ABOUT CLDS RESEARCH
The goal of CLDS research is to provoke, further and improve decisions taken in the real world. Center researchers study a wide range of data, technology and management topics. A key focus is investigating how new technologies disrupt markets, change competition and create new organizations and skills. CLDS research is organized into program areas, described below, and specific projects.
RESEARCH PROGRAM AREAS
DATA IN THE 21ST CENTURY
Forecasting Data, Decision Intelligence and Public Trust
This is a new, future-oriented data research program under development by CLDS principal faculty. The program is organized around the future roles and impacts of next-generation data, decision intelligence, data innovation and public trust. Five, ten and twenty years out. A principal theme will be what steps business and public organizations need to take to prepare for the future. The program will be introduced at Data West in December 2021.
BUSINESS DYNAMISM AND THE DATA ECONOMY
Data is Growing Exponentially, Business Dynamism is Slowing. Why?
The world’s stock of data continues to grow exponentially. Driven by a wide range of new technologies and new business models, from AI, business analytics and machine learning, to new service revenue models and cloud data migration, estimates for data growth vary from an average of 20-30% per year, to almost 60% a year. The exponential growth of data has ushered in a number
of new paradoxes. Data growth is driving increasing investment in information technology spend and ICT asset stock accumulation, yet aggregate economic productivity, globally and by country, fell in the early 2000s, and has remained low ever since. Why?
P. D. Leenheer and J.E. Short, "The Data Dilemma: Data is Growing Exponentially, Business Dynamism is Slowing – Why?" CLDS Working Paper in in Press
DATA INNOVATION, GOVERNANCE AND DATA VALUE
How Much Data Is There? Where Is It? How Is Data In Your Organization Governed? What Is Its Value?
Managers have long known that siloed data severely limits the potential value of the data to the business. In surveys conducted over the period 2017 to 2021, we had expected the explosion of data would allow companies to put more data in the hands of decision makers at all levels in their organizations, positively affecting performance, revenue growth, and the strength of customer relationships. In some cases business and IT leaders report that it has. But in many companies, generating business value from the increasing volume of data is still an aspiration. Data growth has far exceeded revenue growth. Why?
(1) C. Beath, I. Becerra-Fernandez, J. Ross, and J.E. Short, "Finding Value in the Information Explosion," Sloan Management Review, Vol. 53 Number 4 (Summer 2012).
(2) J.E. Short and S. Todd, "What's Your Data Worth?" Sloan Management Review, Vol 58 Number 3 (Spring 2017)
GLOBAL HEALTH POLICY AND DATA ANALYTICS
The world is becoming more interconnected, and health has never been more important.
Globalization has led to a more interconnected society, but with that has also come new and emerging global health challenges that require unprecedented international cooperation. Health emergencies such as the COVID-19 pandemic personify the real-world threats to global society if we do not prioritize public health outcomes and investment. Big data and machine learning approaches are needed to mobilize data in a way that can improve population health outcomes, ensure equity and wellness, and is also needed to inform evidence-based policymaking. Interdisciplinary approaches to improving the health of all is the core focus of this CLDS program area.
Mackey TK, Purushothaman V, Li J, Shah N, Nali M, Bardier C, Liang BA, Cai M, Cuomo RE. Machine Learning to Detect Self-Reporting of Symptoms, Testing Access and Recovery Associated with COVID-19 on Twitter: A Retrospective Big-Data Infoveillance Study. JMIR Public Health Surveill. 2020;6(2):e19509.
DATA SYSTEMS DESIGN AND PERFORMANCE BENCHMARKING
Benchmarking of High-Performance,
Large-Scale Data Systems
Large-scale data - "big data" - is characterized by increasing volumes of data of disparate types (i.e., structured, semi-structured and unstructured) from sources that generate new data at high rates. For example, click streams captured in web server logs. This wealth of data provides new analytic and business intelligence opportunities such as fraud detection, customer profiling, and improved understanding and analysis of risk. As big data systems mature, the pressure to evaluate and compare the performance of these systems will continue to increase. CLDS researchers helped develop the first big data industry benchmark, BigBench.
A. Ghazal, T. Rabl, M. Hu, F. Raab, M. Poess, A. Crolotte and HA. Jacobsen, "BigBench: Towards an Industry Standard Benchmark for Big Data Analytics," SIGMOD’13, June 22–27, 2013, New York, New York, USA.
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