DIASBM2020: Decision Intelligence Analytics with the implementation of Strategic Business Management |
Submission link | https://easychair.org/conferences/?conf=diasbm2020 |
Abstract registration deadline | August 20, 2020 |
Submission deadline | November 7, 2020 |
Decision Intelligence Analytics with the implementation of Strategic Business Management: Title of the book
Short Descriotion of the Edited Volume:
A framework for developing an analytics strategy that includes everything from problem definition and data collection to data warehousing, analysis, and decision making. Best practices in team analytics strategies such as player evaluation, game strategy, and training and performance and how organizations can use analytics to drive additional revenue and operate more efficiently. The keys to building and organizing a decision intelligence analytics team that delivers insights into all parts of an organization. Criteria and tools for evaluating and selecting decision intelligence analytics technologies and the applicability of strategies for fostering a culture that prioritizes data-driven decision making. Each Chapter is carefully segmented that the reader can accumulate the knowledge of Business Intelligence, Decision Making & Artificial Intelligence with Strategic Management.
This Edited volume will be aimed at faculties and managers who want to become sophisticated analytics of Decision Intelligence and provides strategic management support. The idea is to learn just enough analytics that the readers to know what to ask for and where the pressure points are. The volume comprises of techniques and methods of Decision Intelligence analytics with Business Intelligence & Artificial Intelligence of right implementation of Strategic Management and cases, discussions with real-time scenarios.
Submision Dates:
- Abstract Submission: 20th August 2020 (tentative)
- Notification for Abstract: 7th Sept. 2020 (tentative)
- Full Chapter Submission: 7th Nov. 2020 (tentative)
- Notification for full chapter submissions: 21st Nov 2020 (tentative)
- Final CRC Submission: 10th December 2020 (tentative)
- Final Data to Springer: 20th December 2020 (tentative)
All Chapters Information
Chapter – 1:
Analytics techniques: Descriptive Analytics & Predictive Analytics & Prescriptive Analytics
“Descriptive analytics”, which involve preparing the data for subsequent analysis, to “Predictive Analytics” that provide advanced models to forecast and predict future, to the top-notch of analytics called “Prescriptive Analytics” that utilize machine-based learning algorithms and dynamic rule engines to provide interpretations and recommendations.
Chapter – 2:
Using Artificial Intelligence & Analytics for Better Decision-Making and Strategy Management
A framework for developing an analytics strategy that includes problem definition and data collection to data warehousing, analysis, and decision making across the organizations and sectors. Best practices analytics strategies and Artificial Intelligence such as player evaluation, game strategy, and training and performance will be provided.
Chapter – 3:
Artificial Intelligence is being applied to create value across a range of domains
- Customer domain includes Pricing analytics, Predictive subscription and advertising, Customer sentiment analysis, Customer churn analytics, Marketing analytics
- Supply chain/Operations domain includes Supply chain network modeling, Supply chain risk, Geospatial analysis of key operation centers.
- Workforce domain contains Retention, Workforce planning, Safety, Recruiting.
- Finance comprises Interactive financial visualizations, Risk-adjusted planning, budgeting, and forecasting Analytics for control, efficiency, and reduced working capital.
- Risk includes Risk sensing, Reputational risk analytics, fraud and anomaly detection, and prevention.
- Workforce Decision Intelligence & Strategies domain will support Workforce planning & optimization, Workforce transitions, Recruitment Analytics & Retention Risk Analytics.
Chapter – 4:
Business Intelligence and Decision Making
Business Intelligence helps in Strategic Decision Making. This chapter helps extract crucial facts from a vast amount of unstructured data and transform them into actionable information that enables companies to make informed strategic decisions, improving operational efficiency and business productivity.
Chapter – 5:
Amalgamation of business intelligence with corporate strategic management
Amalgamation of business intelligence and corporate strategic management has a direct impact on modern and flexible organizations. This integration helps decision makers to implement their corporate strategies, adapt easily to changes in the environment, and gain competitive advantages.
Chapter – 6:
Enterprise decision management with Strategic focus
Enterprise decision management (EDM) is a closely related discipline that focuses on automating decisions across an enterprise. Decision intelligence is from this point of view a superset of EDM, since it encompasses both manual and automated decision-making processes, unifying them into a common methodology that, when effective, breaks down barriers between quantitative analysis / analytics tools and departments and those with a more qualitative / strategic / management focus.
Chapter – 7:
The rise of decision intelligence: AI that optimizes decision-making
The term for AI applied to the business: decision intelligence. Indeed, a decision intelligence framework helps with the operationalization of AI or ML for real business decisions.
Chapter – 8:
Analytics for Strategic Management
Analytics for Strategic Management aims to create professionals who can bridge this gap, and become sophisticated data consumers. Through lectures, workshops and real-world cases, the authors will learn analytics’ foundational concepts and how to apply those to strategic management.
Chapter – 9:
Analytics Concepts and Processes for Strategic Management:
This chapter introduces analytics as a scientific process involving problem specification, modeling, data collection, analysis, and summary.
Chapter – 10:
Implementation of Decision Intelligence Analytics for Strategic Management:
This chapter formalizes Decision Biases, to Identify the types of biases in a decision making process and learn how to ask for the right information with the models of Strategic Management and to implement and visualize them by using the Statistical tools like SAS, R & Python programming language and Data Visualization tools.
Chapter – 11:
Decision Intelligence Analytics for Competitive Advantage - Boosting the Organization’s Performance
With the right approach, Decision intelligence can be a leading source of competitive advantage. Organizations have an opportunity to use enterprise analytics to drive digital transformation and redefine the customer experience. To accomplish this, data and analytics leaders must create a data-driven culture focused on delivering business outcomes.
Chapter – 12:
Business Lessons from the Sports Data Revolution
This chapter gives an opportunity to engage the strategic thinking management to develop, refine & implement a Decision Intelligence Analytics in the field of Sports, which is emerging in the current era. Sports Data Revolution bangs into the best practices from the sports industry.
Chapter – 13:
Role of Decision Intelligence in Strategic Business Planning
BI basically extract important insights from vast unstructured data & transform them into valuable business information. This chapter provides the understandings of the companies can use this information to make strategic decisions, optimizing operational efficiency and enhance productivity. This information provides important insights of customer trends, buying habit, online shopping stats etc. which can provide immense value to any business.
Chapter – 14:
Decision Intelligence Analytics: Making Decisions through Data Pattern & Segmented Analytics
This chapter concentrates primarily on practical business issues and demonstrates how to apply data warehousing and data analytics to support business decision making. This chapter progresses through a logical sequence, starting with data model infrastructure, then data preparation, followed by data analysis, integration, knowledge discovery to find the pattern, and finally the actual use of discovered knowledge.
Chapter – 15:
What more we need to know (Original contribution of authors Case Studies submissions)
Real-time scenarios will be discussed to adhere to the futuristic plan of dynamic decision making and Strategies. Explain the reasons behind past events by analyzing and summarising data. Predict future outcomes by choosing the appropriate machine learning algorithm to use in a business context. Learn the implementation challenges of creating a data-driven organization. Understand the ethics and regulatory issues involved in making decisions using data.
Editor(s) Information:
Dr. P. Mary Jeyanthi, Faculty – Information Systems & Business Analytics,Institute of Management Technology, Nagpur,India
Dr. Tanupriya Choudhury, Associate Professor;Informatics Dept.,School of CSE University of Petroleum and Energy Studies, Dehradun, India
Dieu Hack-Polay,PhD EdD MA MSc MEd MA BSc BA PGCE QTLS FSET FHEA Chartered FCIPD,Associate Professor,Lincoln International Business School,University of Lincoln, Brayford Pool|Lincoln LN6 7TS|United Kingdom
Professor Dr. Thipendra Pal Singh, Professor and HoD Informatics Dept., School of CS, University of Petroleum and Energy Studies, Dehradun, India
Mr. Sheikh Abujar, Daffodil International University, Bangladesh, Lecturer (Senior Scale), Faculty of Science and Information Technology
Contact Details:
1. Dr. P. Mary Jeyanthi
Email ID: dr.maryprem@gmail.com; pmjeyanthi@imtnag.ac.in;
Mailing Address: Assistant Professor, Institute of Management Technology, Nagpur.35 KM Milestone, Mouza Dorli, Katol Road, Nagpur 441502.Maharashtra, IndiaTelephone No: +91-9962006900https://www.imtnagpur.ac.in/profile?id=50
2. Dr. Tanupriya Choudhury
Email ID: tanupriya1986@gmail.com; tanupriya@ddn.upes.ac.in
Mailing Address: Associate Professor, University of Petroleum and Energy Studies,ENERGY ACRES, UPES, BIDHOLI, via, Prem Nagar, Dehradun, Uttarakhand 248007,India
Telephone No: +91-9910803601 ;+91-9711938087 (Whatsapp)