Trimming The Fat In Your Datacenter

IT managers scrambling to find new ways to appease the corporate bean-counters should take a long, hard look at all the software residing in their budget-munching datacenters.

Fortune 1000 companies routinely spend 30 percent more on datacenter software than necessary—or roughly six percent of the total cost of operating the datacenter—according to a new report by Compass, an IT-consulting firm based in Naperville, Ill.

Mergers and acquisitions, intentional complexity of licensing agreements and the divergent agendas of IT staffers and finance personnel are the main reasons companies waste so much money on datacenter software each year, the report concludes.

So where’s the fat?

“Typically, especially in datacenters running mainframes, a large company will have software suites from multiple vendors that often overlap,” Scott Feuless, a senior consultant at Compass, said in an interview with internetnews.com. “They sell you multiple packages and modules bundled together and that’s where you can start to find redundancy.”

Ironically, most of the vendors selling the software tools used in the datacenter also offer software asset management applications that help customers plan their software purchases to avoid this type of duplication.

But that only solves half the problem.

Compass recommends large companies institute a software asset management (SAM) initiative, essentially a product-by-product, license-by-license, function-by-function audit of all software in the datacenter. Once this review is completed, it’s crucial to establish a process to regularly review new and jettisoned software packages as they cycle through the datacenter.

It seems simple but it doesn’t happen often enough, Feuless said. He noted that business managers are often more obsessed with reducing costs and don’t really know what the software does, they just want to make sure they’re getting the best deal from the vendor.

Also, the tech guys, who are constantly being asked to do more with less, are more focused on getting the job done. If they happen to have more than one tool in their toolbox to solve the problem, so be it. Some might be more comfortable and have more experience using IBM(Quote) software. Another group might prefer offerings from CA (Quote). Some other guy might be in love with BMC (Quote)  tools.

“The two groups are so focused in different areas but for it to work, they really need to work together throughout the process,” Feuless said.

This article was first published on InternetNews.com. To read the full article, click here.

RELATED NEWS AND ANALYSIS

• Huawei’s AI Update: Things Are Moving Faster Than We Think

  FEATURE |  By Rob Enderle,
  December 04, 2020

• Keeping Machine Learning Algorithms Honest in the ‘Ethics-First’ Era

  ARTIFICIAL INTELLIGENCE |  By Guest Author,
  November 18, 2020

• Key Trends in Chatbots and RPA

  FEATURE |  By Guest Author,
  November 10, 2020

• Top 10 AIOps Companies

  FEATURE |  By Samuel Greengard,
  November 05, 2020

• What is Text Analysis?

  ARTIFICIAL INTELLIGENCE |  By Guest Author,
  November 02, 2020

• How Intel’s Work With Autonomous Cars Could Redefine General Purpose AI

  ARTIFICIAL INTELLIGENCE |  By Rob Enderle,
  October 29, 2020

• Dell Technologies World: Weaving Together Human And Machine Interaction For AI And Robotics

  ARTIFICIAL INTELLIGENCE |  By Rob Enderle,
  October 23, 2020

• The Super Moderator, or How IBM Project Debater Could Save Social Media

  FEATURE |  By Rob Enderle,
  October 16, 2020

• Top 10 Chatbot Platforms

  FEATURE |  By Cynthia Harvey,
  October 07, 2020

• Finding a Career Path in AI

  ARTIFICIAL INTELLIGENCE |  By Guest Author,
  October 05, 2020

• CIOs Discuss the Promise of AI and Data Science

  FEATURE |  By Guest Author,
  September 25, 2020

• Microsoft Is Building An AI Product That Could Predict The Future

  FEATURE |  By Rob Enderle,
  September 25, 2020

• Top 10 Machine Learning Companies 2020

  FEATURE |  By Cynthia Harvey,
  September 22, 2020

• NVIDIA and ARM: Massively Changing The AI Landscape

  ARTIFICIAL INTELLIGENCE |  By Rob Enderle,
  September 18, 2020

• Continuous Intelligence: Expert Discussion [Video and Podcast]

  ARTIFICIAL INTELLIGENCE |  By James Maguire,
  September 14, 2020

• Artificial Intelligence: Governance and Ethics [Video]

  ARTIFICIAL INTELLIGENCE |  By James Maguire,
  September 13, 2020

• IBM Watson At The US Open: Showcasing The Power Of A Mature Enterprise-Class AI

  FEATURE |  By Rob Enderle,
  September 11, 2020

• Artificial Intelligence: Perception vs. Reality

  FEATURE |  By James Maguire,
  September 09, 2020

• Anticipating The Coming Wave Of AI Enhanced PCs

  FEATURE |  By Rob Enderle,
  September 05, 2020

• The Critical Nature Of IBM’s NLP (Natural Language Processing) Effort

  ARTIFICIAL INTELLIGENCE |  By Rob Enderle,
  August 14, 2020