Tasks improve the quality of datasets
According to (Shankaranarayan & Even, 2009) specific tasks that could be performed to improve the quality of datasets using SDLC are:
Auditing. Quality defects occur during data processing (e.g., due to miscalculation, or mismatches during integration across multiple sources), or after data is stored (e.g., due to changes in the real-world entity that the data describes). Addressing these defects requires auditing records, monitoring processes, and detecting the existence of defects.
Correction. It is often questionable whether the detected defects are worth correcting. Correction might be time consuming and costly (e.g., when a customer has to be contacted, or when missing content has to be purchased). One might hence choose to avoid correction if the added value cannot justify the cost.
Prevention. Certain methods can prevent data defects or reduce their occurrences during data acquisition, for example, improving data acquisition user interfaces, disallowing missing values, validating values against a value domain, enforcing integrity constraints, or choosing a different (possibly, more expensive) data source with inherently cleaner data

Data Refinement: Datasets are refined at regular intervals and database is checked for the orphan accounts if any in the system. Orphan accounts may result into dangerous issues so it should be handled carefully.
Data Governance implementation: Data Governance Maturity model can be implemented. This will help in achieving the optimization for the datasets.

Actions to optimize record selections and to improve database
According to (Shankaranarayan & Even, 2009), actions that should be performed in order to optimize record selections and to improve database performance from a quantitative data quality are:
Error Detection and correction: Errors may be detected by comparing data to a correct baseline. Errors may also be detected by checking values and by examining time-stamps associated with data. This error detection can be done manually or automated.
Process Control and Improvement: Total Data Quality Management (TDQM) methodology suggests a continuous cycle of data quality improvement which includes defining quality requirements, measuring along these definitions, analyze results, and improve data processes accordingly.
Process Design: Data processes can be built from scratch or, existing processes redesigned, to better manage quality and reduce errors

Maintenance plans and activities to improve data quality
Maintenance:
According to (Porrill, Noria, 2013) the maintenance plans to improve data quality are
• Analyzing the maintenance requirements at regular intervals
• Short-term scheduling of planned maintenance work
• Long-range maintenance planning

• Sorting and grouping of checklists
• Feedback and reporting

Activities
According to (Porrill, Noria, 2013) the activities that to implement to improve data quality are
• Verify the sources of data and assign the responsibilities. Conduct some form of analysis to identify the appropriate maintenance tasks to implement and create a checklist. Streamline the groups of task in an iterative manner.
• Define the metrics for measuring data quality.
• . Feedback information is captured and the information can be summarized on a report and an appropriate course of action is taken in response to each feedback, failure or observation from the data reviews.

Method efficient for planning proactive concurrency control methods
Keep the Integrity of Your Database Secure by Querying Your Transaction Log File: Record all operations to the database transaction log file especially if the system runs on Full Recovery Model. This will allow the system take advantage of a few simple queries to double-check who is running which command in the database. Online Transaction Processing database (OLTP) can be used for the given scenario using two-phase locking and write-ahead logging. (Shebbeare, 2010)

Method to ensure transactions does not produce record-level locking

Authorization and authentication mechanism should be used user’s role level to minimize security risks with in a multiuser environment. The verify method can be used to plan out system effectively and ensure that the number of transactions do not produce record-level locking while the database is in operation. Transaction Log files should be used to verify who accessed the database and at what time.

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References

Phil Nousak, R. P. (n.d.). A Scorecard approach to improving Data Quality. Retrieved from http://www2.sas.com: http://www2.sas.com/proceedings/sugi27/p158-27.pdf
Porrill, D. (2013). Retrieved from Noria: http://www.reliableplant.com/Read/338/effective-maintenance-plan
Ramsin, R., & Paige, R. (2008). Process-centered review of object oriented software development methodologies.
Shankaranarayan, G., & Even, A. (2009). Dual Assesment of Data Quality in customer databaase.
Shebbeare, H. (2010). Retrieved from Microsoft: https://technet.microsoft.com/en-us/security/gg483744.aspx