With the world becoming connected through social media platforms and the use of internet more extensive than ever, there has been a massive explosion of data and content. This has led to an unprecedented need for scaling solutions that store and process data. The traditional approach to address such data growth is to buy progressively more powerful hardware until the database can serve all the traffic.

Even larger companies have dealt with the horror of this scalability as they resorted to using traditional relational databases, eventually hitting limits that were unviable both financially and operationally. Google once ran off of 40,000 MySQL installations and Facebook was at one point spending $1M per month for specialized database hardware to serve their pictures.  These unviable solutions led to a re-evaluation of existing database technologies and led to the Not-Only-SQL (NoSQL)  movement. (more…)

Recently, I was accompanied by Boris Evelson of Forrester Research and Ajoy Kumar, leader of Virtusa’s data warehousing and business intelligence practice for a webinar on Pervasive Business Intelligence.  In the webinar, with attendees from industry and blogging community, Boris and Ajoy discussed the issues associated with pervasive business intelligence as well as best practices and benefits organizations can expect to derive from BI.

Before Boris (@bevelson) began his session, I asked him “what is your definition of pervasive BI?”  His answer was a company in which BI is everywhere, data and information are readily available, and an organization which is poised to compete not only on the basis of its goods or services but on the quality of its decisions as well.  That definition places a culture of “informed decision making” at a premium and views BI as a vehicle to deliver the information upon which critical decisions are based.

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Lately there seems to be many organizations engaged in some type of data management or data warehousing initiative. These data warehousing initiatives include the major components such as the data warehouse, operational data store, data marts, ETL and business intelligence frameworks. However, while there is a common understanding as to what an operational data store is, there seems to be varying ideas as to its purpose; specifically, when would a solution that includes an operational data store be appropriate? (more…)