Open Framework, Information Management Strategy & Collaborative Governance | Data & Social Methodology - MIKE2.0 Methodology
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Information Economics

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MIKE2.0 encourages the use of good information practices within organizations through the application of the principles of Information Development. While people readily talk about "information as an asset", the phrase is seledom turned into meaningful action.

With the transition to an information and knowledge-centric economy, those organisations that innovate with information will be the ones that are seen by their customers and stakeholders as differentiated. Information Economics is a branch of microeconomic theory which seeks to understand the motivations of different participants in a community which can be either internal or across organizational boundaries.

The discipline can be seen as theoretical, with some writers going so far as to suggest that it is the value of the business that is generated that has value rather than the information itself. This view reflects the immaturity of the discussion.

There are two things we can learn from the explosion in era of the information economy. The first is that new and truly disruptive businesses almost always use information in a new way (examples include the way new credit card issuers use loyalty schemes and Amazon’s ability to recommend purchases). The second is that the information associated with truly disruptive businesses more closely adheres to the Small Worlds principle that separation and complexity have a logarithmic relationship. That is, adding more complexity in the information only results in users having to navigate a small number of extra steps.

For instance, the telephone network of the early twentieth century was requiring a linear growth in telephone operators to keep growing, but by the second half of the twentieth century it had innovated to ensure that moving from the simplest transaction (calling next door) and the most complex (calling the other side of the world) only added a small number of exchanges. Similarly, iTunes doesn’t just allow you buy music online, rather it innovates by reducing the number of steps required to relate information on your iPod to the artist and album that you are interested in.

Contents

Implication for information handling in organizations

The trend towards thinking of information as part of the economy is happening in many different ways. Perhaps one of the most important is in the way that business is changing its attitute towards funding "publish and subscribe" information systems such as the enterprise data warehouse.

Flow1.jpg
During 2000-2010, information providers have had to pay for the privilege to have their information incorporated as part of the company information asset. The information consumers similarly have had to pay to extract and benefit from the same data resources. There has been little or no reward for value-adding to the information resource along the way.
Flow2.jpg
More recently, in some organisations, information providers aren’t being asked to pay. The information consumer pays for the data based on the economic benefit of the data and the information asset owner based on the value add that they provide (as shown by the ticks and increased charging). The model acknowledges the value of the information, while repaying the effort required to provide it.
Flow2.jpg
Ultimately, this trend will mean that the information asset owner will become an information “trader”, aiming to be a profit centre (selling internally or externally) – identifying information across the organisation that can be value-added and resold. The group function increasingly becomes part of the “cloud” with data as a service rather than providing a technology platform.

When to consider Information Economics

The information sharing motivations existing and required within and across organisations should be considered as part of phase one, particularly:

Supporting assets

Selected References

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Reference
Link
Description
Date
1Dubov, Lawrence. \"Series: Quantifying Information Assets\"http://masteringdatamanagement.com/index.php/2011/01/11/series-quantifying-information-assets/The article defines the term \"Business Entropy\" and estimates the impacts of poor data quality in terms of losses of Business Entropy. In order to monetize these information losses the article leverages the Economic Value approach developed by Robert Hillard and Sean McClowry 11/01/2011
2Berson, Alex and Dubov, Larry \"Master Data Management and Data Governance\"http://www.amazon.com/MASTER-DATA-MANAGEMENT-GOVERNANCE/dp/0071744584/ref=sr_1_1?ie=UTF8&s=books&qid=1304638855&sr=8-1Chapter 12 of this book discussed techniques for estimation of business benefits for MDM09/11/2010
3Hillard, Robert \"Information Driven Business\"http://www.amazon.com/Information-Driven-Business-Information-Maximum-Advantage/dp/0470625775/ref=sr_1_1?ie=UTF8&qid=1304686315&sr=8-1In addition to many interesting thoughts about business impact of information, page 22 of the book provides quantitative recommendations on the portion of Economic Value of information by industry23/08/2010
4Liliendahl Sorensen, Henrik. “Liliendahl on Data Quality. ROI\"http://liliendahl.wordpress.com/roi/Quote: \"Predicting Return on Investment from a data quality program (and many other business initiatives) is like predicting the weather. There is a myriad of factors, events and not fully understood processes that makes weather forecasting and making a business case for data quality a chaotic discipline.\" It is an important thought justifying the need for the methods other than traditional bottom up approach. It follows from this quote that often it is not feasible/too expensive to estimate the ROI, NPV or EV by applying bottom-up approach 12/06/2010
5Dubov, Lawrence. Serier: Building a Business Case for MDMhttp://masteringdatamanagement.com/index.php/2010/01/19/series-building-a-business-case-for-mdm/The blog series describes how to estimate business benefits of MDM using the Economic Value of Information method and MDM maturity model 23/02/2010
6Liliendahl Sorensen, Henrik. “55 Reasons to Improve Data Quality.” Liliendahl on Data Qualityhttp://liliendahl.wordpress.com/2009/11/22/55-reasons-to-improve-data-quality/It is a good list that can be used as a starting point if an organization wants to do in depth bottom up estimation of inefficiencies due to data quality problems22/11/2009
7Wrazen, Ed. “Using Metrics to Assert a Business Case for Data Quality.” http://www.dataqualitypro.com/data-quality-home/using-metrics-to-assert-a-business-case-for-data-quality.htmlThe article brings some useful metrics and numbers characterising business impact of poor data quality. Some specific quantitative examples include: impact of missing or incorrect zipcode, relationships between duplicates, repeat sales and revenue, financial impacts of inconsistencies in product codes 01/11/2009
8Linthicum, David. “Defining the ROI for Data Integration. Informatica Perspectives, September 23, 2009http://blogs.informatica.com/perspectives/index.php/2009/09/23/defining-the-roi-for-data-integration/Rule of Thump Estimate: ROI from data integration 500-1000% over 5 years23/09/2009
9Joshi, Nitin. “Quantifying Business Value in Master Data Management.”http://www.information-management.com/infodirect/2009_137/master_data_management-10016016-1.htmlSome basic analysis and recommendations on how to quntify business benefits of master data management via Net Present Value (NPV). The article discusses how to evaluate inflows and outflows used for NPV claculations03/09/2009
10Africa, Andrew. “Rely on Data Quality to Survive.” InfoManagement Direct, August 20, 2009http://www.information-management.com/infodirect/2009_135/data_quality_economy_it_business_intelligence_bi-10015920-1.html\"The paper focuses on the impacts of data quality for the orgnizations servicing maintenance contracts with expiration dates. Due to poor quality of customer data and data inconsistencies across systems. Specific numbers provided by the author: Poor data quality is the primary factor driving these revenue detractors:
  • More than 50 percent of maintenance contracts go un-renewed
  • 30 to 60 percent of warranty service products are unregistered
  • More than 50 percent of registration information is typically not actionable. Registration is defined as: complete customer contact information (i.e., phone, address, email, etc.), asset serial number, service contract number, purchase info, etc.\"||20/08/2009
11Karel, Rob. “The ROI for Master Data Management.” Forrester Research, Inc., October 29, 2008. Available online from Forrester Research, Inc.The paper applies \"Total Economic Impact (TEI)\" analysis to MDM. MDM benefits within the model are limted to increased productivity of call centers, mailings\' cost reduction and compliance. The model evaluates the costs of a typical MDM initiative by categories: software license, spftware maintenance fees, hardware, professional services and in-house IT resources 29/10/2008
12Hillard, Robert and McClowry, Sean. “Economic Value of Information.” Mike 2.0.http://mike2.openmethodology.org/wiki/Economic_Value_of_InformationThe article expresses the Economic Value (EV) of information in terms of market capitalization of the enterprise. The authors found that EV of information can be estimated as 20-50% of the market capitalization26/04/2006
13Lawrence, David B. “Economic Value of Information.” Springer 1999http://www.amazon.com/Economic-Value-Information-David-Lawrence/dp/0387987061/ref=sr_1_1?ie=UTF8&s=books&qid=1305337792&sr=8-1One of the first books on the topic, it covers the "evaluation and choice of information sources by individuals and the design and management of information systems by organizations. The book studies the determinants of the value and cost of information, both to the individual and to the organization, provides technqiues for the assessment of the value of information and the comparison of informativeness among alternative sources, and presents principles for the optimal design and management of information systems."1999
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