Why and How To Reinforce Data Quality


In the current world of data-driven businesses, the quality of the data you rely on will help tell you apart from your competition. In fact, poor quality data will often result in a loss of about $17 million annually, according to Gartner. Typically, data will comprise a diversity of details that range from customer information to company secrets.

While there are cases where data quality is compromised due to human error, it might also be compromised by issues with your application, such as the failure of your database. Although human error is easy to avoid, application errors need to be taken more seriously to prevent any losses from crippling your business. Simply put, data management calls for the use of the right staff, technologies, and processes.

Here is why you need to reinforce the quality of your data and how to do it:

Why Data Quality Matters

Managers need information from the analyzed data to make strategic decisions. Without quality data, it can be easy to make mistakes in their decision-making process which can end up resulting in losses not to mention lagging behind in the competition spectrum. Data quality also plays a pivotal role in compliance as regulatory bodies will mainly rely on the same data to determine whether you are compliant or not.

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Lastly, if the data you post is corrupted, your staff members will need to work extra hours to revert it back to its original state. This will eat into the time that they could have been spent getting insights from the data and will only impede productivity.

How to Enforce Data Quality

The management of data quality calls for all aspect of your organization including people, processes and even technology. While human errors will have to be eradicated, errors arising from the applications will have to be dealt with in good time. The initial step would be to define the standard for data quality and set the right metrics to determine this.

Additionally, focus on regular data audits to determine the current state of the data. For a more effective approach, working with a log management tool will help to identify application issues that can easily lead to data corruption. Next, formulate policies on how to proceed with rectifying any issues with your data to make the process effective.

Getting Started

To excel in data quality management, you must first assess the current efforts that are in place for managing quality. Do employees react immediately to data quality issue or do they ignore them for some time? Once you have an idea of the current trend, you can then set and implement the benchmark for data quality management.

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This is the threshold quality you expect from data. It should focus on the nitty-gritty details such as how to add date and name tags to the data. Ensure that you also have both the technology and manpower to help you excel in data quality management. If your IT department is understaffed, acquiring more talent is vital.

Metrics to Use in Data Quality Management

Focusing on measuring success through metrics is wise. Here are a few metrics to focus on:

  • Data to error ratio: determines the number of errors in a set of data with regard to its size.
  • Dark data percentage: dark data is the kind of data that can no longer be used. The more dark data, the less effective it will be to draw value from your database.
  • Time-to-value: this metric helps to determine how long data takes within the database. before being used. The shorter it takes, the more valuable it is.
  • Empty value: this metric simply assesses the number of data sets with empty values.

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The type of data you analyze will affect all aspects of your business along with your chances of successful profit maximization. While preventing errors is vital, focusing on only recording and storing valuable data is essential. Consider the steps above to have control over your data quality.