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Applications Of Big Data For Business Model Innovation

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Question:

Discuss about the Usefulness and Applications of Big Data for Business Model Innovation.
 
 

Answer:

Introduction

This particular literature review considers usefulness and applications of big data for business model innovation. This review study used implications of several companies for including ‘big data’ in Data Driven Business Model (DDBM) implementation. In business model implementation, every organization is considering “data is the new oil” (Hartmann et al. 2014). In case every startup companies, simple yet effective big data business model can change entire business process. In this study, identified business models can serve for purpose of inspiration and considering new business procedures (Richter 2013). DDBM have several possibilities and potential to test the established sectors for several other extensions as well.

In this literature survey, several secondary studies are considered for big data applications in business model incorporation. In the review section, business model framework is defined as in how business entrepreneurs are incorporating them. Prior studies are included in this assessment showing how the outcomes of the studies are contribute to state business model element identification. In later part, big data and its importance is emphasized for analytical discussion. Later, how business model can be benefited, this aspect is demonstrated with DDBM. Finally, the limitations of big data based business model framework are included for study gaps identification.

Big Data for Business Model Innovation: Literature Review

Existing literature reviews have identified that business models encountered evolution since recent years and e-business strategies becoming popular. Groves et al. (2016) opined that business model frameworks so that concept of business model propose value for functional appropriateness. Moreover, the business models have identified that ‘value proposition is necessary for technology and innovation’. Based on the business components, Loebbecke and Picot (2015) demonstrated that business model framework contains major elements as customer, competitors, offering of services, and activities of organizations. Supply factor, resources and production inputs are considered for preparing of business model. However, in case of startup and corporate world, practitioners have proposed other business model so that business model ontology can be applied for DDBM as well. Hartmann et al. (2014) synthesized that business models are major factor for consisting value, process, resource, partner, and customer relationships. Other elements of business model are considered as value chain, value network, and competitive strategy. Schaltegger, Ludeke-Freund and Hansen (2012) claimed that business model framework consists of value preposition / offering services, resources, activity, customer segmentation, and competitors. On the other hand, Carayannis, Sindakis and Walter (2015) depicted that customer relationship, primary partnership options, revenue stream, costing structure, and customer / market segmentation is important as well.

In recent years, big data term is most popular and to some extent, some of the entrepreneurs are not yet sure about its exact meaning. The term has some ambiguities as data set and volume besides the typical database software tools. Kastalli and Van Looy (2013) demonstrated that big data as ‘high-volume, high-velocity, high-variety’ information for creating cost-effective and innovative data processing for business decision-making. Furthermore, big data is defined to pose challenges to integrate different data types and formats so that data velocity can be assessed, speed of data creation can be referred, and data can be processed and analyzed. Minelli, Chambers and Dhiraj (2012) emphasized that demand of new solution in current business model frameworks, is increasing in big data era. Big data can refer to ‘information asset’ for implications that it can lend impressive value to the organization. Veracity for big data refers to reliability consideration for data type and veracity of data is not about data quality; however, it addresses to uncertainty of data as weather forecasting.

 


Collection, storing, and analysis of big data are not effective regarding companies, though companies are interested for creating value proposition. Tan et al. (2015) cited several examples for depicting competitive advantages to data usage and analytics feasibility in certain organizations.

Figure 1: The Data-driven Business-model Framework (DDBM)

(Source: Hartmann et al. 2014, pp. 11)

There are examples such as Wal-Mart that uses big data for business model implementation and incorporation for new and proposed model. Gopalkrishnan et al. (2012) opined that according to main idea behind business model preparation, two fields are considered as in which big data creates value for organizations can be easily identified. Big data can be utilized for incremental business model improvement and current business model optimization for addressing services to the customers. For instance, the services can be offered to optimization of existing services, improving customer relationship, and process of innovation with employee collaboration. On the contrary, Kwon, Lee and Shin (2014) claimed that products and new business models have improvement tendency regarding the data usage. The papers regarding use of data described that in current business, models are often relied on data. However, Chesbrough (2013) cited an exception that some times, business models can be prepared based on models from partner data domain. Therefore, this finding comprises that business model is dependent over organizational data such as company name, addresses, identifiers, classification codes, and banking information. Wu, Guo and Shi (2013) incorporated their study based on six different organizations for utilization of partner domain information. The DDBM framework is chosen to describe and analyze all these different models.

As big data is in early innovation and implementation, still most potential in value creation is unclaimed. Kindstrom and Kowalkowski (2014) identified that industries are on path for rapid change and several new inventions, stakeholders are committed to innovation. Companies and service providers develop proactive measures for achieving initiatives in new business environment.

Conclusion

This literature review encompasses about the big data for business model implementation. Furthermore, proposed DDBM model is discussed as how it presents framework for allowing analysis of DDBMs. As per other studies regarding DDBM application in business, the studies stated that this framework serves several purposes of innovation with big data. Proposed DDBM framework has benefits of practitioners to work in big data research field. Furthermore, the DDBM framework has outlined dimensions so that architecturally, new business models can be developed for companies. Therefore, DDBM framework requires further research for essential contributions in big data field and regarding research area.

 

References

Carayannis, E.G., Sindakis, S. and Walter, C., 2015. Business model innovation as lever of organizational sustainability. The Journal of Technology Transfer, 40(1), pp.85-104.

Chesbrough, H., 2013. Open business models: How to thrive in the new innovation landscape. Harvard Business Press.

Gopalkrishnan, V., Steier, D., Lewis, H. and Guszcza, J., 2012, August. Big data, big business: bridging the gap. In Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications (pp. 7-11). ACM.

Groves, P., Kayyali, B., Knott, D. and Kuiken, S.V., 2016. The'big data'revolution in healthcare: Accelerating value and innovation.

Hartmann, P.M., Zaki, M., Feldmann, N. and Neely, A., 2014. Big data for big business? A taxonomy of data-driven business models used by start-up firms. A Taxonomy of Data-Driven Business Models Used by Start-Up Firms (March 27, 2014).

Kastalli, I.V. and Van Looy, B., 2013. Servitization: Disentangling the impact of service business model innovation on manufacturing firm performance. Journal of Operations Management, 31(4), pp.169-180.

Kindstrom, D. and Kowalkowski, C., 2014. Service innovation in product-centric firms: A multidimensional business model perspective. Journal of Business & Industrial Marketing, 29(2), pp.96-111.

Kwon, O., Lee, N. and Shin, B., 2014. Data quality management, data usage experience and acquisition intention of big data analytics. International Journal of Information Management, 34(3), pp.387-394.

Loebbecke, C. and Picot, A., 2015. Reflections on societal and business model transformation arising from digitization and big data analytics: A research agenda. The Journal of Strategic Information Systems, 24(3), pp.149-157.

Minelli, M., Chambers, M. and Dhiraj, A., 2012. Big data, big analytics: emerging business intelligence and analytic trends for today's businesses. John Wiley & Sons.

Richter, M., 2013. Business model innovation for sustainable energy: German utilities and renewable energy. Energy Policy, 62, pp.1226-1237.

Schaltegger, S., Ludeke-Freund, F. and Hansen, E.G., 2012. Business cases for sustainability: the role of business model innovation for corporate sustainability. International Journal of Innovation and Sustainable Development, 6(2), pp.95-119.

Schmarzo, B., 2013. Big Data: Understanding how data powers big business. John Wiley & Sons.

Tan, K.H., Zhan, Y., Ji, G., Ye, F. and Chang, C., 2015. Harvesting big data to enhance supply chain innovation capabilities: An analytic infrastructure based on deduction graph. International Journal of Production Economics, 165, pp.223-233.

Wu, J., Guo, B. and Shi, Y., 2013. Customer knowledge management and IT-enabled business model innovation: A conceptual framework and a case study from China. European Management Journal, 31(4), pp.359-372.

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