A Conceptual Modeling Framework for Business Analytics

Data analytics is an essential element for success in modern enterprises. Nonetheless, to effectively design and implement analytics systems is a non-trivial task. This paper proposes a modeling framework (a set of metamodels and a set of design catalogues) for requirements analysis of data analytics systems. It consists of three complementary modeling views: business view, analytics design view, and data preparation view. These views are linked together and act as a bridge between enterprise strategies, analytics algorithms, and data preparation activities. The framework comes with a set of catalogues that codify and represent an organized body of business analytics design knowledge. The framework has been applied to three real-world case studies and findings are discussed.

[1]  Eser Kandogan,et al.  From Data to Insight: Work Practices of Analysts in the Enterprise , 2014, IEEE Computer Graphics and Applications.

[2]  Ron Kohavi,et al.  Emerging trends in business analytics , 2002, CACM.

[3]  Padhraic Smyth,et al.  From Data Mining to Knowledge Discovery in Databases , 1996, AI Mag..

[4]  C. Maria Keet,et al.  Modeling Issues, Choices in the Data Mining OPtimization Ontology , 2013, OWLED.

[5]  John Mylopoulos,et al.  Business Intelligence Modeling in Action: A Hospital Case Study , 2012, CAiSE.

[6]  Hendrik Blockeel,et al.  A new way to share, organize and learn from experiments , 2012 .

[7]  Luís Torgo,et al.  OpenML: A Collaborative Science Platform , 2013, ECML/PKDD.

[8]  J. Manyika Big data: The next frontier for innovation, competition, and productivity , 2011 .

[9]  Esteban Zimányi,et al.  BPMN-Based Conceptual Modeling of ETL Processes , 2012, DaWaK.

[10]  S. Viaene,et al.  The secrets to managing business analytics projects , 2011 .

[11]  John Mylopoulos,et al.  Strategic business modeling: representation and reasoning , 2014, Software & Systems Modeling.

[12]  Rajesh Parekh,et al.  Lessons and Challenges from Mining Retail E-Commerce Data , 2004, Machine Learning.

[13]  Geoff Holmes,et al.  Experiment databases , 2012, Machine Learning.

[14]  John Mylopoulos,et al.  Composite Indicators for Business Intelligence , 2011, ER.

[15]  Jose-Norberto Mazón,et al.  A Model-Driven Goal-Oriented Requirement Engineering Approach for Data Warehouses , 2007, ER Workshops.

[16]  Anjana Gosain,et al.  An approach to engineering the requirements of data warehouses , 2008, Requirements Engineering.

[17]  Tim Menzies,et al.  Software Analytics: So What? , 2013, IEEE Softw..

[18]  R. Suganya,et al.  Data Mining Concepts and Techniques , 2010 .

[19]  John Mylopoulos,et al.  Strategic Models for Business Intelligence , 2011, ER.

[20]  Eric Yu,et al.  Modeling Strategic Relationships for Process Reengineering , 1995, Social Modeling for Requirements Engineering.

[21]  Thomas Reinartz,et al.  CRISP-DM 1.0: Step-by-step data mining guide , 2000 .

[22]  Juan Trujillo,et al.  A UML Based Approach for Modeling ETL Processes in Data Warehouses , 2003, ER.

[23]  Jose-Norberto Mazón,et al.  Automatic generation of ETL processes from conceptual models , 2009, DOLAP.

[24]  Paolo Giorgini,et al.  GRAnD: A goal-oriented approach to requirement analysis in data warehouses , 2008, Decis. Support Syst..

[25]  Roy van Beest Project Intelligence: is Data Analytics the new path to value? , 2016 .

[26]  John Mylopoulos,et al.  Non-Functional Requirements in Software Engineering , 2000, International Series in Software Engineering.

[27]  Sotiris B. Kotsiantis,et al.  Supervised Machine Learning: A Review of Classification Techniques , 2007, Informatica.

[28]  Jon Kleinberg,et al.  Algorithms Need Managers, Too , 2016 .

[29]  Raian Ali,et al.  A goal-based framework for contextual requirements modeling and analysis , 2010, Requirements Engineering.

[30]  Panos Vassiliadis,et al.  Conceptual modeling for ETL processes , 2002, DOLAP '02.