Information Quality Assessments and Data Quality Assessments

Requirement: Make a comment based on responses. Two Entries (Each for 100 - 200 words)
1 response:The readings for this week’s modules dive deeper into assessing and measuring data quality and integrity methodology. One technique discussed in the textbook was how organizations could implement and evaluate data quality through subjective evaluations, referred to as Information Quality Assessments (Lee et al., 2006, Chapter 3). These assessments are utilized to give valuable insights into how different data collectors, custodians, and consumers feel about the quality of the data within the organization. Combined with an individual’s importance rankings on numerous data quality dimensions mentioned in the last module’s readings (i.e., believability, accuracy, value-added) (Lee et al., 2006, Chapter 6), extensive analysis can occur. Two analytical approaches are mentioned, role and benchmark gap analysis. Role gap analysis focuses on understanding how different functions within organizations identify and respond to data quality concerns. Benchmark gap analysis combines all responses within an organization and compares them to other organizations. Understanding where your organization compares to others regarding data quality can be essential for advocating and enacting organizational change. Executives and managers must be confronted with proof that the organization’s data quality is lacking before real change occurs. Another technique for assessing and measuring data quality, presented in Chapter 4, centers around establishing quantifiable metrics. These metrics are crucial to identifying shortcomings in data quality. A few of the listed metrics are completeness, believability, and accessibility (Lee et al., 2006, Chapter 4). After defining the metrics for comparison, the authors recommend that finding tools to automate analysis will enhance the speed at which improvements to data quality can be made. Once established, these well-defined metrics to identify data quality will serve as a baseline for future improvements.As mentioned in a few of my previous discussion board posts, I co-oped as a marketing operations manager within the marketing department. The role crosses over with topics discussed in this course due to the heavy involvement with operational and consumer data. This week’s content on utilizing metrics to identify and improve data quality relates directly to my experience in the role. We hosted gated content on the company’s website that required visitors to fill out personal information before accessing the content. Soon after publishing the gated content, we discovered that the quality of the leads coming in from that channel was dramatically lower than other lead generation channels (i.e., social, events, paid search). As a department, we created additional metrics for evaluating leads from gated content. These other metrics included; completeness of the form, value-added scoring for keywords in an individual’s job title, and time spent on the page before converting. The metrics provided sales the ability to prioritize their time on leads based on lead quality. Time is money for salespeople, so our implementation of data quality metrics indirectly led to more revenue.  
2Response:
This week the module was structured around analyzing and assessing data quality in order to improve the quality and increase the utility of the information. One of the major techniques for assessing data quality are DQ surveys. The method to utilize these surveys is to employ a survey acquire the results compare the analysis, determine the discrepancies and their root causes and then to take actions to improve data quality. These assessments enable different stakeholder groups to contribute the level of data quality they experience, these diverse perspectives can be coalesced to improve data quality for the entire organization (Lee et al., 2006, Chapter 3). There are two primary gap analysis methods which are addressed in the reading. The first is Benchmark Gap analysis, addresses the concerns the organization may have with how their data quality is performing relative to other organizations. The second, Role Gap analysis, is meant to determine whether the differences between roles experience different data quality problems and then elucidates options for resolving these issues (Lee et al., 2006, Chapter 3).The text introduces further methods to assess and analyze data quality. These techniques are construed of ratings along dimensions that organizations determine are important to their operations to measure how the firm’s fare (Lee et al., 2006, Chapter 4). Several of the metric coalesce with data quality dimensions like completeness, consistency, and believability (Lee et al., 2006, Chapter 6). These dimensions are of the highest utility when an organization can automate the functions that assess and analyze the metrics set forth by an organization. It also compiles a policy guideline that should create the basis of an organization’s data quality policy. While the guidelines must be adapted to fit the environment, they’re meant for they will always have the same intent: to provide the vision and guidance for a sustainable, viable, and effective data quality practice (Lee et al., 2006, Chapter 11).The material introduced this week has a great deal of potential for increasing data quality within the organizations I have been able to work with in the past. In my co-op experience I worked with a tax firm that had a large array of distinct positions that operated with data. Although I did not experience it while I was employed at this organization, I can see how a Role Gap and Benchmark analysis would provide much utility. After the company would construct the specific metrics, it would use (perhaps value-added, accuracy or relevance). From there the comparison to competitors would allow the firm to see the DQ areas they were weakest in and improve them. This would allow the company to offset intra-industry rivalry by making sure the data quality is staying comparable with other firms in the Big Four or potentially pulling ahead and creating advantages for the firm. (Pearlson, K., 2019).