Big Data, Cloud Computing, and Visionary Leadership

By: Denekew A. Jembere

Licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Introduction

In the current highly competitive market, the speed and frequency in which data is produced and collected from the increasing number of sources pose a challenge for organizations to stay competitive. Understanding this challenge and the mechanisms to cope with it requires innovative leadership.  To summarize the challenges posed, coping mechanisms and opportunities available to utilize this ever-increasing data, this article outlines a review of recent studies on Big Data, Cloud computing, and innovative leadership. Building on the review of the recent studies, this article concludes with a discussion on the need to converge Big Data, Cloud computing, and innovative leadership to facilitate the competitive advantages of organizations.

Big Data

Big Data is a technology that is emerging nowadays for providing data-intensive processing on large-scale datasets and is becoming a key enabler to organizations at meeting their business goals (Gualtieri & Yuhanna, 2014).  Big Data is also implied as an umbrella term for the explosion in the quantity and diversity of high-frequency digital data and not usually coming from traditional sources (Pramana et al., 2017).   

To provide solution approaches, for businesses to deal with a very large number of datasets, studies are being conducted on the current Big Data technologies in different business scenarios. One of such study is this one done aiming at generating timely business performance information (Vera-Baquero et al, 2015). In this study, three different approaches (map-reduce, Impala and secondary indexes) have been undertaken based on Hadoop and HBase. And, this study concludes that a map-reduce approach is suitable for batch processing and presents very high scalability; whereas the Impala based approach has significant improvements over map-reduce for performing real-time queries over HBase. And finally, the use of secondary indexes is proposed with the aim of enabling immediate access to event instances for correlation in detriment of high-duplication storage and synchronization issues. 

While producing Big Data is a natural phenomenon and part of the day-to-day business process for some organizations, others may have to look beyond the traditional ways of data collection to get data sources where they can tap into and capture Big Data in a chipper way to stay competitive. Among such organizations are National Statistical Organizations (NSOs) mentioned in (Harwood & Mayer, 2016) study.  This study proposes the NSOs to redefine their role, use technology to streamline business practices and look to create new statistical products and services.  As a proof of concept, this study used a prototype semantic linked employer-employee database (LEED) which demonstrated the potential benefits of data integration to the NSOs.  After indicating that many datasets need to be linked to maximize the integration potential, the study further explains the challenge posed to maintain the traditional data structures as a result of the increased dimensionality of this approach. As a result, Harwood and Mayer proposed the use of semantic web technology so that NSOs can efficiently and effectively utilize integrated data and Big Data sources for the wide variety of analytic use to extract insights about their users’ demand. 

 The growing types and sources of digital data and their increased speed and frequency of data production is a driver for new computing paradigms, concepts, and solutions. The integration of these data sources into the cyber-physical systems will reduce the time to find a readily available solution to utilize this data.  Kos et al (2015) propose a data flow computing paradigm as one of the possible candidates to solve part of this problem. According to Kos et al, unlike the old business paradigm where a program controls the flow of data, the flow of data defines the structure of a program.   This demands new ways of programming and thinking whereby redefining the interdependence of data and program As a result, Kos et al proposed a benchmarking methodology in which data flow computers are claimed to outrank the control flow computers on a top 500 list for a number of Big Data applications whereby saving time, space, and power, which all cost money. Based on their observation, Kos et al advise data centers, running Big Data applications, to pay attention to these facts, if not earlier, then at least at the next equipment refreshment.

Claiming that the Big Data technology with Machine Learning practices is applicable to all organizations, industries, and sectors where extensive data are generated and can be analyzed for greater insights, Migliore and Chinta (2017) establish a link between the Big Data and decision quality of organizational leaders. The need for considering such new data processing methodology and cost-effective computing infrastructure as well as technology required for processing the ever-growing digital data leads to a discussion on cloud computing.

Cloud Computing

The most recent computing paradigm, which is rapidly moving from the early adopters to mainstream organizations is Cloud computing. Fueled by the market pressure on organizations to stay competitive and the cost of maintaining a stand-alone IT infrastructure, Cloud computing has become one of the top priorities of organizations and their leaders, in their strategic business considerations.  For organizations and their leaders, there are many appealing and readily available Cloud computing offerings such as the pay-as-you-go; the on-demand resources scale up or down; the flexibility of deploying and customizing solutions; the flexible software-as-a-service (SaaS), platform-as-a-service (PaaS) and infrastructure-as-a-service (IaaS).  Because of this flexibility, some manufacturing industries already started reaping the benefits of Cloud adoption, moving into an era of smart manufacturing with the new agile, scalable and efficient business practices, by replacing traditional manufacturing business models (Xu, 2013).   According to Xu, the adoption is typically focused on BPM applications such as human resources, CRM and ERP functions, with Salesforce and Model Metrics being two of the popular early PaaS providers. 

Cloud computing has emerged as one of the major enablers in areas such as the manufacturing industry (Xu, 2013), the digital earth and geospatial studies (Xia et al., 2015); and health nutrition programs (Dávila et al, 2017). As a result, Cloud computing facilitated transforming business models in organizations, helping them align product innovation with business strategy, and creating intelligent processes that encourage effective collaboration. For a typical organization, there are benefits of adopting Cloud computing (Dávila et al, 2017; Xia et al, 2015; Xu, 2013) such as:

• The elimination of time and resource-consuming functions that were essential in traditional IT;
• The use of smart Cloud computing capabilities for customizations and tweaks that the organization might need at a process level;
• Its effectiveness in offering business-to-business (B2B) solutions for commerce transactions between businesses, for instance, between a manufacturer and a wholesaler, or between a wholesaler and a retailer. 
• Its ability to provide high-speed access to solutions enabled by its optimized real-time data tracking; and
• Its elastic resource pooling and dynamic workload balancing features

Although Cloud-computing provides very appealing cost benefits explained above, its privacy and security, as well as data processing boundaries, are the two major areas of concern for holding some organizations back from reaping these benefits.  Bhadauria et al (2014) elaborate on the numerous unresolved security and privacy issues, threatening the Cloud computing adoption and diffusion affecting the various stakeholders which are linked to it. Bhadauria et al provided their advice on the need to ensure security against these virtual threats and, proposed security methodologies to be adopted and maintained. 

Because of the new cost-effective data processing features of Cloud computing, it is positioned well to provide solutions for the growing need to deal with Big data, and its ever-increasing sources, types, and sizes of data production.  Although the Big Data analytics offerings are available in almost all Cloud computing service providers, the use of these technologies requires careful considerations of the security risks and the corresponding solutions available. Organizations and their leaders who would like to adopt and gain the resulting competitive advantages of using Big Data analytics and cloud computing should be well informed about the privacy and security limitations and the available solutions.  In this regard, Migliore and Chinta (2017) suggest including practical steps in the context of visionary leadership, alignment of resources and implementation to use technologies such as Hadoop and Machine Learning practices and succeed in Big Data related projects.  With the ability to envision and utilize insights generated from Big Data processing, visionary leaders can play a great role in transforming the way organizations gain the maximum return on investment (ROI) of the Big Data and Cloud computing technologies.

Visionary leaders for technology adoption

Technology adoption in organizations may require spending the initial cost of a feasibility study and additional costs for implementation, training, etc., Leaders that don’t have a long-term vision for their organization may not be able to see opportunities that could be leveraged from new technological innovations and paradigm shifts. As a result, convincing these leaders about the long-term benefits that the organization would enjoy is an additional step that needs to be taken.

According to Giotopoulos et al (2017), if not coupled with more concrete evidence of actual productivity gains, information communication technology (ICT) policies that simply focus on “raising awareness” are not enough to increase the competitive advantage of an organization. As Giotopoulos et al explain, organizations with visionary leadership which is open to new ideas and committed to growth-driven goals for the business are more likely to adopt and use ICT.  Apart from visionary leadership, an organization’s new technology adoption depends on the organization’s existing infrastructure (Larosiliere et al, 2016) and availability of well-educated and skilled workers (Giotopoulos et al, 2017) in the organization. 

Conclusion

The sources, speed, and frequency of data generation have become a challenge for organizations to stay competitive, in the current market. In addition to processing the ever-increasing data being collected within the organization, organizations are, sometimes, required to tap into additional sources of data.  This increases the challenge to process the enormous amount and types of data collected.  Apart from the volume and type of data, the technology and infrastructure needed to collect and process this enormous data is another challenge.  Moreover, the leadership’s perception of the long-term ROI in technologies and infrastructure needed to process this data requires an organizational visionary quality.  Therefore, the convergence of a clear understanding of the value of Big Data, the use of Cloud computing and, more importantly, the visionary leadership qualities in leaders for adopting new technologies would benefit organizations to stay relevant and completive.

References

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