climbing the big data ladder
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Leveraging your ERP to unlock your information assetsTRANSCRIPT
Climbing the Big Data Ladder
Leveraging your ERP to unlock your information assets
Melbourne, April 2012
2 Climbing the Big Data Ladder © 2012 Deloitte Touche Tohmatsu
Robert Hillard
Robert Hillard is the Deloitte partner leading the
Australian Technology Consulting practice. He is a
specialist in Enterprise Information Management,
which is a key part of the firm’s Technology
capability, and is the author of Information-Driven
Business (Wiley 2010).
Robert was an original founder of MIKE2.0 which
provides a standard approach for Information and
Data Management projects. He continues to
support the initiative as the vice-president and a
board member of the MIKE2.0 Governance
Association, the Swiss non-profit governance body
for MIKE2.0.
Robert has held international consulting leadership
roles and provided advice to government and
private sector clients around the world. He has
more than twenty years experience in the discipline
of Information Management, focusing on
standardised approaches including being one of
the first to use XBRL in government regulation and
the promotion of information as a business asset
rather than a technology problem.
Over many years, Robert has advised large
complex organisations on their Information
Management strategies and specifically how to
leverage these strategies to achieve their business
objectives including major transformations.
3 Climbing the Big Data Ladder © 2012 Deloitte Touche Tohmatsu
“People (will) have more time for leisure activities in
the year 2008. The average work day is about four
hours”
James R. Berry (1968), “40 Years in the Future,” Mechanix Illustrated
4 Climbing the Big Data Ladder © 2012 Deloitte Touche Tohmatsu 4
Despite the continuing reduction in the cost of
computing, it is orders of magnitude more expensive
today to introduce new products or services than it
was 15 or 20 years ago.
5 Climbing the Big Data Ladder © 2012 Deloitte Touche Tohmatsu
“Information is produced by all processes and it is
the values of characteristics in the processes’ output
that are information”
R. M. Losee (November 1998), “A Discipline Independent Definition of Information,” Journal of the
American Society of Information Science
6 Climbing the Big Data Ladder © 2012 Deloitte Touche Tohmatsu
[In the 1920s,] experts predicted that by 1980, every
single woman in North America would have to work
as a telephone operator if growth in telephone usage
continued at the current rate*
Business Data Communications and Networking
Jerry FitzGerald, Alan Dennis
*At the time, all telephone operators were women
7 Climbing the Big Data Ladder © 2012 Deloitte Touche Tohmatsu
Information Growth doesn’t just go on for ever
• 1990: price of storage hit the important physiological threshold of US$1
per megabyte
• Apparently insatiable growth in business data but we expect growth will
slow and transition to the “new economy” in the future
1990
2030s?
8 Climbing the Big Data Ladder © 2012 Deloitte Touche Tohmatsu
What is big data?
• Big data can be very small (e.g., avionics)
• Large datasets aren’t necessarily big (e.g., transactions)
• Big data is complex and hard to isolate (e.g., toll roads)
Big refers to big complexity rather than big volume. Of course, valuable
and complex datasets of this sort tend to grow rapidly and so big data
quickly becomes truly massive.
9 Climbing the Big Data Ladder © 2012 Deloitte Touche Tohmatsu
Information
Trading
Platform
• Traditionally information is
governed as a system
• Value is imposed
• Increasingly trying to put the
right motivations in place
• Moving to an information
economy
• Value is built into the price
10 Climbing the Big Data Ladder © 2012 Deloitte Touche Tohmatsu
ERPs are the trading platform not a bucket
• The investment in enterprise applications has provided a foundation for master
data
• Master data is a set of keys not a map of what’s behind every door
• Sensors, SCADA, mobile devices, location aware services et cetera are all
creating masses of data that should not necessarily go into the ERP
• Finance, though, can argue a position as the information Tsar
11 Climbing the Big Data Ladder © 2012 Deloitte Touche Tohmatsu
Value needs to be modelled not calculated
• Big data adds complexity to the organised, justifying consolidation at the rate
f=logc/i(l+1) where f=complexity factor, c=cost per system, i=cost per interface and l=number of legacy systems
• Data should be valued without trying to identify all of its uses (6 standard
methods)
• ERP master data should be used to provide a standard language and point of
agreed value – it is not the single enterprise store
• Introduce information currency as real concept for exchanging value, ROI should
include both repayment of system cost and the impact on the information
economy
12 Climbing the Big Data Ladder © 2012 Deloitte Touche Tohmatsu
The six methods of valuing information
1
2
Where:
i = influenced
C = control
Intrinsic Value of Information How good and easy to use is the data versus how likely are
others outside the organization to have it also? This the
presumptive value of information, enabling apples-to-
oranges comparisons.
Business Value of Information The value of information to a business process: How good
is the data? How applicable to the business or a particular
business process is it? How quickly can we get fresh data
to the point of the business process?
Loss Value of Information The cost of not having information: What would it cost to
replace the data, and what is the financial impact to the
business if the data were lost over a time period (t)?
Performance Value of Information Value of information to business objectives, represented
as key performance indicator (KPI) targets: How much
does having a unit of information incrementally contribute to
moving closer toward all n KPI targets over a given period?
Economic Value of Information The bottom-line financial value for the information asset:
The Performance Value of Information (PVI) for a revenue
metric, less the cost of acquiring, administering, and
applying the information.
Market Value of Information The income that can be generated by selling, renting or
bartering with this information. How much is a business
partner (p) willing to pay for access to this information?
13 Climbing the Big Data Ladder © 2012 Deloitte Touche Tohmatsu
Putting a value on decommissioning
• A simple approach to estimating the value of decommissioning
legacy systems is based on the complexity that they add to the
introduction of new services.
• Using the past as a basis, c is the investment per new system
and n is the number of system builds expected over a given
period. Investment cost for a domain is therefore c times n.
• However legacy systems add complexity at a rate that rapidly
increases initially before trailing off (logarithmic). The
complexity factor (f) is dependent on the ratio of the cost of
software to development (c) to the cost of interfacing (i):
f=logc/i(l+1)
• The complexity factor can then be applied to the original
investment:
• c x n x (f + 1)
• Note, efficiencies in interfacing similarly provide benefit. As the
cost of interfacing drops the logarithm base increases and the
complexity factor naturally decreases.
This is only a method of identifying a savings trend, but it
provides a good starting point for more detailed modelling of
benefits.
c = likely cost per system
n = number of likely system builds in 5 years
i = cost per interface
l = number of legacy systems in domain
f = complexity factor
f
l
14 Climbing the Big Data Ladder © 2012 Deloitte Touche Tohmatsu
• www.infodrivenbusiness.com
• www.openmethodology.org
• www.twitter.com/rhillard
• www.deloitte.com/au/eim
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