Predictive modeling and advanced analytics
Discussion of technologies and vendors in the overlapping areas of predictive analytics, predictive modeling, data mining, machine learning, Monte Carlo analysis, and other “advanced” analytics.
Eight kinds of analytic database (Part 1)
Analytic data management technology has blossomed, leading to many questions along the lines of “So which products should I use for which category of problem?” The old EDW/data mart dichotomy is hopelessly outdated for that purpose, and adding a third category for “big data” is little help.
Let’s try eight categories instead. While no categorization is ever perfect, these each have at least some degree of technical homogeneity. Figuring out which types of analytic database you have or need — and in most cases you’ll need several — is a great early step in your analytic technology planning. Read more
What colleges should teach in analytics
Based on a Teradata press release calling attention to the small amount of explicit university instruction in business intelligence, I was asked:
Does BI really need a dedicated undergrad track? What sort of BI and analytics-related skills should students look to obtain now in order to be viable in the job marketplace five years out?
My answers were (slightly edited):
- Most important is a basic, intuitive understanding of statistical significance. If you’re looking at an apparent trend, is it real or just random variation?
- Also crucial are general analytic and quantitative problem-solving skills.
- One also should have a comfort level learning how to use new software tools.
- Everybody in business should have those skillsets. So should people in science, medicine, teaching, journalism, government, and most other vocations.
- The more analytically oriented should add basic programming skills, and basic knowledge of SQL. While SQL’s utter dominance is ebbing a bit, it still will be with us for a very long time.
Of course, there are more specialized skills also worth teaching, in a number of areas, starting with statistics and other predictive modeling technologies. But it’s OK to go through life not knowing those.
| Categories: Analytic technologies, Business intelligence, Data warehousing, NoSQL, Predictive modeling and advanced analytics, Teradata | 1 Comment |
What to think about BEFORE you make a technology decision
When you are considering technology selection or strategy, there are a lot of factors that can each have bearing on the final decision — a whole lot. Below is a very partial list.
In almost any IT decision, there are a number of environmental constraints that need to be acknowledged. Organizations may have standard vendors, favored vendors, or simply vendors who give them particularly deep discounts. Legacy systems are in place, application and system alike, and may or may not be open to replacement. Enterprises may have on-premise or off-premise preferences; SaaS (Software as a Service) vendors probably have multitenancy concerns. Your organization can determine which aspects of your system you’d ideally like to see be tightly integrated with each other, and which you’d prefer to keep only loosely coupled. You may have biases for or against open-source software. You may be pro- or anti-appliance. Some applications have a substantial need for elastic scaling. And some kinds of issues cut across multiple areas, such as budget, timeframe, security, or trained personnel.
Multitenancy is particularly interesting, because it has numerous implications. Read more
Citrusleaf RTA
Citrusleaf has released an add-on product called Citrusleaf RTA (Real-Time Attribution). It’s to be used when:
- You want to update dashboards within a minute.
- You want to update predictive models fairly quickly (within the hour?), although it’s not clear to me how much the models are being updated or changed with that latency.
The metrics envisioned are:
- 100 or so ad impressions per person …
- … for 1 billion or so people …
- … stored for 30-90 days …
- … where each ad impression is a fairly short record …
- … stored on disk …
- … but indexed in a way so that the index can fit into RAM.
- 50-100,000 writes per second. (I didn’t ask on what amount of hardware.)
- Several hundred reads per second.
A consistent relational schema is NOT assumed.
Citrusleaf’s solution is:
- Have one index entry for each of the 1 billion people.
- Bang each new object/record to disk. Include in it a pointer to the previous object/record for the same person.
- Each time a new object/record is added, update the index in place so that it now points to the new once. Hence, the index is sized according to the number of people, not according to the total number of objects/records.
- Eventually let objects/records age off in the obvious way.
The downside is that when you do read 100 objects/records per person, you might need to do 100 seeks.
Vertica as an analytic platform
Vertica 5.0 is coming out today, and delivering the down payment on Vertica’s analytic platform strategy. In Vertica lingo, there’s now a Vertica SDK (Software Development Kit), featuring Vertica UDT(F)s* (User-Defined Transform Functions). Vertica UDT syntax basics start: Read more
| Categories: Analytic technologies, Data warehousing, GIS and geospatial, Predictive modeling and advanced analytics, RDF and graphs, Vertica Systems, Workload management | 7 Comments |
Investigative analytics and derived data: Enzee Universe 2011 talk
I’ll be speaking Monday, June 20 at IBM Netezza’s Enzee Universe conference. Thus, as is my custom:
- I’m posting draft slides.
- I’m encouraging comment (especially in the short time window before I have to actually give the talk).
- I’m offering links below to more detail on various subjects covered in the talk.
The talk concept started out as “advanced analytics” (as opposed to fast query, a subject amply covered in the rest of any Netezza event), as a lunch break in what is otherwise a detailed “best practices” session. So I suggested we constrain the subject by focusing on a specific application area — customer acquisition and retention, something of importance to almost any enterprise, and which exploits most areas of analytic technology. Then I actually prepared the slides — and guess what? The mix of subjects will be skewed somewhat more toward generalities than I first intended, specifically in the areas of investigative analytics and derived data. And, as always when I speak, I’ll try to raise consciousness about the issues of liberty and privacy, our options as a society for addressing them, and the crucial role we play as an industry in helping policymakers deal with these technologically-intense subjects.
Slide 3 refers back to a post I made last December, saying there are six useful things you can do with analytic technology:
- Operational BI/Analytically-infused operational apps: You can make an immediate decision.
- Planning and budgeting: You can plan in support of future decisions.
- Investigative analytics (multiple disciplines): You can research, investigate, and analyze in support of future decisions.
- Business intelligence: You can monitor what’s going on, to see when it necessary to decide, plan, or investigate.
- More BI: You can communicate, to help other people and organizations do these same things.
- DBMS, ETL, and other “platform” technologies: You can provide support, in technology or data gathering, for one of the other functions.
Slide 4 observes that investigative analytics:
- Is the most rapidly advancing of the six areas …
- … because it most directly exploits performance & scalability.
Slide 5 gives my simplest overview of investigative analytics technology to date: Read more
The essence of an application
Once upon a time, information technology was strictly about — well, information. And by “information” what was meant was “data”.* An application boiled down to a database design, plus a straightforward user interface, in whatever the best UI technology of the day happened to be. Things rarely worked quite as smoothly as the design-database/press-button/generate-UI propaganda would have one believe, but database design was clearly at the center of application invention.
*Not coincidentally, two of the oldest names for “IT” were data processing and management information systems.
Eventually, there came to be three views of the essence of IT:
- Data — i.e., the traditional view, still exemplified by IBM and Oracle.
- People empowerment — i.e., Microsoft-style emphasis on UI friendliness and efficiency.
- Operational workflow — i.e., SAP-style emphasis on actual business processes.
Graphical user interfaces were a major enabling technology for that evolution. Equally important, relational databases made some difficult problems easy(ier), freeing application designers to pursue more advanced functionality.
Based on further technical evolution, specifically in analytic and consumer technologies, I think we should now take that list up to five. The new members I propose are:
- Investigative analytics.
- Emotional response.
| Categories: Data warehousing, Facebook, Predictive modeling and advanced analytics, Theory and architecture, Web analytics | 1 Comment |
Oracle and Exadata: Business and technical notes
Last Friday I stopped by Oracle for my first conversation since January, 2010, in this case for a chat with Andy Mendelsohn, Mark Townsend, Tim Shetler, and George Lumpkin, covering Exadata and the Oracle DBMS. Key points included: Read more
Application areas for SAS HPA
When I talked with SAS about its forthcoming in-memory parallel SAS HPA offering, we talked briefly about application areas. The three SAS cited were:
- Consumer financial services. The idea here is to combine information about customers’ use of all kinds of services — banking, credit cards, loans, etc. SAS believes this is both for marketing and risk analysis purposes.
- Insurance. We didn’t go into detail.
- Mobile communications. SAS’ customers aren’t giving it details, but they’re excited about geocoding/geospatial data.
Meanwhile, in another interview I heard about, SAS emphasized retailers. Indeed, that’s what spawned my recent post about logistic regression.
The mobile communications one is a bit scary. Your cell phone — and hence your cellular company — know where you are, pretty much from moment to moment. Even without advanced analytic technology applied to it, that’s a pretty direct privacy threat. Throw in some analytics, and your cell company might know, for example, who you hang out with (in person), where you shop, and how those things predict your future behavior. And so the government — or just your employer — might know those things too.
| Categories: Application areas, Predictive modeling and advanced analytics, SAS Institute, Surveillance and privacy, Telecommunications | 2 Comments |
In-memory, parallel, not-in-database SAS HPA does make sense after all
I talked with SAS about its new approach to parallel modeling. The two key points are:
- SAS no longer plans to go as far with in-database modeling as it previously intended.
- Rather, SAS plans to run in RAM on MPP DBMS appliances, exploiting MPI (Message Passing Interface).
The whole thing is called SAS HPA (High-Performance Analytics), in an obvious reference to HPC (High-Performance Computing). It will run initially on RAM-heavy appliances from Teradata and EMC Greenplum.
A lot of what’s going on here is that SAS found it annoyingly difficult to parallelize modeling within the framework of a massively parallel DBMS such as Teradata. Notes on that aspect include:
- SAS wasn’t exploiting the capabilities of individual DBMS to their fullest; rather, it was looking for an approach that would work across multiple brands of DBMS. Thus, for example, the fact that Aster’s analytic platform architecture is more flexible or powerful than Teradata’s didn’t help much with making SAS run within the Aster nCluster database.
- Notwithstanding everything else, SAS did make a certain set of modeling procedures run in-database.
- SAS’ previous plans to run in-database modeling in Aster and/or Netezza DBMS may never come to fruition.