Department of Management

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Author: search.filters.author.Tikoria, JyotiSubject: search.filters.subject.Business analytics

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    Dimensions influencing business intelligence and analytics maturity: a critical analysis
    (Inder Science, 2021-05) Tikoria, Jyoti
    The importance of business intelligence and analytics (BI&A) as a capability in an organisation has grown over the years. In order to assess the state of BI&A, managers today need to identify the critical dimensions which influence the maturity of BI&A capability. A wide range of maturity models (MMs) have been developed, for the purpose of assessment of BI&A capability maturity. Managers may find it difficult to select the appropriate MM to use especially with the changing characteristics of data over time and advent of big data. Each of these models has multiple dimensions which are not necessarily exhaustive and distinct. This study consolidates the large number of dimensions found across 29 BI&A MMs in extant literature. With help of an expert panel, a set of six distinct and critical dimensions needed for assessing BI&A capability in organisations have been identified. Qualitative research tool NVIVO is used for analysis of the research articles.
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    Measuring Business Intelligence & Analytics capability maturity: A study of organizations in India
    (Inder Science, 2021) Tikoria, Jyoti
    While there have been studies for development and evaluation of maturity models for measuring business intelligence and analytics (BI&A) capability maturity of organisations, there has been no empirical study done solely for assessing the BI&A capability maturity of organisations. The purpose of this paper is to measure the BI&A capability maturity in organisations. This has been done through an empirical study with a sample of 145 organisations from different industry sectors in India. BI&A capability maturity has been assessed using six influencing factors. Analysis has been done based on industry sectors and segments, where findings have indicated some distinct industry segments with varying BI&A capability maturity. Organisations have also been grouped based on the maturity of six factors using K-means clustering technique. Each cluster was found to be representing a different level of maturity. The clusters have been reorganised to bring out interesting insights about BI&A capability maturity. The study presents important managerial implications.