May 21 2013
Organizations are aware of what big data has in store, but are not sure if this new technology adds any value to their specific organizational scenarios. Most are thinking "we do not do much with the social media data or web logs or have any real need to analyze such information within our business context". However, the cost effective storage and data discovery platforms that can give new insights are some things people are liking, but they are assuming these are something they may use in the future (and not now).
So, to really understand what could be the best place to start with big data is a great idea to explore.
May 15 2013
Most people segregate the regular data away from big data in their thinking process. The structured relational or non-relational data is often thought of as the regular data and the data in web logs or files is thought of as big data. Traditionally, managers who are successful in last few decades embraced such thinking and reaped great career benefits. However, the new breed of managers have begun to look at the data assets quite differently as they evolve into formulating new strategic drives.
Conceptually, it may be true that big data assets have quite a few differences with the traditional data assets. However strategically, this isolated thinking is leading to segregating the data organizations into two silos. This can make long-term information strategy costly, ineffective and suboptimal for the organizations.
Apr 08 2013
I read the article, "9 warning signs you've been velocitized" by Bruce Kasanoff and was quite impressed by the questions there. The velocity can make us blindsided and we may miss the things happening around us. This is more so within the data warehousing space as the work involves fast paced deliveries and user driven business intelligence or data integration needs that are too detailed or specific in nature (leaving little room to explore.)
Most of the time it is the small series of small successes that reinforce us to pick up the pace and run to an often fatal ending. For example, a production support engineer checking and responding to emails after hours is seen by some as commitment to work or drive.
Dec 11 2012
Traditional extract, transform and load (ETL) has existed since the times when data warehousing evolved to help move data from legacy mainframe applications. Therefore, data movement from files to relational or dimensional databases for the consumption by reporting engines has been the focus of ETL. Even in the data world today where most focus has been on data visualization or analytics or business intelligence, data professionals recognize the importance of effective ETL engines as the backbone.
Nov 27 2012
There has always been an opportunity to create big data. There have also always been opportunities for prediction based analytics in the repeatable processes. For example, a simple thing like driving a car for a mile can generate tons of data, like the oil temperature changes, engine sound profile, traffic encountered, weather conditions faced, break/acceleration usage during the drive, road conditions and so on. Similarly, one can crunch data and keep developing prediction models on what to expect during the drive, like how much time it would take for the one mile drive, how much of gas is expected to be burnt, level of stress expected on the driver during the drive and so on.
In his article, “Big Data is Just a Fad,” Buck Woody concluded, “Big Data...will fade, over time, into the pantheon of other tech buzzwords.