Vijay Khatri

Areas of Expertise

Data lifecycle management, Data visualization, Predictive analytics, Semiotics and database design, Cognitive aspects of data management, Data governance

Academic Degrees

• PhD, University of Arizona, 2002
• MBA, Management Studies, University of Bombay, 1994
• BE, Electronics, Malaviya Regional Engineering College, 1992

Professional Experience

• Associate Professor, Kelley School of Business, Indiana University, 2008–present
• Assistant Professor, Kelley School of Business, Indiana University, 2002–2008
• Assistant Consultant/Consultant, IBM Consulting Group, India, 1995–1997
• Software Engineer, Infosys Technologies Limited, India, 1994–1995

Awards, Honors & Certificates

• 2011 Provost''s Award for Undergraduate Research and Creative Activity, Indiana University
• 2011 Innovative Teaching Award, Kelley School of Business.
• 2006 and 2010, Trustees'' Teaching Award, Kelley School of Business.
• 2008 Harry C. Sauvain Teaching Award, Kelley School of Business.
• 2006 and 2007, Outstanding MSIS Faculty Award, awarded by the graduating MSIS Class.
• 2005 and 2008, Nominated for the best paper award at HICSS in the "Collaboration Systems and Technology" track.
• 2007 Finalist for the best paper award for "Usability of Online Services: The Role of Technology Readiness and Context" by the Decision Sciences Journal.
• 2003 SBC Fellow, Indiana University.
• 2000 Jim L. LaSalle Award for Teaching Excellence by a Graduate Student Instructor, Department of Management Information Systems, University of Arizona.

Selected Publications

• Khatri, Vijay, Sudha Ram, Richard T. Snodgrass, and Paolo Terenziani (2014), “Capturing Telic/Atelic Temporal Data Semantics: Generalizing Conventional Conceptual Models,” IEEE Transactions on Knowledge and Data Engineering, 26(3): 528-548.

Abstract Time provides context for all our experiences, cognition, and coordinated collective action.   Prior research in linguistics, artificial intelligence and temporal databases suggests the need to differentiate between temporal facts with goal-related semantics (i.e., telic) from those are intrinsically devoid of culmination (i.e., atelic).  To differentiate between telic and atelic data semantics in conceptual database design, we propose an annotation-based temporal conceptual model that generalizes the semantics of a conventional conceptual model.  Our temporal conceptual design approach involves: 1) capturing “what” semantics using a conventional conceptual model; 2) employing annotations to differentiate between telic and atelic data semantics that help capture “when” semantics; 3) specifying temporal constraints, specifically non-sequenced semantics, in the temporal data dictionary as metadata.  Our proposed approach provides a mechanism to represent telic/atelic temporal semantics using temporal annotations.  We also show how these semantics can be formally defined using constructs of the conventional conceptual model and axioms in first-order logic.  Via what we refer to as the “semantics of composition,” i.e., semantics implied by the interaction of annotations, we illustrate the logical consequences of representing telic/atelic data semantics during temporal conceptual design.

• Khatri, Vijay and Carol V. Brown (2010), “Designing Data Governance,” Communications of the ACM, Vol. 53, No. 1, pp. 148-152.

Abstract Organizations are becoming increasingly serious about the notion of "data as an asset" as they face increasing pressure for reporting a "single version of the truth." In a 2006 survey of 359 North American organizations that had deployed business intelligence and analytic systems, a program for the governance of data was reported to be one of the five success "practices" for deriving business value from data assets. In light of the opportunities to leverage data assets as well ensure legislative compliance to mandates such as the Sarbanes-Oxley (SOX) Act and Basel II, data governance has also recently been given significant prominence in practitioners'' conferences, such as TDWI (The Data Warehousing Institute) World Conference and DAMA (Data Management Association) International Symposium.The objective of this article is to provide an overall framework for data governance that can be used by researchers to focus on important data governance issues, and by practitioners to develop an effective data governance approach, strategy and design. Designing data governance requires stepping back from day-to-day decision making and focusing on identifying the fundamental decisions that need to be made and who should be making them. Based on Weill and Ross, we also differentiate between governance and management as follows:• Governance refers to what decisions must be made to ensure effective management and use of IT (decision domains) and who makes the decisions (locus of accountability for decision-making).• Management involves making and implementing decisions.For example, governance includes establishing who in the organization holds decision rights for determining standards for data quality. Management involves determining the actual metrics employed for data quality. Here, we focus on the former.Corporate governance has been defined as a set of relationships between a company''s management, its board, its shareholders and other stakeholders that provide a structure for determining organizational objectives and monitoring performance, thereby ensuring that corporate objectives are attained. Considering the synergy between macroeconomic and structural policies, corporate governance is a key element in not only improving economic efficiency and growth, but also enhancing corporate confidence. A framework for linking corporate and IT governance (see Figure 1) has been proposed by Weill and Ross.Unlike these authors, however, we differentiate between IT assets and information assets: IT assets refers to technologies (computers, communication and databases) that help support the automation of well-defined tasks, while information assets (or data) are defined as facts having value or potential value that are documented. Note that in the context of this article, we do not differentiate between data and information.Next, we use the Weill and Ross framework for IT governance as a starting point for our own framework for data governance. We then propose a set of five data decision domains, why they are important, and guidelines for what governance is needed for each decision domain. By operationalizing the locus of accountability of decision making (the "who") for each decision domain, we create a data governance matrix, which can be used by practitioners to design their data governance. The insights presented here have been informed by field research, and address an area that is of growing interest to the information systems (IS) research and practice community.

• Montoya, Mitzi, Anne P. Massey, and Vijay Khatri (2010), “Connecting IT Service Operations to Service Marketing Practices: Trust in IT Service Providers,” Journal of Management Information Systems, Vol. 26, No. 4, pp. 65-85.

Abstract The importance of building relationships with customers and trust in the services provider is well documented in the marketing literature. Conceptually, we extend this logic to the context of internal information technology (IT) services operations through the notion of the service delivery chain. The purpose of the study is to examine how key service mechanisms in operational IT implementation are related to employee perceptions of actual system benefits and trust in the IT services provider. We report on a study with 380 employees of 14 bank affiliates that were recently acquired by a bank holding company. The focus of the study is on postimplementation trust rather than preimplementation or initial trust, and the service provider is viewed as the object of trust rather than the technology. Our findings suggest that training, trial, and social influence are key service mechanisms an IT services provider can use to stimulate trust in the IT services provider and the realization of system benefits.

• Massey, Anne, Vijay Khatri, and Mitzi M. Montoya-Weiss (2007), “Usability of Online Services: The Role of Technology Readiness and Context,” Decision Sciences, Vol. 38, No. 2, pp. 277-308.

Abstract An important prerequisite for the success of any online service is ensuring that customers'' experience—via the interface—satisfies both sensory and functional needs. Developing interfaces that are responsive to customers'' needs requires a perspective on interface design as well as a deep understanding of the customers themselves. Drawing upon research in consumer behavior concerning consumer beliefs about technology, we deploy an alternative way to describe customers based on psychographic characteristics. Technology readiness (TR), a multidimensional psychographic construct, offers a way to segment online customers based upon underlying positive and negative technology beliefs. The core premise of this study is that the beliefs form the foundation for expectations of how things should work and how specific online service interfaces are evaluated by customers. At the same time, usability evaluations of specific online services might be contingent on contextual factors, specifically the type of site (hedonic vs. utilitarian) and access method (Web vs. wireless Web). The aspects of usability examined here are those incorporated into the usability metric and instrument based on the Microsoft Usability Guidelines (MUG). The results of an empirical study with 160 participants indicate that (i) TR customer segments vary in usability requirements and (ii) usability evaluations of specific online service interfaces are influenced by complex interactions among site type, access method, and TR segment membership. As organizations continue to expand their online service offerings, managers must recognize that the interface exists to serve the customers, so their design must be matched to market needs and TR.

• Khatri, Vijay, Iris Vessey, Sudha Ram, and V. Ramesh (2006), “Cognitive Fit between Conceptual Models and Internal Problem Representations: The Case of Geospatio-Temporal Conceptual Schema Comprehension,” IEEE Transactions on Professional Communication, Vol. 49, No. 2, June, pp. 109-127.

Abstract Geospatio-temporal conceptual models provide a mechanism to explicitly represent geospatial and temporal aspects of applications. Such models, which focus on both "what" and "when/where," need to be more expressive than conventional conceptual models (e.g., the ER model), which primarily focus on "what" is important for a given application. In this study, we view conceptual schema comprehension of geospatio-temporal data semantics in terms of matching the external problem representation (that is, the conceptual schema) to the problem-solving task (that is, syntactic and semantic comprehension tasks), an argument based on the theory of cognitive fit. Our theory suggests that an external problem representation that matches the problem solver''s internal task representation will enhance performance, for example, in comprehending such schemas. To assess performance on geospatio-temporal schema comprehension tasks, we conducted a laboratory experiment using two semantically identical conceptual schemas, one of which mapped closely to the internal task representation while the other did not. As expected, we found that the geospatio-temporal conceptual schema that corresponded to the internal representation of the task enhanced the accuracy of schema comprehension; comprehension time was equivalent for both. Cognitive fit between the internal representation of the task and conceptual schemas with geospatio-temporal annotations was, therefore, manifested in accuracy of schema comprehension and not in time for problem solution. Our findings suggest that the annotated schemas facilitate understanding of data semantics represented on the schema.

• Khatri, Vijay, Iris Vessey, V. Ramesh, Paul Clay, and Sung-Jin Park (2006), “Understanding Conceptual Schemas: Exploring the Role of Application and IS Domain Knowledge,” Information Systems Research, Vol. 17, No. 1, March, pp. 81-99.

Abstract Although information systems (IS) problem solving involves knowledge of both the IS and application domains, little attention has been paid to the role of application domain knowledge. In this study, which is set in the context of conceptual modeling, we examine the effects of both IS and application domain knowledge on different types of schema understanding tasks: syntactic and semantic comprehension tasks and schema-based problem-solving tasks. Our thesis was that while IS domain knowledge is important in solving all such tasks, the role of application domain knowledge is contingent upon the type of understanding task under investigation. We use the theory of cognitive fit to establish theoretical differences in the role of application domain knowledge among the different types of schema understanding tasks. We hypothesize that application domain knowledge does not influence the solution of syntactic and semantic comprehension tasks for which cognitive fit exists, but does influence the solution of schema-based problem-solving tasks for which cognitive fit does not exist. To assess performance on different types of conceptual schema understanding tasks, we conducted a laboratory experiment in which participants with high- and low-IS domain knowledge responded to two equivalent conceptual schemas that represented high and low levels of application knowledge (familiar and unfamiliar application domains). As expected, we found that IS domain knowledge is important in the solution of all types of conceptual schema understanding tasks in both familiar and unfamiliar applications domains, and that the effect of application domain knowledge is contingent on task type. Our findings for the EER model were similar to those for the ER model. Given the differential effects of application domain knowledge on different types of tasks, this study highlights the importance of considering more than one application domain in designing future studies on conceptual modeling.

• Ram, Sudha and Vijay Khatri (2005), “A Comprehensive Framework for Modeling Set-based Business Rules during Conceptual Database Design,” Information Systems, Vol. 30, No. 2, April, pp. 89-118.

Abstract Business rules are the basis of any organization. From an information systems perspective, these business rules function as constraints on a database helping ensure that the structure and content of the real world sometimes referred to as miniworld--is accurately incorporated into the database. It is important to elicit these rules during the analysis and design stage, since the captured rules are the basis for subsequent development of a business constraints repository. We present a taxonomy for set-based business rules, and describe an overarching framework for modeling rules that constrain the cardinality of sets. The proposed framework results in various types constraints, i.e.. attribute, class, participation, projection, co-occurrence, appearance and overlappinq, on a semantic model that supports abstractions like classification, generalization/specialization, aggregation and association. We formally define the syntax of our proposed framework in Backus-Naur Form and explicate the semantics using first-order logic. We describe partial ordering in the constraints and define the concept of metaconstraints, which can be used for automatic constraint consistency checking during the design stage itself. We demonstrate the practicality of our approach with a case study and show how our approach to modeling business rules seamlessly integrates into existing database design methodology. Via our proposed framework, we show how explicitly capturing data semantics will help bridge the semantic gap between the real world and its representation in an information system.

• Khatri, Vijay, Sudha Ram, and Richard T. Snodgrass (2004), “Augmenting a Conceptual Model with Geo-spatio-temporal Annotations,” IEEE Transactions on Knowledge and Data Engineering, Vol. 16, No. 11, November, pp. 1324-1338. 

Abstract While many real-world applications need to organize data based on space (e.g., geology, geomarketing, environmental modeling) and/or time (e.g., accounting, inventory management, personnel management), existing conventional conceptual models do not provide a straightforward mechanism to explicitly capture the associated spatial and temporal semantics. As a result, it is left to database designers to discover, design, and implement—on an ad hoc basis—the temporal and spatial concepts that they need. We propose an annotation-based approach that allows a database designer to focus first on nontemporal and nongeospatial aspects (i.e., "what”) of the application and, subsequently, augment the conceptual schema with geospatiotemporal annotations (i.e., "when” and "where”). Via annotations, we enable a supplementary level of abstraction that succinctly encapsulates the geospatiotemporal data semantics and naturally extends the semantics of a conventional conceptual model. An overarching assumption in conceptual modeling has always been that expressiveness and formality need to be balanced with simplicity. We posit that our formally defined annotation-based approach is not only expressive, but also straightforward to understand and implement.