Lesson 9- Data Governance and Ethics.pptx

notes

1. Lesson 9 Data Governance and Ethics

2. What is data governance? “formalizes behavior around how data are defined , produced , used , stored , and destroyed in order to enable and enhance organizational effectiveness” “adds value to our administrative and academic data systems by the establishment of standards that that promote data integrity and enables strategic integrations of information systems” “the discipline which provides all data management practices with the necessary structure, strategy, and support needed to ensure that data are managed and used as a critical asset” a set of guidelines for how people behave and make decisions about data.

3. Important Xstics of Data Governance

4. Data governance is a strategic priority

5. Justification for data governance Value – what could you do that you can’t do now? Costs – what costs are you incurring because data are not well governed? Risks – what risks are you taking because data are not well governed?

6. Principles of Data Governance

7. Data Quality Assessment (DQA) A scientific and a statistical evaluation to determine if data are adequate for their intended use Scientific : check for anomalies, transcription errors, assess effect of any QC deviations, evaluate with professional contextual judgment, etc. Statistical : graphical display of a data set's features and trends; identification of statistical outliers; verification of assumptions underlying statistical tests, etc. The goal of DQA is to assess whether the data that were gathered are adequate for drawing the conclusions needed for this particular project. This means if new data are used for a project, existing data you collected for one project are then used for a different project, or you use data that someone else collected for a project.

8. The Five Steps of Data Quality Assessment 1. Review objectives and data collection design 2. Conduct a preliminary data review 3. Select the statistical method 4. Verify the assumptions of the statistical method 5. Draw conclusions from the data

9. Ethical Issues in Business Intelligence Data Privacy: BI often requires access to large amounts of data, including sensitive information about individuals. Ensuring the privacy of this data is critical to maintaining ethical standards. This includes obtaining proper consent for data collection and implementing robust security measures to protect against unauthorized access. Data Accuracy and Bias : BI systems rely on data to generate insights and make predictions. However, if the data used is inaccurate or biased, it can lead to flawed conclusions and decisions. Organizations must be transparent about the sources of their data and take steps to address biases that may be present. Transparency and Accountability : Lack of transparency can make it challenging for stakeholders to assess the validity and fairness of the insights produced. Organizations should strive to make their BI processes transparent and be accountable for the decisions made based on BI outputs. Data Governance: Establishing clear policies and procedures for data governance is essential for ethical BI practices. This includes defining roles and responsibilities for data management, establishing data quality standards, and implementing mechanisms for oversight and compliance. Intellectual Property and Copyright : BI often involves the aggregation and analysis of data from multiple sources, which may include proprietary or copyrighted information. Organizations must respect intellectual property rights and ensure that they have the appropriate permissions to use and distribute the data. Impact on Stakeholders including employees, customers, and communities. Organizations need to consider the potential consequences of their BI initiatives and strive to minimize any negative impacts while maximizing benefits for all stakeholders.