Question

3. The goal of software process is high quality. Why does better quality imply better timeliness? [3pts

Answer 1

The Dimensions of Data Quality:

Collecting and then managing huge amounts of data is an enormous challenge for any organisation. Key to managing data is in qualifying and guaranteeing the quality of that data so it can be interpreted and acted upon. For data to be high quality it must be, according to the academic Tom Redman, defined as “fit for their intended uses in operations, decision making and planning”.

Two crucial dimensions of data quality are timeliness and accuracy (which we’ll look at in a minute) but all data can be defined as having six quality dimensions. Each of these can be understood through certain questions relating to the data:

• Completeness: Is all the intended data being produced in the data set or is any of it missing?
• Uniqueness: Are any individual pieces of data from the dataset recorded more than once?
• Timeliness: How long is the time difference between data capture and the real world event being captured?
• Validity: Is the data presented in the correct and pre-defined format, type or range so as to be applicable to the given analytical task?​​​​​​
• Accuracy: Does the data matches up with the real world object or event it describes, enabling correct conclusions to be drawn from it?
• Consistency: Is the given dataset consistent and correlative with different representations of the same information across multiple datasets?

Why Accuracy and Timeliness Matter:

In the field of sustainability data management, timeliness and accuracy are fundamental prerequisites when it comes to data analysis. Failings in either of these dimensions can compromise the usefulness of your data. Let’s look at each in turn.

Data Timeliness:

Timing is everything and when you’re capturing, interpreting and then acting on real-time data timeliness can be fundamental. Let’s imagine a scenario in which we’re measuring energy input and some form of output from a manufacturing process. If we were to precisely record our energy input against our output then we could conceivably find the optimum collaboration and adjust accordingly.

Now imagine this system has several inputs and several outputs. Suddenly the importance of data timeliness comes into play. In a dynamic and rapidly evolving system, a second or even a few microseconds between one reading and another can mean one dataset is mismatched against another. In this example it’s essential to ensure that all our data is timely and being interpreted in real-time to ensure the best optimisation of the system.

Data Accuracy:

The second pillar of strong and reliable sustainability data is accuracy. This is the degree to which the data reflects the real world. Ensuring data accuracy isn’t always easy as slight inaccuracies will not paint an entirely unfeasible picture and therefore comparison between ‘real life’ and the dataset will not give cause for concern. In many data driven processes though, a high degree of accuracy can make all the difference, especially over long periods of time, when small inaccuracies can equate to quite fundamental inefficiencies.

Ensuring data accuracy isn’t always easy. Proper configuration of sensor or recording equipment is vital but a reference point is also needed in many cases. This will usually take form of a separate third party dataset from the same time period that can be relied on. Close inspection of how a given dataset relates to and fits with other related datasets can also help to identify inaccuracies. In other words if one dataset produces totally unexpected results based on the findings of another dataset then it’s likely one of those datasets is inaccurate. Establishing a robust data validation process with data accuracy rules and acceptable margins of error is therefore essential.