Data accuracy is crucial because it allows you to make decisions that will provide your consumers with the best possible experience. A positive customer experience increases brand loyalty, customer satisfaction, and profitability for the company. If you’re working with bad data, most of what your consumers desire is an educated assumption. Worse, you may purposefully engage in behaviors that offend your clients.
Data quality measurements evaluate how useful, valuable, accurate, consistent, and safe the data that your firm uses is. Organizations resort to data quality metrics and management to resolve the myriad of problems caused by low-quality data. According to Gartner, by 2022, 70% of enterprises will closely monitor data quality levels, increasing quality by 60% to dramatically lower operational risks and costs.
Build an Understanding of How Data Affects Your Business
Data quality management is the application of a structure that monitors and manages data sets, validates the accuracy of the data, and carries out a number of procedures to obviate data quality issues.
Data that is suitable to be utilized in all planned operations, decisions, and scheduling is the ultimate benchmark for data quality. There are several steps that can be taken by the business to ensure the highest data quality.
For one, determine how business operations, key performance metrics, and data assets are clearly related. List the current data quality problems the organization is experiencing and how they affect sales and other key performance metrics for the company.
Data and analytics executives can start developing a targeted data quality improvement program that precisely defines the scope, the list of stakeholders, and a high-level investment plan once they have established a clear connection between data as an asset and the improvement requirements.
Data Quality Metrics
Metrics for measuring data quality help you assess how useful, precise, profitable, trustworthy, and secure the data your company uses is.
Gartner explains the significance of data quality measurements by stating that poor data quality affects businesses by $12.9 million on average annually.
In addition to causing revenue losses, bad data complicates operations and data ecosystems, results in poor decision-making, and challenges performance and your bottom line.
To ascertain whether your data satisfies quality criteria, particular indicators may need to be included, depending largely on the aims of the business and market. However, the majority of organizational data integrity needs to be assessed at least in the following areas:
- Accuracy
- Consistency
- Completeness
- Integrity
- Timeliness
- Relevance
At Anchor Software, we have systems and solutions that can enhance your data quality and maintain the integrity and relevance of data at all times.
Reach out to Anchor Software today to find out how we can help with your deliveries, your marketing campaign, or your data validation.
