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OBIEE – what’s in a name?

The unwieldy acronym OBIEE stands for Oracle Business Intelligence Enterprise Edition.

The offering is a loosely coupled assembly of a dozen plus components (eight – by some other counts) both acquired and homegrown. Its beginnings go back 12 years ago to nQuire product which first became Siebel Analytics only to be reborn as OBIEE after Oracle's acquisition of Siebel in 2005 and then Hyperion in 2007. The story does not end here as Oracle continues its acquisition spree with the recent (2012) purchase of Endeca for its e-Commerce search and analytics capabilities.

The current intermediate result is a solid contender for the Enterprise BI Platform, firmly placed at the top-right of Gartner's Magic Quadrant along with Microstrategy, Microsoft, IBM, SAP and SAS.

Oracle's page for Oracle Business Intelligence Enterprise Edition 11g summarizes the suite's functionality in following terms (direct quote, with claims about “cost reduction” and “ease of implementation” left TBD)

• Provides a common infrastructure for producing and delivering enterprise reports, scorecards, dashboards, ad-hoc analysis, and OLAP analysis
• Includes rich visualization, interactive dashboards, a vast range of animated charting options, OLAP-style interactions and innovative search, and actionable collaboration capabilities to increase user adoption

And – by and large - it does deliver on the promises.

One of the important features for the enterprise is integration with Microsoft Office (Word, Excel and PowerPoint). What Oracle has dubbed as “Spacial Intelligence via Map Based Visualization” represents a decent integration of mapping capabilities (not quite ESRI ArcGIS but a nice bundled option nevertheless – and no third party components!)

Among other things to consider is tighter integration with Oracle's ERP/CRM ecosystems (no surprises here as every vendor sooner or later tries to be everything for everybody), and for the organizations with significant Oracle presence this would be an important selling point.

Being redesigned with SOA principles in mind, OBIEE yields itself nicely to integration into SOA- compliant infrastructure. Most organizations choose Oracle Fusion Middleware for the task due to more coherence with OBIEE and the rest of Oracle's stack; but it is by no means a requirement– it can be run with any SOA infrastructures, including open source ones.

For mobile BI capabilities, OBIEE offers Oracle Business Intelligence Mobile (for OBIEE 11g), currently only for Apple's devices – iPad and iPhone – downloadable from Apple iTunes App store. Most features of the OBIEE available in the corporate environment are supported on mobile devices, including geo spacial data integration.

NB: Predictive modeling and data mining are not part of OBIEE per se (it cannot even access data mining functions built into Oracle dialect of SQL!) but they could be surfaced through it. Oracle Advanced Analytics platform represents Oracle's offering in this market.

OBIEE ranks second from the bottom in difficulty of implementation (SAS holding the current record); coupled with a relative dearth of expertise on the market and below-average customer support, this should be considered in evaluation of the OBIEE for adoption in the enterprise.

One interesting twist in OBIEE story is Oracle's introduction of Exalytics In-Memory Machine in 2011 – an appliance that integrates OBIEE with some other components such as Oracle Essbase and Oracle TimesTen in-memory database. The appliance trend resurrects the idea of a self-contained system in a new context of interconnected world, and Oracle fully embraces it with the array of products such as Exadata, Exalogic and now – Exalytics. By virtue of coming fully integrated and preconfigured it supposedly addresses the difficulties of installation and integration – at a price; this is designed to be a turn-key solution for an enterprise but its full impact (and validity of the claim) remains to be seen.

So, to sum it up:


It is a solid enterprise class BI platform with all standard features of a robust BI – reports, scorecards, dashboards (interactive and otherwise), OLAP capabilities, mobile apps,
integration with Microsoft Office, SOA compliant architecture. It also includes pre-defined analytics applications for horizontal business processes (e.g. finance, procurement, sales) as well as additional vertical analytical models for the industries (to help to establish common data model)


It is evolving through acquisitions and integration thereof which affects coherence and completeness of vision; no integrated predictive modeling and data mining capabilities,
ranks rather low on ease of deployment and use as well as on quality of support; rather shallow (and therefore expensive) talent pool; with all being factored in, the TCO could
potentially be higher than comparable offerings from other vendors.



Ethical limits of Business Intelligence

Intelligence of all kinds can be gleaned from the mounds of data accumulated from our daily interactions with the outside world such as business intelligence or social intelligence. It then can be used to manipulate our behavior to the benefit of the data collector/analyst.

Here is, for example,  how IKEA and Costco utilize information "to turn browsers into buyers, and making buyers to spend more". A new layout of the store floor or combination of sounds/lights/olfactory stimuli to put us in "buying mode", targeted advertising, mass customization based upon data collected from purchasing history, Facebook, LinkedIn, Google+... For example:

"In research yet to be published, a University of Alberta team has proven that what we smell and hear affects what we buy: When a sample group smelled the relaxing scent of lavender, 77% wanted a soothing iced tea, but when the same group smelled the arousing aroma of grapefruit, 70% reached for an energy drink. When the researchers played Mozart’s Sonata in D Major at a slow tempo, 71% wanted iced tea, but when the piano piece was sped up, 71% wanted an energy drink — an exact reversal."

Where does "legitimate use" stop and "Brave New World"/"1984" take over?

Where is this limit after which these "insights into consumers" behavior become invasion of privacy?



Getting started with Oracle BI: a virtual experience – Part III

(continued from Part II)

Step 5

You are ready to assemble the VMDK files into a working virtual machine. Make sure that VMDK and OVF files are all in the same directory (Figure 1)


Start up VirtualBox application (Figure 1; disregard already imported appliance). The virtual appliance will be created in the default directory - be sure to set up the directory that has enough free space to accommodate files, logs etc.(virtual size for the running appliance will increase the size of VMDK file by ~30%; you need all free space on the hard-drive you can get!)


By default, on Windows machines, the directory will be located on C:\ drive; to change it, go to File > Preference… option. The Default Machine Folder will be under tab [General] - select [Other…] choice from the drop-down box as shown on Figure 2

From the File menu select  [Import Appliance…] option. The “Appliance Import Wizard” screen would appear. Click on [Choose…] button to navigate to the directory where the [Sampleapp_v107_GA.ovf] file is located, and select it.

The next screen will present the summary of the virtual appliance settings including location of the .VMDK files (they have to be in the same directory where .OVF file is).

Click [Import] button to start the process which can take up to several hours - depending on the computer’s caharacteristics.

(continued in Part IV of  Getting started with Oracle BI:  a virtual experience)



Getting started with Oracle BI: a virtual experience – Part II

(continued from Part I)

Step 3.

Download OBIEE Sample application (V107) from the Oracle’s site. The download includes deployment guide, VirtualBox VB Image Key - the deployment descriptor which is needed to convert VMDK image disk file into a working virtual machine, and the VMDK files themselves; the downloads descriptor on the Oracle site is shown on Figure 1.

The VMDK files are hosted at FTP server and you could use an FTP client of your choice (default port 21, user “robic1”, password “1pertg9edq”), or use your browser’s FTP capabilities. I went with the latter option; Figure 2 shows the directory structure for the FTP site:

The number of archives available for download was a bit puzzling, and for some reason- in my experience - the downloaded files were invariably corrupted upon assembly; downloading straight VMDK  files from the Unzipped_Version directory worked for me.

(NB: verfying CHECKSUM - see file [checksum.md5] on Fig.2 - would provide reasonable assurance that the files were not tampered with; for instance, you could use FastSum free utility for this)

As part of the download, click on VB Image Key (.ovf) link shown on the Figure 1; both the OVF key file and four .VMDK files must be in the same directory.

The downloads take approximately 25GB of space. Make sure that you have plenty of space for the download and installation.

(continued in Part III of  Getting started with Oracle BI:  a virtual experience)


Just-in-time Data Warehousing

Just-in-time Data Warehousing

In his article Is an Enterprise Data Warehouse Still Required for Business Intelligence? the author Colin White of BI-Research ponders arguments against Enterprise Data Warehouse(EDW) as foundation for Business Intelligence (BI).

He's got a point. As BI becomes real-time (e.g. quant trading algorithms) there simply is not enough time to persist the data in the data warehouse and then extract it for analysis. At the same time, operating with just current data  there might be not enough information to base the decisions off. The answer, of course, is that the both systems are complementary. The "traditional" data warehouse stores the wealth of information, and operational BI uses it to match transitory patterns of the OLTP records against those mined from the EDW. In a sense, this is very similar to how human intuition works as we are sometimes forced to make split second decisions based on the most insufficient data available drawing upon the experience accumulated up to the moment...

The EDW is not going away anytime soon; the justifications for its existence remain as valid today as they were back in the days of yore. Colin White lists 5 key reasons :

  1. the data was not usually in a suitable form for reporting,
  2. the data often had quality issues,
  3. decision support processing degraded business transaction performance,
  4. data was  often dispersed across many different systems
  5. there was a general lack of historical information.

None of these went away, if anything the problems only got worse as we are moving towards evermore distributed,  voluminous and hetero-formatted data. What did change are the speed and processing power which enabled tasks parallelization and distributed computing at acceptable rates of performance, and advances in software engineering that made design, construction and maintenance of complex software-intensive systems manageable.

A system such as IBM Watson would be impossible without all-in-memory data storage, and clever parallelization strategy splitting tasks across ~2,000 CPUs. This just might be a precursor for a just-in-time Enterprise Data Warehouse where all processes we currently perform sequentially would be done in parallel with sufficient speed to make cleansing, accumulation, and analysis nearly simultaneous.


Data Warehousing: an introduction

Here's a brief (32 slides) introduction into Data Warehousing Concepts for Managers. The target audience of the presentation are managers and architects; the goal was to resolve confusion about basic DW concepts and terminology, and get everybody on the same page.

And this presentation (47 slides) targets developers Data Warehousing Concepts for Developers. There is significant overlap between the two, with developer's version going into greater detail presenting underlying technologies.

Further reading:

For dimensional Data Warehousing  go to the source:

The Data Warehouse Lifecycle Toolkit  by Ralph Kimball 

For normalized Data Warehousing:

 Building the Data Warehouse  by W.H. Inmon