Measuring Data Quality

Last time, in Databasics, we discussed the Data Maturity Lifecycle; that construct that enables us to understand how raw data is transformed into Information and then Intelligence. But as we know, there is no more accurate description of technology than the age-old phrase “Garbage In, Garbage Out”. This is to say, if the raw data going in is not “high quality data”, then the Information and Intelligence coming out will be of little use or value. So how do we quantify and measure Data Quality?
Distilling it down to its core components, Data Quality is the combined measure of the Accuracy, Timeliness and Relevance of each lifecycle component. Consider any of the following:
  • Accurate, relevant data that arrives a day late;
  • Irrelevant information that’s readily available;
  • Incorrect intelligence that shows up right on time.

The fact is that the core measures of Data Quality are never ranked in order of importance, because they are all equally crucial. As the three examples above demonstrate; the absence of any one of the three qualities results in data, information and/or intelligence that is considered “Low Quality”.

Since all of us want our Ninox applications to store and deliver high quality
results, I encourage you to consider the quantifiable and measurable
definitions below:
  • Accuracy– Data is accurate if it is free from error or defect. Accurate data is a precise and exact representation of events, people or objects;
  • Relevance– Data is relevant if it has bearing upon or is related to the question, issue, concept, operation or strategy at hand. Relevant data is pertinent and has a material relationship to a topic of discussion, decision to be made or strategy to be defined and executed; relationship to a topic of discussion, decision to be made or strategy to be defined and executed;
  • Timeliness– Data is timely if it is delivered at such point that it can be taken into consideration during the course of discussion, evaluation or execution. Timely data is delivered to the appropriate personnel such that it can be used to effect change and/or impact decision making.

Once you understand Data Quality, it’s important to understand how you can improve it and maintain it at a high level. And for that, please consider the model below:

This model enables us to understand where we are to go if any of our three quality measures are weak. If Accuracy is what we need, we must consider the source of our raw data. Whether keyed in by hand, imported from spreadsheets or delivered via API conduit, it’s crucial that we ensure that the inflow of content is completely free from error or defect.

We then look to Timeliness. Here, we are fully focused on the delivery model that enables humans or other applications to consume the content. How quickly can data be transformed into information and intelligence and how quickly can that asset be delivered to the human or system needing it. To ensure Relevance, we must design our Ninox solutions such that the consumers of the output receive the output at or before it’s needed and not any later.

And then finally, we consider Relevance. Put simply, Relevance is the answer to the question “Does It Matter?” Assets can be 100% accurate and delivered right on time, but if those assets are not relevant to the matters at hand, what does it matter? When considering relevance, we need to put our users front and center. What matters to them? What decisions do they have to make? When must action be taken? Answer these questions, and you will know what constitutes Timely versus useless as far as the all-important end-user is concerned.

So what does this all mean in the final analysis? Simply this… Creating any Ninox data solution – from the most basic to the most sophisticated – requires us to consider not just the fields and objects in our data model, but also the information delivery methods, frequency requirements and expectations of our users. Consider all three from the very beginning and your applications will consistently contain and deliver high quality data, information and intelligence. Fail to consider all three, and your content quality may come up short.

Happy Ninoxing to you all!