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The End to End BI Concept

Considering the Full Landscape

Cornerstone Solutions® use an End to End BI approach and here’s why. When thinking about Business Intelligence architecture we need to consider the entirety of the component parts as a whole and not simply individually. In other words consideration is made of the full BI system as a whole and the individual components are not treated in exclusivity of each other. This is because the components will need to act interdependently regardless of whether they have been procured from a single or multiple software vendors.

In this respect the BI system is similar to the human body i.e. everything is so closely related that a felt symptom in one area (a pain in the arm) may be caused by an unseen symptom in another part of the body (a problem with an internal organ). To relieve the pain felt in the arm we treat the unseen causal effect in the internal organ. The same concept of unseen relationship and causal effect applies to the Business Intelligence system. Data flows from end to end through the Business Intelligence system in a similar way to blood circulating in the body and must not be blocked, lost or corrupted at any stage. The BI Architect must ensure this.

End to End BI
The components in the BI system are akin to a linked interdependent chain

Interdependency of the BI system

The early phases of a BI implementation can be usefully considered as akin to those of the Rational Unification Process (RUP) stages of Strategy, Inception, Elaboration, and Construction. A well defined BI strategy is very important. However, perhaps paradoxically the Construction phase is often delivered using an Agile delivery method.

Construction frequently commences with source system analysis, end user requirement gathering and the installation and configuration of the software components. If using SAP Business Information Warehouse, cubes and queries will be developed, or if a relational platform is used a dimensional modelling exercise is undertaken and then the physical tables are developed. The ETL system is designed and developed and reports and dashboards are built.

The relationship between these things is one of interdependency. It’s like a linked interdependent chain. This is why in the implementation methodology of BI System Builders we practice our philosophy of End to End BI. End to End BI takes the view that as each component in the BI system has interaction with and therefore dependency on its related components, BI Breakpoints can occur. BI System Builders take full consideration of the interdependencies in the landscape to ensure prevention rather than cure in their End to End BI project delivery method.

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Avoid BI Breakpoints With Cornerstone Solutions

BI System Builders in their End to End BI philosophy refer to the concept of BI breakpoints. The application of our Cornerstone Solutions® will help you avoid BI breakpoints, but what are they?

BI Breakpoints

Here’s an example of a small breakpoint in a BI system with relational tables that illustrates the escalating effort and associated cost. It starts with a poorly defined report specification. In this example no one identifies the specification as being problematic. So the report is developed according to the specification. However, when the report reaches User Acceptance Testing (UAT) it fails. After consultation with the end user that carried out the UAT it is understood that two important report columns are missing, so the report specification is changed. However, the new data columns are not supported by the dimensional model, so this also must be changed requiring modelling and DBA work. This consequence in turn means that the universe and the ETL packages must be changed and so on and so forth. You can see here the interdependence and knock on effect between the components leading to escalating effort and cost.

However, if you know what to look for you can pre-empt where the BI breakpoints might occur and put quality gates in place. Here are some examples of where BI breakpoints might occur in the dimensional model design of a relational database:

  • The use of a data warehouse generated artificial primary key rather a composite primary key on a fact table
  • Snowflaking in a relational model (not SAP OLAP)
  • A table in which its nature is not made explicit though clear naming convention
  • Table joins leading to chasms or fan traps
  • Fact to fact joins (there are exceptions)

Now a dimensional model design can be tricky to fully evaluate while it remains purely on paper or a computer screen but it is possible at this point to put a quality gate in place to minimise the risk of future BI breakpoints.  Firstly, it can be checked for best practice design principles to avoid the types of things listed in the bullet points above.  Another quality gate can occur after the physical model has been generated. This quality gate is to execute business questions against the tables using Structured Query Language (SQL) and to validate that they can be answered. To choose the business questions to be executed via SQL refer to the report specifications or consult the business users.

It is better to write pure SQL statements using a SQL tool than to use SAP BusinessObjects to generate the business questions in queries. It’s a false economy to wait for Web Intelligence reports to be designed to test the physical model. The reason for this is that the design should be validated as soon as possible, and before the universe is generated. To achieve this some test data must be loaded into the tables but it is far more efficient to identify a design weakness at this stage than later on where changes to the physical model will have a knock on effect on the universe design and any dependent reports.

If it becomes apparent in the quality gate that overly complex SQL has to be generated to answer a business question then the design should be revisited and optimized. The business questions should be focussed around any fact to fact joins, snowflaking or bridging tables as these are potential BI Breakpoints. Their existence can lead to very poor performance, miscalculations due to chasms and fan traps, and the prevention of drill across.

To minimise the risk of BI breakpoints occurring the BI Architect should introduce best practice principles along with quality gates early on. Here’s an example of the best practice principle of designing aggregates. The use of aggregates upfront can address three breakpoint areas before they occur:

 

  • Slow query response times
  • Report rendering times
  • Overly complex reports

It is of course vital to select the correct aggregate type to support the end user requirement. There are several different types of aggregates for example invisible aggregates (roll ups), new facts (ETL pre-calculated fields), pre-joined aggregates and accumulating snapshots etc. Aggregates allow the processing effort to be pushed way from the report and into the database engine and ETL program where it can be more efficiently executed.  However, the use of specific aggregates should not be decided upon until the detailed report specifications are available. Choosing the most appropriate aggregates to support the reports requires skill and experience and is not a menial task.

Aggregate tables can significantly reduce the size of the row set returned to the BI reporting tool. As a rule of thumb it is always better for BI reporting tools to work with smaller row sets. It is always best to force as much processing effort as possible back to the ‘gorilla’ of the BI system – the server itself. It can be even better when processing effort and calculations are forced back into the ETL program. However, care should be taken to educate end users when the effort is pushed back into the ETL program for calculated facts, so called ‘New Facts’, because the facts may be semi-additive or not additive at all.

The downside of using aggregate tables is the increased maintenance e.g. administering ten tables instead of five and the ETL effort required to support them. However, their benefits outweigh their cost.

BI breakpoints can manifest in far more areas than just relational tables, they may be seemingly invisible, and costly when they occur because of the independencies of the BI system. However, applying governance to the BI system around breakpoint areas is not always the most popular notion at implementation. This is because it requires the effort to think clever and to apply hard work, rigour, and discipline upfront to pre-empt problems that are almost invisible. We may be tempted to take short cuts, but, it’s better to apply the extra early effort. Entering into small skirmishes and battles in the early stages to iron out BI breakpoints is much better than allowing them to go unchecked and develop into big fire fights later. Regardless of whether your BI system relies on SAP OLAP cubes and queries or relational tables BI breakpoints are a real threat.

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Reducing Total Cost of Ownership In Business Intelligence

Centralising on a single Business Intelligence platform and data warehouse is well known for helping organizations become both more effective and efficient. But it will reduce the total cost of ownership of the Business Intelligence IT system too.

Several efficiencies can be realised. The single Business Intelligence platform can be used to consolidate your numerous disparate reporting mechanisms, meaning that you no longer need to license and maintain them. However, it’s not just the licensing and maintenance costs that can be saved. It can also be expensive to maintain different software from various vendors because the related skill sets must also be maintained. Centralising on a single business intelligence will introduce a common, core skill set that can be shared amongst teams at lower internal cost.

In terms of data, the tedious, error prone, and time expensive practice of manually hand cranking data load can also be replaced with a single ETL tool such Microsoft’s SSIS or Informatica. Not only does this remove the risk of user error in data quality but it brings increased efficiency by freeing team members up to do other work.

A single Business Intelligence platform can also drive down licensing costs. Though no way guaranteed, licensing economies of scale have historically been realised due to business intelligence vendor licensing discounts increasing as the number of user and product licenses bundled into a single purchase increased. This is more cost effective than procuring several small licenses for business intelligence software from different vendors.

This combination of consolidation, easier maintenance, increased efficiency, common skill set, and economy of scale drives down the total cost of ownership.