Skip to Main Content
You are now in the main content area

Research

Representation of DEAL centre with a triangle inside a circle

DEAL's Research Approach

Deal's approach is represented as shown in the above image, with a huge triangle with another triangle at it's center resulting in the formation of four triangles. The center triangle represents Data and the sorrounding three triangles represent Business, Analytics and Regulation. The huge triangle is inside a circle with its corners touching the circle and resulting in the representation of three main themes around it : People, Planet and Profit.

DEAL employs a holistic research approach that addresses both the technical side of digital businesses and the business side of digital technologies while taking into account current societal discussions and ethical debates about datafication and Canada’s Digital Charter (external link) .

DEAL's Strategic Framework for Business Data Analytics stipulates that any given digital enterprise leadership initiative and/or business analytics project with big data sets (in-house and/or external) needs to first consider the regulatory environment (for example, GDPR in the EU), identify the bonafide business needs, problems opportunities, and/or challenges next, and then select the appropriate analytical methods and tools. Finally, the impacts of such business data analytics projects need to be considered, estimated and reflected upon across the triple bottom line of economic profitability, social responsibility, and environmental sustainability.

DEAL's Strategic Framework for Business Data Analytics informs and guides all activities at the centre across research, research-based education, consultancy, service and outreach.

DEAL Research Streams

DEAL Research Stream #1: Data Management for Digital Enterprises (Dr. Mehdi Kargar)

This research stream aims at enabling business users to explore and analyze different types of data. It identifies problems, challenges and opportunities for improving data exploration and data profiling. More specifically, we propose to build systems to resolve three hard problems: (a) search in big graphs and relational databases for business users, (b) team discovery in expert networks, and (c) discovery of business rules and dependencies. By collaborating with industry and working with real-world datasets, we will investigate how cutting-edge research findings can be implemented in real world systems.

  • Business Datasets: Enterprise Structured and Semi- Structured Data
  • Digital Artifacts: Search System for Business Users, Data Curation Systems
  • Industry Sectors: Financial Industry, Technology Firms, Health Industry, Transportation
  • Keywords: Graph Analytics, User-Aware Enterprise Search, Learn to Rank and Form Answers

DEAL Research Stream #2: Applied Machine Learning for Digital Enterprises (Dr. Morteza Zihayat)

This research stream applies advanced machine learning methods and techniques for actionable insight discovery, user modeling for understanding domain specific behaviors, customer relationship management, and user behavior modeling for acquisition and retention. We seek to monitor and improve Key Performance Indicators (KPIs) in order to decrease customer acquisition costs, increase employee retention. We aim to build and deploy state-of-the-art user-centered modelling techniques (e.g., churn modeling and customer lifetime value (LTV) models) in Canadian enterprises.

  • Business Datasets: Enterprise Structured and Unstructured Data
  • Digital Artifacts: Data-Driven Predictive Models, Context-driven Behavioral Patterns
  • Industry Sectors: Digital Media, Health Industry, FinTech
  • Keywords: Machine Learning, Artificial Intelligence

DEAL Research Stream #3: Text Analytics for Digital Enterprises (Dr. Ozgur Turetken)

This research stream addresses the vast amounts of business-relevant textual content in the form of emails, blog posts, consumer reviews and tweets etc. We aim to employ text analytics techniques, methods and tools (such as text clustering, automated question answering, web trend analysis. sentiment analysis, domain-specific classifiers, and conversation bots) to design, develop, and deploy text analytics solutions and services in Canadian enterprises.

  • Business Datasets: Internal Business Text Corpora and External Sources (Social Media)
  • Digital Artifacts: Domain-Specific Classifiers, Conversation Bots, Q&A Systems
  • Industry Sectors: Digital Media, Health Industry, FinTech
  • Keywords: Text Mining, Text Analytics, NLP, Conversation Bots

DEAL Research Stream #4: Social Set Analysis (Dr. Ravi Vatrapu)

This research stream focuses on B2C organizations and applies novel methods from social set analysis to combine in-house business data with external datasets (open, register, and social media). Key business processes supported by this research stream are: community detection for market segmentation, demand forecasting of revenues & customers, modelling of reputational risks from corporate crises, process monitoring dashboards, and maturity models for organizational benchmarking. 

  • Business Datasets: In-House Business Data and External Sources (Digital Media)
  • Digital Artifacts: Social Set Visualizer, Brand Loyalty Quadrant
  • Industry Sectors: Retail, Banking, Education, Design, Culture
  • Keywords: Set Theoretical Segmentation, Digital Brand Parameters, Digital Promotion