If you research some basic facts and figures about various organizations, you will know that most of them are plagued with poor data quality. This eventually ends up in poor business decisions. Meaning that the cost of bad data adds up quickly and affects your business revenue. Based on this, you may think that organizations go to great lengths to ensure best practices for quality data. However, several studies and surveys indicate that organizations are not as focused on maintaining data quality, as they should be. And those who try to maintain it find it quite challenging.
While I was trying to contact Spectrum TV, I went through an online survey about the reasons behind not being able to maintain quality data. The top reason remains to be the human-error. People generate data with their own distinct ways of formatting and handling data. And guess what! They are inherently prone to human error.
Let’s take a swirl around some of the best practices to ensure improved data quality management.
Maintaining Data Quality
Data quality refers to the degree of data being error-free. It also refers to its ability to serve the purpose it is intended to serve. Here are some of the top characteristics, which quality data should have:
Up-to-date.
Accurate.
Completes every required field unless strictly deemed optional.
Unique according to the context and entity.
Reliable and trust-worthy.
Properly formatted across various data sources.
If your organization manages to achieve high-quality data consistently, this means that its better positioned to make critical and strategic business decisions. This will yield useful business insights and generate revenue.
Therefore, here are some tips and practices to ensure effective data quality management.
Establish the Metrics
In order to measure and maintain the data quality, you need to establish a set of effective metrics. These metrics need to be relevant and applicable to your business targets and goals. Without doing so, achieving those goals and targets is not possible.
Establishing metrics to measure data quality is essential to:
Understand the accuracy of your data.
Advising the management and employees on the effectiveness of data quality to get buy-in.
Taking all the corrective actions to improve data quality.
Quantifying incomplete, missing, and inconsistent data.
Make Data Quality a Priority
Do you know that even if half of the company shows commitment to ensure data quality, the organization can expect over 50% data quality! Therefore, all the stakeholders should be aware of and take responsibility for maintaining data quality.
In order to gain enterprise buy-in, it should be ensured that data quality is being promoted and supported at all management levels. This includes the C-suite too. If business leaders and executives don’t make a point to prioritize good quality data, the data managers will also not.
Therefore, make sure that data quality maintenance is emphasized across the organization at every level.
Invest in Internal Training Ventures
Let’s face it. Attaining and maintaining good quality data is no easy feat. It essentially requires a profound understanding of data quality processes, principles, and technologies. And only formal training ventures can impart this knowledge.
In this regard, follow the training track for data management certifications. Some instances to offer effective roadmap are CIMP (Certified Information Management Professional), CDMP (Certified Data Management Professional), CDS (Certified Data Steward), and so on.
Therefore, encourage your data quality staff to earn these certifications. And if possible, finance these certifications for them. This will inform them on:
Basic principles, concepts, and practices relevant to quality management.
Thinking through the benefits of high-quality data.
Knowing the costs of poor quality data.
Creating, delivering, and selling business cases for data quality.
The key principles to build data quality organizations.
The principles, concepts, and practices of the data stewardship program.
Data quality challenges, inherent in data integration.
Investigate the Failures in Data Quality
Without being able to pinpoint data quality failures, you won’t be able to control and rectify the errors that occur. Rectifying data errors can be a time-consuming and daunting task. Once you have corrected the data, you may feel tempted to consider that the task is complete.
Data errors can result from a variety of various causes. Some of the top causes are:
Human error.
Too many sources of data.
The communication gap between various departments.
If you are enlightened about the causes of error, then taking the right actions to prevent these errors will be easier.
Establish a Systematic and Efficient Data Auditing Process
You need to figure out whether the processes that you have implemented to create and maintain data quality are effective or not. Perfecting these processes is an ongoing thing and you can only do it with the help of audits.
To build trust in your data, conducting regular audits on the data that you have in your repositories is the best strategy. Use Spectrum Internet to audit data processes, check for cases of poor data quality and help you improve the data quality immensely. While I was looking up the features of Spectrum 210 DVR, I stumbled upon an informative piece. It indicated that the data audit checks poor quality data in several fields. Some of which are:
Inaccuracies.
Poorly populated fields.
Incomplete data.
Outdated entries
Inconsistencies in formatting.
Duplicate entries.