Martin Heller
Contributing Writer

Performance centers of excellence pay off

analysis
Feb 3, 20092 mins

A survey by analyst firm voke cites the key goals: prioritize, centralize, standardize

I had a telephone conversation the other day with analyst Theresa Lanowitz, now of voke, and formerly of Borland, Taligent, Sun, and Gartner. (I knew her when she worked on JBuilder at Borland.) We were talking about a survey she recently did of mostly large organizations that have established performance Centers of Excellence (CoE).

(For a while, I thought that this conversation wasn’t going to happen, as we called each other and got disconnected multiple times. After about 6 tries, Theresa called me from a landline and said “My iPhone can do everything except make a call without dropping it.” She’s doing better than I would, since AT&T cell phones typically get zero to one bar in my house.)

In some ways, a study like this is a little like a study to establish that most traffic flows at rush hour: You have a good idea of what the results will be qualitatively, but until you see the numbers you don’t really know for sure what they are quantitatively. You would expect a performance CoE to work better than the usual ad-hoc, end-of-cycle performance testing, because measuring and improving performance takes some insight into the architecture and some software engineering and modeling skill, and in fact Theresa’s survey established that having a CoE helped to prioritize, centralize, and standardize performance testing across the enterprise. They typically returned their investment in 12 months and required a modest headcount: mostly under 19, and on average 11. A CoE not only improves performance, it improves quality awareness in the organization.

A few takeaways for success:

  • Design a performance CoE to be scalable, both for company growth and expanded scope
  • Silos are ineffective
  • The line of business is good at identifying bad performance and assigning it a cost
  • A performance CoE can help to assess deployment and operation risks

The study goes on to list several more concrete ways to make a performance CoE effective, some of which are motherhood and apple pie, and some of which are less obvious, but I don’t want to give away too much of Theresa’s study. This 21-page paper is available for subscribers at vokestream.com.

Martin Heller

Martin Heller is a contributing writer at InfoWorld. Formerly a web and Windows programming consultant, he developed databases, software, and websites from his office in Andover, Massachusetts, from 1986 to 2010. From 2010 to August of 2012, Martin was vice president of technology and education at Alpha Software. From March 2013 to January 2014, he was chairman of Tubifi, maker of a cloud-based video editor, having previously served as CEO.

Martin is the author or co-author of nearly a dozen PC software packages and half a dozen Web applications. He is also the author of several books on Windows programming. As a consultant, Martin has worked with companies of all sizes to design, develop, improve, and/or debug Windows, web, and database applications, and has performed strategic business consulting for high-tech corporations ranging from tiny to Fortune 100 and from local to multinational.

Martin’s specialties include programming languages C++, Python, C#, JavaScript, and SQL, and databases PostgreSQL, MySQL, Microsoft SQL Server, Oracle Database, Google Cloud Spanner, CockroachDB, MongoDB, Cassandra, and Couchbase. He writes about software development, data management, analytics, AI, and machine learning, contributing technology analyses, explainers, how-to articles, and hands-on reviews of software development tools, data platforms, AI models, machine learning libraries, and much more.

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