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Business Analytics, Big Data and Knowledge Management (SIGDSA) Track

Thilini Ariyachandra, Xavier University This email address is being protected from spambots. You need JavaScript enabled to view it.
Amit Deokar, Pennsylvania State University This email address is being protected from spambots. You need JavaScript enabled to view it.
Babita Gupta, California State University Monterey Bay This email address is being protected from spambots. You need JavaScript enabled to view it.

Track Description                                                                                 

In recent years, the ability to manage (big) data, information and knowledge to gain competitive advantage and the importance of business analytics for this process has been well established. Organizations are investigating ways to efficiently and effectively collect and manage the data, information and knowledge they are exposed to via various internal and external sources in the networked society. Research contributions in this space continue to enlighten industry on how to handle the various organizational and technical opportunities and challenges when working with big data, knowledge management and analytics. From research on managerial concerns (such as strategy, governance, leadership), process centric approaches and interorganizational aspects of decision support to research on technical considerations when incorporating new data sources and new frameworks for big data, analytics and knowledge management, academic endeavors in this space provides insights on a dynamic and highly relevant field within information systems. The research track seeks research that promote theoretical, design science, pedagogical, behavioral research and emerging applications in innovative areas of analytics, big data, and knowledge management. In keeping with the Blue Ocean Research theme of the AMCIS 2015 conference, the track hopes to emphasize and encourage research that creates and explores new and uncontested areas within analytics, big data and knowledge management in addition to the traditional areas of interest within this space. Given the very broad and encompassing nature of the research area, there is room for new research and manuscripts involving emerging issues, challenges and trends.

Mini-Tracks

Business Analytics for Organizational Performance Management

Benjamin Shao and Robert D. St. Louis

Innovative Approaches to Data and Text Mining

Shuyuan (Lance) Deng, Abhijit (Abbi) Dutt and Huimin (Min) Zhao

Managing (Big) Data as an Asset

Rob Nickerson and Jerry Luftman

Spatial Business Intelligence, Analytics, and Knowledge Management

James B. Pick, Daniel Farkas, Brian Hilton, Avijit Sarkar, Hindupur Ramakrishna, and Namchul Shin


Business Analytics for Organizational Performance Management

Benjamin Shao, Arizona State University This email address is being protected from spambots. You need JavaScript enabled to view it.

Robert D. St. Louis, Arizona State University This email address is being protected from spambots. You need JavaScript enabled to view it.

 

Mini-track Description

The goal of business analytics (BA) is to summarize massive amounts of disparate corporate and customer data into succinct information that can help management better understand their business processes, make informed decisions, and measure and improve organizational performance. BA can provide managers with the ability to integrate enterprise-wide data into metrics that link specific objectives to the performance of different business units. In today’s hypercompetitive environment, accurate real-time BA metrics are even more critical for measuring and enhancing organizational performance. Many technologies contribute to BA solutions, including databases, data warehouses, data marts, analytic processing, social analytics, and data mining, among others. BA needs to acquire data from multiple platforms and provide ubiquitous access. This requirement to leverage so-called “big data” presents numerous managerial challenges. This mini-track aims to promote innovative research in the BA domains of organizational performance measurement and improvement.

Potential Topics:

Real-time business metrics for performance measurement 
Using BA to support the “balanced scorecard” 
Developing the warehouse to support “digital dashboards” 
Critical BA success factors for management control 
Analytical processing 
Techniques for summarizing data 
Assessing organizational performance using BA metrics
Information extraction and report generation
Real-time decision making
Online analytical processing (modeling)
Business performance management (BPM) and improvement
Economic analysis of business analytics 
Business analytics strategies 
Business analytics performance metrics 
Business analytics implementation management
BA governance, implementation, and management
Vigilant information systems
Leveraging big data for performance improvement
Summarizing and visualizing organizational performance metrics
Social analytics on organizational performance management
Predictive analytics

 


Innovative Approaches to Data and Text Mining

Shuyuan (Lance) Deng, Dakota State University This email address is being protected from spambots. You need JavaScript enabled to view it.

Abhijit (Abbi) Dutt, Penn State University This email address is being protected from spambots. You need JavaScript enabled to view it.

Huimin (Min) Zhao, University of Wisconsin-Milwaukee This email address is being protected from spambots. You need JavaScript enabled to view it.


Mini-track Description

Analytics technologies have been consistently creating positive impact on organizational decision making. Data and text mining is a critical area in analytics. As data are being generated at an unprecedented magnitude in recent years, the data structures that need to be processed in analytics are becoming increasingly diverse. The underlying storage architecture is also experiencing a drastic shift, as demonstrated by the wide adoption of non-relational (NoSQL) and distributed databases. New approaches in data and text mining are in demand by organizations to properly analyze such data.

We invite innovative research papers (either completed research or research in progress) using a variety of research methodologies, such as qualitative, quantitative, design science, and theoretical modeling methodologies. We have listed some of the areas here. However, we welcome papers in other relevant areas too.

Potential Topics:              

Challenges in mining unstructured data
Mining big data
Innovative analytic techniques
Data and text mining on distributed computing infrastructures
Legal and privacy issues in data mining
Teaching data and text mining techniques
Data and text mining in bioinformatics
Data and text mining’s impact on organization performance
Data and text mining as a service
Innovative application of data and text mining in organizations
Information security and mining
Tools in data and text mining

 

Managing (Big) Data as an Asset

Rob Nickerson, San Francisco State University This email address is being protected from spambots. You need JavaScript enabled to view it.

Jerry Luftman, Global Institute for IT Management This email address is being protected from spambots. You need JavaScript enabled to view it.


Mini-track Description

Among the most significant changes currently facing organizations are big data and business analytics. Luftman’s annual IT management trends research over the last 15 years has placed big data/business analytics as the number one emerging technology investment around the globe. This, in concert with the trends research also indicating a global increase in the use of IT for revenue generating initiatives, is demanding organizations to address how to leverage this important set of technologies. 

To be successful in leveraging data and business analytics, organizations need to understand how to move from big data to smart data, and more importantly, how to obtain demonstrable value from these important initiatives. 

The impact of big data analytics touches every area of the enterprise – marketing, sales, research, finance, human resources, supply chain, customer relations, legal, etc.  To be successful there is a strong requirement for an organizational leader/manager to provide a new form of information service to the entire enterprise.   As a result, there is currently a debate regarding the role of, or need for, a Chief Data Officer (CDO) or Chief Analytics Officer (CAO) – perhaps as another member of the C-suite.  Or, perhaps such a role is better placed under the CIO, or elsewhere in the organization. There is no one best place in which the responsibilities for the data questions raised above should reside for every organization. Management, however, needs guidance regarding what to take into account in determining the best alternative for their individual situation (e.g., strategy, culture, politics, IT-business relationship).

Clearly defined governance processes for data and business analytics are essential.

The key is for executives to consider data as an asset – to determine how best to manage it, to exploit its potential, as we would with any other asset. How should it be acquired, stored, maintained and put to work.   Recognizing the importance of IT and non-IT organizations working collaboratively is essential.

While there are numerous industry-oriented articles on managing data as an asset, limited academic research has appeared. This dearth of research publications highlights the need for theoretical and empirical investigation into this topic. The purpose of this minitrack is to provide a forum for presenting research in this new and important area.

Potential Topics:

Topics for this minitrack include, but are not limited to, the following:

Data and analytics strategy
Business organization for gaining value from data
How and what data to capture
Storage for the growing availability of data
Feasibility and effectiveness of analytics
IT, business, and industry skills needed by the organization
Data and analytics responsibility within the organization
Ethical questions and considerations and how to deal with them
New business models
Governance of data and business analytics
Responsibility and expertise for:
strategic authority for informational assets
tactical and operational management of data assets
investment and sourcing decisions for deploying data initiatives
achieving organizational data success/value from data initiatives
ensuring data security/privacy compliance
creating innovative data-driven products and services

 


Spatial Business Intelligence, Analytics, and Knowledge Management

James B. Pick, University of Redlands This email address is being protected from spambots. You need JavaScript enabled to view it.

Daniel Farkas, Pace University This email address is being protected from spambots. You need JavaScript enabled to view it.

Brian Hilton, Claremont Graduate University This email address is being protected from spambots. You need JavaScript enabled to view it.

Avijit Sarkar, University of Redlands This email address is being protected from spambots. You need JavaScript enabled to view it.

Hindupur Ramakrishna, University of Redlands This email address is being protected from spambots. You need JavaScript enabled to view it.

Namchul Shin, Pace University This email address is being protected from spambots. You need JavaScript enabled to view it.


Mini-track Description

The mini-track on Spatial Business Intelligence, Analytics, and Knowledge Management seeks to provide a forum for research on varied aspects of geographic information systems (GIS) for business intelligence, analytics, knowledge management, and spatial data management.  This area is becoming an essential aspect for governments and has been growing rapidly over the past decade in business. The mini-track encourages manuscript submission on theory, methodology, applications, behavioral studies, and emerging areas in GIS.  Current areas of interest include spatial big data, spatial knowledge management, theory development, cloud-based GIS, spatial crowdsourcing, spatial workforce, behavioral research, geo-design, privacy and security aspects, mobile location-based applications, and new and emerging areas of GIS.

In concert with the AMCIS 2015 theme, spatial technologies have been undergoing a major transformation based on new and emerging geospatial technologies including space-time, 3-D modeling, LIDAR, unmanned spatial data collection, augmented reality glasses, and virtual reality of place.

The intent is to advance knowledge from a relatively nascent level in light of the continuing geospatial revolution and encourage exchange of findings, methodologies, blue ocean ideas between scholars and practitioners in an area ripe for rapid growth in business and in information programs. The mini-track over the past four years has attracted increasing interest and participation.  It is part of the SIGDSA track and is sponsored by SIGGIS.

GIS and spatial technologies are growing rapidly in business and government.  Increasingly these applications involve business intelligence (BI), big data, analytics, and knowledge management.  Although there is considerable research on GIS technology and geographic information science, there has been relatively little research in spatial decision making in business, government, and organizations. This track seeks manuscripts that address theory, methodology, geo-design, applications, management issues, and behavioral aspects of these topics.  The relevance to research is to build up greater knowledge of the geo-spatial aspects of decision-making and management, and to develop theory and applications, occasionally building on well-known concepts in the Decision Support/Analytics and MIS fields

The mini-track will address the emerging areas of GIS, GIScience, and related technologies such as RFID, imagery, virtual augmentation and reality, location for mobile devices, and sensors, cloud-based GIS, and expansive spatial information. Since this area is expanding and becoming essential in business and government, the mini-track can shed light on a new and evolving field. The mini-track findings, results, and discussions will also inform the leaders, managers, strategic thinkers, and policy makers in many organizations that are building, deploying, and managing applications in these areas.

In concert with the AMCIS 2015 theme, submissions are encouraged in the blue ocean aspects of spatial science and technologies. This mini-track is supported by SIGGIS of AIS.

Potential Topics:

Spatial analytics 
Spatial decision support
Spatial knowledge management
Big Data and GIS
Management decision-making using GIS
Spatial data mining and knowledge discovery
Web-based GIS concepts and applications
GIS and the cloud
Mobile-based GIS concepts and applications
Security and privacy of spatial information
Geo-Design
Theoretical studies
Methodological papers
Case studies 
Investment in and benefits of GIS, spatial BI, or spatial analytics
Managerial concerns in spatial systems
Ethical aspects of GIS and spatial decision-making
GIS workforce, training, and education
Quality measures and evaluation of spatial systems
Systems and software development of GIS
Crowdsourcing and public domain sources of spatial information
Emerging and Blue Ocean areas in spatial science and technologies
 


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