Complications arising from data quality have hoodwinked a significant number of business intelligence projects. In most cases, the issues may not be presented until the system is tested. There are various ways to ensure the data is of high quality, such as increasing awareness on the significance of decision-making and the consequences of inaccurate data (Sherman, 2015). However, ensuring that businesses not only collect relevant data but also handle it in an expressive and viable manner is still a challenge. Nonetheless, the following data quality framework can be used to evaluate data quality.
The first step for the business is to define the goals of the business with regards to the improvement of data quality, stakeholders, rules as well as its impact. For instance, other than filling inaccurate information, make sure each customer’s record is unique to prevent confusion. Assessment is also key. It is critical to evaluate the information about the defined goals of the business. Policies such as data security and access and compliance with the standards of operation are assessed at this stage (Sherman, 2015). Following the assessment is an in-depth analysis. The results obtained from the assessment should be analyzed on multiple angles. It should include an analysis of the gap between the goals of the business and the data collected as well as the causes of poor data quality.
Based on the analysis, improvement plans can then be designed and developed. The plan should be inclusive of the costs incurred, the resources needed, and the timeframe of implementation. The implemented solutions should be the ones determined during the improvement plans stage to be most effective. The final step is verifying the consistency of the information with goals of the business and the rules highlighted in defining the objectives (Sherman, 2015). Regularly communicating the present status and the metrics of data quality to stakeholders is key to making sure discipline is continuously upheld within the organization.
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Sherman, R. (2015). Business Intelligence Guidebook: From Data Integration To Analytics. Waltham, MA: Morgan Kaufman.
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