The impact of big data analytics on firms’ high value business performance

Big Data Analytics (BDA) is an emerging phenomenon with the reported potential to transform how firms manage and enhance high value businesses performance. The purpose of our study is to investigate the impact of BDA on operations management in the manufacturing sector, which is an acknowledged infrequently researched context. Using an interpretive qualitative approach, this empirical study leverages a comparative case study of three manufacturing companies with varying levels of BDA usage (experimental, moderate and heavy). The information technology (IT) business value literature and a resource based view informed the development of our research propositions and the conceptual framework that illuminated the relationships between BDA capability and organizational readiness and design. Our findings indicate that BDA capability (in terms of data sourcing, access, integration, and delivery, analytical capabilities, and people’s expertise) along with organizational readiness and design factors (such as BDA strategy, top management support, financial resources, and employee engagement) facilitated better utilization of BDA in manufacturing decision making, and thus enhanced high value business performance. Our results also highlight important managerial implications related to the impact of BDA on empowerment of employees, and how BDA can be integrated into organizations to augment rather than replace management capabilities. Our research will be of benefit to academics and practitioners in further aiding our understanding of BDA utilization in transforming operations and production management. It adds to the body of limited empirically based knowledge by highlighting the real business value resulting from applying BDA in manufacturing firms and thus encouraging beneficial economic societal changes

Developing and Deriving Value from Big Data Analytics Capabilities

In this big data age, big data analytics (BDA) has come to occupy a large role in becoming a major competitive differentiator for companies with many companies significantly accelerating the pace of their investments in BDA (Abbasi et al., 2016). As companies increasingly bet on BDA as the next competitive frontier, there is an imminent need for business leaders to clearly understand and rationalize the economic value gained from costly BDA investments by measuring their impact on objective measures of firm performance (Mikalef et al., 2020). Borrowing from prior empirical literature on IT capabilities and economic value, some scholars have drawn a positive relationship between BDA capabilities, which are built by assembling an array of resources that include a mix of big data, technology, human, and organizational resources among others and firm performance while others have failed to capture commensurate value from BDA investments (Gupta & George, 2016; Wamba et al., 2017; Popovič et al., 2018;). More work is required to understand and articulate the value creation process from capability building to value realization (Grover et al., 2018). While the BDA literature has been very prolific in defining the ingredients that go into building a BDA capability, not much work has been done to highlight the contributions of the manager as a potential source of BDA value creation (Mikalef et al., 2020). The IT-Business value literature has previously demonstrated that resource synchronization and orchestration is a prerequisite to develop and leverage resources strategically (Cragg et al., 2011). Using the resource orchestration framework as a theoretical foundation, this paper addresses the following research questions – 1) How do managers contribute to firm performance by bundling resources to build superior BDA capabilities? 2) How do managers mobilize, coordinate, and deploy these capabilities in concert with firm strategy and market context, and how does that moderate the relationship between BDA capabilities and performance outcomes? 3) Can managerial ability explain the differential performance outcomes in firms with otherwise BDA capability parity? This study will employ a quantitative research approach using a survey targeting top, middle, and operational level analytics managers in publicly traded companies drawn from multiple industries to measure BDA and BDA Managerial Capability given various market contingencies. The survey data will draw measures of firm performance from the Compustat database. The study adds to the scholarly literature by explicating the importance of effective resource management and the contribution of managers to the resource exploitation aspects of value realization from capabilities. From a practical viewpoint, the study enables companies to understand the processes and activities required to create and deploy high-quality BDA capabilities along with the organizational context and strategies necessary to produce superior firm performance

From big data to big performance – exploring the potential of big data for enhancing public organizations’ performance : a systematic literature review

This article examines the possibilities for increasing organizational performance in the public sector using Big Data by conducting a systematic literature review. It includes the results of 36 scientific articles published between January 2012 and July 2019. The results show a tendency to explain the relationship between big data and organizational performance through the Resource-Based View of the Firm or the Dynamic Capabilities View, arguing that perfor-mance improvement in an organization stems from unique capabilities. In addition, the results show that Big Data performance improvement is influenced by better organizational decision making. Finally, it identifies three dimensions that seem to play a role in this process: the human dimension, the organizational dimension, and the data dimension. From these findings, implications for both practice and theory are derived

Linking business analytics to decision making effectiveness: a path model analysis

While business analytics is being increasingly used to gain data-driven insights to support decision making, little research exists regarding the mechanism through which business analytics can be used to improve decision-making effectiveness (DME) at the organizational level. Drawing on the information processing view and contingency theory, this paper develops a research model linking business analytics to organizational DME. The research model is tested using structural equation modeling based on 740 responses collected from U.K. businesses. The key findings demonstrate that business analytics, through the mediation of a data-driven environment, positively influences information processing capability, which in turn has a positive effect on DME. The findings also demonstrate that the paths from business analytics to DME have no statistical differences between large and medium companies, but some differences between manufacturing and professional service industries. Our findings contribute to the business analytics literature by providing useful insights into business analytics applications and the facilitation of data-driven decision making. They also contribute to manager's knowledge and understanding by demonstrating how business analytics should be implemented to improve DM

How does big data affect GDP? Theory and evidence for the UK

We present an economic approach to measuring the impact of Big Data on GDP and GDP growth. We define data, information, ideas and knowledge. We present a conceptual framework to understand and measure the production of “Big Data”, which we classify as transformed data and data-based knowledge. We use this framework to understand how current official datasets and concepts used by Statistics Offices might already measure Big Data in GDP, or might miss it. We also set out how unofficial data sources might be used to measure the contribution of data to GDP and present estimates on its contributions to growth. Using new estimates of employment and investment in Big Data as set out in Chebli, Goodridge et al. (2015) and Goodridge and Haskel (2015a) and treating transformed data and data-based knowledge as capital assets, we estimate that for the UK: (a) in 2012, “Big Data” assets add £1.6bn to market sector GVA; (b) in 2005-2012, account for 0.02% of growth in market sector value-added; (c) much Big Data activity is already captured in the official data on software – 76% of investment in Big Data is already included in official software investment, and 76% of the contribution of Big Data to GDP growth is also already in the software contribution; and (d) in the coming decade, data-based assets may contribute around 0.07% to 0.23% pa of annual growth on average

Introduction for the special Issue on BIG DATA

Today we live in the era of Big Data Revolution, overwhelmed with data, information and knowledge that is spread all over the web, in social media sites, in smartphones contributing so in creating a large base of big data. In their best seller book ‘Big Data: A Revolution That Will Transform How We Live, Work, and Think’ (2013) Mayer-Schönberger and Cukier argue that thanks to the internet, social networking, smartphones and credit cards, more data is being collected and stored about us than ever before – and this has created an opportunity for firms and managers to easily and cheaply capture and store massive amounts of data in a way that was simply impossible before. In this scenario the winners will be those that have the abilities, the intelligence, the creativity and the tools to elaborate these data for grasping insights and knowledge from the available data and to be able to use and exploit them for continuous innovation, for improving firms performance, for creating new products and services as well as for establishing new innovative business models. According to a study realized by McKinsey  (Manyika  et al, 2011) to analyze the impact of big data analysis for  innovation, competition, and productivity they argue that there are 5 broad ways in which using data can create value: Big data can unlock significant value by making information transparent and usable at much higher frequency. As organizations create and store more transactional data in digital form, they can collect more accurate and detailed performance information on everything from product inventories to sick days, and therefore expose variability and boost performance.Big data allows ever-narrower segmentation of customers and therefore much more precisely tailored products or services. Sophisticated analytics can substantially improve decision-making. Big data can be used to improve the development of the next generation of products and services. On the other hand, the availability of big data has created a new era also for data analysis and elaboration to discover unknown patterns, to find out what customers want, what they evaluate,  to get closer to customers, to gain a wealth of information about their behaviors and preferences, as well as to identify new market trends and new opportunities to remain competitive. The ability of firms to aggregate, elaborate and analyze the data is becoming a key competitive advantage resource. Different researches have evidenced the role and the outcomes that the big data analysis could bring to firms in terms of innovation, efficiency, productivity, quality and customer satisfaction. In a survey study of more than 3,000 business executives, managers and analysts from organizations, MIT Sloan Management Review, in collaboration with the IBM Institute for Business Value found out that executives are oriented toward managing the business based on data-driven decisions and it is the use of business information and analytics that differentiates them within their industry (Lavalle et al, 2010). Moreover, the content and information that customers create in web 2.0 platforms constitute a valuable asset for firms to directly tap into the customer’s preferences and needs, as the most valuable source for attaining direct and reliable market information. In this environment, more and more firms are building their competitive advantage on their ability to collect, analyze and act on data. Therefore, the capability of firms to tap into data, to analyze and interpret them to gain insights and to ensure a more effective decision making process has become an essential ingredient towards innovative thinking and creativity. Therefore, there is a need of matching the analytical capacities with the creativity in order to interpret big data in an innovative way. It is in this aim that we have realized these special issue of the journal to publish some  research articles that use statistical and analytical models to elaborate big data for a large range of issues and sectors and for establishing new innovative insights. The issue presents four research papers that focus on providing practical cases of exploiting big data for grasping new insights and opportunities. In particular: The article on ‘Big Data and Knowledge-intensive entrepreneurship: trends and opportunities in the tourism’, the authors Del Vecchio et al., focuses on the growing relevance of Big Data as valuable source of knowledge impacting on the creation and execution of knowledge-intensive entrepreneurship. The article provides a detailed description regarding the opportunities offered by Big Data by demonstrating, with practical applications from the tourism field, how the large amount of knowledge distributed in the web can support the conception and execution of an entrepreneurial process more aligned with the customers' needs and focused on the actual market's trends.   Carpita and Simonetti in the article ‘Big Data to Monitor Big Social Events: Analysing the mobile phone signals in the Brescia Smart City’ present the implications that big data analysis  could provide for Municipal administration to plan future events, and more generally to develop policies for the ‘smart city. They use the statistical methods to process and assess really high mass of data and information extracted from mobile phone signals in order to  improve the quality of the big social events that take place in the city as well as to create the conditions for developing useful reports for territorial marketing. The paper on ‘Ontological analysis for dynamic data model exploration’ by Hobbs et al., explores the expressive approaches to data analysis. The authors provide an aggregate model that utilizes ontological tools to create domain models in a way that it allows for a distributed and parallel implementation necessary for big data analysis. In the article ‘Implementation of a Web-based Application for Predicting Best Training Recommenders for Princess Norah University Employees’ the authors Mohammad and  Alhaidey propose the realization of  a recommender system that would help in the decision making process and planning of the training course offered by organizations for their employees. The recommender system is based on using data mining techniques that allows the observer to discover specific patterns and knowledge from large databases and carrying out predictions for outputs.

Understanding the Importance and Impact of Technology in an Accounting Setting: Work Outcomes and Relationships with Clients

This study explores how technology positively or negatively impacts the accounting profession, and specifically, the impact on work outcomes (i.e. the effectiveness and efficiency of work) and relationships with clients. Three types of technology tools were featured in this study: Accounting and Analytics, Robotic Process Automation, and Communication Technology Tools and Platforms. Our research questions were (1) How much do technology tools improve the efficiency and effectiveness of the accountant? and (2) How much do technology tools affect the relationship with clients? After surveying professionals in the accounting field, we concluded that accountants believe that Communication softwares improve their efficiency and effectiveness the most, with Accounting and Analytics softwares just behind. We can also conclude that technology has a positive, or at the very least, neutral, effect on the relationship between professionals and their clients. Overall, it was found that in the accounting field, technology has a positive impact on work outcomes and relationships with clients

How can SMEs benefit from big data? Challenges and a path forward

Big data is big news, and large companies in all sectors are making significant advances in their customer relations, product selection and development and consequent profitability through using this valuable commodity. Small and medium enterprises (SMEs) have proved themselves to be slow adopters of the new technology of big data analytics and are in danger of being left behind. In Europe, SMEs are a vital part of the economy, and the challenges they encounter need to be addressed as a matter of urgency. This paper identifies barriers to SME uptake of big data analytics and recognises their complex challenge to all stakeholders, including national and international policy makers, IT, business management and data science communities. The paper proposes a big data maturity model for SMEs as a first step towards an SME roadmap to data analytics. It considers the ‘state-of-the-art’ of IT with respect to usability and usefulness for SMEs and discusses how SMEs can overcome the barriers preventing them from adopting existing solutions. The paper then considers management perspectives and the role of maturity models in enhancing and structuring the adoption of data analytics in an organisation. The history of total quality management is reviewed to inform the core aspects of implanting a new paradigm. The paper concludes with recommendations to help SMEs develop their big data capability and enable them to continue as the engines of European industrial and business success. Copyright © 2016 John Wiley & Sons, Ltd.Peer ReviewedPostprint (author's final draft

IPO Ready? Illuminating the Dark Box of Private Equity

The use of public equity data can help combat the challenges private equity funds currently face regarding data availability. The goal is to create a model to provide guidance to both investors and entrepreneurs in the decision-making process. The data gathered would provide insight on how close a private company is to a successful Initial Public Offering (IPO). The idea is that a model, showing the average financial metrics of companies within certain industries during an IPO, can provide new perceptiveness as to how the private company is performing