Goals and Objectives
The goals of the Acadia Institute for Data Analytics are to foster collaboration and the sharing of data analytics methods, technologies, and ethical practices among its stakeholders.
More specifically, the Institute will gather active stakeholders as members (academic researchers, teachers, associations, companies, government departments, etc.); foster communications and collaboration among members; communicate and coordinate relevant meetings and other events, and make connections with other data analytics organizations. The Institute will share knowledge of technologies and methods and promote the ethical use of data analytics methods that protect the privacy of individuals and the security and integrity of data.
The Data Runway project will help build on AIDA's early success by creating a specialized data analytics stream within the Rural Innovation Centre, called the Data Runway. The Data Runway will focus on a significant gap in the Atlantic Canadian innovation ecosystem by supporting businesses who wish to specialize in data analytics and machine learning and/or use these technologies to create a competitive advantage. AIDA is devoted to growing new data analytic start-ups and building data science know-how and expertise within existing, high-growth companies in Nova Scotia and Atlantic Canada. The Data Runway is a unique addition to the NS innovation ecosystem. Unlike current business incubation and accelerator models that provide basic business support services, the Data Runway will provide access to specialized technical expertise and a wide network of data analytics/business experts. AIDA will:
- Offer data analytics consultation and leadership to both new start-ups/mature companies in Nova Scotia.
- Provide expertise to plan, organize, direct and lead full-scale data analytics projects and business ventures.
- Provide advice in all areas of the data analytics project cycle: business problem formation, data understanding, data warehousing, data preparation, and predictive model development, evaluation, interpretation, and deployment.
- Source specialized student talent for companies interested in hiring data analytics expertise.
- Provide training sessions in the above methodological and technical areas to help companies in Nova Scotia realize the benefits of data analytics.
- Assist with applications for project funding, early start-up funding, and venture capital.
- Assist new start-ups in developing their network of potential customers, advisors, partners, and investors.
- Discover common problems with start-ups working in the data analytics space and develop novel solutions to these problems and best practices.
- Foster and support an Atlantic Canada data analytics eco-system through outreach to other accelerators, collaborate on conferences, symposia and other initiatives with NS groups such as CARET, BDANS, and the Big Data Institute at Dalhousie University.
Acadia University has identified detailed measures for monitoring and evaluation of the Data Runway project. Success for the program will be measured with regard to population and economic development goals that are important to the Province of Nova Scotia such as;
- To increase inter-provincial migration and international immigration, much of which will happen through the retention of international students and students from other provinces;
- To assist in the development of new start-ups and new business ventures within existing companies in the areas of (exportable) data analytics products and services;
- To work with Acadia University and other post-secondary institutions to educate and employ Nova Scotians, including those of First Nations and African descent in the growing fields of data engineering, data science, data analysis, and business intelligence;
- To continue to contribute to university-based applied R&D for industrial benefit and economic growth;
- To encourage injections of venture capital for the development of new data analytics start-ups; and
- To positively impact expansion in the tourism, fisheries and agricultural sectors through data collection, visualization, analysis, and predictive modeling