Our latest on energy efficiency of computing over time, now out in Electronic Design

My colleague Sam Naffziger (AMD) and I just published our latest article titled “Energy efficiency of computing: What’s next?” in the magazine Electronic Design.  Here’s the abstract, which didn’t make it into the actual online article:  

Today’s computing systems operate at peak output a tiny fraction of the year, so peak output energy efficiency (which has slowed since the turn of the millennium) is not the most relevant efficiency metric for such devices. The more important question is whether computing efficiency in idle and standby modes (which are more representative of “typical use”) can be improved more rapidly than can peak output efficiency.  This article demonstrates that in the past eight years, the answer to that question has been a resounding yes, and we expect those more rapid efficiency improvements for computers in typical use to continue for at least the next few years.

Our original work (2011) on efficiency trends showed the energy efficiency of computing had doubled every 1.6 years since the beginning of the computer age:

Koomey, Jonathan G., Stephen Berard, Marla Sanchez, and Henry Wong. 2011. “Implications of Historical Trends in The Electrical Efficiency of Computing.”  IEEE Annals of the History of Computing.  vol. 33, no. 3. July-September. pp. 46-54. [http://doi.ieeecomputersociety.org/10.1109/MAHC.2010.28]

In that work, I didn’t examine the post-2000 period in detail.  When I re-analyzed the 2011 data, I found that peak output efficiency had slowed after 2000, with a doubling time of 2.6 years.  That result makes sense, because Dennard scaling ended in 2000 or so.  Figure 1 in our new article shows the effect of that change.

When AMD approached me with new data, I leapt at the chance to see what trends were implied in those data.  As Figure 1 shows, their trend in peak output efficiency from 2008 to 2016 lies almost exactly on the trend I found in our 2011 data.

The key insight of the new article is that there are different measures of efficiency, and that a focus on peak output efficiency is not as appropriate for many types of computing devices (whose energy use is dominated by long periods of idle, standby, and sleep).  We show that a focus on what we call “typical use” efficiency reveals more rapid improvements than are evident in peak-output efficiency in the 2008 to 2016 period, as shown in Figure 2.

This new article is an expanded look at the data we first put forth in IEEE Spectrum last year:

Koomey, Jonathan, and Samuel Naffziger. 2015. “Efficiency’s brief reprieve:  Moore’s Law slowdown hits performance more than energy efficiency.” In IEEE Spectrum. April. [http://spectrum.ieee.org/computing/hardware/moores-law-might-be-slowing-down-but-not-energy-efficiency]

I summarize our latest work on computing efficiency here:  http://www.analyticspress.com/computingefficiency.html

If you’d like a copy of the original 2011 article or the Electronic Design article with the related appendices, please email me.  The full reference is:

Koomey, Jonathan, and Samuel Naffziger. 2016. “Energy efficiency of computing:  What’s next?” In Electronic Design. November 28. [http://electronicdesign.com/microprocessors/energy-efficiency-computing-what-s-next]

My review of Mann and Toles, The Madhouse Effect: How Climate Change Denial is Threatening our Planet, Destroying our Politics, and Driving Us Crazy

The sterling reputations of Michael E. Mann and Tom Toles precede them, and in The Madhouse Effectthey do not disappoint.  I confess that I read all the cartoons first (I’m a big Tom Toles fan).  Then I dug in to the text, and found it equally enjoyable.

The writing is brilliantly clear and concise.  The science is unfailingly accurate.  And the cartoons add an immediacy, accessibility, and passion to the book that “normal” books about the climate problem usually lack.

There are many excellent treatments of climate science for lay people, but most talk about climate science without explaining first what science is and what we can reasonably expect of it.  Chapter 1, titled “Science:  How it Works”, takes on that challenge (see also Chapter 4 in my 2008 book, Turning Numbers into Knowledge: Mastering the Art of Problem Solving, titled “Peer Review and Scientific Discovery”).

Chapter 1 is critically important to understanding what I see as the key purpose of this book.  The campaign of denial and deceit against climate science is an attack on rational thinking and scientific inquiry more generally, and this book is a counterattack against that effort for a non-technical audience.

Others have made this case, notably Naomi Oreskes in Merchants of Doubt, and Mann and Toles echo and support Oreskes’ arguments, with additional context and color from Professor Mann’s experience as a practicing climate scientist who has faced down the deniers on more than one occasion (even prevailing in court).

Mann and Toles also don’t shrink from naming names, and this is one of the most important contributions of the work, particularly for practicing journalists.  The rogue’s gallery of deniers and delayers is a who’s who of people who journalists shouldn’t cite on this topic (or probably any topic).  Their credibility is shot in the scientific community, and they should be treated as the cranks and crackpots that they are.

This assessment sounds harsh, but most of these bad actors have been at this game for decades, and their strategy is one that they’ve used many times before.  Here’s how I summarized it in Cold Cash, Cool Climate:  Science-based Advice for Ecological Entrepreneurs:

The supporters of the deniers follow a particular strategy, one that was well honed by the corporate responses to various public health and environmental issues, as documented by Naomi Oreskes and others.  They make excuses that parallel the high level talking points summarized at Skeptical Science:  

It’s not a problem.

If it is a problem, we didn’t cause it.

Even if we caused it, fixing the problem would be too expensive and cost too many jobs.

These are exactly the same points industries used in fighting government action on cigarettes, asbestos, seat belts, air bags, lead in paint and gasoline, catalytic converters, ozone depletion, acid rain, and any number of other related issues, and we need to start treating it as a deliberate strategy instead of just a legitimate line of argument to be analyzed and assessed in isolation.  That doesn’t mean industry will never raise real issues about whether and how to regulate a particular environmental problem, just that we should be more than a little skeptical whenever we hear this self-serving way of framing issues. It is especially important for members of the news media to understand this tactic, because they often unwittingly serve as megaphones for industry arguments of this form.  If they realized that this strategy is a deliberate one, they might be a bit more careful in how they characterize these stories.

Those of us who’ve been studying climate science and solutions for decades have grown weary of the deniers being treated as serious contributors to the debate.  This book makes a strong case for voting them off the island.

The only minor criticism I’d raise of The Madhouse Effect is that the treatment of the economic case for rapid climate mitigation is a bit less strong than I’d prefer, but Professor Mann isn’t an economist or a technologist, so this isn’t really surprising.  Perhaps for his next popular treatment he’ll bring in a collaborator to take a more detailed crack at that aspect of the problem!

This is just a quibble, however. It is rare to find a book on a complex topic that is so clearly written, compelling, and (dare I say it?) fun.  I loved it, and you will, too.

References

Koomey, Jonathan. 2008. Turning Numbers into Knowledge:  Mastering the Art of Problem Solving. 2nd ed. Oakland, CA: Analytics Press.

Koomey, Jonathan G. 2012. Cold Cash, Cool Climate:  Science-Based Advice for Ecological Entrepreneurs. Burlingame, CA: Analytics Press.

Mann, Michael E., and Tom Toles. 2016. The Madhouse Effect: How Climate Change Denial is Threatening our Planet, Destroying our Politics, and Driving Us Crazy. New York, NY: Columbia University Press.

Oreskes, Naomi, and Eric M. Conway. 2010. Merchants of Doubt: How a Handful of Scientists Obscured the Truth on Issues from Tobacco Smoke to Global Warming. New York, NY: Bloomsbury Press.

Our new Oil Climate Index 2.0 is live!

We just expanded and updated the Oil-Climate Index (OCI) from the first version that we released in 2015.

Project collaborators at the Carnegie Endowment for International Peace, Stanford University, and the University of Calgary have now collected enough open-source data to model the climate impacts of 75 global oils—25 percent of current production. Our results can be found on the new OCI 2.0 web tool at OCI.CarnegieEndowment.org. The OCI’s new look and functionality include a global oil map, oil field boundaries, flaring data, carbon tax calculator, in-depth comparison tools, information on related oils, and more.

This release features a new OCI publication, “Getting Smart About Oil in a Warming World.” And you can view demonstration videos that pose critical questions about oil-climate responsibilities and strategies. Stay tuned for a forthcoming report that highlights promising supply chain innovations in the oil sector. These and all related publications, events, and media are (or will be, for future pubs) archived on Carnegie’s OCI webpage.

We look forward to introducing the OCI 2.0 and its many energy and climate applications to you.

Reference

Koomey, Jonathan, Deborah Gordon, Adam Brandt, and Joule Bergeson. 2016. Getting smart about oil in a warming world. Washington, DC: Carnegie Endowment for International Peace.  October 5. [http://carnegieendowment.org/2016/10/04/getting-smart-about-oil-in-warming-world-pub-64784]

My online class, Modernizing Enterprise Data Centers for Fun and Profit, starts again next Monday (September 26th)

Cern datacenter
Photo credit: By Hugovanmeijeren (Own work) [GFDL or CC-BY-SA-3.0-2.5-2.0-1.0], via Wikimedia Commons

I’ve been struggling for years to convince executives in large enterprises to fix the incentive, reporting, and other structural problems in data centers.  The folks in the data center know that there are issues (like having separate budgets for IT and facilities) but fixing those problems is “above their pay grade”.  That’s why we’ve been studying the clever things eBay has done to change their organization to take maximal advantage of IT, as summarized in this case study from 2013:

Schuetz, Nicole, Anna Kovaleva, and Jonathan Koomey. 2013. eBay: A Case Study of Organizational Change Underlying Technical Infrastructure Optimization. Stanford, CA: Steyer-Taylor Center for Energy Policy and Finance, Stanford University.  September 26.

That’s also why I’ve worked with Heatspring to develop the following online course, the latest version of which starts September 26th and goes through November 6th, 2016:

Modernizing enterprise data centers for fun and profit

I wrote an article for the September 2015 issue of DCD focus with the same name, which describes the rationale for the class.

Here’s the course description:

This is a unique opportunity to spend six weeks learning from Jonathan Koomey, a Research Fellow at the Steyer-Taylor Center for Energy Policy and Finance at Stanford University, and one of the foremost international experts on data center energy use, efficiency, organization, and management.

This course provides a road map for managers, directors, and senior directors in Technology Business Management (TBM), drawing upon real-world experiences from industry-leading companies like eBay and Google. The course is designed to help transform enterprise IT into a cost-reducing profit center by mapping the costs and performance of IT in terms of business KPIs.

Executives in this course will gain access to templates and best practices used by leaders in your data center. You’ll use these templates to complete a Capstone Project, in which you will propose management changes for your organization to help increase business agility, reduce costs, and move their internal IT organization from being a cost center to a cost-reducing profit center.

I’m excited about this class, but we need more signups. Please spread the word!

Sign up, or find out more…

Also see the related super-short course for upper management:   Data Center Essentials for Executives:  A Beginner’s Guide

New online class:  Data center essentials for executives–a beginner’s guide

Photo Credit: University of Hertfordshire, licensed under a Creative Commons Attribution-Share Alike 3.0 unported license.

Many of you know that I’ve been teaching an online class about data transformation for a couple of years now.  That class, now titled Modernizing Enterprise Data Centers for Fun and Profit, is targeted at Director and Senior Director level executives who work for VPs and C level executives.  It delves into great detail about how to transform organizations to take full advantage of the power of information technology, and is scheduled to be given again between September 26th and November 6th, 2016.

I’ve now developed an introductory class (in collaboration with Heatspring) specifically targeted to VP and C level executives who want to transform their data centers into cost reducing profit centers.  It’s called Data Center Essentials for Executives–A Beginner’s Guide.    For a modest investment of time (about 1.5 hours in total) this short course offers a high level summary of steps every company can take to improve the business performance of its IT organization.

Students can sign up at any time and take the class whenever is convenient. I encourage those who sign up to reach out to me via email with specific questions.

Go here to sign up, or email me for more details!

Why “deep dive” journalism is in rapid decline

Mother Jones has a terrific piece describing the economics of doing big stories like the influential muckraking  piece they did on private prisons.  That story lead to dramatic results:

This June, we published a big story—Shane Bauer’s account of his four-month stint as a guard in a private prison. That’s “big,” as in XXL: 35,000 words long, or 5 to 10 times the length of a typical feature, plus charts, graphs, and companion pieces, not to mention six videos and a radio documentary.

It was also big in impact. More than a million people read it, defying everything we’re told about the attention span of online audiences; tens of thousands shared it on social media. The Washington Post, CNN, and NPR’s Weekend Edition picked it up. Montel Williams went on a Twitter tear that ended with him nominating Shane for a Pulitzer Prize (though that’s not quite how it works). People got in touch to tell us about their loved ones’ time in prison or their own experience working as guards. Lawmakers and regulators reached out. (UPDATE: And on August 18, the Justice Department announced that it will no longer contract with private prisons, which currently hold thousands of federal inmates—a massive policy shift.)

In the wake of our investigation, lots of people offered thoughts similar to this, from New Yorker TV critic Emily Nussbaum:

Incidentally,that Shane Bauer Mother Jones undercover investigation is literally why journalism exists and why we have to pay for it.

That’s a great sentiment, and we agree! But it also takes us to a deeper story about journalism and today’s media landscape. It starts with this: The most important ingredient in investigative reporting is not brilliance, writing flair, or deep familiarity with the subject (though those all help). It’s something much simpler—time.

And of course, time is money!  Here’s the key takeaway:

Conservatively, our prison story cost roughly $350,000. The banner ads that appeared in it brought in $5,000, give or take.

And this is the quandary in which the media find themselves.  The world is getting more complicated, and the need for “deep dive” factual journalism is greater than ever, but the cash cow of classified ads, which funded such activities in the past is all but gone, and the media world is under increasing financial pressure.  That’s why we probably need alternative business models for investigative media in our increasingly complex technological age.

Facts and Fiction in Energy Transitions – podcast posted today!

I had a wide ranging conversation with Chris Nelder that he recorded for his Energy Transitions podcast series, posted today.    We covered a lot of ground, as the description reveals:

Should we tweak our markets to keep nuclear plants alive, or forget about markets and pay for them another way… and do we really need them at all to keep the grid functioning? Is nuclear power really declining because of overzealous environmentalists, or are there other reasons? Is it possible to balance a grid with a high amount of variable renewables and no traditional baseload plants? Is cost-benefit analysis the right way to approach energy transition? How much “decoupling” can we do between the economy and energy consumption, and how can we correctly measure it? Why are we so bad at forecasting energy and economic growth, and how can we do it better? How will energy transition affect the economy?

Good thing I came up for air in between topics!  I enjoyed chatting with Chris–I always learn something from him.  And I can chalk up this opportunity to Twitter, because I met him through our Twitter interactions.

To listen, go here.

2015 State of the Climate:  Hot, Hot, Hot!

The Bulletin of the American Meteorological Society just released the 2015 state of the climate. Download it here.

NOAA gives a nice summary, which I condense to its key points below.

1. Greenhouse gases were the highest on record.
2. Global surface temperature was the highest on record.
3. Sea surface temperature was the highest on record.
4. Global upper ocean heat content highest on record.
5. Global sea level rose to a new record high in 2015.
6. Tropical cyclones were well above average, overall.
7. The Arctic continued to warm; sea ice extent remained low.

If anyone still has any doubt that the earth is warming and humans are responsible, they should read this document!

Surprise!: US data center electricity use has been growing slowly for years

Lawrence Berkeley National Laboratory, in collaboration with experts at Stanford (me), Carnegie Mellon, and Northwestern, today released our latest analysis of electricity used by data centers in the US.  Surprisingly, electricity use in data centers has been roughly flat since the financial crisis with little growth projected to 2020, even though delivery of computing services has been increasing rapidly.  As I’ve argued for years, the level of inefficiency in enterprise data center facilities leaves lots of room for improvement, and the market is finally getting that message.

Here are the first couple of paragraphs of the executive summary:

This report estimates historical data center electricity consumption back to 2000, relying on previous studies and historical shipment data, and forecasts consumption out to 2020 based on new trends and the most recent data available. Figure ES-1 provides an estimate of total U.S. data center electricity use (servers, storage, network equipment, and infrastructure) from 2000-2020. In 2014, data centers in the U.S. consumed an estimated 70 billion kWh, representing about 1.8% of total U.S. electricity consumption. Current study results show data center electricity consumption increased by about 4% from 2010-2014, a large shift from the 24% percent increase estimated from 2005-2010 and the nearly 90% increase estimated from 2000-2005. Energy use is expected to continue slightly increasing in the near future, increasing 4% from 2014-2020, the same rate as the past five years. Based on current trend estimates, U.S. data centers are projected to consume approximately 73 billion kWh in 2020.

Many factors contribute to the overall energy trends found in this report, though the most conspicuous change may be the reduced growth in the number of servers operating in data centers. While shipments of new servers into data centers continue to grow every year, the growth rate has diminished over the past 15 years. From 2000-2005, server shipments increased by 15% each year resulting in a near doubling of servers operating in data centers. From 2005-2010, the annual shipment increase fell to 5%, partially driven by a conspicuous drop in 2009 shipments (most likely from the economic recession), as well as from the emergence of server virtualization across that 5-year period. The annual growth in server shipments further dropped after 2010 to 3% and that growth rate is now expected to continue through 2020. This 3% annual growth rate coincides with the rise in very large “hyperscale” data centers and an increased popularity of moving previously localized data center activity to colocation or cloud facilities. In fact, nearly all server shipment growth since 2010 occurred in servers destined for large hyperscale data centers, where servers are often configured for maximum productivity and operated at high utilization rates, resulting in fewer servers needed in the hyperscale data centers than would be required to provide the same services in traditional, smaller, data centers.

Here’s the full reference:

Shehabi, Arman, Sarah Smith, Dale A. Sartor, Richard E. Brown, Magnus Herrlin, Jonathan G. Koomey, Eric R. Masanet, Nathaniel Horner, Inês Lima Azevedo, and William Lintner. 2016. United States Data Center Energy Usage Report. Berkeley, CA: Lawrence Berkeley National Laboratory. LBNL-1005775.  June 27. [http://eta.lbl.gov/publications/united-states-data-center-energy-usag]

Is stabilizing the climate impossible?

Your climate thought for today, via Goodreads:

“Impossible is just a big word thrown around by small men who find it easier to live in the world they’ve been given than to explore the power they have to change it. Impossible is not a fact. It’s an opinion. Impossible is not a declaration. It’s a dare. Impossible is potential. Impossible is temporary. Impossible is nothing.”

― Muhammad Ali

My webinar for DOE + EPA today: Why we can’t accurately forecast the future

Graph of energy and electricity/GDP indices taken from Hirsh and Koomey 2015 (subscription required).  The graphs illustrate structural changes in the relationship between energy and economic activity that have confounded modelers in the past and will no doubt do so in the future.

Today I gave a webinar for EPA and DOE staff titled “Past performance is no guide to future returns:  Why we can’t accurately forecast the future”.  I first gave a version of this talk at the Energy and Resources Group at UC Berkeley on September 28, 2011.  It built upon our Climatic Change article (with Irene Scher) titled “Is accurate forecasting of economic systems possible?” (subscription required), and grew into Chapter 4 of Cold Cash, Cool Climate:  Science-based Advice for Ecological Entrepreneurs, titled “Why we can’t accurately forecast the future” and my open access ERL article titled “Moving beyond benefit-cost analysis for climate change”.

Here’s the talk description:

This webinar explores why (with few exceptions) models of economic systems do not yield accurate predictions about the future. Predictions can be accurate when systems have consistent structure (geographically and temporally) and when there are no surprises, but neither of these conditions holds for virtually all economic systems. Physical systems can exhibit structural constancy, so predictions based on physical sciences can be accurate (barring surprises). The webinar also explores implications of this irreducible uncertainty, introduces ways to cope with it, and discusses responsible use of economic modeling tools in the face of such modeling limitations. The talk explores these issues using examples of forecasts of US primary energy use, oil prices, electricity demand, and the costs of nuclear power.

You can download the slides here.

Addendum, June 1, 2016: Some of the participants in the workshop wanted to understand why economic modelers have a hard time accepting the thesis of my talk.  I pointed them to a November 30, 2014 NYT blog post by Paul Krugman about the sociology of economics that is revealing.  The Krugman post refers to a study that will be fascinating for anyone interested in how the economic community operates.

A very old phone, at the Point Sur lighthouse in California. Photo taken in late March 2016. We've come a long way!

A very old phone, at the Point Sur lighthouse in California.  Photo taken in late March 2016.  We’ve come a long way!

My article in DCD Focus:  Modernizing enterprise data centers for fun and profit

In September 2015, Data Center Dynamics published my article titled “Modernizing enterprise data centers for fun and profit”, which describes the rationale for my upcoming online class.  The current incarnation of that class starts again May 2, 2016.

Here are the opening paragraphs of the DCD article:

Twenty first century data centers are the crown jewels of global business. No modern company can run without them, and they deliver business value vastly exceeding their costs. The big hyperscale computing companies (like Google, Microsoft, Amazon, and Facebook) are the best in the industry at extracting that business value, but for many enterprises whose primary business is not computing, the story is more complicated.

If you work in such a company, you know that data centers are often strikingly inefficient. While they may still be profitable, their performance still falls far short of what is possible. And by “far short” I don’t mean by 10 or 20 percent, I mean by a factor of ten or more.

Read more…

The course will teach people how to bring their data centers into the twenty first century, turning them from cost centers into cost-reducing profit centers.

Sign up here!

Our journal article critiquing a recent Energy Policy article on nuclear costs has just been released online

Our article critiquing the recent Lovering et al. Energy Policy article on the costs of nuclear power was just published online by Energy Policy (the article is now considered to be “in press” with only minor tweaks to come once they assign the article to an issue).  My colleagues Nate Hultman at the University of Maryland and Arnulf Grubler of Yale University and the International Institute of Applied Systems Analysis teamed up with me on the critique.

Here’s the abstract:

Lovering et al. (2016) present data on the overnight costs of more than half of nuclear reactors built worldwide since the beginning of the nuclear age. The authors claim that this consolidated data set offers more accurate insights than previous country-level assessments. Unfortunately, the authors make analytical choices that mask nuclear power’s real construction costs, cherry pick data, and include misleading data on early experimental and demonstration reactors. For those reasons, serious students of such issues should look elsewhere for guidance about understanding the true costs of nuclear power.

Here are the references for the relevant articles:

Koomey, Jonathan, Nathan E. Hultman, and Arnulf Grubler. 2016. “A reply to "Historical construction costs of global nuclear power reactors”.“ Energy Policy.  April. [http://dx.doi.org/10.1016/j.enpol.2016.03.052]


Lovering, Jessica R., Arthur Yip, and Ted Nordhaus. 2016. "Historical construction costs of global nuclear power reactors.”  Energy Policy.  vol. 91, 4//. pp. 371-382. [http://www.sciencedirect.com/science/article/pii/S0301421516300106]

Alex Gilbert, Ben Sovacool, Phil Johnstone, and Andy Stirling also did a critique of Lovering et al. that should appear in the same issue (it’s also “in press”):

Gilbert, Alexander, Benjamin K. Sovacool, Phil Johnstone, and Andy Stirling. “Cost overruns and financial risk in the construction of nuclear power reactors: A critical appraisal.”  Energy Policy.  [http://www.sciencedirect.com/science/article/pii/S0301421516301690]

Our understanding was that the editor sought out a response to our critiques from Lovering et al, but we don’t know when that one will appear.  We were told initially that it would appear at the same time as our articles.

Please email me if you’d like a copy of our article, or download directly if you have access through your university.

Upcoming class: Modernizing enterprise data centers for fun and profit

jgkoomey:

Cern datacenter
Photo credit: By Hugovanmeijeren (Own work) [GFDL or CC-BY-SA-3.0-2.5-2.0-1.0], via Wikimedia Commons

I’ve been struggling for years to convince executives in large enterprises to fix the incentive, reporting, and other structural problems in data centers.  The folks in the data center know that there are issues (like having separate budgets for IT and facilities) but fixing those problems is “above their pay grade”.  That’s why we’ve been studying the clever things eBay has done to change their organization to take maximal advantage of IT, as summarized in this case study from 2013:

Schuetz, Nicole, Anna Kovaleva, and Jonathan Koomey. 2013. eBay: A Case Study of Organizational Change Underlying Technical Infrastructure Optimization. Stanford, CA: Steyer-Taylor Center for Energy Policy and Finance, Stanford University.  September 26.

That’s also why I’ve worked with Heatspring to develop the following online course, the third instance of which starts May 2nd and goes through June 12th, 2016:

Modernizing enterprise data centers for fun and profit

I also wrote an article for the September 2015 issue of DCD focus with the same name, which describes the rationale for the class.

Here’s the course description:

This is a unique opportunity to spend six weeks learning from Jonathan Koomey, a Research Fellow at the Steyer-Taylor Center for Energy Policy and Finance at Stanford University, and one of the foremost international experts on data center energy use, efficiency, organization, and management.

This course provides a road map for managers, directors, and senior directors in Technology Business Management (TBM), drawing upon real-world experiences from industry-leading companies like eBay and Google. The course is designed to help transform enterprise IT into a cost-reducing profit center by mapping the costs and performance of IT in terms of business KPIs.

Executives in this course will gain access to templates and best practices used by leaders in your data center. You’ll use these templates to complete a Capstone Project, in which you will propose management changes for your organization to help increase business agility, reduce costs, and move their internal IT organization from being a cost center to a cost-reducing profit center.

I’m excited about this class, but we need more signups by early May. Please spread the word by sending this blog post to upper level management in the company where you work.

Sign up, or find out more…

Latest course starts May 2nd, 2016!