Random Curry

(h/t GarfieldMinusGarfield)

Hoo boy…

Genworth in deal to buy bank, seeks TARP money

Hartford To Convert To S&L To Tap Treasury Funds

According to the second article, we now have American Express and GMAC structured as bank holding companies as well. When I heard the news about The Hartford on Friday, I was in line at the bank (my real, actual bank), and I laughed out loud - I’m sure everyone thought I was nuts.

Two observations:

Tell me again when we’re getting some adults in charge of our national store?

Riot…

The Matrix Runs Windows

Where Alan Greenspan echoes the typical cry from management: When All Else Fails, Blame IT!!

Greenspan: Bad data hurt Wall Street computer models

Hey Alan - everything you talk about are management decisions, not IT decisions. For example, using only 20 years of historical data is a management decision…Sorry to have to point that out to you…

At the risk of incurring the wrath of The Washington Post by linking to them, here’s an article with current interest that exposes a broader issue:

Thousands Face Mix-Ups In Voter Registrations

It occurs to me that this is a classic Customer Data Integration issue - name pattern matching and verification of the “right” information across multiple databases. There are several responses to this issue, most of which involve trying to include every record possible in the final result set.

I find it interesting that the Republican response to this is to automatically disqualify all those records that do not match, which is curious to me because I doubt that they would do that in the course of their own businesses. It leads me to believe that they really don’t want to fix the issue because it serves them politically (third major election in a row, folks). Food for thought on what really matters to some people…

I’m always for explaining things in the simplest way possible, so here’s a link to a great (and simple) introduction to the Semantic Web:

The Semantic Web, explained with Lolcats

Ican has cheezburger, indeed…

A great article linking to a great paper on the state of Semantic technology in the enterprise world…

New report places Semantic Web ‘On the Cusp’ of something big

I call your attention to the conclusion of the author, David Provost:

The business value of the Semantic Web has moved away from being a debate to the point where the technology is proving itself to be commercially competitive. Increasingly, innovators, entrepreneurs, and business managers are beginning to understand how to recognize, define, and pursue the market opportunities made possible by this technology.

It’s always exciting to watch a concept go from the theoretical, academic world to concrete business application - I continue to believe that Semantics is making that journey, and is worthy of everyone’s attention. Good stuff.

Most likely the greatest “get off my lawn” moment of a year filled with “get off my lawn” moments:

I am soooooooo glad I’m not a Raiders fan…

Mike Walker shares his observations on Day 2 of the Gartner Enterprise Architecture Summit:

Gartner EA Summit - Day 2

I’m particularly interested in the presentation by Brian Burke, and I’m sorry I missed it. It seems that the consensus in the field is settling on the idea that the most successful enterprise architects are more like masters of Jiujitsu and less like aggressive, type-A personalities. This is really encouraging for someone like me, who embraces the philosophy of picking one’s battles and passive influence, if not the physical aspects of it.

Perhaps there’s a future for me here after all…

In Part 1 of this article, we explored the issues surrounding risk management in IT organizations. Part 2 of the article describes the role of the Actuary in business and how this concept could apply to issues facing IT.

—–

The Actuary
The dictionary defines an actuary as “someone versed in the collection and interpretation of numerical data (especially someone who uses statistics to calculate insurance premiums).” The
actuarial function is very important to the insurance industry since the accurate prediction of risk allows the accurate calculation of premium rates and loss exposure, which demonstrates the soundness of the business. Actuaries have access to decades of “experience” data to base their calculations, and a well-established certification process to ensure competence.

The application of this skill set would pay great dividends to an IT department that is interested in quantifying the risk associated with the continuance of “unapproved” behavior of business uers, such as shadow data analysis systems. For example, the statistical analysis of hours spent by business staff on gathering data for these systems and the reconciliation of numbers produced by similar systems could predict the financial effect of the continuation of the practice. From this data, decision-makers can weigh the costs of lost productivity with the benefits of the more local control and flexibility that these systems provide and make an intelligent decision on the continuance of the practice, as opposed to today’s practice of anecdotal decisions.

The Requirements
Why haven’t more enterprises embraced this sort of detailed analysis in their IT departments? Why have initiatives such as Enterprise Risk Management failed to gain much traction outside of regulatory compliance? The answer lies in data itself. Actuaries have decades of experiential information at their disposal to do their jobs, mainly collected by public agencies such as the United States Census, hospital records, accident records, and the like. This data is collected and made available as part of the normal operation of organizations that are external to the actuarial team in the enterprise, so the business incurs little if any cost in obtaining the data.

Business users and IT departments do not generally track staff utilization to the point where it would be useful for statistical analysis, and they would need to establish policies and infrastructure to collect the data within the organization. The cost of obtaining this infrastructure and training staff in its use would need to be added to any project plan implementing a risk management program. The fact that the cost would be borne entirely by the enterprise is a strong disincentive to undertake such an effort.

There is a more significant hurdle to cross than cost, however – the definition of the costs themselves. How difficult would it be to collect this data? Here are some examples:

• When a business user creates a “spreadmart*,” how much time is devoted to obtaining
  and reconciling data, and how much time is devoted to the analysis itself?

• What is the cost of retrieving quality data in a spreadmart and in the enterprise data
  warehouse?

• What is the cost in time and materials for a local hard drive crash where spreadmart data
  is lost?

• What is the reconciliation cost of data calculated by formulas that deviate from the
  corporate standard in a spreadmart?

• What is the potential cost to the enterprise if a spreadmart metric is incorrect?

Very few, if any, enterprises collect data on staff utilization and costs at this level of detail, mainly because there are few automated methods for data collection, and the testimony of staff members in status reports or time sheets is fairly unreliable for statistical purposes. Yet, it is precisely this level of detail that is required to perform the analysis necessary to quantify risk.

Another requirement for actuarial analysis is scholarship regarding risk management standards in
the IT profession. Here, insurance actuaries have a huge advantage due to the relative age of the professions: information technology has been practiced for just over 50 years, where insurance has been practiced for hundreds of years, and entire university curricula is devoted to the subject.

However, this should not be a great impediment to the adoption of actuarial philosophy to information technology, provided that IT is viewed as simply another business process. The statistical measures of risk should be similar, although the specific situations may be different. The analytical techniques should be similar enough to encourage adoption.

—–

In the final installment of this article, we will bring the issues and definitions together to propose a potential solution to risk management issues in IT organizations.

* The term “Spreadmart” was coined by Wayne Eckerson, Director of Research for TDWI, to
describe the primary implementation of a shadow data analysis system as a spreadsheet with
data obtained from enterprise systems that functions as a data mart.