Tableau explain why FC Barcelona is still the best team in Spain (and the whole World)

Introduction

Futbol Club Barcelona, also known as Barça is a professional football club, based in Barcelona, Catalonia, Spain. Founded in 1899 by a group of Swiss, English and Catalan footballers led by Joan Gamper, the club has become a symbol of Catalan culture and Catalanism, hence the motto "Més que un club" (More than a club). Unlike many other football clubs, the supporters own and operate Barcelona. It is the world's second-richest football club in terms of revenue, with an annual turnover of $613 million and the third most valuable sports team, worth $2.6 billion.The official Barcelona anthem is the "Cant del Barça" and it is knows to all 480 000 000 fans around the World.

Team Philosophy

Johan Cruyff and Charly Rexach returned to Barcelona in 1988 and began to install a philosophy that would change the way people play and view football forever. Both Cruyff and Rexach admit that it wasn't a completely new philosophy. Admitting that it was one adapted from ideas given to them by Michels, and one which many believe had been given to Europe by the Hungarian side of the early 1950s. The foundation of this philosophy was, and still is, built upon the basic template of touch, technique, maintaining possession, and stretching the pitch with continuous circulation of the ball (Tiqui-Taka). Elements that, at the time, were not valued by many Barcelona supporters (Hunter, 2012)



The chart down (OptaPro) shows the power of Barcelona over other La Liga teams, its play philosophy of short passes and ball possession.





Team Discipline and attractivness (support by fans)

Learn About Tableau

Why FC Barcelona is better than any team, this season also?!

Learn About Tableau

Conclusion

With this in mind, it seems that a lot of clubs steal ideas from Barcelona that they see on the surface, such as tactics, and implement them in the short term, but fail to intertwine them into their own specifically moulded model. It is one thing to use Barcelona as inspiration, but it must be remembered that Barcelona's philosophy is tailored to THEIR own needs, no one else's, and the coaching and playing staff have grown together surrounded by it. They have lived by it and through it. Due to Barcelona's on field success, many are using various aspects of the Barcelona model to shape their own coaching methods, training programmes, and playing style etc. Often missing the point that a club philosophy needs to be self defined and fully committed to by all. There are many different styles of playing football, Barcelona have their own unique style of playing, but it should be remembered that this playing style is born out of wider and deeper beliefs in the cultural values of their personal, independent philosophy.

Benefits from BI in Hospitality

Every Business today take advantages of Business Intelligence solutions, so Hospitality is one of them.
I tried to spot the most important benefits that hospitality industry has from Business Intelligence:


- Profile Guest & Business Segments by any combination of criteria. Break out, analyze and compare these segments on demographics, stay patterns, etc.

- Identify your best guests & uncover those with the highest potential for additional nights or services

- Perform Drill Down & Side-By-Side Analysis or filter on any variable with no limitations to “dimensions” and no cubes to rebuild

- Compare alternative target segments or multiple characteristics side-by-side, even if they overlap

- Track Performance across time: Guests, Return Rate, Length of Stay, Frequency, Recency, Room Rate, upgrades, etc. Track by: Division, Product Line, Guest

Segment, Booking Source/Channel, Geography/Property…any variable on file

- Identify Challenges & Opportunities to quickly spot where your business or guest segments are excelling or under-performing. Monitor changes and easily drill down to see the factors driving this performance

- Access Executive Dashboards tailored for at-a-glance & measuring performance, right at the fingertips of managers across your organization

SSAS 2005: Cube Performance Tuning Lessons

Intro.

A recent project has forced me (which is a good thing) to learn both the internals of SSAS 2005 as well as various performance tuning techniques to get maximum performance out of the OLAP server. It goes without saying that the grain of both your underlying data warehouse's Dimensions & Facts will drive how large your cubes are (total cube space). It also should be a given that Processing Time & Query (MDX) Execution Time usually compete with one another. Given the same grain of a model, the more Grain Data, Indexing, and Aggreggations you process upfront should generally result in a more performant end-user experience (but not always). And while ETL & Cube Processing time is of importance, in the real-world it is the end-user experience (capability and performance) which drives the DW/BI solution's adoption!

Throw-out unused Attributes/Optimizing Attributes/Leverage Member Properties

The more dimensional attributes you create infers a larger cubespace, which also means more potential aggregations and indexes. Take the time to review with your clients the proposed set of attributes and be sure they all provide value as a 1st class Dimension Attribute. Also, if you find attributes are used primarily for informative purposes only consider leveraging Member Properties instead of creating an entire Dimension Attribute. Also, the surrogate key for your dimensions almost never add business value, delete those attributes and assign the keyColumns of your dimension's grain member (ie it's lowest level) attribute to the surrogate key. If an attribute participates in a natural hierarchy but is not useful as a stand-alone attribute hierarchy you should disable it's hierarchy via the AttributeHierarchyEnabled setting. Finally, be aware that if you have a 'deep' dimension (ie like 19 million members) at its lowest grain, any additional attributes you add will incur much overhead as they have a much higher degree of cardinality.

Set Partition Slices

The question of whether or not you must explicitly set a partition's SLICE property is clearly documented incorrectly in SQL Server 2005 Books Online (BOL). For all but the simplest partition designs you should consider setting the SLICE property to match the source property (ie the dataset definitions should match across both properties). For those who do not know, a partition's SLICE is useful for query execution purposes, the SLICE tells the formula|storage engine which partition(s) hold the data that is being requested of it. Please see resources section below for more information on this.

Optimizing Attribute Relationships

Attribute relationships are the developer's mechanism to inform the OLAP server of the relation between attributes. Just like Fact Tables (measure groups) relate to dimension in various ways (Regular, Referenced, Fact, Many-to-Many), dimension attributes can relate to one another in various forms (One-to-One or One-to-Many).Also, you can set the RelationshipType to Flexible or Rigid. If you know your member's change over time (ie reclass), make sure to leave this setting as Flexible, otherwise set it to Rigid. Take the time to thoroughly review your attribute relationships and ensure that both represent their natural hierarchy and are optimized!

Scalable Measure Group Partitioning & Aggregation Strategies

This is one of the better known techniques but it is still of utmost importance. Make sure to design your measure group's partitions to optimize their performance (both processing and query execution). If your system has a 'rolling window' requirement also account for this in your ETL design/framework. You should almost always partition your measure groups by the DATE_KEY and match the underlying relational data warehouse (RDW) partitioning scheme. The basics of this is your 'hot' (the current period) partition should be optimized for query-execution time via setting a different Aggregation Design as opposed to the 'colder' (older) partitions. Also, if your main storage device (ie SAN usually) cannot hold all of your required data, consider leveraging Remote Partitions to offload the extreme 'cold' partitions to slower storage.

Continuously Tune Aggregations Based On Usage

Most documentation in the community clearly states the order of creating effective aggregations is to first leverage the Aggregation Design Wizard, enable the Query Log, and then re-tune the aggregations using the Usage-Based Tuning Wizard. What is not mentioned (near enough anyway) is to continuously retune your aggregations using a refreshed Query Log using the Usage-Based Tuning Wizard. By doing so you are ensuring your aggregations are periodically revised based up recent, real-world usage of your cubes.

Warming the Cache

Another well known technique...by issuing real-world MDX queries onStartUp of the MSOLAP service your cube's cache will be pre-optimized.

Be Mindful of Many-to-Many Dimensions Performance Implications

While M:M dimensions are a powerful feature of SSAS 2005, that power comes at the cost of query-execution time (performance). There are a few modeling scenarios where you almost have to leverage them but be aware that if you are dealing with large amounts of data this could be a huge performance implication at query-time.

Control of the Client Application (MDX): That is the Question

A lesser discussed matter yet still very important is how much control you have over the MDX issued to your cubes. For example, Excel Pivot Tables and other analytical tools allow the user to explore your cubes with freedom pending the security (no Perspectives are not a security measure). If you can write (or control) the MDX being issued by the end-user then obviously you have more control to ensure that actual MDX is optimized.

Got 64-Bit? Multi-Cores?

For enterprise-class Microsoft DW/BI engagements forget about x86/single-core, period. Analysis Services can chew through (process) more data, in higher-degrees of parallelization with x64 multi-core CPUs. Storage|Formula engine cache rely on memory...long-story short, Analysis Services has been designed to perform at higher levels of scalability when running on multi-core/x64 CPUs. Also, be sure to set Analysis Service's Min/Max Thread settings properly for both Query & Processing.

Conclusion

I am dedicated to life-long learning. I cannot take full credit for my content above as much of this knowledge was the work of others as well as my own testing. The resources section listed below gives credit where it is due accordingly. Take the time to learn and implement highly-performant SSAS 2005 cubes to ensure your project's stakeholder’s first query is a performant one!

Resources

Mosha Pasumansky's Blog (MDX 'Father'): http://sqlblog.com/blogs/mosha/default.aspx

Microsoft SQL Server 2005 Analysis Services (best SSAS 2005 OLAP internals book out!) by SAMS Publishing: http://safari.samspublishing.com/0672327821

SQL Server Analysis Services 2005 Performance Tuning Whitepaper (a great tuning document): download.microsoft.com/download/8/5/e/85eea4fa-b3bb-4426-97d0-7f7151b2011c/SSAS2005PerfGuide.doc

HP Solutions with Microsoft SQL Server: http://h71028.www7.hp.com/enterprise/cache/3887-0-0-0-121.html

My Friends & Colleagues at Scalability Experts