Big Data: 12-dimensional Extreme Information Management (EIM)

Big Data isn’t just ‘Big’, it’s ‘Extreme’.  IBM describes Big Data as having the characteristics of extreme volume, velocity, and variety.  Forrester added variability.  McKinsey Global Institute next threw in value as a fifth ‘V’ descriptor.

Well, seemingly not to be outdone, Gartner says there’s actually a lot more to Big Data.  Mark Beyer of Gartner says that there are actually 12 dimensions to Big Data, or as he calls it, Extreme Information.  Beyer says volume, velocity and variety are only three of the dimensions that need to be considered, and just focusing on those instead of all areas of  an organization’s information infrastructure can be a mistake.

“Extreme Information Management (EIM) is the concept that your current information infrastructure must be intentionally managed along twelve complementary dimensions to meet the challenges of the 21st century Information Age”.

To explain the concept of 12-dimensional Extreme Information, Beyer presents a graphic that shows three tiers of information management, with four dimensionsoin each of the tiers.

On the first tier, there are the familiar Big Data characteristics of Volume, Velocity and Variety, as well as a dimension of Complexity.  In the diagram, the axis representing the volume dimension is explicitly labeled as being associated with ‘Big Data’.

The second tier has more to do with attempting to understand and analyze the information.  It consists of the dimensions of Technology, Pervasive Use, Classification and Contracts.

And, the third tier considers aspects related to replenishing and cleansing data to maintain relevance.  The dimensions on this tier consist of Validation, Linking, Fidelity, and Perishability.

Organizations that are able to adopt and utilize information to the fullest are the most likely to succeed.  Gartner says that organizatinos that have a modern information management system will outperform their peers by 20 percent.

Beyer sees three big opportunities coming from the use of Extreme Information Management:

1. Marketing Processes can be improved by analyzing social data to understand network relationships and social sentiment
2. Logistics and Supply Planning can improve with better data and metrics obtained with monitoring operations
3. Competitive advantage in operations is possible by collecting and using ‘high-fidelity’ data sets

But Beyer says that there are problems and challenges that must be overcome to deal with Extreme Information:

• Governance issues related to managing and interacting with large data sets
• Integration issues related to bringing together many kinds of data from applications, data stores and monitors using a variety of tools

Beyer expects that Extreme Information Management will become increasingly important and gives examples that include the public sector for dealing with disaster and emergency response, in health care for managing electronic health records, and in utilities for managing data being collected by smart meters.