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. / Informatics / MIT (Information Systems) / Unrestricted

In today’s turbulent business environment, an increasing number of businesses and organizations are finding themselves confronted with a crushing pressure to constantly reinvent themselves. Given such a business environment, coupled with high stakes and fierce competitiveness, it is quite unsurprising that most change efforts are reported to fail. Change communication, or rather the lack thereof, has been pointed out as one of the most important reasons. In recent years, many scholars have started to differentiate between participatory and programmatic change communication, elicited by a reconceptualization of change as continuous and emergent rather than episodic and planned. Participatory and programmatic are two diametrically different ways to approach change communication, and thus, they each require different tools, techniques and strategies. Several scholars have called attention to the shortage of tools, techniques and strategies suitable for participatory change communication in particular, however, empirical research remain scarce. At the same time, the data revolution is right upon us. Businesses that fail to harness the true potential of their data see themselves outcompeted by those who do. Even though data since long has been prophesied to transform management altogether, it is only in recent years that its application within the various subfields of management research has received serious attention by academia. However, the application of data in change management in general, and change communication in particular, has largely been left unaddressed.  In this thesis, we are exploring the role of data in the context of change communication through a case study at Skanska Rental, one of Sweden’s largest construction equipment rental companies. We delimit our study to only treat one of the most important aspects of data-driven communication; namely, data visualization. Our findings indicate that data visualization can facilitate change communication by encouraging it to be participatory. In particular, we find that data visualization has the merit of enthusing its audience, aligning the perception of the current state of affairs, reinforcing a data-driven culture, and facilitating interpersonal communication. We also call attention to three important considerations; (1) the democratization of data requires transparency trade-offs, (2) data visualization cannot replace interpersonal communication, but at most facilitate it, and (3) communication, although one of the most important, is not the sole precursor of successful change. We conclude our thesis by addressing the practical and theoretical implications of our conclusions, and lastly, by suggesting directions for future research in data-driven change communication. / I dagens turbulenta omvärld utsätts företag och organisationer för ett ständigt förändringstryck som inte visar några tecken på att avta. Givet ett sådant klimat, där mycket står på spel och där konkurrensen är förkrossande, är det inte förvånande att de flesta förändringsinitiativ misslyckas. Förändringskommunikation, eller snarare bristen därav, har ofta pekats ut som en av de viktigaste orsakerna till detta. På senare tid har många forskare börjat göra skillnad mellan två olika typer av förändringskommunikation: deltagande respektive programmatisk. Att betrakta förändringskommunikation genom denna dikotomi har föranletts av en ny syn på förändring i stort; snarare än att se förändring som episodisk och planerad har många forskare istället konceptualiserat förändring som kontinuerlig och framväxande. Olika tekniker, verktyg och strategier lämpar sig olika väl för de två olika typerna av förändringskommunikation. Många forskare har varnat för en bristande förståelse för i synnerhet de tekniker, verktyg och strategier som lämpar sig för den deltagande typen av förändringskommunikation. Under de senare åren har det förändringstryck många företag och organisationer står inför snarast ökat i styrka på grund av den datarevolution vi befinner oss mitt upp i. Trots att det sedan länge har förutspåtts att data har potentialen att vända spelplanen för företagsledning upp och ner, så är det bara på senare år som ämnet har åtnjutit företagsledningsforskningens fulla uppmärksamhet. Däremot finns det fortfarande ett underskott på akademisk forskning som adresserar hur data kan användas inom förändringsledning i allmänhet och förändringskommunikation i synnerhet. I denna masteruppsats utforskar vi den roll som data kan spela inom förändringskommunikation. Vårt empiriska material inhämtar vi genom en case-studie hos Skanska Rental, ett av Sveriges största uthyrningsföretag inom byggbranschen. Vi avgränsar vår studie genom att endast behandla en utav de viktigaste aspekterna av data-driven kommunikation, nämligen datavisualisering. Våra resultat indikerar att datavisualisering kan underlätta förändringskommunikation genom att göra den deltagande. I synnerhet finner vi att datavisualisering har potential att entusiasmera dess publik, linjera bilden av nuläget, förstärka en data-driven organisationskultur och fungera som en utgångspunkt för mellanmänsklig kommunikation. Vi identifierar också tre viktiga reservationer mot denna slutsats; (1) demokratisering av data fordrar ställningstagande gällande transparens, (2) datavisualisering kan inte ersätta mellanmänsklig kommunikation, och (3) kommunikation, om än viktig, är inte den enda förutsättningen för framgångsrik förändring. Vi avslutar vår uppsats med att adressera de praktiska och teoretiska implikationer som vår slutsats resulterar i, samt föreslår inriktningen för framtida forskning inom data-driven förändringskommunikation.