Big data

Big data

Big data basically refers to large-scale, increasingly growing, widely distributed, and diverse collection of data assets necessitating use of data analytics solutions to derive valuable insights that can be leveraged to gain sustainable development and growth (Girard, 2015).  Big data can help an organization gain useful information and/or knowledge regarding a wide array of issues, ranging from customer expectations and preferences to creation of innovative and quality business products (Turner, Schroeck, & Shockley, 2013). However, how can big data analytics support strategic project management?

To start with, big data analytics helps trace valuable patterns in diverse datasets that drive efficiency and effectiveness in project portfolio management. The world of project management has large datasets on projects that remains largely unused and/or idle, and they may be holding the edge to an efficient management of project portfolios. Not surprisingly, it would be challenging or impossible for any project manager to manually extract or filter knowledge, patterns, relationships, trends, or facts from large data resources. This necessitates big data analytics to derive insightful patterns and drive better accuracy, because there are minimal chances for miscalculations or unwanted human error (Hu & Kaabouch, 2014). According to Girard (2015), patterns extracted from big data analytics provides the bigger picture about a project to bridge existing and potential gaps and voids in project portfolio management. As a result, there are better chances of mitigating potential project challenges and risks, thus helping avoid project management pitfalls (Leal, 2015).

Secondly, making sufficient sense of the unused datasets can help derive key project elements for better decision-making with respect to managing resources (human resources, equipment, timeline, and budget), overcoming risk factors and quality issues, change management, and scope management. Big data bolsters the sense-making process involving disparate datasets, which could otherwise be based on guesswork, expert advice, or mere previous experiences. Combining big data analytics with subject-matter expertise and past experiences is an integral element of successful project management (Leal, 2015). Girard (2015) argues that big data analytics forms an key pillar of project management at a time marred by depressing statistics about failed projects due to poor understanding of cost projections, milestones, scheduling, task prioritization, team management, and resource management.

Thirdly, there are projects whose requirements are not clearly understood upfront or are extremely complex to manage using conventional approaches, translating to inherent risks that can lead to depressing delays, excessive cost overruns, quality issues, or even total failure. Processing large and fragmented structured and unstructured datasets to extract useful facts, ideas, correlations, and patterns may facilitate innovation, predictive risk management, while seizing new opportunities. In addition, big data analytics may facilitate the selection of the best implementation strategies as well as the break down of large and complex projects into manageable packages, deliverables, and milestones towards successful delivery. Visualization of poorly-defined requirements and project complexities supported by big data is critical to strategic management of projects that are not clearly understood upfront (Girard, 2015; Hu & Kaabouch, 2014; Leal, 2015).

Lastly, big data may be used in stakeholder management – which encompasses a major element of project management. More precisely, big data analytics may help understand the requirements and expectations of the large number of stakeholders involved, including employees, business managers, executives, customers, the community, suppliers and vendors, and regulators. In addition, big data analytics may be used to understand crucial insights associated with conflicting requirements among stakeholders, and political issues and controversies triggered by perceived exclusion. These are issues that could otherwise jeopardize coordination, collaboration, project progress, and acceptance if not handled properly – on time and adequately. Big data analytics could drive valuable stakeholder management insights for better decision-making, and continuous communications and reporting (Girard, 2015; Hu & Kaabouch, 2014; Leal, 2015).

It is evident that big data constitutes a valuable tool for government agencies in their strategic project management processes because it supports informed and smart use of diverse data resources. This is critical to meeting accountability requirements expected of government agencies.

II

There is no universal definition of big data. Basically, organizations must implement a sound strategic big data plan, information foundation, and analytics solutions that support the increasingly growing and dynamic volume, variety, veracity, and velocity of datasets (IBM, n.d.; Turner et al., 2013). The convergence of volume, variety, veracity, and velocity of data constitute the four dimensions definition of big data (Tai, 2015).

Volume refers to the quantity or scale of structured (from systems such as ERP and CRM solutions that are directly related to an organization) and unstructured (from external systems such as social media) data. Most organizations in the U.S. handle data resources in the excess of 100 Terabytes. Data created daily accounts for approximately 2.5 trillion Gigabytes, and it is expected there will be close to 43 trillion Gigabytes of data by 2020. The growth of data can be attributed to proliferation of PCs, smartphones, expanding internet of things (IoT), wearable technologies, increased adoption of enterprise information systems, and growing social media trends. Growing data stored, processed, and shared across these technologies constitute to the volume big data dimension (Hu & Kaabouch, 2014; IBM, n.d.; Tai, 2015).

The variety dimension refers to the diverse forms and sources of structured and unstructured datasets that need to be managed and analyzed. The form diversity is triggered by the wide range of technologies, for example, wearable devices (holding health-related data), CCTVs (video surveillance records), databases, and social media tweets, posts, and comments. In addition, there are diverse emails, images, and videos from a wide range of systems, which triggers data warehousing and data mining challenges (Hu & Kaabouch, 2014; IBM, n.d.; Tai, 2015).

Veracity encompasses the uncertainty, volatility, and validity problems surrounding rapidly growing data assets. The data resources handled by modern semi-automated and automated systems cannot be trusted for decision-making processes, because of potential inaccuracies, ambiguities, biasness, anomalies, biasness, and quality issues. These are issues that are brought about by increased data generation and diversity, and they need to be adequately managed. The business value of such data resources is derived through big data analytics, and constitutes the veracity big data dimension. In today’s dynamic information and competitive world, the value of data resources may depreciate within a day. Therefore, big data initiatives require a proactive management approach to uphold the sight of authenticity, accuracy, quality, and validity of data and associated insights (Hu & Kaabouch, 2014; IBM, n.d.; Tai, 2015).

The velocity dimension entails actively streaming data. For example, the New York Stock Exchange (NYSE) captures approximately 1 terabyte of trading data during every business session. There are close to 2.5 network connections per person globally. Incorporation of sensors and GPS tracking into consumer appliances (such as TVs and refrigerators) and modern cars as well as the growing IP-based networks have led to increasingly growing streaming internet traffic. More precisely, IP-based networks facilitate accumulation of data from many equipments and information systems such as CCTV systems, HVAC implementations, building management solutions, and ERP and CRM applications (Hu & Kaabouch, 2014; IBM, n.d.; Tai, 2015).

References

Girard, J. (2015). Strategic Data-Based Wisdom in the Big Data Era. IGI Global.

Hu, W. C., & Kaabouch, N. (2014). Big Data Management, Technologies, and Applications. Information Science Reference.

IBM. (n.d.). Infographics: The Four V’s of Big Data. Retrieved from http://www.ibmbigdatahub.com/infographic/four-vs-big-data

Leal, J. G. (2015). Handbook of Research on Effective Project Management through the Integration of Knowledge and Innovation. IGI Global.

Tai, N. (2015). Dimensions of Big Data. Retrieved from http://www.klarity-analytics.com/2015/07/27/dimensions-of-big-data

Turner, D., Schroeck, M., & Shockley, R. (2013). Analytics: The real-world use of Big Data in financial services. IBM Global Business Services.

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