Requirements for the Summary-Reflection Paper:
For each reading article, you are responsible for writing a summary-reflection essay ( around 2 pages or more as needed) (after you read it). This essay should reflect your reflections of the concepts and theoretical frameworks discussed and the confusions you may have.Rather than simply repeat or even copy/paste from original reading materials, please focus on your understanding of the concepts and theoretical frameworks. To be more specific, besides a brief summary, you should have a major section dedicated to the confusions you have with the concepts and models in the reading materials. You should comment on those concepts and models with your own opinions based on working experiences, readings from the library, and reflections of your previous learning.
If you just repeat abstract concepts from the reading materials, you will not earn a high score. You should connect concepts with job/internship experiences.
The following rubrics of essay evaluation could be used to guide your writing. All essays are single-spaced with top and bottom margins of 1 inch and left and right-side margins of 1 inch and the font size should be 12.
Integration of big-data ERP and business analytics (BA) Contents lists available at ScienceDirect Journal of High Technology Management Research journal homepage: www.elsevier.com/locate/hitech Integration of big-data ERP and business analytics (BA) Zhengzhong Shia,⁎, Gang Wangb aDecision & Information Sciences, Charlton College of Business 209, The University of Massachusetts at Dartmouth, USA bDecision & Information Sciences, Charlton College of Business 214, The University of Massachusetts at Dartmouth, USA A R T I C L E I N F O Keywords: Business analytics ERP Big data Maturity model Portfolio perspective Sustainable competitive advantages A B S T R A C T Technology advancements in cloud computing, big data systems, No-SQL database, cognitive systems, deep learning, and other artificial intelligence techniques make the integration of tra- ditional ERP transaction data and big data streaming from various social media platforms and Internet of Things (IOTs) into a unified analytics system not only feasible but also inevitable. Two steps are prominent for this integration. The first, coined as forming the big-data ERP, is the integration of traditional ERP transaction data and the big data and the second is to integrate the big-data ERP with business analytics (BA). As ERP implementers and BA users are facing various challenges, managers responsible for this big-data ERP-BA integration are also seriously chal- lenged. To help them deal with these challenges, we develop the SIST model (including Strategic alignment, Intellectual and Social capital integration, and Technology integration) and propose that this integration is an evolving portfolio with various maturity levels for different business functions, likely leading to sustainable competitive advantages. 1. Introduction On July 1, 2014, SAP, the world-renowned ERP vendor, released its own Certified Spark Distribution,1 starting its journey of a brand new collaboration2 with Databaricks3 - the rising star behind the increasingly dominant distributed analytic platform Apache Spark. In three years, as the fruit of this collaboration, SAP announced its new product Vora in 2017. “SAP Vora is an in-memory, distributed, query-processing engine … which extends the Apache Spark framework. … allows you to inexpensively process enterprise and Hadoop data for real-time business applications and analytics.”4 This collaboration between SAP and Databricks exemplifies the integration of both the business transactional data generated by traditional ERP systems and the big data streaming from multiple social media platforms, mobile phones, and Internet of Things (IOTs) into a unified analytics system and represents a critical milestone in the evolution of ERP systems. We would coin a new term - the big-data ERP5 - to represent the innovation of integrating the transaction data and the big data. Further, we would like to name https://doi.org/10.1016/j.hitech.2018.09.004 ⁎ Corresponding author. E-mail addresses:
[email protected] (Z. Shi),
[email protected] (G. Wang). 1 Apache Spark™ is a fast and general engine for large-scale data processing. https://spark.apache.org/, Accessed on June 21, 2017. 2 https://databricks.com/blog/2014/07/01/integrating-spark-and-hana.html, accessed on June 20, 2017. 3 Databaircks is a company, founded by the team who created Apache Spark and it provides a Unified Analytics Platform that accelerates innovation by unifying data science, engineering, and business. https://databricks.com/unified-analytics-platform, Accessed on June 21, 2017. 4 White Paper, https://www.sap.com/documents/2017/05/82e66ce9-b97c-0010-82c7-eda71af511fa.html, Accessed on September 19, 2017 5 In this paper, the big-data ERP broadly refers to systems responsible for managing daily operations and producing large amount of business transactional data, IOT (Internet of Things) data, social media data, etc. It represents a critical evolution from traditional ERP systems that process only transactional data. Journal of High Technology Management Research 29 (2018) 141–150 Available online 06 October 2018 1047-8310/ Published by Elsevier Inc. T http://www.sciencedirect.com/science/journal/10478310 https://www.elsevier.com/locate/hitech https://doi.org/10.1016/j.hitech.2018.09.004 https://doi.org/10.1016/j.hitech.2018.09.004 mailto:
[email protected] mailto:
[email protected] https://spark.apache.org/ https://databricks.com/blog/2014/07/01/integrating-spark-and-hana.html https://databricks.com/unified-analytics-platform https://www.sap.com/documents/2017/05/82e66ce9-b97c-0010-82c7-eda71af511fa.html https://doi.org/10.1016/j.hitech.2018.09.004 http://crossmark.crossref.org/dialog/?doi=10.1016/j.hitech.2018.09.004&domain=pdf the integration between this big-data ERP and the business analytics (BA) as the big-data ERP-BA integration. The big-data ERP system is both the source of data for the BA system and the major consumer of the insights produced by the BA system. Managers as well as smart machines/devices apply those insights to make decisions and judgements such as optimizing inventories, assessing supplier risks, selecting efficient routing paths, evaluating marketing effectiveness, etc. As ERP implementers and business analytics users are facing various challenges (Gupta & George, 2016; Strong & Volkoff, 2010), managers responsible for this big-data ERP-BA integration are facing challenges in strategic alignment, inter-organizational collaboration, and technological integration. On the one hand, the integration of transactional data from a traditional ERP system and the big data streaming from multiple sources is challenging in terms of distributed sensor management, data center management, use of the non-SQL database, config- uration of the complex big-data ERP system, etc. On the other hand, a business analytics6 (BA) system refers to the technologies that can be used to dig into the data supplied by the big-data ERP system to produce insights and help with the ERP supported operations management and strategic positioning for the ultimate value creation (Lavalle, Lesser, Shockley, Hopkins, & Kruschwitz, 2011). To effectively manage this big-data ERP-BA integration, firms are facing the challenges of developing the strategic thinking, hiring and training highly qualified people, making them work together effectively, connecting the big-data ERP and BA systems, and maintaining a reliable and durable infrastructure. Users of the integrated big data ERP-BA system face the challenge of both ex- ploiting existing processes and exploring untested fields. With data exploitation, the BA system can help identify opportunities for incremental improvements through adjusting parameters of and/or reconfiguring business processes embedded in the big-data ERP system. With data exploration, the BA system may be used to analyze the transactional and the social media/IOTs big data for strategic decision-making regarding product and manufacturing disruptive innovations (Fan & Gordon, 2014). While the integration of the big-data ERP and BA technological systems itself is challenging, beyond it, through continuous interactions, social and intellectual exchanges are unavoidable between people from both the ERP and BA organizations. Subsequently, social/intellectual capitals are developed and nurtured, transforming the inter-organizational structures and processes for future exchanges that substantiate the big-data ERP-BA integration.7 This aspect of ERP-BA integration sets up the environment for the effective collaboration among people across inter-organizational structures and processes. Moreover, since the big-data ERP and BA systems are critical for the long-term survival and prosperity of any sizable businesses in today's global competition, the strategic management of this ERP-BA integration is always valuable and highly preferable. To help with managing this challenging big-data ERP-BA integration task, based on classic information systems management models and recent advancements in analytics, artificial intelligence, cloud computing research and practice, we develop a SIST model (including Strategic alignment, Intellectual and Social capital integration, and Technology integration) and propose that this big-data ERP-BA integration is an evolving portfolio with various maturity levels for different business functions. We conclude that this integration, as a moving target with path dependencies, is likely to sustain competitive advantages. 2. The big-data ERP-BA integration –SIST model The SIST model in Fig. 1 illuminates the three layers for the big-data ERP-BA integration. The Strategic Alignment layer sets the value proposition and constructs the blueprints for the big-data ERP-BA integration. The second layer is the Intellectual and Social Capital Integration substantiating the ERP-BA integration in terms of integration of structures, processes, and people and acting as the environment within which both the alignment model formulation (at the first layer) and technology integration (at the third layer) occur. The Technology Integration layer is about the integration of the big-data ERP and BA technical systems to implement those blueprints constructed at the first layer for value realization. 2.1. The strategic alignment (SIST) for the big-data ERP-BA integration The strategic alignment for the big-data ERP-BA integration is the conceptual strategic thinking about the value proposition and the construction of blueprints for the integration. Conceptually, both ERP and BA organizations can be value centers with external and internal orientations as depicted in Fig. 2 (adopted from Venkatraman, 1997). Firstly, ERP and BA organizations are profit centers as profits may come from providing consulting and other related services to external customers. A good example of this aspect of the alignment is the integration between Facebook's consumer-facing operational system at www.facebook.com and the business- facing analytics (BA) system at https://analytics.facebook.com. Behind the two systems, in Facebook, there is an end-user facing organization responsible for the frontend big-data ERP system's development, operations, and maintenance and there is also a business-customer facing organization responsible for providing social media analytics (Fan & Gordon, 2014). Facebook makes profits as it provides services of social media analytics to customers. Secondly, as investment centers, both ERP and BA organizations can invest in each other or third parties for joint research and 6 Wikipedia defines business analytics as making extensive use of “statistical analysis, including explanatory and predictive modeling, and fact- based management to drive decision making. … Analytics may be used as input for human decisions or may drive fully automated decisions.” https://en.wikipedia.org/wiki/Business_analytics, Accessed on September 22, 2017. In our view, all data processing techniques, such as statistical analysis, machine learning, deep learning, reinforcement learning, other AI techniques, etc., should be included (Davenport, 2006; Silver et al., 2016; Silver et al., 2017). 7 In the following, to make the writing more concise, the big-data ERP-BA integration is normally interchangeable with the ERP-BA integration. Otherwise, there will be specific explanations. Z. Shi, G. Wang Journal of High Technology Management Research 29 (2018) 141–150 142 http://www.facebook.com https://analytics.facebook.com https://en.wikipedia.org/wiki/Business_analytics technology development. The collaboration between SAP and Databricks demonstrates the mutual investments between a major ERP firm and an increasingly dominant big data analytics firm. The profit and investment centers are mostly externally oriented with the former facing the external service consumers and the latter targeting external technology providers. These two perspectives are related to strategic decisions on the scope of technologies, competencies, and governance8 (Henderson & Venkatraman, 1993). Thirdly, both ERP and BA organizations should provide the best possible services to each other at the lowest cost and play the roles of service and cost centers. Walmart's integration of its Hadoop9 cluster system with its daily operational system (as a part of its big-data ERP system) demonstrates both the service and cost center roles played by its ERP and BA organizations.10 Cost and service centers are internally oriented and relate to decisions on system architecture, processes, and people's skills11 (Henderson & Venkatraman, 1993). Lastly, according to Venkatraman (1997), the big-data ERP-BA strategic alignment should be specified in terms of the horizontal strategic and operational integration across the two organizations and the vertical strategic fit