Journal:Data science as an innovation challenge: From big data to value proposition
| Full article title | Data science as an innovation challenge: From big data to value proposition |
|---|---|
| Journal | Technology Innovation Management Review |
| Author(s) | Kayser, Victoria; Nehrke, Bastian; Zubovic, Damir |
| Author affiliation(s) | Ernst & Young |
| Year published | 2018 |
| Volume and issue | 8(3) |
| Page(s) | 16–25 |
| DOI | 10.22215/timreview/1143 |
| ISSN | 1927-0321 |
| Distribution license | Creative Commons Attribution 3.0 Unported |
| Website | https://timreview.ca/article/1143 |
| Download | https://timreview.ca/sites/default/files/article_PDF/Kayser_et_al_TIMReview_March2018.pdf (PDF) |
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Abstract
Analyzing “big data” holds huge potential for generating business value. The ongoing advancement of tools and technology over recent years has created a new ecosystem full of opportunities for data-driven innovation. However, as the amount of available data rises to new heights, so too does complexity. Organizations are challenged to create the right contexts, by shaping interfaces and processes, and by asking the right questions to guide the data analysis. Lifting the innovation potential requires teaming and focus to efficiently assign available resources to the most promising initiatives. With reference to the innovation process, this article will concentrate on establishing a process for analytics projects from first ideas to realization (in most cases, a running application). The question we tackle is: what can the practical discourse on big data and analytics learn from innovation management? The insights presented in this article are built on our practical experiences in working with various clients. We will classify analytics projects as well as discuss common innovation barriers along this process.
Keywords: analytics, big data, digital innovation, idea generation, innovation process
Introduction
Understandably, much effort is being expended into analyzing “big data” to unleash its potentially enormous business value.[1][2] New data sources evolve, and new techniques for storing and analyzing large data sets are enabling many new applications, but the exact business value of any one big data application is often unclear. From a practical viewpoint, organizations still struggle to use data meaningfully or they lack the right competencies. Different types of analytics problems arise in an organizational context, depending on whether the starting point is a precise request from a department that only lacks required skills or capabilities (e.g., machine learning) or rather it stems from a principal interest in working with big data (e.g., no own infrastructure, no methodical experience). So far, clear strategies and process for value generation from data are often missing.
Much literature addresses the technical and methodical implementation, the transformative strength of big data[3], the enhancement of firm performance by building analytics capability[4], or other managerial issues[5][1] Little work covers the transformation process from first ideas to ready analytics applications or in building analytics competence. This article seeks to address this gap.
Analytics initiatives have several unique features. First, they require an exploratory approach—the analysis does not start with specific requirements as in other projects but rather with an idea or data set. To assess the contribution, ideation techniques and rapid prototyping are applied. This exploration plays a key role in developing a shared understanding and giving a big data initiative a strategic direction. Second, analytics projects in their early phase are bound to a complex interplay between different stakeholder interests, competencies, and viewpoints. Learning is an integral part of these projects to build experience and competence with analytics. Third, analytics projects run in parallel to the existing information technology (IT) infrastructure and deliver short scripts or strategic insights, which are then installed in larger IT projects. Due to a missing end-to-end target, data is not only to be extracted, transformed, and loaded, but also needs to be identified, classified, and partly structured. So, a general process for value generation needs to be established to guide analytics projects and address these issues.
Here, we propose an exact configuration and series of steps to guide a big data analytics project. The lack of specified requirements and defined project goals in a big data analytics project (compared to a classic analytics project) make it challenging to structure the analytics process. Therefore, the linear innovation process serves as reference and orientation.[6] As Braganza and colleagues[7] describe, for big data to be successfully integrated and implemented in an organization, clear and repeatable processes are required. Nevertheless, each analytics initiative is different and the process needs to flexible. Unfortunately, the literature rarely combines challenges in the analytics process with concepts from innovation management. Nevertheless, an integration of the concepts from innovation management could guide the analytics work of formulating digital strategies, organizational anchoring of the analytics units and their functions, and designing the analytics portfolio, as well as the underlying working principles (e.g., rapid prototyping, ideation techniques).
References
- ↑ 1.0 1.1 McAfee, A.; Byrnjolfsson, E. (2012). "Big data: The management revolution". Harvard Business Review 90 (10): 60–8. PMID 23074865.
- ↑ Wamba, S.F.; Gunasekaran, A.; Akter, S. et al. (2017). "Big data analytics and firm performance: Effects of dynamic capabilities". Journal of Business Research 70: 356–65. doi:10.1016/j.jbusres.2016.08.009.
- ↑ Wamba, S.F.; Akter, S.; Edwards, A. et al. (2015). "How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study". International Journal of Production Economics 165: 234-46. doi:10.1016/j.ijpe.2014.12.031.
- ↑ Akter, S.; Wamba, S.F.; Gunasekaran, A. et al. (2016). "How to improve firm performance using big data analytics capability and business strategy alignment?". International Journal of Production Economics 182: 113–31. doi:10.1016/j.ijpe.2016.08.018.
- ↑ Devenport, T.H.; Harris, J.G. (2007). Competing on Analytics: The New Science of Winning. Harvard Business School Press. pp. 240. ISBN 9781422103326.
- ↑ Cooper, R.G. (1990). "Stage-gate systems: A new tool for managing new products". Business Horizons 33 (3): 44–54. doi:10.1016/0007-6813(90)90040-I.
- ↑ Braganza, A.; Brooks, L.; Nepelski, D. et al. (2017). "Resource management in big data initiatives: Processes and dynamic capabilities". Journal of Business Research 70: 328–37. doi:10.1016/j.jbusres.2016.08.006.
Notes
This presentation is faithful to the original, with only a few minor changes to presentation and grammar. In some cases important information was missing from the references, and that information was added. The original article lists references alphabetically, but this version — by design — lists them in order of appearance.
