Category Archives: OBI EE Development

Minimalist Design for the Criteria Tab

MinimalistBathroom

The picture above is an example of minimalist design. It features an uncluttered and simplistic visual scheme that is almost relaxing to the eye and mind. Optional or unnecessary elements are removed. It focuses on only the bare essential elements – the essence of the building or room’s function. Notice there isn’t even a towel rack.

It’s not really for me – it reminds me too much of a concrete future dystopia that I’ve seen too many times in sci-fi movies. It also doesn’t look comfortable at all. But hey that’s just me; I could be wrong.

However when it comes to OBI report design, minimalist is the only way to go when it comes to the Criteria Tab. Whereas architecture and design are subjective, when it comes to keeping the criteria tab minimalist there is a strong, demonstrable benefit to this philosophy. It’s so important that this is the very first thing I look for when I am called in for a performance healthcheck review.

This post will take a quick look into why. Read the rest of this entry

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Build Your Own Time Hierarchy

Every single BI system has a time/date/period/calendar hierarchy in it. If you use the BI Apps, it actually comes with 2 different tables for this purpose alone, along with a ton of OOB capability. However, when you are faced with a pure custom system from scratch, how do you build one? What does it look like? What sort of data should it have in it?

I’m going to lay out what a decent Time Hierarchy looks like from an OBI and data model perspective. From there I’ll then demonstrate how to actually build one yourself instead of relying on complex code. Along the way we’ll mix in some best practices and theory as well.

Your mileage may vary depending on the specifics of your system, but my goal is to make the design and building of your hierarchy take only 3 hours and last the lifetime of your project (or career). Read the rest of this entry

The Single Most Important Thing to Know About the OBI RPD

Over my OBI career I’ve had to review dozens of customer and consultant developed RPDs as part of either a formalized health check engagement or simply a developer code review.  Frequently the customer has some problems that they can’t solve, or that they have solved with a work-around of some sorts.  In some cases this is simply due to not fully understanding the concepts and what you can do with those concepts, but sometimes there is something far worse at play: bad advice from supposed experts.

Twice this year I have come across customers who have been told by other “experts” that Dimensional Hierarchies aren’t that important or they should leave the Content tab empty.  One of these so called experts was even an actual Oracle employee!  This is disastrous advice as I will demonstrate in this post.

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OBI’s Nested Aggregation > Ralph Kimball

Ok so I put a provocative title on this post for a reason.  This post will explore why some of Kimball’s concepts may be out dated when newer query generation or database capabilities are taken into account.  Specifically, I’m going to discuss the OBI concept of Nested Aggregation, also known as Dimension Based Aggregation, in detail.  Using this OBI Modeling technique you can relax a bit on one of the Kimball Dimensional Modeling rules.  I’ll show how it works in a simplified manner, plus identify some things to be careful with and how it does not alleviate you from doing real modeling work. Read the rest of this entry

Tuning Time Series Metrics

In this post I am going to explore some performance issues related to OBI’s time series functions.  Released back in OBI 10g, the ToDate() and Ago() functions brought a significant improvement to the process of easily creating a variety of time series metrics.  In older versions of Siebel Analytics, creating time series was a very manual effort involving a lot of aliases and special joins that could at time become a little confusing to the developer.  They did have a wizard called the Time Series Wizard to assist, but if you are like me you never use wizards J.  The Time series functions however solved that; using them is a piece of cake, requiring only a minor enhancement to the Date dimension.

All is rosy with the world then, correct?  Well not so fast.  The reality is that these functions do some very strange things behind the scenes in order for them to work properly.  So strange in fact that the database engine typically has some difficulty figuring out what to do.  One thing I’ve learned over the years when it comes to database engine performance – keep it simple if you want it to run fast.

As it turns out these strange things that OBI does for the Time Series functions in fact cause a decent performance hit when compared with the old technique.  This short post will discuss this in more depth. Read the rest of this entry

QA for OBI (Part 2 – The Tests)

In the last post I discussed some general, overall topics to prepare you for a better QA cycle.  In this post I’d like to get into the details a bit more to discuss some of the actual tests you can perform across both ETL and OBI layers.  In particular I’m going to focus on data testing for ETL and OBI.

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QA for OBI (Part 1 – Planning)

It seems that every client and every project (I think I’m in the 40’s now) has a different way of QA’ing their OBI system.  This holds true for whether you are doing a BI Apps deployment or simply a new deployment of a custom solution.  This post will set the foundation for the discussion of what needs to be QA’d and how to break the multi-layered solution into more manageable units.

I get the feeling sometimes that my customers are expecting QA to be something perfectly pre-canned that I can simply show up with, fill in the cells in a spreadsheet, and off we go.  The reality is that each project by nature is different, starting from a different place, executed in a different manner, and with different people involved.  It follows that some of the specifics of the QA cycle will therefore be different as well.

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Degenerate Dimensions and Aggregates

An interesting topic came up recently on my latest project that I think is quite informative on how OBI works under the hood.  It involves how to model degenerate dimensional fields into OBI when there are aggregates present.  Individually each of these two topics are relatively well know with solutions for how to handle them, but when combined they can get pretty tricky.  This is especially true when you are using the BI Apps, which to a large degree still rely on the old method of manually mapping Time Series (Ago & ToDate) metrics (as opposed to the OBI 10.x + technique of Time Series functions) which require an explosion of Logical Table Sources (LTSs).

The core of this post concerns what do you do when some of your aggregates don’t have all of the degenerate fields that your base fact has?  What do you need to do to model them properly?  In the explanation I will shed a bit more light on how OBI “thinks” so that you too can model things properly the first time.

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Prototyping with OBI

I’ve always felt that traditional approaches to developing BI content left much to be desired.  Going through a traditional requirements approach with users to determine what reports should do, then spending a significant amount of time documenting them, only to finally show them to users after development and find out they aren’t happy with the result is something that I suspect most of you have encountered.  I believe a better way to come to a solution that makes users happy is by starting right away with a Prototype.  In this article I’ll discuss the generals of prototyping, and specifically how OBI is well suited to this approach.

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Making Dims & Facts Work Together

This is a very common RPD modeling question on IT Toolbox – it comes up every week it seems.  The problem is stated something like this:

I have 2 fact tables and 3 dimension tables.  One of the dimension tables doesn’t work with Fact #2 while the other 2 dimension tables work with both facts.  When I make a report with all 3 dimensions and both facts, Fact #2 is incorrect or missing. Read the rest of this entry