The Data and Information Management area comprises competencies related to tools and techniques for managing data with database systems. At the highest level, competencies within this area are related to two questions (a) how to use a database and (b) how to build a database. Most of this competency area will focus on the classic relational model. In the past several years, driven by evolving functional and non-functional (quality) needs of an organization, alternatives to the classic relational model have emerged. Illustrative samples will be examined of these popular alternatives known as non-relational or NoSQL models.
Electives: Following the trend towards big data and analytics, there is increasing need for professionals in this area and thus opportunities for specialization. While specialized programs exist that produce data scientists, this area has also emerged as an important area for graduates from IS programs. To support the design of this specialization to an undergraduate IS program, two specialization areas, Data and Business Analytics and Data and Information Visualization, are identified.
Competency Area Statement
We currently live in a data driven age. Data has emerged as the new oil that drives an organization: The successful operation of modern organizations relies on the effective use of their operational data. Database management systems (DBMS) are the engines of this data driven world.
Data collected and used by an organization is broadly divided into two types (i) line of business data and (ii) customer behavior data. Traditionally data management has focused on online business data. For example, when a ride request is made to a ride sourcing company (Uber, Lyft, etc.), what data is needed to meet that request? When a purchase is made in a grocery store what is the flow of data during that transaction? Line of business data is used to support core business processes of the organization. Alternatively, based on the purchase patterns of a shopper or the volume or location of ride requests, how can a grocery store or a ride sourcing company make their operation more effective? The answer to this question is based on customer behavior data (who bought what, when etc.). Whatever type of data it may be, many fundamental questions are the same: How do you gather, organize, curate, and process data to help run an organization or extract actionable information to increase effectiveness?
The use of data involves three aspects (i) management (ii) security and (iii) analytics.
We will study tools and techniques for managing data with database systems. At the highest level we will study two questions (a) how to use a database and (b) how to build a database. For more than three decades, the relational model has been the predominant model of data management. Most of this module will focus on the classic relational model. In the past several years, driven by evolving functional and non-functional (quality) needs of an organization, alternatives to the classic relational model have emerged. We will examine illustrative samples of these popular alternatives known as non-relational or NoSQL models.
Competencies: Graduates will be able to:
| Competency | Dispositions | Skill Level | |
| DATA.MGMT.1 | Query the relational model | Meticulous, Self-directed, Purpose-driven | Analyze |
| DATA.MGMT.2 | Design relational databases | Self-directed, Purpose-driven, Meticulous | Create |
| DATA.MGMT.3 | Programming database systems using functions and triggers | Meticulous, Self-directed, Purpose-driven | Apply |
| DATA.MGMT.4 | Secure a database | Meticulous, Self-directed, Purpose-driven | Analyze |
| DATA.MGMT.5 | Compare tradeoffs of different concurrency modes | Self-directed, Meticulous, Inventive | Apply |
| DATA.MGMT.6 | Develop non-relational models | Meticulous, Self-directed, Purpose-driven | Create |
Suggestions Accepted for consideration for the next Edition:
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