Business Intelligence and Data Science Difference Reading

Data Science Central have shared an updated article from Bill Schmarzo, the Difference Between Business Intelligence and Data Science and Kirk Borne shared this useful summary diagram.

Big Data Storymap

This is really great diagram EMC have created setting out the current state and future state of BI and Big Data with the business challenges.

Cortana Analytics gives us some great tools to be able to assist with this storymap.

Shift in Market Leaders

Gartner Magic Quadrant for Business Intelligence and Analytics Platforms reports the market shift in leaders

Data Warehousing and Modern BI technical pattern

There is a great article explaining the Data Warehousing and Modern BI technical pattern which is deployed via Cortana Intelligence Solutions. The artcile shows a hybrid EDW scenario and how it can be implemented on Azure.





















This model uses Azure SQL Data Warehouse, Azure Analysis Services,  Azure Blob Storage, 
Azure HDInsight and Azure Data Factory. More details can be found here.

Microsoft's Modern Data Warehouse

This article “Modernizing” Your Data Warehouse with Microsoft depicts how the new Microsoft data platform stack fits together.  This article also makes reference to The Modern Data Warehouse  paper from The Data Warehousing Institute (TDWI) providing interesting insight into the future of data warehousing. The replacement of data warehouse platforms over the next 3 years will empower organizations in their quest to leverage big data, provide more business insight, integrate platforms and provide scalability. The Microsoft Whitepaper provides further information.

BI Semantic Model (BISM)

The term BI Semanitc model has been bounded around a lot over the last year. I have come across a few really good articles which explain this in more depth.  The definition is that the  BI Semantic Model is one model for all end user experiences.

Here are my a few of articles that clearly explain this term with a really helpful diagram. 

Updated: Analysis Services – Roadmap for SQL Server Code Name “Denali” and Beyond
http://blogs.technet.com/b/dataplatforminsider/archive/2010/11/12/analysis-services-roadmap-for-sql-server-denali-and-beyond.aspx

So, what is the BI Semantic Model?
http://cwebbbi.wordpress.com/2012/02/14/so-what-is-the-bi-semantic-model/

Understanding the SQL Server 2012 BI Semantic Model (BISM)
http://www.mssqltips.com/sqlservertip/2818/understanding-the-sql-server-2012-bi-semantic-model-bism/

The Multi Facets of a Data Scientist

The Data Scientist role is an interesting evolution in the BI and Data Mining filed.  This role is the new name for a quant and an e-branding of statistical literacy as "data science". There are a few interesting quotes I have seen in the last few weeks defining data scientists which I thought were worth sharing.

•    Defines a Data Scientist as a person with mathematical and statistical skills, an investigative mind, an understanding of computer languages like C++ and Java and ability to write code. http://www.forbes.com/sites/tomgroenfeldt/2012/01/17/big-data-needs-data-scientists-or-quants-or-excel-jockeys/
•    A data scientist is somebody who is inquisitive, who can stare at data and spot trends. It's almost like a Renaissance individual who really wants to learn and bring change to an organization. http://www-01.ibm.com/software/data/infosphere/data-scientist
•    A data scientist includes not only crunching numbers, but also visualizing the results. http://www.technologyreview.com/view/425561/big-data-means-business-needs-mathematicians/
•    The data scientists look for patterns as branches and tributaries join and pull away from the “river.” http://www.forbes.com/sites/danwoods/2012/07/23/what-is-a-data-scientist-tom-wheeler-of-clickfox/2/
•    A new class of engineer, the "data scientist," whose job it is to perform the sophisticated mathematical gymnastics required to extract actionable information from this mass of numbers. http://www.technologyreview.com/view/425561/big-data-means-business-needs-mathematicians/

An article in computing http://www.computing.co.uk/ctg/analysis/2201583/computing-research-the-power-of-data-science explained the difference between the BI specialist and Data Scientist.

“ BI specialists understand data and analytics, their focus tends to be on the technical aspects such as implementing the software and controlling how data is stored within the system….. A data scientist is generally less concerned with the technical nuts and bolts and more interested in the analytical side, revealing the messages that lie hidden in the data and uncovering insights that can deliver immediate competitive advantage”

Data Scientists have good mathematical and communication skills, who will be analytic innovators through their ability to gain insight from big data. A recent study from EMC shared further insight on this new field http://practicalanalytics.files.wordpress.com/2012/01/datascientistinfographic.jpg

Cube Synchronisation Scale Out Methods

There are 4 Analysis Services Synchronisation Methods

• Analysis Services Synch Method
• Backup/Restore Database
• Attach/Detach Database
• Robocopy Method

You can read more after the Querying and Processing cubes for scale out section

http://sqlcat.com/sqlcat/b/technicalnotes/archive/2008/03/16/analysis-services-synchronization-best-practices.aspx

Synchronize Analysis Services Databases
http://msdn.microsoft.com/en-us/library/ms174928.aspx

Other useful articles for scaling out Analysis Services are

REAL PRACTICES: Performance Scaling Microsoft SQL Server 2008 Analysis Services at Microsoft adCenter
http://sqlcat.com/sqlcat/b/whitepapers/archive/2011/03/14/real-practices-performance-scaling-microsoft-sql-server-2008-analysis-services-at-microsoft-adcenter.aspx

Scale-Out Querying with Analysis Services
http://sqlcat.com/sqlcat/b/whitepapers/archive/2007/12/16/scale-out-querying-with-analysis-services.aspx (download white paper http://technet.microsoft.com/library/Cc966449 )

Analysis Services 2008 R2 Performance Guide
Includes
Design Patterns for Scalable Cubes
Tuning Query Performance
Tuning Processing Performance
Special Considerations
http://sqlcat.com/sqlcat/b/whitepapers/archive/2011/10/10/analysis-services-2008-r2-performance-guide.aspx

Data Warehouse Physical Architecture Options

Custom Data Warehouse Architecture
Scale not defined
Custom design
  
Fast Track Reference Architecture
Scale between 4 and 80TB
Rapid Deployment and Use reference configurations optimized for data warehousing.
http://www.microsoft.com/sqlserver/en/us/solutions-technologies/data-warehousing/fast-track.aspx

HP Business Data Warehouse Appliance
Optimised for up to 5TB
Highly tuned solution that integrates hardware and software.
http://www.microsoft.com/sqlserver/en/us/solutions-technologies/appliances/hp-bdw.aspx

Parallel Data Warehouse
Scale to 100s of terabytes
Parallel processing (MPP) architecture to gain scalable performance, flexibility, and hardware choices with the most comprehensive data warehouse solution available.
http://www.microsoft.com/sqlserver/en/us/solutions-technologies/data-warehousing/pdw.aspx