Data as a business asset

Data as a business asset

If I look back at my career so far, I’ve spent a lot of time with data in the context of all sorts of business intelligence, data warehouse, analytics and master data projects. Even though each and every one of those projects was unique due to different business and technology contexts there was one common denominator: I was building data as a business asset, something that was tangible.

Let’s have a closer look at the office building we work in. It is an asset that is insured, it is on the balance sheet and we maintain it and keep it clean. Why would we not do the same with an organisation’s data?

Building out data as a business asset means improving the quality of the data assets. Some important properties of data that define data quality are:

  • Relevance: the usefulness of the data in the context of your business.
  • Clarity: the availability of a clear and shared definition for the data.
  • Consistency: the compatibility of the same type of data from different sources.
  • Timeliness: the availability of data at the time required and how up to date that data is.
  • Accuracy: how close to the truth the data is.
  • Completeness: how much of the required data is available.
  • Accessibility: where, how, and to whom the data is available or not available.
  • Cost: the cost incurred in obtaining the data, and making it available for use.

 

Data as a business asset

Example: the Challenger disaster

Let’s have a look at one the components of this data quality wheel in more detail: relevance. Data by itself is meaningless. It is only when you add context and relevance that data gets turned into information. An example to illustrate this concept is the Challenger disaster, which has been used as a case study in many discussions of engineering safety and workplace ethics. Edward Tufte provided an interesting view regarding the availability of data and related decision making processes surrounding this historic moment.

Let’s assume that you are the decision maker. The date is January 27th, 1986. The Space Shuttle launch team meets to discuss the launch of the space shuttle. There are concerns regarding the launch weather conditions, which have never been tested at the expected temperatures. Normally, a launch like this would have some flexibility in terms of go or no-go. However, this launch will be different. The first teacher in space will be aboard. Also, the president has a scheduled live satellite conference that will be broadcast across the globe. If the launch does not proceed, it will be a public relations issue.

The engineers gathered around this day are the best and brightest. They know of the issues that could occur with the space shuttle. Specifically, they are concentrating on the booster rockets. They are a critical and somewhat delicate piece of machinery. The question is how will the booster rockets really handle cold weather conditions?  Will the O-rings, the most sensitive part of the booster rockets, be able to handle the stress? The engineers will analyse data from prior launches and tests to determine this.

The first handout you are given is illustrated below. This is a sampling of the damage that occurred to O-rings during prior launches. Using the legend on the right, you can determine the damage that occurred. Any of the images on the right placed over the lines in the boosters (lines represent O-rings) represent damage. For this sample, there was no damage.

Data as a business asset

The next sample provides some additional detail. Most of the boosters perform well. However, a closer look shows that some of the boosters do perform poorly. It is hard to see whether a correlation exists between damage and temperature. The graphs that have been shown so far are attractive (in that they show a representation of the booster), but are difficult to use for analysis. The trend cannot be shown. What happens when temperature decreases?

Data as a business asset

The next graph takes the same data, but instead of using a code for damage, a damage index is applied. Although the sample size is small, you can see that decreasing temperature leads to further damage. Furthermore, the predicted temperature for launch has not been tested. Not by a long shot. The prediction is that damage will be very high.

By simply changing the way the data is displayed, even a room full of everyday persons could see that the possibility of severe O-ring damage would be high. Although most of the engineers for the booster manufacturer stated there would be trouble, the heads of NASA and the government decided that the launch should proceed, with devastating results.

Data is NOT information

The important conclusion of this article is that simply having data is not enough. Without the ability to place the results within some context (damage index) and relevance (launch/no launch), we wouldn’t have the information as to what that cold temperature could do. And if you don’t process this information (synthesis), you probably wouldn’t understand that the booster rockets could potentially fail.

Data as a business asset

At Karabina we help our clients to improve the quality of their data assets and to unlock the right level of understanding in their businesses.Contact us.

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