The projects will provide an opportunity for businesses and learners to collaborate to identify and address real business challenges.

The projects, which can be short, will allow the student to apply the data management concepts and techniques presented in the classes to address the sponsors business challenges. Some examples are:

• Identify the value of data in relation to information and knowledge; define the concepts of data, information and knowledge
• Outline the approaches in preparing data for analysis when the data is sourced from multiple diverse environments
• Apply data quality and data profiling in data analysis, explain the various approaches for improving data quality 
• Define Data Governance components to managing the data and information assets of the business. 
• Apply the analytical process to break down complex data problems into smaller steps for effective management and analysis
• Define the operational considerations in architecting BI and Analytic Platforms and the factors to be considered in preparing big data for analysis and the need for various repositories for managing the data (e.g. warehouses, sandboxes)

You should submit a high-level proposal/business problem statement including relevant data sets and definitions, a list of acceptable tools (if applicable), and expected deliverables. Business datasets could be provided based on a non-disclosure agreement or in an anonymized/synthetic data format that is relevant to your organization and business problem. The  course instructors will review the documents to confirm the scope and timing of the proposed problem and its alignment with the capstone course requirements.

Analytics solution may be applicable for (however they are not limited to) the following topics:

1. Demand for social services (healthcare, emergency services, infrastructure, etc.)
2. Customer acquisition and retention
3. Merchandising for trade areas (categories)
4. Quantifying Customer Lifetime Value
5. Determining media consumption (mass vs digital)
6. Cross-sell and upsell opportunities
7. Develop high propensity target markets
8. Customer segmentation (behavioral or transactional)
9. New Product/Product line development
10. Market Basket Analysis to understand which items are often purchased together
11. Ranking markets by potential revenue
12. Consumer personification

To ensure students’ learning objectives are achieved, we recommend that the datasets are at least 20,000+ rows in size. Data need to be ‘clean’. If more than one database is provided, which must be conjoined, students will be required to integrate them. This supports the learning experience and minimizes partner data preparation.