no plans to change that.
- Sybase has discontinued all the business intelligence types of products Aleri and Coral8 were developing. Rather, Sybase is OEMing Panopticon, which it reports has been well received. Other than the discontinuation of the BI efforts, there seem to be few Aleri or Coral8 features missing from the merged Sybase ESP product.
- Truviso continues to be out of the picture.
- I have more to say about StreamBase separately.
- I have more to say about Sybase and IBM, which I’ll get to when I can.
- I have nothing new on Progress Apama. I also know little about any of the open source efforts.
Meanwhile, if you want to see technically nitty-gritty posts about the CEP/streaming area, you may want to look at my CEP/streaming coverage circa 2007-9, based on conversations with (among others) Mike Stonebraker, John Bates, and Mark Tsimelzon.
| Categories: Business intelligence, IBM and DB2, StreamBase, Streaming and complex event processing (CEP), Sybase, Truviso | 4 Comments |
Terminology: Operational analytics
It’s time for me to try to define “operational analytics”. Clues pointing me to that need include:
- The term investigative analytics has gotten considerable traction.
- I generally contrast “investigative” and “operational” analytics, for example in the last line of the post linked above, or in my recent introduction to Odiago WibiData.
- It’s clear that I’m conflating several different things in the term. (See for example the operational analytics sections of my posts on eight kinds of analytic database or definitional challenges for 2011.)
- I’m pretty negative about the utility of alternate terms such as “operational BI”.
But as in all definitional discussions, please remember that nothing concise is ever precise.
Activities I want to call “operational analytics” include but are not limited to (and some of these overlap): Read more
| Categories: Analytic technologies, Business intelligence, Predictive modeling and advanced analytics | 6 Comments |
Where Datameer is positioned
I’ve chatted with Datameer a couple of times recently, mainly with CEO Stefan Groschupf, most recently after XLDB last Tuesday. Nothing I learned greatly contradicts what I wrote about Datameer 1 1/2 years ago. In a nutshell, Datameer is designed to let you do simple stuff on large amounts of data, where “large amounts of data” typically means data in Hadoop, and “simple stuff” includes basic versions of a spreadsheet, of BI, and of EtL (Extract/Transform/Load, without much in the way of T).
Stefan reports that these capabilities are appealing to a significant fraction of enterprise or other commercial Hadoop users, especially the EtL and the BI. I don’t doubt him.
| Categories: Business intelligence, Datameer, EAI, EII, ETL, ELT, ETLT, Hadoop | 4 Comments |
Commercial software for academic use
As Jacek Becla explained:
- Academic scientists like their software to be open source, for reasons that include both free-like-speech and free-like-beer.
- What’s more, they like their software to be dead-simple to administer and use, since they often lack the dedicated human resources for anything else.
Even so, I think that academic researchers, in the natural and social sciences alike, commonly overlook the wealth of commercial software that could help them in their efforts.
I further think that the commercial software industry could do a better job of exposing its work to academics, where by “expose” I mean:
- Give your stuff to academics for free.
- Call their attention to your free offering.
Reasons to do so include:
- Public benefit. Scientific research is important.
- Training future customers. There’s huge academic/commercial crossover, especially as students join the for-profit workforce.
| Categories: Business intelligence, Data warehousing, Infobright, Petabyte-scale data management, Predictive modeling and advanced analytics, Scientific research | 7 Comments |
Ingres deemphasized, company now named Actian
Ingres, the company, is:
- Changing its name to Actian.
- Deemphasizing Ingres, the product.
- Emphasizing a set of products that don’t exist yet (or at least aren’t shipping), namely lightweight mobile apps that are business-intelligence-plus-an-action, and technology for building them. These are called “Action Apps”, and are discussed on the Actian company blog.
- Positioning all this as something to do with “big data” (what a shock).
It turns out that Actian was the name of an ancient athletic competition commemorating Augustus’ defeat of Anthony at Actium, a battle that was more recently memorialized in the movie Cleopatra. Frankly, I think Cleopatra Software might have been a more interesting company name, although that could mean execs would have to arrive at sales calls rolled up in a carpet.
| Categories: Actian and Ingres, Business intelligence, Hadapt, Market share and customer counts, VectorWise | 10 Comments |
“Big data” has jumped the shark
I frequently observe that no market categorization is ever precise and, in particular, that bad jargon drives out good. But when it comes to “big data” or “big data analytics”, matters are worse yet. The definitive shark-jumping moment may be Forrester Research’s Brian Hopkins’ claim that:
… typical data warehouse appliances, even if they are petascale and parallel, [are] NOT big data solutions.
Nonsense almost as bad can be found in other venues.
Forrester seems to claim that “big data” is characterized by Volume, Velocity, Variety, and Variability. Others, less alliteratively-inclined, might put Complexity in the mix. So far, so good; after all, much of what people call “big data” is collections of disparate data streams, all collected somewhere in a big bit bucket. But when people start defining “big data” to include Variety and/or Variability, they’ve gone too far.