Data quality has never been more important than a the present time. Whether required in order to perform an analysis of the latest marketing figures or as a means to gauge the effectiveness of a new type of software, little should be left to the imagination.

This is why industry experts have created five “pillars” which should always be included within any data mining project. Our team at Retail Shake has summarised each in in detail. Let us have a closer look.

Accuracy

How accurate is the information being collated? We are not only referring to updated real-time information in this sense (as will be discussed later). Accurate data should also be realistic in nature; particularly in terms of expectations. For example, is it logical to assume that all customers will be able to afford a specific product or service at its current price? Accurate information will ensure that it can be properly interpreted.

Reliability

Our data engineering specialists also place a great deal of emphasis upon the reliable nature of the information collected. Does it coincide with industry trends or might it instead be flavoured by in-house expectations? Ideally, any viable data should be backed up by similar industry-recognised findings and/or ongoing research. Otherwise, organisations will run the risk of taking the wrong decisions based off of spurious advice.

Completeness

How comprehensive is the data in question? Does it include all of the necessary parameters or are some fields unavailable? An example will help to cement this point.

Let us imagine for a moment that a business is collecting customer data to be used in an upcoming marketing campaign. Some client details are complete while others lack vital information such as active email addresses or social media accounts. In this case, gaps in the data will inevitably lead to an inefficient campaign.

There may also be times when incomplete data can result in sales professionals performing additional research that might not have otherwise been required. This takes away from their primary duties and once again, less-than-optimal results will be generated.

Relevance

Data quality is also centred around relevance. Some firms may attempt to collate as much information as possible. However, these details are of little value if they are irrelevant to the task at hand.

Firms must instead focus upon collecting only pertinent information that will have a direct impact upon the ways in which they approach a project. For instance, it makes little sense to accumulate information related to summer fashion trends when designing a wardrobe for the autumn. Therefore, analysts should modify their efforts so that the right data is presented at the appropriate time.

Timeliness

While there is much to be said about data quality in terms of longevity (sometimes known as its “evergreen” nature), there are also instances when updated information can render old approaches useless. If out-of-date details happen to be collected, the chances are high that incorrect decisions will be made.

A significant amount of effort should therefore be placed upon the assurance that any data represents the most recent findings. Companies might otherwise make incorrect decisions that cost time as well as money.

These five pillars of data quality represent extremely powerful tools when used correctly. Unfortunately, some analysts fail to take such concepts into account. This is also why a growing number of customers are choosing to partner with the team at Retail Shake. Transparency is paramount and we guarantee that our services will always reflect the latest industry trends.

Data mining is both an art form and a science, so why leave anything to chance? Our team is always here to help.