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
Open issues in database and analytic technology
The last part of my New England Database Summit talk was on open issues in database and analytic technology. This was closely intertwined with the previous section, and also relied on a lot that I’ve posted here. So I’ll just put up a few notes on that part, with lots of linkage to prior discussion of the same points. Read more
Interesting trends in database and analytic technology
My project for the day is blogging based on my “Database and analytic technology: State of the union” talk of a few days ago. (I called it that because of when it was given, because it mixed prescriptive and descriptive elements, and because I wanted to call attention to the fact that I cover the union of database and analytic technologies – the intersection of those two sectors is an area of particular focus, but is far from the whole of my coverage.)
One section covered recent/ongoing/near-future trends that I thought were particularly interesting, including: Read more
Research agenda for 2010
As you may have noticed, I’ve been posting less research/analysis in November and December than during some other periods. In no particular order, reasons have included: Read more
Introduction to Gooddata
Around the end of the Cold War, Esther Dyson took it upon herself to go repeatedly to Eastern Europe and do a lot of rah-rah and catalysis, hoping to spark software and other computer entrepreneurs. I don’t know how many people’s lives she significantly affected – I’d guess it’s actually quite a few – but in any case the number is not zero. Roman Stanek, who has built and sold a couple of software business, cites her as a key influence setting him on his path.
Roman’s latest venture is business intelligence firm Gooddata. Gooddata was founded in 2007 and has been soliciting and getting attention for a while, so I was surprised to learn that Gooddata officially launched just a few weeks ago. Anyhow, some less technical highlights of the Gooddata story include: Read more
Ray Wang on SAP
Ray Wang made a terrific post based on SAP’s annual influencer love-in, an event which I no longer attend. Ray believes SAP has been in a “crisis”, and sums up his views as
The Bottom Line – SAP’s Turning The Corner
Credit must be given to SAP for charting a new course. A shift in the management philosophy and product direction will take years to realize, however, its not too late for change. SAP must remember its roots and become more German and less American. The renewed focus must put customer requests and priorities ahead of SAP’s bureaucracy. The emphasis must focus on the relationship. When that reemerges in how SAP works with customers, partners, influencers, and its own employees, SAP will be back in good graces. In the meantime, its time to get to work and deliver. Oracle’s Fusions Apps are coming soon and competitors such as IBM, Microsoft, Epicor, IFS, and SalesForce.com will not relent.
I recall the 1980s, when SAP’s main differentiator, at least in the English-speaking US, was a total commitment to customer success, and when it could be taken for granted that SAP would do business ethically. Things change, and not always for the better.
Anyhow, the reason I’m highlighting Ray’s post is that he makes reference to a number of interesting SAP-cetric technology trends or initiatives. Read more
| Categories: Analytic technologies, Business intelligence, MOLAP, Memory-centric data management, SAP AG, Solid-state memory | 1 Comment |
Boston Big Data Summit keynote outline
Last month, Bob Zurek asked me to give a talk on “Big Data”, where “big” is anything from a few terabytes on up, then moderate a panel on cloud computing. We agreed that I could talk just from notes, without slides. So, since I have them typed up, I’m posting them below.
This week at the Teradata Partners user conference
Teradata tells me that its press embargoes are ending at 9:00 this morning. Here are some highlights of what’s going on, although names, dates, and details will have to await conversations and press releases this week.
- Teradata is productizing “private cloud,” under names including “Teradata Enterprise Analytics Cloud,” “Teradata Agile Analytics Cloud,” and “Teradata Elastic Mart Builder.” I.e., Teradata hopes to leapfrog Greenplum in its “Enterprise Data Cloud” strategy. This is only fair, in that Greenplum lifted the idea from Teradata and eBay in the first place. It also provides major support for what I think is an extremely sensible trend. Give or take issues of who announces and ships what a couple months before or after a competitor, my early thinking is that the main differences between Greenplum and Teradata in this regard will be:
- Virtual as opposed to just physical data marts, based on robust workload management software. (Advantage: Teradata)
- Pricing, deployment options. (Advantage: Greenplum)
- Features that don’t directly relate to enterprise/private cloud. (Advantage: Either, often Teradata.)
- Teradata is generally strengthening its data movement technology, e.g. for making various appliances work in sync. I’m not too clear yet on the details of that. I think this is what Teradata’s phrase “ecosystem management” refers to.
- Teradata is (pre-)announcing – at least as a statement of direction — an appliance based on solid-state drives (SSDs). I’ve thought for a while that Teradata was a leader in thinking through the issues around solid-state memory in data warehousing, so it makes sense that they’re among the leaders in actually coming to market as well. I plan to say more after meeting with, e.g., Carson Schmidt.
- Teradata has achieved a 300%ish speed-up in geospatial processing. I gather this is largely a byproduct of the parallel analytics work Teradata did around strengthening its SAS integration. However, there don’t seem to be a lot of Teradata geospatial users yet.
- Teradata Express, Teradata’s free Windows-based crippleware, is being ported to Amazon EC2 and VMware as well. Presumably to avoid cannibalizing Teradata product sales, there are quite a few limitations on Teradata Express, including system capacity, database size, and “no production use.”
- Teradata continues to extend its optimizations to handle queries issued by business intelligence tools. Previously, the focus of what Teradata discussed in this regard was query rewrite. But soon automatic recommendation and creation of Aggregate Join Indexes – i.e.., materialized views – will be included as well.
Thinking about analytic speed
For a variety of reasons, I don’t plan to post my complete Enzee Universe keynote slide deck soon, if ever. But perhaps one or more of its subjects are worth spinning out in their own blog posts.
I’m going to start with analytic speed or, equivalently, analytic latency. There is, obviously, a huge industry emphasis on speed. Indeed, there’s so much emphasis that confusion often ensues. My goal in this post is not really to resolve the confusion; that would be ambitious to the max. But I’m at least trying to call attention to it, so that we can all be more careful in our discussions going forward, and perhaps contribute to a framework for those discussions as well.
Key points include:
1. There are two important senses of “latency” in analytics. One is just query response time. The other is the length of the interval between when data is captured and when it is available for analytic purposes. They’re often conflated — and indeed I shall do so for the remainder of this post.
2. There are many different kinds of analytic speed, which to a large extent can be viewed separately. Major areas include:
- Data exploration. In-memory OLAP is a huge trend, and QlikView is a hot BI product line.
- Budgeting/planning. In an unprecedentedly frightening economy, annual planning/forecasting cycles may well be too slow.
- Operational integration. This is probably the biggest current area of mission-critical IT advancement. Not coincidentally, it is also the mainstay of the most expensive and complex data warehousing technologies. It’s also an ongoing area of application for event/stream processing, aka CEP.
- General or deep analytics. This is what I seem to spend much of my time writing about — data warehousing price/performance, parallelized data mining, and much more.
- Data administration. Ease of data mart spin-out and administration is becoming a major concern. And of course analytic appliance and DBMS vendors have been telling ease-of-deployment, low-DBA-involvement kinds of stories at least since Netezza first came to market.
There certainly are relationships among those; e.g., a really great analytic DBMS could help speed up any and all of the last three categories. But when assessing your needs, you can go quite far viewing each of those areas separately.
3. It is indeed important to carefully assess your need-for-speed. Acceptable levels of analytic latency vary widely, ranging from sub-millisecond to multi-month. Read more
| Categories: Analytic technologies, Business intelligence, Data warehousing, Presentations | Leave a Comment |
Aster Data enters the appliance game
Aster Data is rolling out a line of nCluster appliances today. Highlights include:
- Configurations ranging from 9 6.25 terabytes to 1 petabyte of user data. (Edit: Here’s the up-to-date data sheet.)
- A $50K “Express Edition” price for <1 terabyte of user data. Unfortunately, that’s the only stated price.
- The option of bundled MicroStrategy.
- “MapReduce” in the name, which suggests something about the positioning — i.e., enterprise decision support, rather than Aster’s usual web/”frontline” emphasis. (Edit: That also fits with Aster’s recent MapReduce-for-.NET announcement.) (Edit: Actual name is Aster MapReduce Data Warehouse Appliance.)
- Claims that because Aster runs effectively on cheaper, more truly “commodity” hardware than competitors, you get more hardware bang for the buck if you buy from Aster.
I don’t have a lot more to add right now, mainly because I wrote at some length about Aster’s non-appliance-specific, non-MapReduce technology and positioning a couple of weeks ago.
| Categories: Analytic technologies, Aster Data, Business intelligence, Data warehouse appliances, Data warehousing, Database compression, MapReduce, Pricing | 16 Comments |
An example of what’s wrong with big vendors’ approaches to BI (SAP in this case)
I just found Chris Kanaracus’ article about SAP’s rollout last month of its “clear enterprises” strategy. The money quote comes from Sara Lee, the user SAP seems to have trotted out:
But Sara Lee has not yet decided to purchase the software, and there are substantial underlying tasks to perform as well, he added.
“This is giving us the horsepower [to analyze data] but we need to have harmonized and structured data underneath it.”
This is from the leading test user of the product?
Business intelligence and the associated data management processes need to be reimagined, and I’m increasingly coming to suspect that the big BI conglomerates aren’t up to the task.