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.

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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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