Climbing the Big Data Ladder

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Leveraging your ERP to unlock your information assets

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Climbing the Big Data Ladder

  1. 1. Climbing the Big Data Ladder Leveraging your ERP to unlock your information assetsMelbourne, April 2012
  2. 2. Robert Hillard 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 Robert Hillard is the Deloitte partner leading the the first to use XBRL in government regulation and Australian Technology Consulting practice. He is a the promotion of information as a business asset specialist in Enterprise Information Management, rather than a technology problem. which is a key part of the firm’s Technology Over many years, Robert has advised large capability, and is the author of Information-Driven complex organisations on their Information Business (Wiley 2010). Management strategies and specifically how to leverage these strategies to achieve their business objectives including major transformations.2 Climbing the Big Data Ladder © 2012 Deloitte Touche Tohmatsu
  3. 3. “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 Illustrated3 Climbing the Big Data Ladder © 2012 Deloitte Touche Tohmatsu
  4. 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.4 Climbing the Big Data Ladder © 2012 Deloitte Touche Tohmatsu 4
  5. 5. “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 Science5 Climbing the Big Data Ladder © 2012 Deloitte Touche Tohmatsu
  6. 6. [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 women6 Climbing the Big Data Ladder © 2012 Deloitte Touche Tohmatsu
  7. 7. 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 2030s? 19907 Climbing the Big Data Ladder © 2012 Deloitte Touche Tohmatsu
  8. 8. 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.8 Climbing the Big Data Ladder © 2012 Deloitte Touche Tohmatsu
  9. 9. • 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 Information Trading Platform  9 Climbing the Big Data Ladder © 2012 Deloitte Touche Tohmatsu
  10. 10. 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 Tsar10 Climbing the Big Data Ladder © 2012 Deloitte Touche Tohmatsu
  11. 11. 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 economy11 Climbing the Big Data Ladder © 2012 Deloitte Touche Tohmatsu
  12. 12. The six methods of valuing information Intrinsic Value of Information Performance Value of Information How good and easy to use is the data versus how likely are Value of information to business objectives, represented others outside the organization to have it also? This the as key performance indicator (KPI) targets: How much presumptive value of information, enabling apples-to- does having a unit of information incrementally contribute to oranges comparisons. moving closer toward all n KPI targets over a given period? Where: i = influenced C = control Business Value of Information Economic Value of Information The value of information to a business process: How good The bottom-line financial value for the information asset: is the data? How applicable to the business or a particular The Performance Value of Information (PVI) for a revenue business process is it? How quickly can we get fresh data metric, less the cost of acquiring, administering, and to the point of the business process? applying the information. Loss Value of Information Market Value of Information The cost of not having information: What would it cost to The income that can be generated by selling, renting or replace the data, and what is the financial impact to the bartering with this information. How much is a business business if the data were lost over a time period (t)? partner (p) willing to pay for access to this information?12 Climbing the Big Data Ladder © 2012 Deloitte Touche Tohmatsu 1
  13. 13. Putting a value on decommissioning • A simple approach to estimating the value of decommissioning c = likely cost per system legacy systems is based on the complexity that they add to the n = number of likely system builds in 5 years introduction of new services. i = cost per interface • Using the past as a basis, c is the investment per new system l = number of legacy systems in domain and n is the number of system builds expected over a given f = complexity factor 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 f 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. l13 Climbing the Big Data Ladder © 2012 Deloitte Touche Tohmatsu
  14. 14. • www.infodrivenbusiness.com • www.openmethodology.org • www.twitter.com/rhillard • www.deloitte.com/au/eim14 Climbing the Big Data Ladder © 2012 Deloitte Touche Tohmatsu
  15. 15. About DeloitteGeneral information only Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private company limitedThis presentation contains general information only, and none of Deloitte Touche Tohmatsu Limited, by guarantee, and its network of member firms, each of which is a legally separate andits member firms, or their related entities (collectively the “Deloitte Network”) is, by means of this independent entity. Please see www.deloitte.com/au/about for a detailed description of the legalpresentation , rendering professional advice or services. structure of Deloitte Touche Tohmatsu Limited and its member firms.Before making any decision or taking any action that may affect your finances or your business, you Deloitte provides audit, tax, consulting, and financial advisory services to public and private clientsshould consult a qualified professional adviser. No entity in the Deloitte Network shall be responsible spanning multiple industries. With a globally connected network of member firms in more than 150for any loss whatsoever sustained by any person who relies on this presentation. countries, Deloitte brings world-class capabilities and deep local expertise to help clients succeed wherever they operate. Deloittes approximately 170,000 professionals are committed to becoming the standard of excellence. About Deloitte Australia In Australia, the member firm is the Australian partnership of Deloitte Touche Tohmatsu. As one of Australia’s leading professional services firms. Deloitte Touche Tohmatsu and its affiliates provide audit, tax, consulting, and financial advisory services through approximately 5,400 people across the country. Focused on the creation of value and growth, and known as an employer of choice for innovative human resources programs, we are dedicated to helping our clients and our people excel. For more information, please visit our web site at www.deloitte.com.au. Liability limited by a scheme approved under Professional Standards Legislation. Member of Deloitte Touche Tohmatsu Limited © 2012 Deloitte Touche Tohmatsu

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