8 causes of poor data quality, and how automation can help

The quality of data is integral to its usability. It’s what renders a data set able to serve the need that it was collected for. Poor data quality, then, means that the data collected cannot serve its intended purpose.

The first step to addressing and preventing poor data quality (as much as is possible) is to understand what causes the problem in the first place.

So, what causes poor data quality? And how can automation software alleviate the issue?

The properties of poor data

First, it’s important to establish what makes data ‘poor’. Many factors can compromise a data set, and you should look out for data that is:

  • Incomplete: the data has empty fields and lacks needed data points
  • Lacking in accuracy and consistency: data is stored in incorrect fields and incorrect formats
  • Incorrect: data that is not valid — perhaps because it is outdated, or mis-transposed
  • Redundant: duplicate copies of the same data, or excessive data that is not useful/needed
  • Non-standard: data in a non-supported format, meaning that your systems are unable to process it correctly

Poor quality data will have one or more of these properties, for one or more reasons. So, let’s take a closer look at what causes these data integrity issues.

The causes of poor data quality

1.      Manual data entry (a.k.a. human error)

Manual data entry has little to recommend it. One of the main issues with manually inputting data from A to B is that it is prone to human error. Humans are not perfect. And with a task as dull as data entry, it can be all too easy to make mistakes.

Such mistakes could be anything from a small unnoticed typo, to a completely missed entry. A human might inadvertently fill data into the wrong field. It’s not out of malice — it’s pure mistake.

This is, at least, an easy cause to address. Instead of subjecting human team members to data entry, automation software can handle the process faster, more efficiently, and with absolute accuracy.

Automation won’t get bored, and it’s not prone to human error. Provided you configure your automation system with the right rules and integrations, it will vastly improve the accuracy of your organisation’s stored data.

2.      Lack of complete information

You can’t avoid poor data quality if you don’t collect needed data properly in the first place. Data ingestion is the start of the data lifecycle. And if it’s flawed, the rest will be too.

Incomplete data ingestion = incomplete data = poor data quality.

3.      Transformation errors

Data doesn’t always come in the format you require. So, this is where data transformation enters a workflow.

Transformation changes the format of the data to match your storage. For example, changing mobile phone numbers from ‘07XXX’ to ‘+447XXX’. Incorrect conversions, however, lead to poor data quality.

This is another key area of data quality management where automation software helps. Automation can act as an ETL tool, providing a robotic consistency to transformation efforts. And, in the event of an anomaly with the data, automation software will also detect and alert to issues.

4.      Unmanaged data decay

Data is not immune to change. Over time, data quality will decay as it becomes invalid. Customers acquire new addresses, new phones, new email addresses. Contacts leave companies, and companies change their relationship with you. Once sought-after tech falls into obscurity when new innovations come along, or hype cycles drop.

In short, failing to cleanse and/or update data results in poor data quality.

Vigilance is an important part of managing data decay. However, automation software can also help here via rule-based database cleansing. This allows automation software to automatically remove archaic (outdated) data, as well as reformat inaccurate entries.

5.      Inconsistent data entry standards

Beyond mistakes and errors, another cause of poor data quality is not having a standard understanding of how the data should be collected, transformed, stored, or represented.

A good example is to imagine you are storing data about American states. Without a clear set standard for how to record this data, one state could have multiple names/entries. I.e., New York, NY, New York State, etc.

All these entries mean the same thing. But, they won’t get grouped together because of the non-standard entry practices. So, you need to set and reinforce organisation-wide standards to avoid inconsistency issues. Better yet, set your automation tool to recognise incongruent data entries, and automatically update them.

6.      Siloed information/lack of integration

This brings us to the next cause of poor data quality: siloed data. If data is not well integrated across a business, it can result in several departments storing duplicate data in a variety of formats. This, obviously, is inefficient. It can also make future attempts at integration harder.

Poor integration can also mean that valuable data gets locked away, rather than used to help the business. While solving integration challenges can be complex, automation can once again be applied. In this scenario, automation can act as middleware to link disparate point solutions into one connected, data-fluid ecosystem.

7.      Poor migration

Migration is an occasional necessary evil when it comes to data management. It also represents a potential cause of poor data quality.

This is particularly the case, for instance, when you need to update your legacy systems. If the data migration is not carefully managed, data points can end up missing or irregular — making for an inaccurate, incomplete database.

So, you can (and should) use automation as a mass data migration tool, both for legacy and up-to-date systems.

8.      Broken processes

Poor data quality doesn’t just happen during collection and storage efforts. Indeed, a poor process could result in data being manipulated incorrectly, producing non-valid outputs.

The fix to this is to review your processes and ensure they are running optimally. Only when you’ve fixed your processes should you seek to automate them.

Poor data quality

Data is always changing. It always needs collecting, transforming, and updating. And all these steps in the data lifecycle represent opportunities for poor practice or mistakes to cause a degradation of the data quality.

The battle against poor data quality, then, is an ongoing one. But, by knowing these potential causes and applying the right technological help, you have a strong starting point for high-quality data.

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