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The Technology Economics of the Mainframe: Mainframe Computing Still Growing in Banking
Despite perceptions, mainframe computing continues
to grow in banking. But from an economic standpoint, that may not be a
bad thing.
Contrary to conventional wisdom, mainframe computing is growing. In
fact, financial services has been passed by only a few sectors in terms
of growth in mainframe
MIPS,
or million instructions per second. But maybe this is good news:
Multiyear results show that "mainframe-heavy" organizations are more
economically efficient in supporting business computational demands and
have more upward scalability than distributed-server-heavy
organizations.
Decisions about computer platform choices and options typically are made
without consideration of true business impact from a cost-of-goods or
other perspective. As a consequence of what we know about technology
economics today, however, platform choices can be based on factual
criteria and should not be decided as a "fashion statement."
During the 50-year history of what we now call "technology economics,"
it has always been clear that demand for computing is increasing and
that upward expense pressure is a fact of life in what many have called
the Information Age. Between 2006 and 2010, demand for processing cycles
(MIPS, servers and the like) slowly approached an 18% annual growth
rate in the big banks, while storage demand has been growing at 45% or
more per year.
With infrastructure spending -- on computing power, networks, storage,
help desks and so on -- historically accounting for 57% of overall IT
expense, it is likely that it is the largest component of an
organization's "IT Cost of Goods." As such, it is worthy of
investigation and analysis.
Previous research that exlpored the dynamics of platform economics
indicated that firms with a mainframe computing platform bias
("mainframe heavy") exhibited a lower IT Cost of Goods and overall IT
costs in situations in which the mainframe was a suitable platform.
Conversely, "server heavy" firms were at an economic disadvantage --
higher IT Cost of Goods and overall infrastructure costs. Additional
research updates these earlier findings, continuing to chart the
interaction (and value) of computing choices and real bottom-line
business impacts.
Key cost of goods metrics were identified for the sectors under study.
Within each sector, analyses were performed to determine average levels
of both mainframe and distributed server usage relative to business
volumes/revenue. Within each sector, two groups were identified --
"mainframe heavy" and "distributed-server heavy," relative to average
levels of usage.
Within these two groups (by industry), IT Cost of Goods was computed and compared.
The research database for this study contained data from 498 companies
across 20 sectors, spanning the years 2008 to 2011. Data elements
include the amount of computational resources along with key business
performance parameters.
Across the 498 companies studied, on average, computational needs grew
far faster than revenue. MIPS capacity grew at 2.33 times the rate of
revenue growth, while distributed server deployments grew at 3.5 times
the rate of revenue growth. Additionally, firms that had higher
mainframe growth had 25% lower distributed server growth and exhibited
approximately
67 percent more-effective cost containment than those with less
mainframe intensity. The implication is that the required computational
growth is roughly three times more economically efficient in a mainframe
environment.
Further, organizations with high mainframe intensity had 39% more upward
scalability in that they could support revenue growth with 61% less
investment than those that were distributed-server-intense. And
organizations with high mainframe intensity maintained their leverage in
terms of lower IT Cost of Goods. Across sectors, the gap widened by 3%;
in banking, the gap widened by 2%, with mainframe platforms maintaining
an astounding 67% cost advantage at the unit cost-per-core transaction
level.
This research reveals a pattern that indicates that mainframe-heavy
organizations are more economically efficient in supporting the
computational demands of increased revenue than distributed-server-heavy
organizations. Such patterns are critical to observe and understand as
computational demand increases in the global economy, in business and
government, and in our daily lives.
It is likely that 2012 is the "
technology economic tipping point"
-- the point at which demand for computing growth outstrips the ability
of Moore's Law to offset increased costs. This perhaps is also the
point at which organizations that have not learned by codifying the
patterns of technology costs and value will see their tech expenses
spiral out of control, leading to uninformed demands to cut IT expenses.
On the flip side, those oganizations that have an understanding of
technology economics will find themselves in a position of extreme
competitive advantage.
Howard A. Rubin is founder of Rubin Worldwide, a research and
advisory firm focused on the economics of business technology.
Howard.Rubin@rubinworldwide.com
