When dealing with the large amounts of data associated with the “big data” class of business intelligence efforts, there is a quandary:

• Do you show “all” the data?
• Do you show an aggregated subset of the data?

When you attempt to show all the data in a “big data” environment, you run the risk of data overload as well as “processing” overload.  When you aggregate, you run the risk of skipping over the detail that many analysts may require.

Step 1: Architecture

In this quandary, business intelligence professionals need to look at the architecture of their “big data” environment and make the proper decisions on the performance of their data environments.  Tableau Software, for example, offers many options associated with their “big data” visualizations. From options for existing/traditional data warehouse environments to new analytical environments based on Hadoop or MapReduce technologies, data visualization products like Tableau give BI/DW architects the opportunity to make those decisions rather than being stuck on one side of the “big data” quandary.

Telecom Take

Telecom environments are a long way from being configured with a single data source for operational BI or analytics.  Mergers and acquisitions (M&A) as well as growing nature of telecom products and customers will ensure that a single data source is more of a “journey” than a destination.  Yet, there is the need to create operational BI and analytical data visualization layers similar to the example below:

Being able to mix and match as necessary will be a key architectural attribute for data visualization teams.  This will be a strong requirement in putting information in the hands of frontline customer service reps as well as strategic account management teams to manage customer expectations.

Does your telecom organization’s BI/DW architecture allow for these types of mix and match connections for data visualization?

Post your comments below or email (John.Myers@BlueBuffaloGroup.com) / twitter (@BlueBuffaloGrp) me directly.