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A number of newsworthy trends are highlighting the growing recognition of risks of exposing individuals’ personal and private information. The numbers and sizes of data breaches are exploding along with emerging indignance over corporations using and selling what is believed to be personal or private information have motivated the creation of a growing inventory of global regulations address the need to secure and protect individuals’ personal and private data. In this talk we will discuss data sensitivity, concepts of information risk, along with trends and ideas for instituting data governance techniques to improve overall data protection while reducing the constraints presumed to be imposed by data policies.
View the presentation, Data Governance and Trends in Protecting Sensitive Data
Early data warehouses and marts used for business intelligence that targeted senior management reporting were called “decision support systems,” but what does it really mean to “support” a decision? More commonly these systems are used to facilitate decision-making in a broad array of scenarios, providing guidance for the decision before it is made and reinforcement of the process after the decision is made, all with the intent to help people make better decisions. Understanding what type of information is expected to make a particular decision is one step in considering the variables that are relevant for improving the process as well as ways that the right amount of information can streamline or optimize those variables. This talk introduces a simple model that abstracts some of the key facets of a decision-making scenario and can help highlight where potential gaps in knowledge can impede optimal decision-making. The talk will then consider how “decision quality” can be assessed using a number of dimensions such as informedness, timeliness, completeness, and optimality of outcome. In turn, we describe how a model for a decision-making scenario encompasses at the very least these characteristic features:
- Business use case
- Relevant actors
- Business question to be answered
- Inputs to the decision
- Decision points in the scenario
- Choices presented to the decision-maker
- Desired outcomes
- Performance measures
Attendees will learn about:
- Describing desired outcomes of a decision
- Identifying the decision points within a business process flow
- Assessing what types of decisions are properly informed
- How actors can be guided to make optimal choices
- Leveraging information that will lead to better overall optimal performance
View the presentation, Using a Decision-Making Model to Improve Decision Quality
Establishing a business case for introducing and developing a data quality management program is often predicated on the extent to which data quality issues impact the organization and the return on the investment in data quality improvement. While there is frequently real evidence of hard impacts directly associated with poor quality data, unfortunately, we too often resort to examples where flawed data has led to business problems. Anecdotes help raise awareness of data quality as an issue, but developing a performance management framework that helps to identify, isolate, measure, and improve the value of data within the business contexts requires correlating business impacts with data failures and then characterizing the loss of value that is attributable to poor data quality.
View the presentation, Establishing a Business Case for Data Quality Improvement
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