A RESEARCH CRITIQUE ON the Role of Big Data in Shaping Ambidextrous Business Process Management

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1. INTRODUCTION
2. REVIEW
   • Methodology
   • Inferences
3. CRITIQUE
4. CONCLUSION

• References

 

INTRODUCTION

In this age of technology, researchers are increasingly showing interest in comprehending organizations’ outlook towards information and communication technologies in discovering and redesigning their business processes to add value their businesses (Zancul et al. 2016; Scuotto et al. 2017), as organizations are increasingly realizing the significance of information and data to stay competitive. By utilizing big data in combination with business intelligence tools organizations tend to drive intelligent insights to support their business processes (Boisot and Canals 2004; Del Giudice and Della Peruta 2016). Big data is used to define data sets which were complicated to analyze using past data analysis tools.

Luca Dezi, Gabriele Santoro, Heger Gabteni and Anna Claudia Pellicelli in 2017, conducted a case study titled as “The role of big data in shaping ambidextrous business process management”, in which they studied the impact of big data on business process management and its role in the organizations’ ambidextrous approach in their business processes. The researchers have elaborated on how big data can aid in streamlining and develop the business process management (BPM) in terms of innovation and redesigning (exploration and exploitation).

(Dezi, L. et al. 2017)have performed a case study on three organizations from the service industry, one banking firm, and two retail firms. Through in-depth semi-structured interviews with different personnel from various functional levels of organizations and questionnaires, the researchers were able to underpin the hurdles and benefits of implying big data in business process discovery and redesigning.

1.  REVIEW

In this case study’s literature review, the researchers (Dezi, L. et al. 2017) have first highlighted the importance of business process management BPM, a business process can be defined as series of work activities on a timeline having inputs and outputs with a distinct beginning and an end. Recently businesses have shown interest in the relationship business process management and ambidexterity of business operations. It is believed that adopting innovative and redesigning practices can enhance firms’ chances of survival and success.

Organizations can manage their present resources and capabilities and explore new capabilities and information hand in hand to manage in an ever competing environment. A vital resource that scholars suggest that organizations use to achieve ambidexterity is the medium of communication and information technology. While information technology has aided organizations to explore and exploit relevant hidden opportunities, businesses, in general, have neglected the importance of big data in discovering and redesigning their business processes. In this era of rapid information technology, the importance of big data is increasingly been realized by progressive organizations.

BPM has gained its importance by effectively refining business processes, management of knowledge and development of business processes.

Ambidexterity of an organization can be defined as the multi-faceted capabilities of an organization to manage its operations efficiently while consistently thriving to adapt and respond to change, by developing and introducing the latest practices and products, by being flexible, adaptable and versatile, and by transforming and infusing latest technology and business opportunities into their process development and optimization.

Organizations nowadays are heavily reliant on data for information and knowledge to gain a competitive advantage for this purpose they employ big data in combination with various business intelligence tools to gain intelligence for their business processes. Collection of data with a combination of technology that analysis, incorporates and reports knowledgeable insights by filtering and correlating unstructured data, is known as big data management.

Big data is large unstructured sets of data that are heterogeneous and are analyzed in real- time to discover hidden data insights that aid in strategic decision-making resulting in added value to the business and its processes.

Big data aids in streamlining internal processes of businesses and increasing their efficacy and efficiency by reducing bottlenecks. It also assists businesses to recognize customer behavior and aiding them to customize their customer offerings, products, services and prices. Big data enables business processes to be more flexible, agile and responsive in turn elevating the organization’s competencies for innovating and redesigning their business operations.

Methodology

In this case study, researchers have adopted an exploratory and qualitative research method, based on multiple case studies involving three service-oriented firms, one from banking and two from retail, who have adopted big data approach in their business processes.

Semi-structured detailed interviews of several employees from different hierarchical levels of the organization performing various functions including IT and R&D. Questionnaires, follow up emails and phone calls were also included in data collection. The secondary source of information included business publications, corporate and online information. The firms were kept anonymous to avoid any misinterpretation, as firms preferred to remain anonymous to keep their strategic aspects unrevealed.

Inferences

According to the data collected in the research, big data has shown a significant role in the discovery and redesign of business processes.

Big data has provided knowledgeable information that has eliminated hurdles and elevated the efficiencies of business processes, improved risk assessment and administration, made decision-making more effective and prompt. It has enabled retail companies to optimize their workforce and made them more customer-centric by supporting analyses of customers’ shopping behaviors and their product line. Resultantly, customers were provided with better offerings and experience, in turn, making these firms more agile and adaptive to market change.

Big data has created an open channel of knowledge exchange between the retailers and vendors from which companies can derive enormous potential value for their businesses. It has enhanced and improved revenue and pricing business models experimentation based on purchase processes, sales, products and logistics.

Harnessing potential value from big data also poses some challenges and prerequisites. Big data management requires firms to have a sound IT architecture, right technologies and people with data analyst skills to extract the highest potential value from big data, and management personnel who can adapt to data-driven decision making. Skilled employees for handling and analyzing big data must be employed and change in management of people with the right mindset is required to reap the highest benefits incurring from implying big data in discovery and redesigning of business processes.

Firms should also adhere to the legalities which are imposed for the usage of big data.

2.   CRITIQUE

Researchers have made a sincere effort in lime lighting the significance of big data management in shaping organizations’ BPM. This case study could have been more comprehensive if a comparative analysis with other similar case studies was done (Schneider and Wagemann 2010). Statistical analysis between other related case studies (Gustafsson 2017) of various aspects of big data impact on BPM would have given this research a more elaborative and quantitative dimension which could have been correlated for better comprehension.

The methodology of research also lags in pursuing random samples for its data gathering. It seems that the researchers have gathered interviews’ and questionnaires’ data from conveniently accessible personnel in the company and not by random sampling, thus introducing a sampling bias (Ishak and Abu Bakar 2014). Even the size of the interaction sample is not stated in the report. Moreover, the specific designations of the interviewees were also not mentioned. Words like ‘manager’ or ‘employee’ lack specific functionality of the personnel in an organization which engenders uncertainty about the credibility of the interviewees’ responses and lacks credence concerning specific aspects of the research.

The domain of research is narrow concerning the types of businesses that were selected i.e. banking and retailers which are related to the service industry only. The inferred data would have held more in-depth perception if the research would have focused on a more varied and considerably larger sample of organizations (Ishak and Abu Bakar 2014).

The case study does not display a direct relationship between the extent of the impact of big data and various aspects of business processes, like, the percentage range of increased efficiency in processes, cost saved, sales increased or revenue generated, post big data implementation. A quantitative matrix of such measures would have made the research inferences more credible for empirical evidence.

One of the main challenges which the researchers fail to highlight is the element of initial cost that needs to be overcome to introduce big data management in business process management, and a suitable size range of business operations that would benefit most from utilizing big data management in their business processes.

Deeming the selected sample of organizations anonymous limits the case study to highlight certain aspects of their business operations, like, the type of retailers or bankers, their business stretch, clientele and so forth, which could have aided in further segregation of the findings. Organizations’ anonymity can also pose challenges in verifying certain resultant facts for other researchers and scholars.

3.  CONCLUSION

Researchers, in this case study, have successfully implied the importance of big data for achieving an organization’s ambidexterity in its business operations by efficiently using the data for discovering and redesigning business processes, along with effective decision making to deliver value to the business.

Although researchers have taken a qualitative approach to this research, a slender exploration of quantitative measures would have given this case study’s inferences a much vivid picture.

A random sample selection of personnel and organizations would have presented bias-free resultants. And a selection of larger and varied sample would have made this case study’s inferences more concise.

References

Boisot, M. and Canals, A., 2004, “Data, information and knowledge: have we got it right?”,

Journal of Evolutionary Economics, 14(1), pp. 43-67.

Del Giudice, M. and Della Peruta, M.R., 2016, “The impact of IT-based knowledge management systems on internal venturing and innovation: a structural equation modeling approach to corporate performance”, Journal of Knowledge Management, 20(3), pp. 484- 498.

Dezi, L., Santoro, G., Heger Gabteni, H. and Pellicelli, C., 2017, “The role of big data in shaping ambidextrous business process management – Case studies from the service industry”, Business Process Management Journal, 24(5), pp 1163-1175.

Gustafsson, J., 2017, “Single case studies vs. multiple case studies: A comparative study”

Ishak, N. and Abu Bakar, A., 2014, “Developing Sampling Frame for Case Study: Challenges and Conditions”, World Journal of Education.

Schneider, C. and Wagemann, C., 2010, “Standards of good practice in qualitative

comparative analysis (QCA) and fuzzy-sets”, Comparative Sociology, 9(3), pp 397-418.

Scuotto, V., Santoro, G., Bresciani, S. and Del Giudice, M., 2017, “Shifting intra-and inter-

organizational innovation processes towards digital business: an empirical analysis of SMEs”,

Creativity and Innovation Management, 26(3), pp. 247-255.

Zancul, E.D.S., Takey, S.M., Barquet, A.P.B., Kuwabara, L.H., Cauchick Miguel, P.A. and Rozenfeld, H., 2016, “Business process support for IoT based product-service systems (PSS)”, Business Process Management Journal, 22 (2), pp. 305-323.

 

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