Over the last 40 years, companies have spent a better part of their data budget on building data marts and analyzing structured data. While this was primarily done because the companies had easier access to data, technologies and talent necessary to get the job done.
Fast forward to 2013, where the millennials are generating several hundred petabytes of unstructured data with Instagram, Facebook, Twitter and its brethren.
By some estimates the flood of social media generated unstructured data constitutes approximately 80% of all data generated today and continues to be labeled as Big Data. The term “Big” Data has been genericized, over-used, mis-used and hyped so much that, Big Data is a term used to describe just about any type of data.
So what is Big Data?
Big Data is data whose scale, diversity, and complexity require new
architecture, techniques, algorithms, and analytics to manage it and extract value and hidden knowledge from it.
Big Data is a data set so large it cannot be managed in conventional database management systems with acceptable performance and at acceptable cost.
Here is a more pragmatic view of Big Data as I see it: several business
scenarios that fall under the general classification of large data sets, large (and complex) analysis, large (and concurrent number of users) and the
proliferation of anywhere, anytime, available Cloud infrastructure.
I prefer to call it Large Data.
As one of the most pervasive buzzwords of this era, “Big Data” gets mentioned in every possible connotation and permutation while describing its 3 major characteristics
A vast variety of users and practitioners forget the other “V”- Value. Value of data and its ability to drive meaningful business analysis is paramount to business users. Yet, most practitioners insist on focusing on volume, variety and velocity. While the latter three have their pros, one cannot under
estimate the undeniability and omni-presence of Value.
Armed with the right use case, mining the large data set with the appropriate set of business questions will usually yield compelling and relevant insights for your business. These insights then lead to actionable intelligence and informed, fact based decision making. For example, one of the largest oil companies is analyzing their accounts payable information (structured data) in conjunction with social data (IM chat, SMS, emails, etc) to identify duplicate payments made to vendors. The resulting ROI from this exercise goes straight to the bottom line!
In conclusion, executives should be asking questions along the lines of:
- What are the best use cases for us to drive meaningful value from the large data we have access to?
- What value can we extract from our data to increase profits/revenue or decrease costs?
- What can we learn about customer behavior and up-sell them on additional product/services?
- Who are our most profitable customers and what else to they want?
- Who are our least profitable customers and what additional value can we provide them?