EMA Blog: Business Intelligence and IT Management

OpenStack’s huge momentum is undeniable. IBM, RedHat, NetApp, Rackspace, HP, Dell, Cisco, Intel and even VMware have committed significant funds and human resources to this project. But why would companies, that are otherwise competing rather fiercely, sit on one table to build an OpenSource cloud platform?

OpenStack’s huge momentum is undeniable. IBM, RedHat, NetApp, Rackspace, HP, Dell, Cisco, Intel and even VMware have committed significant funds and human resources to this project. But why would companies, that are otherwise competing rather fiercely, sit on one table to build an OpenSource cloud platform?

With its roots in mainframe job scheduling, workload automation is often seen as a relic in today's age of cloud, Big Data, mobile management and DevOps. Do we even still need workload automation as a separate discipline or should we simply roll the management of batch jobs into other automation disciplines, such as IT process automation? Is the market for workload automation software stagnating or is there still potential for growth?

Almost one year into the discussion about the Software Defined Data Center (SDDC), it is time to take inventory of the state of the discipline. As a reminder, the ultimate goal of the SDDC is to abstract and centralize the
management of compute, network, storage, operating systems, middleware and applications in order to dynamically place workloads where they can run in the most cost efficient, secure and compliant manner.

Almost one year into the discussion about the Software Defined Data Center (SDDC), it is time to take inventory of the state of the discipline. As a reminder, the ultimate goal of the SDDC is to abstract and centralize the
management of compute, network, storage, operating systems, middleware and applications in order to dynamically place workloads where they can run in the most cost efficient, secure and compliant manner.

As I review my series of #100linesOnBIDW blogs over the last couple of weeks, I found myself looking at the Data Management posting. I covered when to apply schemas, Big Data, and data governance. What I left out was technical implementation concepts for data management systems like row vs. column orientation; in-memory vs. spinning disk primary storage; and symmetric multiprocessing (SMP) vs. massively parallel processing (MPP). Processing and storage were the “developments” of 2012. I left 2013 for the “how to use” Data Management platforms.

If I told you in September that the Baltimore Ravens would win the Super Bowl in February and you did nothing with that information ( i.e. place a bet, announce your prognostication skill, etc. ), was my prediction worth anything? Much like “if a tree falls in the forest and no one is there to hear it, did it make a sound?”, you need action from a piece of analysis, prediction, etc. to make it worthwhile.

Albert Einstein once said:

Edward de Bono, a noted expert on creative thinking, once said:

When Aesop created the fable about the shepherd boy who cried wolf, the message was clear: