Data Quality (Case Study Sample)

Information Technology Student’s Name Institution Recommend at least three specific tasks that could be performed to improve the quality of data sets using the software development life cycle (SDLC) methodology. Include a thorough description of each activity per each phase. An Information Technology specialist performs several activities to improve data sets' quality using the Software Development Life Cycle (SDLC) methodology. For example, elimination of data redundancy, data input validation, eradication of the false null values, safeguarding data values, data conflict resolution, establishment and application of standards, and suitable classification and use of data values (Stefanos, 2019). Eliminating false null values improves data quality sets. Null values are variables with no value. A null value is different from having a value of O, and no two null values are equal. Null values stipulate data is not applicable, is unknown, or needs to be added later. The presence of null values in comparison, logical, and data operators sometimes leads to unknown results instead of true and false results, which leads to application errors. Therefore, minimizing null values helps to reduce queries, reports, and possible maintenance effects. Another strategy to improve data sets' quality using SDLC involves ensuring data values fall within its distinct domain (Stefanos, 2019). All fields need defined data according to the design requirements. In case the social security is 11 characters, then there is a need to validate data within the specific domains. On the other hand, the resolution of information conflict is mandatory. Recent times have seen growth of information. Mokokwe (2018), the internet offers a wide range of data sources that provide information to a broad spectrum of information users. The Web easily facilitates the publication and spread of false data across different sources. Therefore, data fusion helps resolve conflicts from various sources while developing values that mirror the actual world. Recommend the actions that should be performed to optimize record selections and improve database performance from a quantitative data quality assessment. Prevention improves database performance while optimizing record selection because a transaction that requires a new lock is terminated in case of a deadlock. During termination of a transaction, changes taking place roll back, leading to a release of the locks acquired during a transaction. The transaction then undergoes execution. Deadlock prevention avoids conditions that bring about deadlocks. Detection improves database performance because it monitors the database for deadlocks. Detection of any deadlock leads to abortion of a transaction while other transactions take place. Avoidance is another one, which avoids progressing, and conflicting transactions as locks are acquired in succession. Avoidance escalates the action-response time. Suggest three maintenance plans and three activities that could be performed to improve data quality. Maintenance plans prevent inconsistencies while ensuring regular back-up (Shichkina, 2019). Besides, maintenance optimizes the database while supporting a sorting program to remote servers. The three maintenance plan include: Regular database checkup ensures information integrity through supplementation of real time authentication efforts. The updates ensure effective measurement of data quality after the implementation of each strategy (Shichkina, 2019). The checkup ensures refreshment of old information, making the data valid in the future. The operations team should possess an in-depth understanding of information in the database to ensure operations and communications improvements. In-depth knowledge of the database involves identifying the different data entry trends and specific sections where corrupt data accumulates. The next step requires the identification of the bottlenecks that prevent data collection processes. In the long run, all the measures ensure data quality. Data management ensures data quality because it comprises multifaceted approach where individuals capture, access, influence, and update records (Shichkina, 2019). In the capturing process, all the stakeholders obtain required information despite the activities, for example, on-site meetings, mail communication, campaigns, among many others. Three activities that improve data quality include preventative, corrective, and adaptive maintenance (Mokokwe, 2018). Prevention measures ensure the smooth running of the database, which reduces costly repairs, downtime, and low productivity. On the other hand, corrective actions resolve failure or restore operation states. Corrective activities are common, especially when checkups fail, resulting in mechanical issues. Adaptive strategies ensure the database remains functional in a changed and changing situation. Suggest methods that would be efficient for planning proactive concurrency control methods and lock granularities. Assess how your selected method can be used to minimize the database security risks that may occur within a multiuser environment. The methods include Isolating sensitive databases: do not store your database in a directory with public sharing access or within a web server's scope. A more likely attack is via SQL injection (Chen, 2019). 90% of all hacks occur using this technique, and a surprisi...