David Taylors Business Intelligence Blog

On August 5, 2009, the SAP BusinessObjects Innovation Center announced a prototype for moving Sets Technology directly into the Semantic Layer.

Potential Future of Sets

Since the EOL announcement of BusinessObjects Performance Manager and two of the three analytic engines, Process Analysis and Predictive Analysis, there has been an ongoing question on the future of Set Analysis.  I have long been an advocate of the incredible power and importants of sets to help customers get a better understand of how data is changing under the covers.  If you have 100 more customers, that’s great… but how many did you lose and how many did you gain?  You may have lost 50 and actually added 150.  What about my “Gold” Customers?  How many new Gold Customers do I have?  Where did they go?  Did they become Silver members?

We have many customers who have successfully implemented Set Analysis; however the solution had not had significant R&D investment for some time and was tied to the legacy metrics engine of Performance Manager.  How could this technology be integrated into the new XI 3.X platform?

Sets and the Semantic Layer – I’m in Heaven

The Innovation Center has delivered a prototype which show Sets integrated directly into the Semantic Layer.  This prototype is a plug-in to the universe that allows you to create these “sets” , static or dynamic – all in the query panel.  Semantic layer sets lets customers leverage the complexity of  sets natively within that environment.  This is a huge step forward.

Check out the 15 minute video on the SDN website

You can see that this implementation can support all the classic calendar sets which will support of joiners, leavers, stayers, which are at the core of Set Analysis.  In this labs implementation there is support for both Visual Data and Visual Sets; however I hope this is expanded in the future to support Freehand SQL and an imported list.

I think this is a fantastic new innovation and needs to be added to the core product ASAP.  If you think so too, let your voice be heard:  http://sets.uservoice.com

I’ll keep you up-to-date with additional innovations around sets and may provide some additional posts on this topic if there’s interest.  Let me know.

«Good BI»

I was recently working with a universe and attempting to calculate a ratio between two numbers and coming up with some strange results.  I wanted to share this with you so you didn’t waste as much time as I did trying to solve the problem.

Always Returning Zero

In my example, I had two measures and i wanted to calculate the ratio between this.  I was working on a call center universe and I needed to calculate First Call Resolution Ratio.  That would be First Calls/Total Calls.  The problem was that my calculation was always returning 0.  Here is what the SQL looked like:

select count(dbo.Current_Facts.first_call) /count(dbo.Current_Facts.call_id)
from dbo.Current_Facts

There is nothing wrong with the SQL.  The problem was with the data type.  In my case, both the numerator and denominator are integers, therefore the resulting value is also an integer.  Since there are always less first calls than total calls, the values would be a fraction and therefore were truncated to 0.

An Easy Fix

Once you realize the problem, the fix is simple.  Simply modify the numerator to be a float value.  This will cause the calculate to result in a float value.  In my case I changed the first_call value as casted it as a float so my SQL came out like this:

select cast(count(dbo.Current_Facts.first_call) as float) /count(dbo.Current_Facts.call_id)
from dbo.Current_Facts

Although this should fix the problem, there are probably databases that may require you to be more specific in which case you might modify the SQL to look like this:

select cast(cast(count(dbo.Current_Facts.first_call) as float)/cast(count(distinct dbo.Current_Facts.call_id) as float) as float)
from dbo.Current_Facts

Hopefully this little tip will safe you tons of time spent scratching your head.

«Good BI»

So what is Multipass SQL?

Multipass SQL. To calculate comparisons or to correctly calculate non-additive measures in report break rows, the query tool must break the report down into a number of simple queries that are processed separately by the DBMS. The query tool then automatically combines the results of the separate queries in an intelligent way. Breaking a single complex request into several small requests is called multipass SQL. Multipass SQL also allows drilling across several conformed data marts in different databases, in which the processing of a single galactic SQL statement would otherwise be impossible. Finally, multipass SQL gives the aggregate navigator a chance to speed up the report, because each atomic SQL request is simple and easily analyzed by the aggregate navigator. (from Ralph Kimball)

Welcome Multipass 2.0 with BusinessObjects!

Okay – it’s not new, but I dubbed it 2.0 because in my opinion BusinessObjects takes a superior approach to the Multipass SQL problem.

BusinessObjects employs a number of techniques to solve traditional multipass problems in a unique way.  Users of BusinessObjects can get the power of Multipass SQL without the traditional contraints and overheads of inserting records into temporary tables and requiring the report creator to engage in a number of complex steps to get the desired results.  (Anyone ever run out of temp space?)

Highlighted here are some of the most common scenarios in which BusinessObjects employs Multipass 2.0 techniques.

Single dimension across multiple fact tables – The IT organization can configure the Universe (BusinessObjects Semantic Layer) to generate multiple SQL statements when measures exist in from multiple fact tables.

Using the grains of measurement in the same query -  Sometimes you want to be able to do create a query that shows Previous YTD, YTD, QTD, MTD for the same measure, e.g. Revenue.

Requiring the end results to be used in a calculation – Displaying values such as % contribution, ratios (revenue per employee), etc.

Semi-additive measures – My personal favorite.  These measures are traditionally measures that aggregate over all dimensions EXCEPT time.  Some examples are stock level, account balance.  The stock level in the database display the number that are on-hand as of the end of the day.  The account balance displays how much money is in my account and the end of the day.  If I look at these figures across geographies, then they need to be summed up; however if I want to look at the values for the current month, I should only use the value as of today.

Analyzing a subset of data – Sometimes I want to be able to analyze a subset of data to another subset of data.  For example, get all the latest customer transactions and compare them to the historical transactions.

In every single one of these cases, BusinessObjects delivers the results that the user needs without creating temporary tables and inserting thousands of records.  Instead BusinessObjects uses a number of techniques including:

• Derived Tables
• Multi-SQL Universe Options
• Universe Contexts
• Query-Drill
• Report Variables

How To Whitepaper Available

If I’ve whet your appetite for more information, then you will want to download the whitepaper from the SAP SDN site.  This whitepaper explains in detail exactly how to accomplish each of these scenarios using BusinessObjects.  I’ve had some problems with these documents from time to time, so I’ve also made them available from my blog directly.  You can download the whitepaper here and the powerpoint here.

The bottom line is that modern high performance databases are experts at handling extremely complex SQL.  Their optimization engines mean that it is no longer necessary to break queries down into managable pieces and perform multiple passes in the traditional sense.

What Do You Think?

If  you have a complex query scenario that can’t be solved by one of the techniques used in this whitepaper, I’d love to hear from you.

Enjoy.

«Good BI»

So how we do do this exactly? How can I use BusinessObjects to create custom groups on-the-fly? How can I get beyond my requirements for multi-pass SQL and speed up my reports? The answer is Sets.

Set Data

All the information regarding sets is located is the tables called sets_*. The most important tables are:

• set_set – contains the set name, description, etc.
• set_set_detail – contains all the set member information including member key, join date, leave date.

Here is a universe I created that you can download and use with the sample database provided in my earlier blog post. We need to add the set_set and set_set_detail tables to our WebIntelligence Universe so that the set member information they contain can be used within our query.

Here you can see that we created a join to the DW_CRM_CONTACT table. We also created a self-join on the set_set_detail table. The self join make sure that we reference the edate value of 12/31/2999, which is the default edate for current members of any set. In the case of a dynamic set, If a member had left the set then the edate would not be equal to 12/31/2999.

In addition to this, we also joined the set_set table to the set_set_detail table using the setversion_id key. (It may appear that the setversion_id is always the same as the set_id, but it is not so only join the tables via the setversion_id)

Here is a look at the Custom Product Groups

BEST PRACTICE: Whenever you may need to alias a table. If you alias a table, then all references to that table should be aliased. You can see that because I needed to reference the set_set_detail and set_set tables twice, once for products and once for contacts, I aliased both.

User Prompts

After joining the tables, we needed to create the filters for our custom groupings to make it easy for users to select which groups they would like to use on the report.

I specifically defined the filters a multi, which will allow the user to select more than one filter. This is important because it provides even more power for your end-users. Now they can do direct comparisons of one group to another, even if a member (e.g. contact) exists in more than one group. The only downside is that it means that a member can be counted twice, this is why I recommended that when you define your sets you should use naming conventions that identify the sets as being mutually exclusive.

Here is what the prompt will look like in action:

In my universe I did not use cascading prompts to first limit the sets by folder, but that is certainly an option since folder information is located in the set_project table.

Getting Results

After configurating the universe and setting up the prompts this is an example of the results you will see. I have run a report and selected multiple custom groups which are mutually exclusive. These groups on based on the current periods sales amounts:

Here you can see the report is broken down by my custom groups which were defined and processed by the Sets Engine.

Summary

I’ve really enjoyed taking you on a quick tour of Set Analysis and showing you some of the ways that you might get more value out of your existing data assets. Sets offers so many solutions around data analysis:

• Custom Groupings – which allows the organization to more nimble as changes such as mgmt structures, mergers and acquisitions take place.
• Classic Segmentation – Joiners, Leavers, Stayers (which in turn are perfect for analysis by a predictive tool so that you can see why are people joining or leaving? What are the influencers?)
• Advanced Analysis – “Give me all quad-pack customers who have never purchased phone accessories from a company owned retail store.”

The ability to divide customers up into categories of: highly profitable down through :unprofitable, and using that to drive the business because the bottom line of any business is the customer and your organization’s ability to make a profit.

Although dashboards are currently in vogue and everyone wants one, I often wonder how organizations are analyzing the information beyond the dashboards. I have nothing against dashboards, but I would encourage you to ask yourself this. Once we see that sales are falling, what am we going to do? How am we going to do root cause analysis? Were all customers affected? What about products? Segmentation might well hold the answer to these yet unsolved mysteries.