One of the key questions is who should sponsor Information Managements.  The governance sections of MIKE2.0 describes operational organizations and how to get there, but makes the assumption that the CEO, CIO, CFO etc. are supportive of the initiative and will act as sponsors.  What happens when they’re not?

Actually, it seems that this is the case more often than you would wish with many senior executives unwilling to commit to the proper management of information.  It’s not hard to work out the reason why, in most companies (and increasingly in many government organizations) the CEO is only appointed for a short contract with rapid rotation of new talent into the role.  No wonder the CEO acts like a politician looking for the “quick fix” common sense answer that they can put in place within their term and position themselves to be extended (analogous to a politician seeking re-election),

There is hope, however, by looking at the board.  In most companies, board members have a longer tenure than CEOs and also feel more exposed to legal issues.  A quick conversation about the issues of ledger versus non-ledger data (discussed before in this blog) highlights to board members how great their exposure is if they don’t mandate better governance.  Judicious use of passionate middle managers can complete the pincer movement and before you know it the CEO sees Information Management as a mandatory activity and a quick win.

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.

The concept of a single version of the truth has gained currency in Information Management to the point of being a mantra and I believe it is appropriate to introduce a few words of caution.

If I can be philosophical for a moment, Information Management theory is starting to turn the many old views of the world, including the way physics describes objects.  This isn’t a long bow to draw for Information Management professionals: Imagine a white statue in the park.  If you put on rose colored glasses, what color is the statue?  If you get everyone in the park to put on rose colored glasses, what color is the statue?  If you cover the statue in rose colored cellophane?  If you paint in statue with rose colored paint?

Survey any group and you are likely to get different answers to the color of the statue under the different circumstances I’ve outlined here.  If you at least said that painting the statue changed its color then you are admitting that it is the information (in this case color) that you receive that is important rather than what might exist in any deeper layers.

Those same rose colored glasses can apply to the enterprise data warehouse.  One version of the truth that forces everyone to see the data through rose colored glasses does not make the data rose colored!  Accountants, however, have thought long and hard about this and have rules for how you can clean-up variances that can’t be reconciled.  The most important thing is to ensure that all observers agree rather than just observe the same result, and that includes reasonable outsiders, executives and analysts.

In the wake of a recent adverse court finding for a major media company in Australia, I was interviewed by The Age on the business dangers of email.  I argued that communication within and between companies should be embraced rather than feared, but proper governance inevitably meant that one-on-one emails were not the best way to manage this unstructured content.

You can read the full article at http://www.theage.com.au/news/business/teamwork-avoids-dangers-of-oneonone-emails/2007/09/09/1189276544211.html or listen the podcast at the same site.

We’re often asked to compare approaches to managing structured and unstructured data and attempts to bridge the gap between the two.  Traditionally, technology practitioners who worried about unstructured data have been entirely different group to those that worried about structured data.

In fact, there are three types of data, structured, unstructured and a hybrid (records-oriented) grouping of semi-structured.  They have much in common and are all part of the enterprise information landscape.  In order to look at ways to leverage the relative strengths of the different types of data, it is important to first understand how they are used.

There are three primary applications of data within most enterprises.

The first is in support of operational processes.  In the case of structured data, these processes are usually complex from a system perspective but often quite transactional from a human perspective.  In the case of semi-structured and unstructured data, there is often less system intervention or interpretation of the data with a heavy reliance on human interpretation.

Secondly, each of the three is used for analysis.  In the case of structured, it is easy to understand how the analysis is undertaken.  With semi-structured/record data, analysis can be divided into aggregation of the structured components and a manual analysis of the free-text.  With unstructured, analysis is usually restricted to searching for like terms and manually evaluating the documents.

Finally, all three types of data are used as a reference to back-up decisions and provide an audit trail for operational processes.

MIKE2.0 recommends approaches to governance, architecture and integration which are independent of the structure of the data itself.

The majority of effort associated with all data, regardless of its form, is gaining access to it at the time when it’s needed.  In all three cases, there are processes to lookup or search the data.  SQL for structured data, lookups for semi-structured and tree-oriented folders for unstructured.  Increasing, the techniques for finding all three types are converging in one set of processes called Enterprise Search.

Ironically, despite the power of search, successful implementations are really mandating the implementation of common metadata and the use of a single enterprise metadata model.  Again, MIKE2.0 takes the information architect through these requirements in a lot of detail.

In the future, organisations can expect to keep all three forms of data (structured, semi-structured records and unstructured documents) in the same repositories.  However, there is no need to wait for this future utopia to begin leveraging all three in the same applications and managing them in a common way.

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.

Welcome to Information Development - a blog dedicated to the subject of Information Management. Complementary to the MIKE2.0 Methodology which is provides a structured competency for Information Development, this is a collection of perspectives on how the management of information has tremendous impacts on business, technology and society.