Technological advances, particularly in the areas of processing and storage have made it possible to gather an unprecedented vast and heterogeneous amount of data. The evolution of the internet, particularly Social media, the internet of things, and mobile technology together with new business trends has precipitated us in the age of Big data and add complexity to the integration task. The objective of this study has been to explore the question of data heterogeneity trough the deployment of a systematic literature review methodology. The study surveys the drivers of this data heterogeneity, the inner workings of it, and it explores the interrelated fields and technologies that deal with the capture, organization and mining of this data and their limitations. Developments such as Hadoop and its suit components together with new computing paradigms such as cloud computing and virtualization help palliate the unprecedented amount of rapidly changing, heterogeneous data which we see today. Despite these dramatic developments, the study shows that there are gaps which need to be filled in order to tackle the challenges of Web 3.0.

Organizational decisions are significant to an organizations' future. Research shows that many organizations tend to make decisions based on previous experiences, ‘gut feeling’ and assumptions rather than facts. This is troubling since decision-makers leave the organization’s future to the hands of chance. It is impossible to foresee the future, but accurate factual data can be of guidance towards a successful path. The food industry is one industry which must undertake tremendous efficiency efforts due to current climate change in order to sustain a growing population while resources are diminishing. Therefore, organizations must be efficient with resource allocation. Here accurate decisions must be made. Much research has been conducted into the food industry regarding sustainability practices, and data-driven approaches have become widely regarded as promising in sustainability practices due to the new industry paradigm of industry 4.0 and availability of data. Fact-based decision making based on the collection, storage, and analysis of data has been widely studied and coined Business Intelligence & Analytics (BI&A). This study is based upon Scandinavian food producers and processors and explores the current adoption and utilization of BI&A towards efficient production and its challenges. Through an explorative approach with interviews and analysis of organization reports, the current progress into BI&A towards efficient production and accompanying challenges were identified. Findings show that BI&A is applied to varying degrees for storage of data, and to monitor, analyze, and identify targets of action within energy consumption, food waste, material waste, and supply chain. Challenges identified were integration both internally within organizations and externally in the supply chain, economic, and leadership. To manage these challenges, managerial complications are provided. Further research could investigate the data collection processes in production, understanding the role of BI&A for top managers in decision making, investigate BI&A strategies towards efficient production, and provide empirical results from other parts of the supply chain.

Over the past years, the value of data and DDDM have increased significantly as technological advancements have made it possible to store and analyze large amounts of data at a reasonable cost. This has resulted in completely new business models that has disrupt whole industries. DDDM allows businesses to rely their decisions on data, as opposed to on gut feeling. Up until this point, literature is eligible to provide a general view of what are the major challenges corporations encounter when implementing a DDDM strategy. However, as the field is still rather new, the challenges identified are yet very general and many corporations, especially B2B MNCs selling consumer goods, seem to struggle with this implementation. Hence, a single-case study on such a corporation, named Alpha, was carried out with the purpose to explore what are their major challenges in this process. Semi-structured interviews revealed evidence of four major findings, whereas, execution and organizational culture were supported in existing literature, however, two additional findings associated with organizational structure and consumer behavior data were discovered in the case of Alpha. Based on this, the conclusions drawn were that B2B MNCs selling consumer goods encounter the challenges of identifying local markets as frontrunners for strategies such as the one to become more data-driven, as well as the need to find a way to retrieve consumer behavior data. With these two main challenges identified, it can provide a starting point for managers when implementing DDDM strategies in B2B MNCs selling consumer goods in the future.

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