9 Practices to ensure Improved Data Quality Management

Today, enterprises have to do deal with a lot of data because of the data-centered nature of various operations. The data helps in determining the market trends, setting in motion smooth operations and is a major precursor of some very important business decisions. So, the significance of good quality data cannot be discounted.

The major problem that companies face today is bad quality data. If data is of bad quality, it can lead to a wrong decision which can ultimately lead to loss of a hefty amount of revenue in no time. Not only this, it can ruin a company’s reputation and make it lose valuable customers. Imagine if Spectrum cable packages doesn’t live up to the promise of providing HD channels. It will definitely lose customers, right? That’s the case with companies and quality data as well.

To save your business from unrecoverable losses, you can employ these 9 effective practices and ensure improved data quality management.

Involve the Top-Tier Management

Often, whenever a breakdown occurs because of poor quality data, top tier management is not involved. Ensuring data quality is not just the work of a few. Top-tier management has to be involved as well to make this process smooth. This might even encourage others to be more understanding and responsible for this process. 

Make Improving Data Quality an Organization-Wide Concern

You are unlikely to achieve data quality goals if you don’t make the entire organization responsible for this process. Getting only half of your organization involved will not take you any farther than achieving 50 percent data quality. And 50% is not good enough for your important business decisions. Therefore, it is crucial that every person in your organization acts responsibly and plays his/her part in improving data quality.  

Develop Metrics to Assess Data Quality

You need to install metrics to assess data quality. It is important because you need to make sure that the data aligns with your objectives and targets. These metrics encompass Consistency, Accuracy, Completeness, Audibility, Orderliness, Uniqueness and Timeliness and go by the acronym ‘ACCIT.’ All five of these steps need to be carefully considered while assessing data quality as this data is going to be used for various business tasks.

Inspect the Errors and their Sources

You might be tempted to just install the data quality standards and leave things be in case of data quality failure. But this is not going to work in the long run. The error will keep occurring and correcting these errors, again and again, will take up a lot of your precious time. To avoid this situation, it is always better to locate the source of the data error or failure. It can be as simple as a single person’s mistake or the data may appear to be faulty because of too many sources. Whatever the reason may be, it is always a good idea to locate it before taking action.

Set up firm Guidelines for Data Governance

To ensure that your data is credible and can be used to make important business decisions, you must set up rigid guidelines for data governance. Besides the IT and data administrators, delegates from your business’s various departments should also get involved to set these guidelines and make these work. This is because every department may have its unique operations and frameworks and thus may need guidelines specific to these operations. Hence to ensure data quality, it is critical to get everyone on board.

Formally Train Your Staff

Ensuring good quality data is not easy. It requires a careful grasp of various complex systems, rules, and technologies. Therefore, it is always a good idea to formally train your staff to equip them with the skills required for this job. There are multiple certifications that your staff can get to ensure quality management like Certified Data Management Professional (CDMP), Certified Data Steward (CDS) or, Certified Information Management Professional (CIMP). 

Frequently Get the Data Audited

Once you set up frameworks and systems to ensure data quality, don’t think that your job is done. While you may have invested a great deal of time in setting up these frameworks, they might not be fool-proof. So, frequently perform audits to check for data errors. It will help you save a lot of time and energy.

Hire Multiple Data Stewards

Hire data stewards for every department to ensure that data quality guidelines are being met at every step. It is important to hire a different data steward for every department because each department may have its own rules and operations and a specialized steward may closely follow the guidelines set up for a particular department only.

Keep the Sources Same

Important business operations and decisions must be based on the same trustworthy sources of data. To make this work, keep everyone in your organization well-informed about the sources that meet the company’s quality standards so that everyone may use these to get various business tasks done. 

Final Words

Data quality management is crucial for making important business decisions and completing analytical assignments. Data should be of such quality that one can find accurate information and make decisions right away just like you can quickly decide which package to choose from a variety of Spectrum Packages handpicked for cable customers. To ensure improved data quality management, these practices are worth spending your time and energy on.

Huynh Nguyen

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