Climate change is increasingly a mainstream issue. There is growing momentum to address the issue at a number of levels, in the corporate sector, with private citizens and within government.

There is, however, a real danger with the trend towards carbon neutrality that is analogous to the poor systems designs. As described by the United Nations, climate change is just 1 of the 36 areas of Sustainable Development. An Information Development approach can help:

• Understand the impacts of decisions and how they impact other areas of Sustainability (e.g. how a programme that helps reduce a retailer’s carbon footprint by stopping imports from Africa impacts other areas).

• Collaboratively develop standards and share lessons learned. This will be a major benefit as the transformation organizations must go through is so significant. This isn’t necessarily “trade secrets” but an open and transparent forum for them to share information.

• Collaborative Business Intelligence platform to make objective decisions. Decisions can be based on a historical evidence and make us of historical models. Policy analysis, researches and the public can share different forms of information as part of the process.

• Information Sharing across different organisations, involving operational and analytical information. Enablers include open and common standards, search and collaboration. Some information can be anynoymised while other content can seen by both parties.

• Collaboratively develop standards and share lessons learned. This will be a major benefit as the transformation organizations must go through is so significant. This isn’t necessarily “trade secrets” but an open and transparent forum for them to share information.

• Measure progress based on standard metrics. We need standards because when we don’t account for what we produce, we may get unexpected issues. Information Quality issues are analogous to the pollutants we see from poor sustainability design.

The Information Development approach to Sustainable Development can be applied to design the interaction points between the different areas. It also means the ability to make fact-based decisions, share information between systems and provide easy access to information from complex, federated sources.

In this post, I want to remind readers that information is not abstract, it is something real and follows the laws of physics.  Information theory talks about encoding information using predictable patterns, which have to be represented by some type of device.  Such a device would typically use electrical energy in some form to represent each bit so it is no surprise that conservation of energy laws apply: that is creating one piece of information must destroy another.

Why does this matter?  Information is finite and discovering one thing inevitably means that something else is either lost or in some way reduced in value.  Anyone with a physics background might think of this as an extension on the Heisenberg uncertainty principle.

From a business management perspective, it means that the enterprise customer list cannot be used in an infinite number of ways without degenerating the value of the content.  While intuitively true, I argue that it is also mathematically true, through the fact that applying information such as customer details also derives information about its application.  Deriving information about its application must reduce information (significant or otherwise) from elsewhere.  Usually, this reduction is significant – the process of finding out a customer needs a new service usually reduces the confidence in earlier analysis and limits the ability to target the same customer in other ways.

Why would an executive care?  There are two main reasons why every business and technology executive should consider the quality of data modelling to be core to their success.

The first is that information is a valuable economic asset (as argued in MIKE2.0 in the article the Economic Value of Information).  Customer data, performance data, analytical information all combine to be an asset that is often worth multiples of billions of dollars.  If a company had billions of dollars worth of gold, I’d expect business executives to want to review and understand how such a valuable asset was housed!  Given that the data model is usually the main home for the information asset, the same should also be true.  The data model cannot be delegated to junior technical staff!

Increasingly there is another reason for elevating the data model.  Legacy information is becoming an obstacle to business transformation.  As the price of storage dropped during the 1990s, new systems began also storing ancillary data about the parties involved in each transaction and substantially more context for the event.  Context could, for example, include the whole sales relationship tracking leading up to a transaction, or the staff contract changes that led to a salary change.  With the context as part of the legal record, there are operational, regulatory and strategic reasons requiring that any new or transforming business function do nothing to corrupt the existing detail.  The data model is the only tool we have to map new business requirements to old data.

Given the complexity of data modelling, it’s not surprising that executives have shied away from speaking to technologists about the detail of individual models.  A discussion on normalization principles would be enough to put most decision makers off!

In the Small Worlds Data Transformation Measure article MIKE2.0 introduces a set of simple metrics to indicate whether on average the data models of an organization or doing a good job of managing the information asset.  Using the principle that information makes most sense in the context of the enterprise, it measures the level of connectivity and the degree of separation on average across a subset or all of the data models housing the information assets.

I am continually struck by the lack of formal valuation models to information. Considerng how much organizations spend on building and maintaining information assets and how valuable they are to the health of the business, you would think it would be an area that would receive more focus.

While I’ve seen a number of academic papers on assessing the Economic Value of Information, the practically implemented cases are few and far between.  I have done some development on “Assessment-oriented” models that can be value in formulating a strategy, such as the Economic Value of Information model in MIKE2.0.

An Information Value Assessment should provide a mechanism to assign an economic value to the information assets an organization holds and  the resulting impacts of Information Governance practices on this value.   It could also measure whether the return outweighs the cost and the time required to attain this return.

Governance models are just one way of assessing value.  Other simple techniques could include:

• Mastering - how many systems hold this common data?

• Latency - if I load this data into a warehouse in an hourly fashion as opposed to weekly what are the gains?
• Quality - RI issues, accuracy issues
• Reach - how many people read my blog?  who are the readers?

I think this is an area where industry models will greatly improve, similar to what has occurred in the past 10 years in the infrastructure space.  The lack of model points to the immaturity of information management as a competency and the strict building of information with technology.   I would welcome any other opinions on technqiues.