Analytics in Sports

I am fan of football and my favorite team is FC Barcelona. Combining sports, specially football with Analytics is just amazing.

Mike Walsh, futurist

In our VK2012 was invited Mike Walsh and he had a keynote about technology future. He instisted that the future of the World is DATA and claimed the most important profession of the future will be Data Scientist. Let the future begin and let say I am a Data Scientist.

Universe in your table

Microsoft Surface brings the Universe in your desk

BI for Customers

BI for everyone, does it sound familiar!

It is a fact that Business Intelligence was dedicated to big companies, enterprises because they have that amount of data to be considered interesting for analytics and BI. Now, Gartner started the idea of Bi for mid-size and small businesses, so they need attention too based on BI surveys. But, have you ever thought for a BI solution in Customer Level, or more detailed do you think you can handle a personal BI solution.
ELA will give you the answer.

ELA solution for Customer Intelligence

What ELA is actually?

Elegant Analytics represents the name of a general BI solution in or group of methodologies in Analytics that adapts to every Business profile. In this case, ELA will provide solution for personal finance and planning of your budget. The name of the product is PFI (Personnal Finance Intelligence). Inspired by the TV Show “Luksusfellen” here in Norway, this BI end-user tool may be a solution for all these who fail to maintain well their own economy and for those who want to perform their economy as well. The purpose of this project is to create a Customer Analytical Cube that would process data for each bank costumer using his/her history for its own benefit and then answer you most important queries that users do against their own data.

This solution will include also benchmarking against an Imaginary subject (Ola Nordman) that can be Min, Max or Avg of the customer’s measures in a certain region, for a period of time, similar age group, sex and income levels.

For having more controle and planning your own economy, will be an extra parameter as Target, so users (bank customers) will put their targets for costs and income a month, quarter or a year ahead and always will be warned when they are about to achieve the amount they targeted.

If you want to read more then follow the link where you can download the full project.

Microsoft Business Intelligence

Picture worth 1000 words

Here is the ultimate answer for your question: "How is my Business?"

Another approach to Time Intelligence

Best Parctices for Time Scale solution
This is an original method that I use when I build SSAS Cubes and it is time to share it with you

Time Intelligence is a common issue for every OLAP structure because Time as dimension apperars in every OLAP project, in every Cube you build, despite business model or type. To handle Time Intelligence good in calculations, aggregations and optimization, you need to use Timescale as well. With Timescale I mean: MonthToDate (MTD), YearToDate (YTD), LastYear(LY) etc..., all these very important to everyday use of Business Intelligence solutions.

Now I will take to technical steps to implement this genius way of handling with Timescale and Time calculations.
First you create a Table for Timescale in the source (in you DB, DWH or Data Mart), with 3-4 columns and 3-4 records for example. Here is a sample for that:

Based on this table you create a Dimension Timescale where Columns are Attributes and Records are Members of that Dimension.
After that, I go to DSV of our Cube, on every Fact Table that needs Timescale (usually all need Timescale) I add Named Calculation FK_Timescale with value 'PE', as in the image above:

I create a relationship FK_Timescale of the Fact Table to Timescale table in Data Source View (DSV) and after I build the Cube I do the same in Dimension Usage, where I create Regular relation between Fact Table and Timescale Dimension as shown above:

I create 2 name sets for MTD and YTD right after Calculate; and the MDX for that is shown above :

CALCULATE;
-- Period to date
-- Month to Date
[Timescale].[Timescale].[MTD] = Sum(MTD([Time Dim].[Hierarchy].CurrentMember),[Timescale].[Timescale].[PE]);
-- Year to Date
[Timescale].[Timescale].[YTD] = Sum(YTD([Time Dim].[Hierarchy].CurrentMember),[Timescale].[Timescale].[PE]);

Now, you have ready implemmented Timescale in the Cube for all your Measures, so you do not need to calculate Timescales for each Measure. Instead of having MTD(YTD) Revenue, you just use Revenue measure and change Timescale from PE to MTD(YTD). Test this with Excel, through Data Connection to OLAP Cube and enjoy possibilities. This way is proven more dynamic, flexible and optimized for query performance.

I would be very pleased and that will help me keeping posting good things about BI, if you find time from your busy schedule to suggest, critic or to share with love this blog or this particular content.

Regards,
Besim