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Agile Analytics by Ken Collier

I am currently reading a book by Ken Collier, called “Agile Analytics, A Value-Driven Approach to Business Intelligence and Data Warehousing”. This book is specifically written for Agile BI and Data Warehouse projects and includes a BI project scenario for a factitious company called FlixBuster. The...

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Big EDW!

Posted by Anahita | Posted in Agile, Business Intelligence, Data Warehouse, Technology | Posted on 09-01-2012

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Big Data is changing the way we need to look at Enterprise Data Warehousing. Previously I posted about big data  in Big Data – Volume, Variety and Velocity!. I also posted about the supporting projects from Apache Hadoop, such as Hbase and Hive in Big Data, Hadoop and Business Intelligence. Today I want to introduce a new concept, or better say an original idea. Big EDW!  Yes, Business Intelligence and Data Warehousing also will have to turn to Big BI and Big EDW!

So what makes the fabric of Big EDW and Big BI Analytics? The answer is the ability to analyse and make sense of Big Data, which covers not only the 20% of the structured data that organisations keep on their relational and dimensional databases, but also the vast remaining 80% unstructured data scattered in digital and web documents such as Microsoft Word, MS Excel, MS PowerPoint, MS Visio,  MS Project, as well as web data such as social media, wikis, web sites and other formats such as pictures, videos, and log files. I have posted about the meaning of unstructured data  previously  in On Unstructured Data.

Traditionally Enterprise Data Warehouse is a centralised Business Intelligence System, containing the required ETL programs to access various data sources,   transformation and load into a well designed dimensional model.  The front end BI access tools such as reporting, analytical and dashboards then is used on their own or integrated with the organisations interanet, to give the right users timely access to relevant information for analysis and decision making activities.

The Big Data does not quite  fit into this model for three main reasons, volume, variety and velocity of change and growth. Big EDW will need to break some of the traditional data warehousing concepts, but once done, it will create value that has many folds of magnitude.

Big EDW, should have the ability to be quick and agile in dealing with Big Data. It has to make it available for quick access to many new available data sources  in high volume. Enhanced design patterns or new use cases  have to emerge to make this possible. These patterns and use cases  should make use of more intelligent and faster methods of providing the relevant data when  required. This could be achieved by many methods such as  dimensional modelling, advanced mathematical/statistical models such as bootstrap and jackknife sampling to provide more accurate results for more accurate approximation for mean. median, variances, percentiles and standard deviation of big data.   Apache Hadoop  plays an essential role with projects such as  MapReduce, HDFS, HSQL (Hive SQL) and HBase. New central monitoring tools should be developed and embedded within the Big EDW to handle big data metadata such as social media sources, text analysis, sensor analysis, search ranking, etc.  Parallel Machine Learning and Data Mining, being looked at recently via projects such as Apache Mahout and Hadoop-ML combined with Complex Event Processing (CEP), amongst faster SDLC and project methodologies such as agile scrum for handling the Big EDW life cycle are also becoming standard in the realm of Big EDW.

Note that the phrase “Big EDW”  is not used anywhere else and is the naming that I thought could fit EDW growth in to a system that can also accommodate and manage  Big Data!