I’m kind of a car nut in my spare time. I was reading through an article this week on how to be successful on the track, and it occurred to me that the same advice would apply to IT convergence and integration. The article is titled “Why Not Going Slow Is More Important Than Going […]
I’m kind of a car nut in my spare time. I was reading through an article this week on how to be successful on the track, and it occurred to me that the same advice would apply to IT convergence and integration. The article is titled “Why Not Going Slow Is More Important Than Going Fast.” It talks about a common mistake by both professional and amateur drivers (I’ve made it myself): focusing on the wrong part of the racetrack.
In a converged infrastructure deployment or, come to think of it, in a go-to-market plan, we often make this same mistake. Let me explain.
Let’s start with the racing lesson to set the stage. Race car drivers like speed, so they often focus on power plays, living for that top speed on the straights because it is both the most exhilarating and, to be totally frank, the easiest part of a course. However, if you do the math, the car spends more time on the slow portions of a course than it does on straightaways. Increasing the speed 10 percent on the slow portions of the course will have a much bigger impact than working at top speed in the straights.
This isn’t obvious because you think faster is better, but it’s average speed you are working to increase not peak speed. When facing a choice, a race car driver is better off focusing on increasing their slow speed time than their top speed time.
I ran into a similar situation while doing my Windows 10 testing. I had a little Acer box with a Celeron processor that was painfully slow because it had a 500 GB magnetic drive and only 4 GB of memory. I swapped in an SSD drive and 16 GB of memory and found . . . it was still slow. I hadn’t realized the Celeron was not only relatively slow but single-core (it has been a long time since I’ve had a Celeron box). I should have focused on bringing the processor up to two cores first then looked at the memory and hard drive. I likely would have created a faster system for a lot less money.
In any converged or complex system what you want to increase is the total system performance. It is really easy to buy and implement faster networking, faster memory and even faster processors, but in each instance the system is likely to bottleneck someplace else. We tend to become enamored with these new technologies which we toss at performance problems like folks playing darts when instead we should be focused on system metrics and overall system performance.
This is one of the reasons I historically prefer the VCE approach to creating a converged system. With VCE you start with a system, not a collection of often poorly matched components. The VCE approach results in better utilization because everything was designed to work together.
I see this all the time in product planning. A great deal of focus is spent on time-to-market but very little on building demand for the result or even assuring there is a market for the offering. Doing market research is time consuming, but if it isn’t done, not only could the product not reach its potential you may find, as I did at one time, that there was no conceivable customer that wanted to buy the damn thing.
We often tend to avoid the slow hard stuff (like vetting a name) in order to just get the product out the door quickly. We can see the result with products like the Apple Watch, which came to market quickly but is currently finding demand illusive and having to overcome a name that doesn’t have an “i” as the first letter. Google Glass was worse. Google was so aggressive at getting this product out the door they may have destroyed the consumer market for this entire class of products as a result. In this instance, the slow parts of this process may not be slowing the product to market, but they aren’t getting done. The result is poor market performance.
I think this racing advice applies to any system or process. For instance a while back I suggested companies focus on tools like Experfy or BMC’s M-Control solution to correct the data scientist bottleneck.
The time spent identifying and speeding up the slowest parts of the process will have the greatest impact on overall process/system performance and thus should be prioritized even if they aren’t that much fun. Rather than making the fast parts of anything faster, focus on the slow parts, because that is where the best improvements will be found. Or just start with a system and focus on the system speed as a better way to identify the critical path that will provide the best return on your investment.
However you get there, focusing on not going slow will always be a better strategy than going fast.
Photo courtesy of Shutterstock.
Ethics and Artificial Intelligence: Driving Greater Equality
FEATURE | By James Maguire,
December 16, 2020
AI vs. Machine Learning vs. Deep Learning
FEATURE | By Cynthia Harvey,
December 11, 2020
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
FEATURE | By Samuel Greengard,
November 05, 2020
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
FEATURE | By Cynthia Harvey,
October 07, 2020
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 2021
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
As President and Principal Analyst of the Enderle Group, Rob provides regional and global companies with guidance in how to create credible dialogue with the market, target customer needs, create new business opportunities, anticipate technology changes, select vendors and products, and practice zero dollar marketing. For over 20 years Rob has worked for and with companies like Microsoft, HP, IBM, Dell, Toshiba, Gateway, Sony, USAA, Texas Instruments, AMD, Intel, Credit Suisse First Boston, ROLM, and Siemens.
Datamation is the leading industry resource for B2B data professionals and technology buyers. Datamation's focus is on providing insight into the latest trends and innovation in AI, data security, big data, and more, along with in-depth product recommendations and comparisons. More than 1.7M users gain insight and guidance from Datamation every year.
Property of TechnologyAdvice. © 2025 TechnologyAdvice. All Rights Reserved
Advertiser Disclosure: Some of the products that appear on this site are from companies from which TechnologyAdvice receives compensation. This compensation may impact how and where products appear on this site including, for example, the order in which they appear. TechnologyAdvice does not include all companies or all types of products available in the marketplace.
