Business process improvement by means of Big Data based Decision Support Systems: a case study on Call Centers

Big Data is a rapidly evolving and maturing field which places significant data storage and processing power at our disposal. To take advantage of this power, we need to create new means of collecting and processing large volumes of data at high speed. Meanwhile, as companies and organizations, such as health services, realize the importance and value of joined-up thinking across supply chains and healthcare pathways, for example, this creates a demand for a new type of approach to Business Activity Monitoring and Management. This new approach requires Big Data solutions to cope with the volume and speed of transactions across global supply chains. In this paper we describe a methodology and framework to leverage Big Data and Analytics to deliver a Decision Support framework to support Business Process Improvement, using near real-time process analytics in a decision-support environment. The system supports the capture and analysis of hierarchical process data, allowing analysis to take place at different organizational and process levels. Individual business units can perform their own process monitoring. An event-correlation mechanism is built into the system, allowing the monitoring of individual process instances or paths

Methodologies in Predictive Visual Analytics

abstract: Predictive analytics embraces an extensive area of techniques from statistical modeling to machine learning to data mining and is applied in business intelligence, public health, disaster management and response, and many other fields. To date, visualization has been broadly used to support tasks in the predictive analytics pipeline under the underlying assumption that a human-in-the-loop can aid the analysis by integrating domain knowledge that might not be broadly captured by the system. Primary uses of visualization in the predictive analytics pipeline have focused on data cleaning, exploratory analysis, and diagnostics. More recently, numerous visual analytics systems for feature selection, incremental learning, and various prediction tasks have been proposed to support the growing use of complex models, agent-specific optimization, and comprehensive model comparison and result exploration. Such work is being driven by advances in interactive machine learning and the desire of end-users to understand and engage with the modeling process. However, despite the numerous and promising applications of visual analytics to predictive analytics tasks, work to assess the effectiveness of predictive visual analytics is lacking. This thesis studies the current methodologies in predictive visual analytics. It first defines the scope of predictive analytics and presents a predictive visual analytics (PVA) pipeline. Following the proposed pipeline, a predictive visual analytics framework is developed to be used to explore under what circumstances a human-in-the-loop prediction process is most effective. This framework combines sentiment analysis, feature selection mechanisms, similarity comparisons and model cross-validation through a variety of interactive visualizations to support analysts in model building and prediction. To test the proposed framework, an instantiation for movie box-office prediction is developed and evaluated. Results from small-scale user studies are presented and discussed, and a generalized user study is carried out to assess the role of predictive visual analytics under a movie box-office prediction scenario.Dissertation/ThesisDoctoral Dissertation Engineering 201

Methodologies in Predictive Visual Analytics

abstract: Predictive analytics embraces an extensive area of techniques from statistical modeling to machine learning to data mining and is applied in business intelligence, public health, disaster management and response, and many other fields. To date, visualization has been broadly used to support tasks in the predictive analytics pipeline under the underlying assumption that a human-in-the-loop can aid the analysis by integrating domain knowledge that might not be broadly captured by the system. Primary uses of visualization in the predictive analytics pipeline have focused on data cleaning, exploratory analysis, and diagnostics. More recently, numerous visual analytics systems for feature selection, incremental learning, and various prediction tasks have been proposed to support the growing use of complex models, agent-specific optimization, and comprehensive model comparison and result exploration. Such work is being driven by advances in interactive machine learning and the desire of end-users to understand and engage with the modeling process. However, despite the numerous and promising applications of visual analytics to predictive analytics tasks, work to assess the effectiveness of predictive visual analytics is lacking. This thesis studies the current methodologies in predictive visual analytics. It first defines the scope of predictive analytics and presents a predictive visual analytics (PVA) pipeline. Following the proposed pipeline, a predictive visual analytics framework is developed to be used to explore under what circumstances a human-in-the-loop prediction process is most effective. This framework combines sentiment analysis, feature selection mechanisms, similarity comparisons and model cross-validation through a variety of interactive visualizations to support analysts in model building and prediction. To test the proposed framework, an instantiation for movie box-office prediction is developed and evaluated. Results from small-scale user studies are presented and discussed, and a generalized user study is carried out to assess the role of predictive visual analytics under a movie box-office prediction scenario.Dissertation/ThesisDoctoral Dissertation Engineering 201

An Integrated Model of Business Intelligence & Analytics Capabilities and Organizational Performance

Organizations can leverage business intelligence and analytics (BI&A) to transform themselves through a holistic integration process. Contrary to this proposition, many organizations implement BI&A without aligning or integrating it with organizational strategies. Some implement BI&A in a very ad hoc manner without any plans to leverage it. From a research point of view, we lack an integrated framework that can inform both academics and practitioners about adroit applications with business intelligence and analytics capabilities in organizations. We examine what significant BI&A capabilities organizations need to create value from BI&A. We conceptualize second-order constructs that affect the BI&A value-creation process: innovation infrastructure capability, customer process capability, business-to-business (B2B) process capability, and integration capability. We propose that these higher-order BI&A capabilities influence organizational performance through BI&A effectiveness’s the mediation effect. We developed a questionnaire instrument and collected data from 154 firms in India. Partial least squares analysis provides broad support for our hypotheses. Our contributions include identifying and empirically assessing key BI&A capabilities that directly impact how effectively an organization implements BI&A

Combining Big Data And Traditional Business Intelligence – A Framework For A Hybrid Data-Driven Decision Support System

Since the emergence of big data, traditional business intelligence systems have been unable to meet most of the information demands in many data-driven organisations. Nowadays, big data analytics is perceived to be the solution to the challenges related to information processing of big data and decision-making of most data-driven organisations. Irrespective of the promised benefits of big data, organisations find it difficult to prove and realise the value of the investment required to develop and maintain big data analytics. The reality of big data is more complex than many organisations’ perceptions of big data. Most organisations have failed to implement big data analytics successfully, and some organisations that have implemented these systems are struggling to attain the average promised value of big data. Organisations have realised that it is impractical to migrate the entire traditional business intelligence (BI) system into big data analytics and there is a need to integrate these two types of systems. Therefore, the purpose of this study was to investigate a framework for creating a hybrid data-driven decision support system that combines components from traditional business intelligence and big data analytics systems. The study employed an interpretive qualitative research methodology to investigate research participants' understanding of the concepts related to big data, a data-driven organisation, business intelligence, and other data analytics perceptions. Semi-structured interviews were held to collect research data and thematic data analysis was used to understand the research participants’ feedback information based on their background knowledge and experiences. The application of the organisational information processing theory (OIPT) and the fit viability model (FVM) guided the interpretation of the study outcomes and the development of the proposed framework. The findings of the study suggested that data-driven organisations collect data from different data sources and process these data to transform them into information with the goal of using the information as a base of all their business decisions. Executive and senior management roles in the adoption of a data-driven decision-making culture are key to the success of the organisation. BI and big data analytics are tools and software systems that are used to assist a data-driven organisation in transforming data into information and knowledge. The suggested challenges that organisations experience when they are trying to integrate BI and big data analytics were used to guide the development of the framework that can be used to create a hybrid data-driven decision support system. The framework is divided into these elements: business motivation, information requirements, supporting mechanisms, data attributes, supporting processes and hybrid data-driven decision support system architecture. The proposed framework is created to assist data-driven organisations in assessing the components of both business intelligence and big data analytics systems and make a case-by-case decision on which components can be used to satisfy the specific data requirements of an organisation. Therefore, the study contributes to enhancing the existing literature position of the attempt to integrate business intelligence and big data analytics systems.Dissertation (MIT (Information Systems))--University of Pretoria, 2021.InformaticsMIT (Information Systems)Unrestricte

NEW ARTIFACTS FOR THE KNOWLEDGE DISCOVERY VIA DATA ANALYTICS (KDDA) PROCESS

Recently, the interest in the business application of analytics and data science has increased significantly. The popularity of data analytics and data science comes from the clear articulation of business problem solving as an end goal. To address limitations in existing literature, this dissertation provides four novel design artifacts for Knowledge Discovery via Data Analytics (KDDA). The first artifact is a Snail Shell KDDA process model that extends existing knowledge discovery process models, but addresses many existing limitations. At the top level, the KDDA Process model highlights the iterative nature of KDDA projects and adds two new phases, namely Problem Formulation and Maintenance. At the second level, generic tasks of the KDDA process model are presented in a comparative manner, highlighting the differences between the new KDDA process model and the traditional knowledge discovery process models. Two case studies are used to demonstrate how to use KDDA process model to guide real world KDDA projects. The second artifact, a methodology for theory building based on quantitative data is a novel application of KDDA process model. The methodology is evaluated using a theory building case from the public health domain. It is not only an instantiation of the Snail Shell KDDA process model, but also makes theoretical contributions to theory building. It demonstrates how analytical techniques can be used as quantitative gauges to assess important construct relationships during the formative phase of theory building. The third artifact is a data mining ontology, the DM3 ontology, to bridge the semantic gap between business users and KDDA expert and facilitate analytical model maintenance and reuse. The DM3 ontology is evaluated using both criteria-based approach and task-based approach. The fourth artifact is a decision support framework for MCDA software selection. The framework enables users choose relevant MCDA software based on a specific decision making situation (DMS). A DMS modeling framework is developed to structure the DMS based on the decision problem and the users\u27 decision preferences and. The framework is implemented into a decision support system and evaluated using application examples from the real-estate domain

Management decision making in the age of big data : an exploration of the roles of analytics and human judgment : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Management at Massey University, Auckland, New Zealand

This thesis explores the effects of data analytics and human judgment on management decision making in an increasingly data-driven environment. In recent years, the topics of big data and advanced analytics have gained traction and wide-spread interest among practitioners and academics. Today, big data is considered a buzzword by some and an essential prerequisite for future business success by others. Recent research highlights the potential of big data analytics for decision making, but also points out critical challenges and risks. The aim of this research is to take an in-depth look at management decision making by using qualitative case studies and critical incidents to carefully examine managers' decision-making processes. This exploration evolves around the two main research questions: i) How do managers perceive the role of advanced analytics and big data in the decision-making process? ii) How do managers perceive the alignment of advanced analytics and big data with more traditional decision-making approaches such as human judgment? The content and thematic analyses of data from 25 semi-structured interviews with managers, executives, and business analysts from nine organizations provided several key insights. Managers were found to rely on data and human judgment in their decision making to varying extents and in different roles. The processes followed by the decision makers depended on the decisions at hand, the managers’ characteristics and preferences, as well as environmental factors. The findings empirically support the development of an ecological systems framework, which provides a holistic picture of managerial decision making in the age of big data. The study contributes by applying the dual process theory to the context of data-driven decision making. Practical implications for organizations are derived from the findings and identify organizational considerations and prerequisites. The influence of the managers’ environments on decision making emphasizes the organizations’ need to utilize a holistic approach when adopting a data-driven decision-making culture

Asiakaslähtöinen analytiikkatuen arviointi

Analytiikka on tärkeä osa organisaatioiden päivittäistä arkea ja dataa pyritään hyödyntämään erilaisten analyysien ja analytiikkatyökalujen avulla. Analytiikan tärkeyden takia sen elinkaarta on pyritty hallitsemaan erilaisin elinkaarimallien avuilla ja vaikka alalla on useita erilaisia malleja elinkaarien ymmärtämiseen, niin niiden käyttö on vaihtelevaa ja riippuu paljon kyseessä olevasta organisaatiosta. Elinkaarimallit keskittyvät myös pitkälti erilaisten analytiikkaratkaisujen kehittämiseen ja tuotantoon viennin jälkeistä tukemisen vaihetta kuvattu ja tutkittu kirjallisuudessa vähäisesti. Tässä tutkimuksessa tutkittiin analytiikan tukemista analytiikan elinkaarimallin CRISP-DM:n (The Cross Industry Standard for Process for Data Mining) laajennettuun versioon pohjaten. CRISP-DM laajennettu versio sisältää viimeisessä vaiheessaan erilaisia analytiikan tukemiseen liittyviä tehtäviä, jotka toimivat tutkimuksen teoreettisena viitekehyksenä. Teorian pohjalta muodostettiin haastattelurunko, jonka avulla haastateltiin eri organisaatioiden analytiikan kokonaisvaltaisia asiantuntijoita ja kerättiin kokemuksia ja huomioita analytiikan elinkaaresta ja erityisesti sen tukemisesta. Haastattelujen lisäksi tutkimuksessa esitellään tarpeellisessa laajuudessa analytiikkaan, sen tukemiseen ja elinkaarimalleihin liittyvä teoria ja olemassa oleva tutkimus. Haastatteluiden perusteella tunnistettiin erilaisia tukemisen malleja, tukemiseen liittyviä haasteita, analytiikkaratkaisujen ajankohtaisuuteen liittyviä huomioita ja haasteita. Haastatteluissa korostui se, että onnistuneen tuen toimittamiseksi analytiikkatiimien kommunikaatio liiketoiminnan kanssa oli tärkeää. Haasteiksi nostettiin analytiikan tekijöiden vaihtuvuus, analytiikkaratkaisujen suuri määrä, liiketoiminnan omistajuuden puute suhteessa analytiikkaratkaisuihin ja resurssien vähäisyys. Analytiikan paremmaksi tukemiseksi nostettiinkin esille liiketoiminnan syvempi sitoutuminen analytiikkaratkaisujen tukemiseen ja ylläpitämiseen. Analytiikan liiketoimintahyötyjen arviointi oli haastateltavien organisaatiossa alkeellista. Tulosten vertailu valittuun elinkaarimalliin (CRISP-DM) paljasti sen, että mallin tarjoamat tehtävät kohtasivat osittain haastateltavien todellisuuden kanssa, mutta osittain mallin tarjoama jäi ohueksi.Analytics is an important part of organizations' daily routines, and data is sought to be utilized through various analyses and analytic tools. Due to the importance of analytics, its lifecycle has been managed with the help of various lifecycle models. Although there are several different models for understanding lifecycles in the field, their usage varies and depends heavily on the organization in question. Lifecycle models also focus largely on the development and support phase of the analytics has been described and studied to a limited extend in the literature. This study investigated the support of analytics based on an extended version of the analytics lifecycle model CRISP-DM (The Cross Industry Standard for Process for Data Mining). The extended version of CRISP-DM includes various tasks related to supporting analytics in its final phase, which serve as the theoretical framework for the study. Based on the theory, an interview frame was formed, which was used to interview analytics experts from different organizations, collecting experiences and insights on the analytics lifecycle and specifically its support. In addition to the interviews, the study presents the necessary extent of theory and existing research related to analytics, its support, and lifecycle models. Based on the interviews, various support models, challenges related to support, observations on the quality over time of analytics solutions, and challenges of relevance of analytics were identified. The interviews highlighted the importance of effective communication between analytics teams and the business for delivering successful support. Challenges included turnover of analytics personnel, a large number of analytics solutions, a lack of ownership of analytics solutions in relation to the business, and limited resources. To improve support for analytics, deeper commitment from the business in supporting and maintaining analytics solutions was emphasized. Assessment of the business benefits of analytics was rudimentary in the inter-viewed organizations. Comparing the results to the chosen lifecycle model (CRISP-DM) revealed that the tasks provided by the model partly aligned with the reality of the interviewees, but partly fell short

Applied business analytics approach to IT projects – Methodological framework

The design and implementation of a big data project differs from a typical business intelligence project that might be presented concurrently within the same organization. A big data initiative typically triggers a large scale IT project that is expected to deliver the desired outcomes. The industry has identified two major methodologies for running a data centric project, in particular SEMMA (Sample, Explore, Modify, Model and Assess) and CRISP-DM (Cross Industry Standard Process for Data Mining). More general, the professional organizations PMI (Project Management Institute) and IIBA (International Institute of Business Analysis) have defined their methods for project management and business analysis based on the best current industry practices. However, big data projects place new challenges that are not considered by the existing methodologies. The building of end-to-end big data analytical solution for optimization of the supply chain, pricing and promotion, product launch, shop potential and customer value is facing both business and technical challenges. The most common business challenges are unclear and/or poorly defined business cases; irrelevant data; poor data quality; overlooked data granularity; improper contextualization of data; unprepared or bad prepared data; non-meaningful results; lack of skill set. Some of the technical challenges are related to lag of resources and technology limitations; availability of data sources; storage difficulties; security issues; performance problems; little flexibility; and ineffective DevOps. This paper discusses an applied business analytics approach to IT projects and addresses the above-described aspects. The authors present their work on research and development of new methodological framework and analytical instruments applicable in both business endeavors, and educational initiatives, targeting big data. The proposed framework is based on proprietary methodology and advanced analytics tools. It is focused on the development and the implementation of practical solutions for project managers, business analysts, IT practitioners and Business/Data Analytics students. Under discussion are also the necessary skills and knowledge for the successful big data business analyst, and some of the main organizational and operational aspects of the big data projects, including the continuous model deployment