CHALLENGES OF ADVANCED ANALYTICS MATURITY MODEL DEVELOPMENT

Significance to understand the advanced analytics ecosystem maturity is increasing caused by constantly growing data volumes and demand for advanced analytics including automated decision making based on data or process automation. The analytics maturity assessment helps to identify strengths and weaknesses of the organization’s analytics ecosystem and can provide detailed action plan to move to the next level. The focus of the paper is to review and analyse analytics maturity models to assess their application as frame to build a new analytics maturity model or replicate with time adjustment any of reviewed models. The literature review and publicly available assessment models provided by analytics sector were used to review and analyse analytics maturity models.  Fifteen models were reviewed and four of them analysed by twelve characteristics. Summary of four models includes analytics maturity levels, domains, accessibility of questionnaire, discloser of maturity level detection and authors assessment of several characteristics. Comprehensive descriptions of analytics maturity levels were available for many models. Solid recommendation sets for each maturity level provided for the most disclosed models. One of the most important components, approach to detect specific maturity level, was not transparent or disclosed with limitations. However, it is possible to develop a new model or replicate in some extent based on models reviewed in this paper, but it requires extensive professional experience in advanced analytics and related functions.

READINESS OF LATVIA’S ORGANIZATIONS FOR ADVANCED ANALYTICS

The advanced analytics is one of the core tools to provide competitive advantage, sustainable development and foster productivity of the organization. Digital transformation and advanced analytics are two key trends in the emerging age of data, analytics, and automation. Digital transformation is the process of transforming how businesses operate when faced with digital disruption. Advanced analytics is the application of predictive and prescriptive models to analyse large, complex datasets in order to make critical business decisions. The focus of the paper is to assess the maturity level of advanced analytics in the organizations of Latvia by region, size and industry. Assessment was done by several domains like Organization, People, Data, Analytics, Technologies. The quantitative online survey was performed to assess the readiness of Latvia’s organizations for advanced analytics. The questionnaire was developed based on an academic literature review, reports and publications by researchers, analytical sector, industry experts and Author’s professionals experience in advanced analytics industry. The overall readiness level of Latvia’s organizations is 2.4 in 5 points scale. It differs by region, size of the organization and industry. Most of organizations do not have Analytics strategy, majority use spreadsheets based analytical tools, half of organizations use mostly only internal data, more than third part of organizations do not have any analytical resources. It leads to conclusion that majority of Latvia’s organizations are far from ability to improve productivity, be able to maximize the potential of the digital environment, to exploit data to make data-driven and automated decisions and are far from 21st century digital opportunities. Thus, puts under danger the sustainability of the organizations itself.

Identifying Interesting Knowledge Factors from Big Data for Effective E-Market Prediction

Knowledge management plays an important role in disseminating valuable information. Knowledge creation involves analyzing data and transforming information into knowledge. Knowledge management plays an important role in improving organizational decision-making. It is evident that data mining and predictive analytics contribute a major part in the creation of knowledge and forecast the future outcomes. The ability to predict the performance of the advertising campaigns can become an asset to the advertisers. Tools like Google analytics were able to capture user logs. Large amounts of information ranging from visitor location, visitor flow throughout the website to various actions the visitor performs after clicking an ad resides in those logs. This research approach is an effort to identify key knowledge factors in the marketing sector that can further be optimized for effective e-market prediction

Mining the FX electronic inter-dealer market

This paper examines the feasibility of applying data mining techniques to testing market efficiency hypothesis using a high frequency, up to one thousand of a second, electronic brokerage data. Results suggest the existence of a pattern of negative autocorrelation in returns of DEM/USD over relative short lags (less than 40 seconds). However, this pattern is not feasible by two reasons: (1) the structure of autocorrelation pattern is inconsistent and changes too rapidly (2) the largest potential speculative profit is smaller than the regulated tick size. These results indicate that dealers have engaged in any potential profitable speculations based on past price information

A Predictive Analytic Model for Value Chain Management

Value chain management has gone through various stages of automation, integration and optimization in the past decades. While an optimization model for value chain deals with business scenarios under known circumstances, a predictive value chain model deals with probable circumstances in the future. Predictive analytics is succeeding optimization in the evolution of technologies supporting value chain management. This paper proposes a forward looking value creation model that combines the important concepts of value chain management and predictive analytics. An enterprise model for value chain predictive analytics that facilitates the convergence of information, operations and analytics is presented

Advanced Analytics Success Factors - A Case Study

Companies are increasingly taking into use advanced analytics solutions. Advanced analytics solutions are computer programs that analyze data, make predictions on the future, and give optimization-based recommendations on courses of action for achieving pre-determined business goals. Analytics solutions employ sophisticated statistical and mathematical models, and are often offered by third parties. Companies use analytics solutions to improve the efficiency of their operations. This thesis studies whether the distinction between analytics and advanced analytics made in literature is well-founded. The second aim of this study is to find out, what contributes to an analytics initiative’s success. The study begins with a literature review synthesizing the findings of previous analytics research. The resulting synthesis identifies four distinct stages in an analytics project. They are acquiring data, transforming it into insights, communicating the insights, making business decisions, and finally implementing the decisions. Factors that contribute to each stage’s success are identified. The hypotheses that were developed in the theoretical part of the thesis are subsequently tested empirically using the single case study method and semi-structured interviews. The case study confirms the findings of earlier research. Analytics can be viewed as a process with clearly identifiable stages. Specific measures can be taken to improve the success of each stage. The results obtained suggest that an analytics initiative should always be preceded by a thorough goal definition stage. This is a finding that earlier research has not emphasized sufficiently. The study offers business executives a clear roadmap for managing analytics initiatives. It formulates clear action points and allocates parties the responsibility for executing them. The study also highlights some ordinary pitfalls preventing companies from fully benefitting from the results of analytics initiatives. Finally, the study points out new interesting research opportunities in the intersection of information systems science and cognitive science. A key difficulty in using analytics effectively is that the reasoning behind the insights created by the solutions are often complex. Cognitive science could provide us tools for making the insights easier to digest. Lastly, the study highlights that process decoupling will eventually be applied to analytics initiatives. Future studies should research how the stages of an analytics initiative can be separated from each other, and outsourced to parties performing them the most effectively

Deteção de fraude em acidentes de trabalho no Munícipio de Oeiras

Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Risk Analysis and ManagementOs acidentes de trabalho em Portugal têm vindo a aumentar de forma gradual. Paralelo a este fenómeno, o conceito de fraude é cada vez mais abordado por ser um tema que preocupa as organizações públicas e privadas, devido à sua difícil deteção e prevenção. A utilização de técnicas para detetá-las significa identificar tendências gerais de comportamentos suspeitos ou possíveis de fraude. É neste contexto que se insere esta tese, que apresenta um modelo preditivo capaz de prever a ocorrência de fraude em acidentes de trabalho no município de Oeiras. De forma a cumprir o objetivo foi recolhido o histórico dos acidentes ocorridos nos últimos cinco anos na organização e aplicado os algoritmos estudados na revisão de literatura. Através da análise e da comparação dos modelos construídos, é possível concluir que a sua eficácia ficou aquém do esperado. No entanto, reproduzindo a mesma análise para uma base de dados segregada por apenas uma categoria de lesão, foram obtidos melhores resultados.Occupational accidents in Portugal have been gradually increasing. Analogous to this increase, the concept of fraud is increasingly addressed because it is a topic that concerns public and private organizations, due to its difficult detection and prevention. Using techniques to detect them requires identification of general trends in suspicious or possible fraud behaviour. It is in this context that this thesis is inserted, presenting a predictive model capable of predicting the occurrence of fraud in work accidents in the municipality of Oeiras. In order to fulfil the objective, the accidents that occurred in the last five years in the organization were collected and the algorithms studied in the literature review were applied. Through the analysis and comparison of the built models it is possible to conclude that its effectiveness was below the expected. However, reproducing the same analysis for a database segregated by only one category of injury, better results were obtained

Advanced analytical methods for fraud detection: a systematic literature review

The developments of the digital era demand new ways of producing goods and rendering services. This fast-paced evolution in the companies implies a new approach from the auditors, who must keep up with the constant transformation. With the dynamic dimensions of data, it is important to seize the opportunity to add value to the companies. The need to apply more robust methods to detect fraud is evident. In this thesis the use of advanced analytical methods for fraud detection will be investigated, through the analysis of the existent literature on this topic. Both a systematic review of the literature and a bibliometric approach will be applied to the most appropriate database to measure the scientific production and current trends. This study intends to contribute to the academic research that have been conducted, in order to centralize the existing information on this topic

A marketing view of the customer value: Customer lifetime value and customer equity

Throughout this research the customer valuation trend in marketing is going to be reviewed, emphasizing Customer Lifetime Value and Customer Equity measures. The main theoretical contributions in the development and evolution of the Customer Lifetime Value concept are analysed. Customer Lifetime Value is also differentiated from Customer Equity and Customer Profitability analysis to estimate customer value in terms of firm profitability. Customer Lifetime Value and Customer Equity concepts are formally defined. Additionally, a classification of a set of published researches into Customer Lifetime Value and/or Customer Equity is developed. This classification has been posited according to several criteria that serves as a guide to key requirements for developing these types of models. Finally, several conclusions, suggestions and future research streams are highlighted