Emotion is Also a Data Point

We must strike a balance between relying on data and emotion when making decisions. A glaring example of this is our last Presidential election here in America. Clinton relied on data and lost. Trump played on people’s emotions and won.


Tools for predictive analytics took a hit in presidential election


Keep It Simple

Simplicity is great if it works. By nature, Business Intelligence is not simple – there are lots of moving parts and a lot of coordination. However, resigning yourself to complexity is not necessary, in my opinion. There may be simpler quick wins along the way. It’s a matter of understanding customer needs and focusing on what they really expect versus “the whole nine yards.”

At a large American health care provider, the prevailing theory was that a new BI system needed to be built. The new system would address a large swath of data analysis requirements in many departments. Meanwhile, a doctor doing cancer research was manually maintaining data in a simple spreadsheet. Moreover, data arrived in plain text format with a lot of “garbage” characters in it. The doctor dutifully read through the text data and picked out what she needed for her spreadsheet. The doctor did this on a regular basis. It was a Herculean task every single time, to say the least. A piece of software to help her sift through the raw data and automatically pull out what’s relevant is all it took to get her to the grant money she was seeking.

Sure, a hospital may need the new system in the near future, but small problems also need to be addressed and now. The small amount of time it takes to implement a simple solution may be well worth it in terms of value to the client and the learning experience for future BI development. In fact, clients who get early simple results sometimes realize they are not ready for a full blown BI project, nor require it. That’s a good thing because nobody wants to find out well into a major undertaking that a customer wasn’t ready. Small engagements can reveal potential blind spots.

Our experience shows us that BI projects tend to be large and that people tend to think large because of it. Whenever people think large, the small things tend to elude them. Rory Sutherland – a marketing expert – gave a very interesting TED talk some time ago discussing the paradox of grand solutions with low or no impact. We’ve had instances where BI projects were reconsidered in light of smaller and more effective proposals.

None of this is to say that BI projects have little or no value. In fact, the exact opposite is true – business intelligence, when implemented correctly, brings out the best in businesses. However, BI can sometimes be a much more successful proposition if we address the small things.

Observations on Oracle BI

Not quite in the “magic” spot within Gartner’s famous quadrant, Oracle still is a formidable competitor in the business intelligence arena.

Oracle Business Intelligence Enterprise Edition (OBIEE) Version 11g is now significantly more substantial than the versions before it. The feature set and system requirements are greatly expanded resulting in mildly shocking sizing. Someone opined whether Larry Ellison is sticking to the “Fusion” strategy at Oracle by weaving as many products as possible into one thereby making it more expensive, more resource hungry, more dependent on consultant hours, and more appliance oriented. This may have been a humorous comment rather than an opinion, but there could be some truth to it.

A “small” proof-of-concept (POC) installation based on Oracle’s own cookbook came with a recommendation for 300 Gigabytes of disk space – and that’s only for the software. In practice, disk usage came close to 200 Gigabytes when ODI ETL was running. This was due to the number and size of temporary files that were being created during the run. Again, the database software and the data in it were on a separate server.

Another revelation came during a presentation of said POC when the laptop with 4 Gigabytes of memory was unable to run the necessary client side Java applets to show off parts of ODI user interface (BIACM). Therefore, we concluded that developers must make sure their workstations are adequately equipped.

Exalytics, Exadata, and Exalogic are Oracle’s specialty appliances configured to handle high availability. These are not cheap by any means, and software complexity lends itself very well to these pieces of hardware. It is possible to avoid the appliances, but if performance requirements are high then it may be very difficult to even come close with alternate solutions.

Constraints aside, Oracle’s differentiation is their pre-built applications – OBIA – which were, presumably, developed in collaboration with various industry leaders. For example, Student Information Analytics (SIA) is an application that could be installed, configured, and used by the Education industry out of the box with little extra work if the delivered product satisfies business requirements. Even if it does not, a fully built baseline system may be a better start than a blank slate. Currently, there are ready made applications for a number of verticals.

In short, 11g OBIEE suite is a capable monster of a software product that takes money and knowledge to implement. It does, however, provide a wealth of analytics options and that can lead to a good return on investment if done right.

Rumors have it, the next version of OBIEE – 12c – may include built-in visual data lineage tools.