Agile predictive analytics – the heart of the matter
I’ve already suggested that several apparent issues in predictive analytic agility can be dismissed by straightforwardly applying best-of-breed technology, for example in analytic data management. At first blush, the same could be said about the actual analysis, which comprises:
- Data preparation, which is tedious unless you do a good job of automating it.
- Running the actual algorithms.
Numerous statistical software vendors (or open source projects) help you with the second part; some make strong claims in the first area as well (e.g., my clients at KXEN). Even so, large enterprises typically have statistical silos, commonly featuring expensive annual SAS licenses and seemingly slow-moving SAS programmers.
As I see it, the predictive analytics workflow goes something like this Read more
| Categories: Investment research and trading, Predictive modeling and advanced analytics, SAS Institute, Telecommunications, Web analytics | 22 Comments |
Terminology: Data mustering
I find myself in need of a word or phrase that means bring data together from various sources so that it’s ready to be used, where the use can be analysis or operations. The first words I thought of were “aggregation” and “collection,” but they both have other meanings in IT. Even “data marshalling” has a specific meaning different from what I want. So instead, I’ll go with data mustering.
I mean for the term “data mustering” to encompass at least three scenarios:
- Integrated (relational) data warehouse.
- Big bit bucket.
- Big bit stream.
Let me explain what I mean by each. Read more
| Categories: Data warehousing, Investment research and trading, Streaming and complex event processing (CEP), Sybase, Teradata | 12 Comments |
Hope for a new PostgreSQL era?
In a comedy of briefing errors, I’m not too clear on the details of my client salesforce.com’s new PostgreSQL-as-a-service offering, nor exactly on what my clients at VMware are bringing to the PostgreSQL virtualization/cloud party. That said:
- PostgreSQL is good technology.
- MySQL is narrowing the gap, but PostgreSQL is still ahead of MySQL in some ways. (Database extensibility if nothing else.)
- PostgreSQL has a lot of users. (Many of them in academia and/or Russia.)
- Neither EnterpriseDB (which now calls itself “The enterprise PostgreSQL company”) nor the PostgreSQL community leadership have covered themselves with stewardship glory.
- A significant number of interesting DBMS products can be regarded as PostgreSQL forks (e.g. Greenplum, Aster Data nCluster, Netezza if you squint, and Vertica if you stand on your head*).
- PostgreSQL advancement is not dead. For example, Hadapt beta users are running actual PostgreSQL on many nodes each.
- There’s no assurance that Oracle will be a benevolent MySQL steward forever. (Specifically, Oracle’s “Play nicely with others” antitrust commitments expire in 2014.)
So I think it would be cool if one or the other big company put significant wood behind the PostgreSQL arrow.
*While Vertica was originally released using little or no PostgreSQL code — reports varied — it featured high degrees of PostgreSQL compatibility.
| Categories: Aster Data, EnterpriseDB and Postgres Plus, Greenplum, MySQL, Netezza, Open source, salesforce.com, Vertica Systems | 8 Comments |
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.
Clarifying SAND’s customer metrics, positioning and technical story
Talking with my clients at SAND can be confusing. That said:
- I need to revise my figures for SAND’s customer count way downward.
- SAND finally has a reasonably clear positioning.
- SAND’s product actually seems to have a lot of features.
A few months ago, I wrote:
SAND Technology reported >600 total customers, including >100 direct.
Upon talking with the company, I need to revise that figure downward, from > 600 to 15.
Exasol update
I last wrote about Exasol in 2008. After talking with the team Friday, I’m fixing that now. 🙂 The general theme was as you’d expect: Since last we talked, Exasol has added some new management, put some effort into sales and marketing, got some customers, kept enhancing the product and so on.
Top-level points included:
- Exasol’s technical philosophy is substantially the same as before, albeit not with as extreme a focus on fitting everything in RAM.
- Exasol believes its flagship DBMS EXASolution has great performance on a load-and-go basis.
- Exasol has 25 EXASolution customers, all in Germany.*
- 5 of those are “cloud” customers, at hosting providers engaged by Exasol.
- EXASolution database sizes now range from the low 100s of gigabytes up to 30 terabytes.
- Pretty much the whole company is in Nuremberg.
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 |
StreamBase catchup
While I was cryptic in my general CEP/streaming catchup, I’ll say a bit more regarding StreamBase in particular. At the highest level, non-technically:
- StreamBase once planned to conquer the world.
- However, StreamBase really only sold effectively in the financial trading and intelligence markets.
- StreamBase retrenched, focusing almost exclusively on the financial trading market.
- With StreamBase LiveView, StreamBase is expanding from embedded operational analytics to do (also operational) business intelligence as well.
- StreamBase is hopeful that, perhaps starting with Version 2 or so, LiveView will be successful outside the financial trading market.