Analytic technologies
Discussion of technologies related to information query and analysis. Related subjects include:
- Business intelligence
- Data warehousing
- (in Text Technologies) Text mining
- (in The Monash Report) Data mining
- (in The Monash Report) General issues in analytic technology
The latest privacy example — pregnant potential Target shoppers
Charles Duhigg of the New York Times wrote a very interesting article, based on a forthcoming book of his, on two related subjects:
- The force of habit on our lives, and how we can/do deal with it. (That’s the fascinating part.)
- A specific case of predictive modeling. (That’s the part that’s getting all the attention. It’s interesting too.)
The predictive modeling part is that Target determined:
- People only change their shopping habits occasionally
- One of those occasions is when they get pregnant
- Hence, it would be a Really Good Idea to market aggressively to pregnant women
and then built a marketing strategy around early indicators of a woman’s pregnancy. Read more
| Categories: Predictive modeling and advanced analytics, Specific users, Surveillance and privacy | Leave a Comment |
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 |
The 2011/2012 Gartner Magic Quadrant for Business Intelligence Platforms — overview 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:
- (This post) 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.
- Third-party analytics, pulling together and expanding on some points I made in the first three posts.
Gartner’s 2011/2012 Magic Quadrant for Business Intelligence Platforms is out. I shall now comment, much as I did on the recent Gartner Magic Quadrant for Data Warehouse Database Management Systems, and at more length than I did on the Gartner MQ for BI Platforms three years back.
I have one current link.
The first thing to note about any Gartner Magic Quadrant is its biases. Some of the bigger grains-of-salt for me were:
- Gartner’s Magic Quadrant methodology has some seriously silly aspects, for example giving high importance to breadth of sales channels as part of “Completeness of Vision”. (Basically, “Completeness of Vision” might as well have been called “Sales and Marketing Maturity”.)
- Gartner based the whole Magic Quadrant report on a survey of 1364 users, of which 1244 — i.e. 91.2% — were vendor-supplied references.
My concerns about that latter point include: Read more
| Categories: Business intelligence | 3 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
Comments on SAS
A reporter interviewed me via IM about how CIOs should view SAS Institute and its products. Naturally, I have edited my comments (lightly) into a blog post. They turned out to be clustered into three groups, as follows:
- SAS faces a number of challenges, not unlike those faced by other high-priced legacy technology vendors.
- It is used by organizations who have large budgets to pay for the product and to pay people to be expert on the product’s intricacies.
- SAS has not integrated with scale-out analytic DBMS technologies as well or quickly as had been hoped, or as earlier marketing suggested was likely.
- SAS has not been strong in helping its users do agile predictive analytics.
- SAS’ strengths are concentrated in product breadth:
- Lots of statistical algorithms.
- Various vertical products that make the modeling techniques more accessible in specific application domains.
- Various approaches to engineering for scalability — no one of those has been a table-thumping success to date, but SAS has the resources to keep trying.
- Some level of integration with its own business intelligence and text analytics products.
- For any particular use case, the burden of proof is on SAS alternatives to show that they have enough pieces in the toolkit to meet the needs.
- SPSS (now owned by IBM) also has legacy issues.
- KXEN is focused on marketing use cases.
- Mahout has been one of the less successful Hadoop-related open source projects.
- R-based technology is still maturing.
- The modeling capabilities (as opposed to just scoring) bundled into RDBMS and well-parallelized tend to be pretty limited. Apparent exceptions tend to just be R repackaged.
| Categories: Analytic technologies, Data warehousing, Hadoop, IBM and DB2, KXEN, Predictive modeling and advanced analytics, SAS Institute | 18 Comments |
Comments on the analytic DBMS industry and Gartner’s Magic Quadrant for same
This year’s Gartner Magic Quadrant for Data Warehouse Database Management Systems is out.* I shall now comment, just as I did on the 2010, 2009, 2008, 2007, and 2006 Gartner Data Warehouse Database Management System Magic Quadrants, to varying extents. To frame the discussion, let me start by saying:
- In general, I regard Gartner Magic Quadrants as a bad use of good research.
- Illustrating the uselessness of — or at least poor execution on — the overall quadrant metaphor, a large majority of the vendors covered are lined up near the line x = y, each outpacing the one below in both of the quadrant’s dimensions.
- I find fewer specifics to disagree with in this Gartner Magic Quadrant than in previous year’s versions. Two factors jump to mind as possible reasons:
- This year’s Gartner Magic Quadrant for Data Warehouse Database Management Systems is somewhat less ambitious than others; while it gives as much company detail as its predecessors, it doesn’t add as much discussion of overall trends. So there’s less to (potentially) disagree with.
- Merv Adrian is now at Gartner.
- Whatever the problems may be with Gartner’s approach, the whole thing comes out better than do Forrester’s failed imitations.
*As of February, 2012 — and surely for many months thereafter — Teradata is graciously paying for a link to the report.
Specific company comments, roughly in line with Gartner’s rough single-dimensional rank ordering, include: Read more
WibiData, derived data, and analytic schema flexibility
My clients at Odiago, vendors of WibiData, have changed their company name simply to WibiData. Even better, they blogged with more detail as to how WibiData works, in what is essentially a follow-on to my original WibiData post last October. Among other virtues, WibiData turns out to be a poster child for my views on derived data and the corresponding schema evolution.
Interesting quotes include:
WibiData is designed to store … transactional data side-by-side with profile and other derived data attributes.
… the ability to add new ad-hoc columns to a table enables more flexible analysis: output data that is the result of one analytic pipeline is stored adjacent to its input data, meaning that you can easily use this as input to second- or third-order derived data as well.
schemas can vary over time; you can easily add a field to a record, or delete a field. … But even though you start collecting that new data, your existing analysis pipelines can treat records like they always did; programs that don’t yet know about the new cookie are still compatible with both the old records already collected, and the new records with the additional field. New programs fill in default values for old data recorded before a field was added, applying the new schema at read time.
schemas for every column are stored in a data dictionary that matches column names with their schemas, as well as human-readable descriptions of the data.
Interesting aspects of the post that don’t lend themselves as well to being excerpted include:
- How the Produce-Gather “analysis calculus” — i.e. framework — works.
- How this all ties into Apache projects (and sub-projects) such as Hadoop, HBase, and Avro.
| Categories: Data models and architecture, Data warehousing, Derived data, NoSQL, WibiData | 3 Comments |