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The four characteristics of decision support systems

The four characteristics of decision support systems

There are four defining characteristics of decision support systems that differentiate them in the massive world of software. They collect, store, analyse and present information. 

The success of any decision support system depends on how well the builders understand those four characteristics. So let’s take a closer look.

Decision support systems help people make decisions

A decision support system is a particular type of information and communications technology whose purpose is to provide information about the circumstances and context of the environment in which a person is making a decision. The four characteristics of decision support systems reveal how this special form of software collects information from the world, the surrounding environment, the prevailing circumstances and the present situation, and how it then packages that information in a form and in a manner that makes it useful to the person making a decision about that context.

Four characteristics of decision support

Data is at the heart of every decision support system: every time, without exception. Data, data, data! All decision support systems are data systems, because it is data that delivers useful information about the contexts in which decisions are made. Thus, every single one of these four characteristics of decision support systems must be present if the system is going to be useful.

One: Data collection

The first of the four characteristics of decision support systems is that any such system must collect data. A familiar example of a decision support system is the Bureau of Meteorology website. This system collects information about the temperature. There are mechanisms in place to collect information such as how hot it is. Thermometer gauges connected over a countrywide network all become part of a massive decision support system. This system is useful for anyone making a decision where the heat of the day is a key consideration.

Without that network of thermometers collecting data, you couldn’t have a system that tells you about the temperature, past, present or future. You have to have that information in order to have a decision support system; it’s based on the mechanisms of data collection.

So the first defining characteristic of a decision support system is that it collects data.

Two: Data Management

Once you have the data, you have to manage it. First and foremost, that means somewhere to store it. This is the second characteristic of a decision support system. With the weather bureau’s temperature system, for example, all of the records they have collected from their temperature gauges every day over the past 100 years or more need to be stored where they can be accessed when needed. That usually means a database – and it always means a computer-based technology.

If you want a system that delivers useful decision support, you need a system that manages data, plain and simple. This is why data management is the second defining characteristic of a decision support system.

Three: Data Analysis

The third of the characteristics of decision support system is where all of the value is usually created: data analysis. This is the intellectual property layer. Anyone can collect and manage data; not everyone can do analysis.

Data analysis is vital because this is what ultimately makes information and raw data really useful in the context of making big decisions. Something like raw temperature data is rarely useful in and of itself. It needs to be analysed, normalised, standardised and harmonised to meet the needs of the users who may benefit from it.

You can analyse the temperature data you collect from the whole country in different ways. In the world of decision support systems, data analysis is the difference between helping a farmer plan the next day’s harvest and informing an airline of the flying conditions at 30,000 feet.

Different forms of analysis can make the same information useful in multiple contexts, specifically targeted to different places, situations, people and needs. Data analysis is at the core of what makes one system useful and another useless. Data analysis is the third defining characteristic of a decision support system.

Four: Data Presentation

The fourth characteristic of a decision support system is data presentation. This is all about how you deliver your information to your end users. That presentation varies depending on whether the data is being presented on a website, via an app, or with a table or a chart. Blue lines, red lines, pie charts, bar graphs…

How you present the information you collect, manage and analyse makes all the difference to how useful your decision support system will be. Don’t forget that any information has to be useful – and usable – if it’s to have value for the decision maker. When we’re talking about decision support systems, presentation is all about being usable.

To go back to our temperature example, you can present information about how hot it is outside using a sun icon to accompany the number that indicates the temperature – 20, 30, whatever. Or, if you need to give the user an immediate heads up that you’re talking about a cold day rather than a hot one, you can use a snowflake icon instead. When either icon pops up on a notification bar, the user already has an idea of what it’ll be like outside before they even read the actual numbers.

The different forms of presentation are the direct interface between the user and your data. Presentation is the only way your user can interact with your data. This is why it’s so important, and it’s why presentation is the fourth of the characteristics of decision support systems.

Information that is relevant and timely is useful

So there we have the four defining characteristics of a decision support system. The system’s role is to deliver information to people responsible for making decisions – information that’s relevant and timely and that tells them what they need to know about the context in which they are making those decisions.

To sum up, a decision support system is a computer-based information technology with four components:

  1. data collection;
  2. data storage;
  3. data analysis; and
  4. data presentation.

Any computer system that has these four characteristics is a decision support system. And everything that applies to how you create and manage decision support systems – and how you build services around them – will be applicable to it.


Photo Credit: 'GDC Europe 2010 Talks, Conversations, Presentations' by Official GDC on Flickr. CC by 2-0. 

Carl Sudholz

Principled Leader at AGContext
Carl Sudholz has over ten years' experience in creating decision support systems and decision support services for public, private and non-for-profit organisations. Carl holds a Bachelor of Science, a Masters of Sustainable Practice and certifications in PRINCE2 Project Management and Business Analysis. Carl's expertise in the methods of creating first-class decision support services is a rare commodity. Especially so, because in this increasingly complex age of big problems, decision support services are ever more important to deliver but ever more difficult to create.