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 future of enterprise application software
Sarah Lacy argues that enterprise application software is due for a change. Her reasons seemingly boil down to:
- Users are increasingly eager for friendlier, consumer-like technology.
- The current generation of apps was installed long enough ago — often before the Year 2000 deadline — that enterprises are willing to contemplate rip-and-replace.
I’m inclined to agree, although I’d add some further, more technological-oriented drivers to the mix.
Changes I envision to enterprise applications include (and these overlap):
- Better integration with communication technology.
- Social software.
- Better stakeholder-facing interfaces.
- Voice control.
- Better integration with analytic technology.
- Dashboard-first UIs.
- Search-first UIs.
- Alert-first UIs.
- Analytic assessment aids (job performance, supplier desirability, expense approval, etc.).
- Automated decisioning.
- Some true analytic apps, interesting or otherwise.
- Better use of different kinds of data.
- Text.
- Machine-generated.
- Analytically-derived data.
| Categories: salesforce.com, Software as a Service (SaaS), Text | 5 Comments |
Quick notes on MySQL Cluster
According to the MySQL Cluster home page, today’s MySQL Cluster release has — give or take terminology details 🙂 — added transparent sharding (Edit: Actually, please see the first comment below) and a memcached interface. My quick comments on all this to a reporter a couple of days ago were:
- Persistent memcached is a useful thing. Couchbase’s sales illustrate that point: http://www.dbms2.com/2012/02/01/couchbase-update/
- MySQL has always given good performance when used just as a key-value store, e.g. http://www.dbms2.com/2010/08/22/workday-technology-stack/ . So it’s reasonable to hope the memcached interface will have good performance out of the box.
- MySQL’s clustering capabilities have long been weak, providing a window of opportunity for companies and products such as Schooner Information and dbShards. The gold standard for clustering is:
- Efficient transparent sharding: http://www.dbms2.com/2011/02/24/transparent-sharding/
- Synchronous replication at much better than two-phase-commit speeds. http://www.dbms2.com/2011/10/23/schooner-pivots-further/
I don’t really know enough about MySQL Cluster right now to comment in more detail.
| Categories: Clustering, memcached, MySQL, NoSQL, OLTP, Open source | 2 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
Hadoop-related market categorization
I wasn’t the only one to be dubious about Forrester Research’s Hadoop taxonomy (or lack thereof). GigaOm’s Derrick Harris was as well, and offered a much superior approach of his own. In Derrick’s view, there’s Hadoop, Hadoop distributions, Hadoop management, and Hadoop applications. Taking those out of order, and recalling that no market categorization is ever precise:
- “Hadoop applications” is a catch-all category. Since Derrick offered suitable caveats around the label, I’m fine with what he said.
- Hadoop management software commonly comes in the form of suites. Derrick’s discussion was solid.
- Derrick seems to want to define “Hadoop” as being whatever is in the relevant Apache projects. Cool. He does seem to wind up on both sides of the “MapR and DataStax put Hadoop MapReduce on top of something that isn’t HDFS — so is that Hadoop or isn’t it?” question, but that’s a tough ambiguity to avoid.
- Derrick could have been a little clearer on the subject of Hadoop distributions.
Let’s drill down into that last one. Derrick refers to Hadoop distributions as “products” that:
package a set of Hadoop projects (MapReduce, Hive, Sqoop, Pig, etc.) in a way that in theory makes them integrate more naturally, and to run both smoothly and securely.
While that’s a reasonable recitation of the idea’s benefits, I’d rather say that a “distribution” of open source software comprises: Read more
| Categories: Cloudera, Hadoop, MapReduce, Open source | Leave a Comment |
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 |
Comments on the 2012 Forrester Wave: Enterprise Hadoop Solutions
Forrester has released its Q1 2012 Forrester Wave: Enterprise Hadoop Solutions. (Googling turns up a direct link, but in case that doesn’t prove stable, here also is a registration-required link from IBM’s Conor O’Mahony.) My comments include:
- The Forrester Wave’s relative vendor rankings are meaningless, in that the document compares apples, peaches, almonds, and peanuts. Apparently, it covers any vendor that includes a distribution of Apache Hadoop MapReduce into something it offers, and that offered at least two (not necessarily full production) references for same.
- The Forrester Wave for “enterprise Hadoop” contradicts itself on the subject of Hortonworks.
- The Forrester Wave for “enterprise Hadoop” is correct when it says “Hortonworks … has Hadoop training and professional services offerings that are still embryonic.”
- Peculiarly, the Forrester Wave for “enterprise Hadoop” also says “Hortonworks offers an impressive Hadoop professional services portfolio”. Hortonworks will likely win one or more nice partnership deals with vendors in adjacent fields, but even so its professional services capabilities are … well, a good word might be “embryonic”.
- Forrester Waves always seem to have weird implicit definitions of “data warehousing”. This one is no exception.
- Forrester gave top marks in “Functionality” to 11 of 13 “enterprise Hadoop” vendors. This seems odd.
- I don’t know why MapR, which doesn’t like HDFS (Hadoop Distributed File System), got top marks in “Subproject integration”.
- Forrester gave top marks in “Storage” to Datameer. It also gave higher marks to MapR than to EMC Greenplum, even though EMC Greenplum’s technology is a superset of MapR’s. Very strange. (Edit: Actually, as per a comment below, there is some uncertainty about the EMC/MapR relationship.)
- Forrester gave higher marks in “Acceleration and optimization” to Hortonworks than to Cloudera and IBM, and higher marks yet to Pentaho. Very odd.
- I’m not sure what Forrester is calling a “Distributed EDW file store connector”, but it sounds like something that Cloudera has provided via partnership to a number of analytic DBMS vendors.
- Forrester’s “Strategy” rankings seem to correlate to a metric of “We’re a large enough vendor to go in N directions at once”, for various values of N.
- Forrester is correct to rank Cloudera’s “Adoption” as being stronger than EMC/Greenplum’s or MapR’s. But Hortonworks’ strong mark for “Adoption” baffles me.
| Categories: Cloudera, Data warehousing, EMC, Greenplum, Hadoop, Hortonworks, MapR, MapReduce, Pentaho | 11 Comments |