Outline of the Book
The book argument is developed along three main axes, likewise. In particular, we consider first (Part I) Strategy issues related to the growing relevance of Big Data and analytics for competitive advantage, also due their empowerment of activities such as, e.g., consumer profiling, market segmentation, and new products or services development. Furthermore, the different chapters will also consider the strategic impact of Big Data and analytics for innovation in domains such as government and education. A discussion of Big Data-driven Business Models conclude this part of the book. Subsequently, (Part II) considers Organization, focusing on Big Data and analytics challenges for governance, evaluation, and managing change for Big Data-driven innovation. Finally (Part III), the book will present and review case studies of Big Data Innovation Practices at the global level.
Thus, Chap. 8 aims to discuss examples of Big Data and analytics applications in practice, providing fact-sheets suitable to build a “map” of 10 interesting digital innovations actually available worldwide. Besides an introduction to the factors considered in the choice of each innovation practice, a specific description of it will be developed. Finally, the conclusion will provide a summary of all arguments of the volume together with general managerial recommendations.
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2 Competitive Advantage Definition: Old and New Notions . . . . . 4
1.2.1 From Sustainable to Dynamic . . . . . . . . . . . . . . . . . . 5
1.2.2 From Company Effects to Network Success. . . . . . . . . 6
1.3 The Role of Big Data on Gaining Dynamic
Competitive Advantage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3.1 Big Data Driven Target Marketing . . . . . . . . . . . . . . . 6
1.3.2 Design-Driven Innovation . . . . . . . . . . . . . . . . . . . . . 8
1.3.3 Crowd Innovation. . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.4 Big Data Driven Business Models . . . . . . . . . . . . . . . . . . . . . 10
1.5 Organizational Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.5.1 Skill Set Shortages . . . . . . . . . . . . . . . . . . . . . . . . . . 12
1.5.2 Cultural Barriers. . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
1.5.3 Processes and Structures . . . . . . . . . . . . . . . . . . . . . . 13
1.5.4 Technology Maturity Levels . . . . . . . . . . . . . . . . . . . 13
1.5.5 Organizational Advantages and Opportunities . . . . . . . 13
1.6 Case Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
1.7 Recommendations for Organizations . . . . . . . . . . . . . . . . . . . . 17
1.7.1 Ask the Right Questions . . . . . . . . . . . . . . . . . . . . . . 17
1.7.2 Look Out for Complementary Game
Changing Innovations . . . . . . . . . . . . . . . . . . . . . . . . 18
1.7.3 Develop Sound Scenarios . . . . . . . . . . . . . . . . . . . . . 18
1.7.4 Prepare Your Culture . . . . . . . . . . . . . . . . . . . . . . . . 18
1.7.5 Prepare to Change Processes and Structure . . . . . . . . . 19
1.8 Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
In this chapter we have discussed how big data can be utilized to achieve competitive advantage. We opened this discussion by reviewing the evolution of our understanding of competitive advantage and highlighted two advancements: on one hand competitive advantage is a dynamic ever evolving effort, as opposed to a sustainable asset. On the other hand shift from organizational to community ownership of competitive advantage.
Then we illustrated how big data can affect different aspects of a business model that offer add-on capabilities to organizations. In particular, we discussed the effects of big data on marketing, innovation and business model design (Kearney 2014), touching upon the different forms of collaborative organization upstream and laterally to provide value added offerings.
Current challenges for existing organizations were highlighted as well as organizational advantages from embracing big data and business opportunities that open up in the new era. Two case studies were discussed: Groupon and the Pharmaceuticals big data consortium, from different points of view. We highlighted the importance of scenario planning and the monetization of big data driven business models, as well as the role of big data to set into gear big changes in different sectors.
The question as to whether big data will provide a differential advantage to one company over another is still open. Whole sectors seem to embrace big data concurrently leaving their weakest links behind to perish. In addition, technology companies seem to have an advantage over others, as they have ample expertise to deploy in data analysis. This leads to big technology providers making inroads to
previously traditional sectors, such as, e.g., travel and retail. Thus, big data is fussing out industry boundaries and competition, perhaps even our understanding of
what competitive advantage is.
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.1.1 New Notions of Public Service:
Towards a Prosumer Era? . . . . . . . . . . . . . . . . . . . . . 24
2.1.2 Online Direct Democracy . . . . . . . . . . . . . . . . . . . . . 25
2.1.3 Megacities’ Global Competition . . . . . . . . . . . . . . . . . 25
2.2 Public Service Advantages and Opportunities. . . . . . . . . . . . . . 26
2.2.1 New Sources of Information: Crowdsourcing . . . . . . . . 26
2.2.2 New Sources of Information:
Internet of Things (IoTs) . . . . . . . . . . . . . . . . . . . . . . 27
2.2.3 Public Talent in Use . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.2.4 Private–Public Partnerships . . . . . . . . . . . . . . . . . . . . 31
2.2.5 Government Cloud Data . . . . . . . . . . . . . . . . . . . . . . 31
2.2.6 Value for Money in Public Service Delivery . . . . . . . . 32
2.3 Governmental Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
2.3.1 Data Ownership . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
2.3.2 Data Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
2.3.3 Privacy, Civil Liberties and Equality. . . . . . . . . . . . . . 34
2.3.4 Talent Recruitment Issues . . . . . . . . . . . . . . . . . . . . . 35
2.4 Case Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
2.5 Recommendations for Organizations . . . . . . . . . . . . . . . . . . . . 39
2.5.1 Smart City Readiness . . . . . . . . . . . . . . . . . . . . . . . . 39
2.5.2 Learn to Collaborate . . . . . . . . . . . . . . . . . . . . . . . . . 40
2.5.3 Civic Education and Online Democracy . . . . . . . . . . . 41
2.5.4 Legal Framework Development . . . . . . . . . . . . . . . . . 41
2.6 Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
2.6 Summary
This chapter discussed the impact of big data in the context of public service provision and new opportunities for public service organization and structure that may transform the role of governments in societies. We started our analysis by discussing developments in public service provision, which treats citizens as prosumers (proactive consumers) of public service delivery, moves towards direct online democracy, and finally, to active engagement and a global smart megacities competition for resources and talent.
In this context, governments seek to gain an advantage by utilizing a) new sources of data, such as Crowdsourcing, Internet of Things, b) engage public talent, c) institutionalize private–public partnerships and d) seeks for new models of valuefor-money public provision. Despite its potential, the adoption of big data and analytics are not without challenges, particularly for central governments. Of particular interest are the challenges regarding data ownership, data quality, privacy, civil liberties, and equality, as well as public sector’s ability to attract big data analyst talent.
We showcased two case studies demonstrating how new forms of public service provision. Barcelona Smart City provides an example par excellence of collaboration between the private and public sector for regional redevelopment. Haiti’s emergency support during the 2010 earthquake disaster demonstrates how big data in the hands of passionate volunteers can organize and support with life-critical emergency services, providing a life example as to what can be achieved through the blend of human intuition and available big data integration and advanced analytics. Like most sociotechnical changes, challenges reside in the social sphere of technology acceptance and use, as well as with the regulation of such technology, hence our recommendations are directed towards auditing readiness for Smart City development, reskilling public servants with partnership management skills, developing public’s mentality of civic participation and updating legal frameworks to cope with developments in the big data area.
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
3.1.1 From Institutionalized Education to MOOCs . . . . . . . . 49
3.2 MOOC Educational Model Clusters . . . . . . . . . . . . . . . . . . . . 51
3.2.1 University-Led MOOCs . . . . . . . . . . . . . . . . . . . . . . 51
3.2.2 Peer-to-Peer MOOCs . . . . . . . . . . . . . . . . . . . . . . . . 52
3.3 The Role of Big Data and Analytics . . . . . . . . . . . . . . . . . . . . 54
3.4 Institutional Advantages and Opportunities from MOOCs . . . . . 55
3.5 Institutional Challenges from MOOCs. . . . . . . . . . . . . . . . . . . 57
3.6 Case Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
3.7 Recommendations for Institutions . . . . . . . . . . . . . . . . . . . . . . 62
3.8 Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
While MOOCs facilitated the distance learning aspects of formal education institutions, they also introduced an even more fundamental educational change; they opened up participation to education by enabling a peer-to-peer learning. MOOCs open new income streams for traditional institutions by not only relying on state education subsidies and student tuition, but though employment recruiting services, syndication, and sponsoring, as well as by advertising income, selling student information to potential employers or advertisers. Big data analytics can enable the personalization of the online learning process that was missing in previous online instruction methods and facilitate this to happen at a global scale. Used as a pedagogical tool in learning analytics, big data along with educational data mining and teaching analytics can all be seen as three aspects of the same solution to raising the education standards of the youth without necessarily increasing the number of educators required, thus making institutions more cost efficient. Utilizing existing knowledge and education models relating to student effort and success, institutions can use big data technologies to monitor students’ interaction with the system, alerting administrators when student engagement patterns change in order to initiate communication with the student, or indeed send a predetermined default email communication. Big data in the form of learning analytics focus on capturing student behavior and correlating it to achieving learning objectives, educational data mining seek to design predictive analytics models for student attainment while teaching analytics helps educator translate such findings into better course design and student support procedures and interventions.
The reality however is quite different, MOOCs platforms do not share some of the basic assumptions about student motivation and institutional norms, expectations, and obligations, so preexisting educational models are not a good fit for their analysis. Moreover, as the Livemocha case suggested, MOOCs could utilize gamification or other learning from online communities and worlds to ensure continuous engagement and commitment.
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
4.2 Implications of Big Data for Customer Segmentation . . . . . . . . 69
4.3 Implications of Big Data as a Value Proposition . . . . . . . . . . . 69
4.4 Implications of Big Data for Channels . . . . . . . . . . . . . . . . . . 70
4.5 The Impact of Big Data on Customer Relationships . . . . . . . . . 71
4.6 The Impact of Big Data on Revenue Stream . . . . . . . . . . . . . . 72
4.7 The Impact of Big Data on Key Resources
and Key Activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
4.8 The Impact of Big Data on Key Partnerships . . . . . . . . . . . . . . 74
4.9 The Impact of Big Data on Cost Structures . . . . . . . . . . . . . . . 75
4.10 Organizational Advantages and Opportunities . . . . . . . . . . . . . 76
4.11 Organizational Challenges and Threats . . . . . . . . . . . . . . . . . . 77
4.11.1 Creativity and Innovation Capability Deficit . . . . . . . . 77
4.11.2 Interrogating Big Data . . . . . . . . . . . . . . . . . . . . . . . 77
4.11.3 Plug and Play Architectures . . . . . . . . . . . . . . . . . . . . 78
4.12 Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
4.12 Summary
This chapter has discussed business models as the architectural logic that identify how business elements (such as a business structure, business processes, infrastructure, and systems) fit together to coordinate value creation (Osterwalder et al.
2005). It then went on to describe the impact of big data on each of the elements as identified in the Business model canvas proposed by Osterwalder and Pigneur (2010). In particular, it has discussed the new impetus that big data and IoTs technologies give to mass customization and personalization of product and services.
Furthermore, the chapter has investigated big data as a value proposition in its own right and how an industry may be created around the sales of big data and big data analytics technology, analytics consultancy and data scientist recruitment.
The chapter has also touched upon big data solutions for B2B and B2C logistics as well as for customer relationship management and customer service.
Furthermore, the chapter has analyzed the impact of big data on revenues, as it has facilitated new forms of value creation from the emergence of new currencies, in combination with social media and cryptocurrencies. Then, it has been also described how big data has emphasized the value of ‘utility from’ (as opposed to ‘ownership of’) capital resources. It has also been explained how this shifts the emphasis away from the organization as an entity towards an understanding of the organization as an a dynamic process of value creation. Also, the chapter has explored this a bit further by understanding the implications of key partnerships in the big data era and the spanning of organizational boundaries. It has also discussed the monetization implications; the opportunities and challenges it raises for accounting, budgeting and performance metrics. With these in mind, the chapter has highlighted some of the advantages, opportunities challenges and threats around business model innovation, relating them more to the readiness of organizations to assume such an undertaking, rather than describing the many possible big data driven business models to evolve in the future.
Finally, the chapter concludes acknowledging that while so far big data has been used to improve existing business models, far more futuristic scenarios will emerge in combination with other emerging technologies.
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
5.1 Introduction to Big Data Governance . . . . . . . . . . . . . . . . . . . 83
5.1.1 Big Data Types . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
5.1.2 Information Governance Disciplines . . . . . . . . . . . . . . 87
5.1.3 Industries and Functions . . . . . . . . . . . . . . . . . . . . . . 90
5.2 Big Data Maturity Models. . . . . . . . . . . . . . . . . . . . . . . . . . . 91
5.2.1 TDWI Maturity Model . . . . . . . . . . . . . . . . . . . . . . . 91
5.2.2 Analytics Business Maturity Model . . . . . . . . . . . . . . 93
5.2.3 DataFlux Data Governance Maturity Model. . . . . . . . . 94
5.2.4 Gartner Maturity Model . . . . . . . . . . . . . . . . . . . . . . 95
5.2.5 IBM Data Governance Maturity Model . . . . . . . . . . . . 96
5.3 Organizational Challenges Inherent with Governing
Big Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
5.4 Organizational Benefits of Governing Big Data . . . . . . . . . . . . 99
5.5 Case Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
5.6 Recommendations for Organizations . . . . . . . . . . . . . . . . . . . . 101
5.7 Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
5.7 Summary
This chapter attempted to introduce the readers with several guiding principles for data governance in a big data environment. Indeed, organizations need to take the right step in time to get the best out of enterprise data and big data. Without data governance, the data will be inconsistent, unreliable and unrepeatable. Data governance helps ensure that metrics are defined consistently within the organization. Clearly documented standards and definitions mean everybody can understand precisely what everybody else is talking about. Data governance provides confidence in key decisions, limit organizational costs and prevent analysis and reporting issues. Data governance encourages the measurement of successes and failures.
Furthermore, maturity models provide a framework for organizations to measure their success in managing data and information as an enterprise asset. Thus, data governance maturity models can be used and looked at as references in communication, awareness building, and the marketing of data governance. Big data governance is more than standards, reporting, and prioritization of projects; it is a business function providing structure for maintaining high data standards and securing against the risks of data theft or loss. Within ‘Big Data’ projects, privacy and regulatory controls play a pivotal role.
Finally, big data governance is not static; it must evolve over time to meet the changing objectives of the organization. Indeed, with the inclusion of big data, e.g., in decision-making or operations, changes may be bigger and, hence, governance has to be more comprehensive.
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
6.2 Digital Business Evaluation Using Big Data . . . . . . . . . . . . . . 106
6.3 Organizational Advantages and Opportunities . . . . . . . . . . . . . 108
6.3.1 Customer Value Proposition . . . . . . . . . . . . . . . . . . . 109
6.3.2 Customer Segmentation. . . . . . . . . . . . . . . . . . . . . . . 110
6.3.3 Channels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
6.3.4 Customer Relationship . . . . . . . . . . . . . . . . . . . . . . . 111
6.4 Organizational Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
6.4.1 Key Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
6.4.2 Privacy and Security . . . . . . . . . . . . . . . . . . . . . . . . . 114
6.4.3 Cost Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
6.5 Cases Studies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
6.6 Recommendations for Organizations . . . . . . . . . . . . . . . . . . . . 121
6.6.1 Hardware . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
6.6.2 Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
6.7 Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
6.7 Summary
This chapter discussed how big data and analytics can be used to evaluate business performance. It described six steps that summarize this process: Goals, Selection of Data, Processing Data, Data Mining, Evaluation and Visualization & Feedback. It then presented an analysis of the advantages and opportunities of using big data and analytics, identifying customer value proposition, customer segmentation, channel diversity, and better customer relationship as the most important ones. On the other hand, it also analyzed the challenges that organizations are facing when they want to adopt these technologies and create organizational advantage and highlights the importance of having skilled people in this area that is relatively new and thus the talent supply is scarce. Privacy and Security and the relatively high costs were also identified as challenges. Finally, this chapter closes with the analysis of two case studies: Facebook and Netflix, to demonstrate how these organizations have used big data and analytics to evaluate and shape their business models.
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
7.1 Introduction: Big Data—The Innovation Driver . . . . . . . . . . . . 125
7.2 Big Data—The Key Innovative Techniques . . . . . . . . . . . . . . . 126
7.2.1 Integration of Data Platforms . . . . . . . . . . . . . . . . . . . 127
7.2.2 Testing Through Experimentation . . . . . . . . . . . . . . . . 128
7.2.3 Real-Time Customization . . . . . . . . . . . . . . . . . . . . . 128
7.2.4 Generating Data-Driven Models . . . . . . . . . . . . . . . . . 128
7.2.5 Algorithmic and Automated-Controlled Analysis . . . . . 129
7.3 Big Data: Influence on C-Level Innovative Decision Process . . . 129
7.3.1 Stimulating Competitive Edge . . . . . . . . . . . . . . . . . . 130
7.3.2 Predictive Analytics: Data Used to Drive Innovation. . . 130
7.4 The Impact of Big Data on Organizational Change. . . . . . . . . . 132
7.4.1 An Incentivized Approach . . . . . . . . . . . . . . . . . . . . . 133
7.4.2 Creating a Centralized Organizational ‘Home’ . . . . . . . 133
7.4.3 Implementing the Changes—First Steps . . . . . . . . . . . 135
7.5 Methodologies for Big Data Innovation. . . . . . . . . . . . . . . . . . 135
7.5.1 Extending Products to Generate Data . . . . . . . . . . . . . 135
7.5.2 Digitizing Assets . . . . . . . . . . . . . . . . . . . . . . . . . . . 135
7.5.3 Trading Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
7.5.4 Forming a Distinctive Service Capability. . . . . . . . . . . 136
7.6 New Big Data Tools to Drive Innovation . . . . . . . . . . . . . . . . 137
7.6.1 The Hadoop Platform . . . . . . . . . . . . . . . . . . . . . . . . 137
7.6.2 1010DATA Cloud Analytics . . . . . . . . . . . . . . . . . . . 137
7.6.3 Actian Analytics. . . . . . . . . . . . . . . . . . . . . . . . . . . . 138
7.6.4 Cloudera . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138
7.7 Models of Big Data Change . . . . . . . . . . . . . . . . . . . . . . . . . 139
7.7.1 Big Data Business Model . . . . . . . . . . . . . . . . . . . . . 139
7.7.2 The Maturity Phases of Big Data Business Model . . . . 139
7.7.3 Examples of the Business Metamorphosis Phase . . . . . 142
7.8 Big Data Change Key Issues . . . . . . . . . . . . . . . . . . . . . . . . . 143
7.8.1 Storage Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143
7.8.2 Management Issues. . . . . . . . . . . . . . . . . . . . . . . . . . 144
7.8.3 Processing and Analytics Issues . . . . . . . . . . . . . . . . . 144
7.9 Organizational Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . 145
7.9.1 Data Acquisition . . . . . . . . . . . . . . . . . . . . . . . . . . . 145
7.9.2 Information Extraction . . . . . . . . . . . . . . . . . . . . . . . 146
7.9.3 Data Integration, Aggregation, and Representation . . . . 146
7.10 Case Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147
7.11 Recommendation for Business Organizations . . . . . . . . . . . . . . 149
7.12 Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150
7.12 Summary
This chapter has discussed how big data has become a key organizational asset, which represents a strategic basis for business competition. The radical development illustrated in previous sections is now influencing business organizations to consider new innovative techniques on maximizing the potentials of big data. We highlighted that the success of many organizations demands new skills as well as new perspectives on how the epoch of big data could advance the speed of business processes. One important factor of this technological process is the new analytics tools that have evolved together with new progressive business models. In this chapter, we explored the innovative capabilities of the growing big data phenomenon and addressed various issues concerning its methodologies for changes. Our findings are substantiated by describing the real-life cases of Adobe and Hewlett Packard organizations, which are globally considered as one of the biggest drivers of big data analytics.
We have addressed various issues concerning big data’s innovation techniques and modeling strategies adopted by many large business organizations. Without a doubt, it is clearly seen that big data is now integrated into the business processes of most organizations not because of the buzz it attracts but for its innovative capabilities to completely transform any business landscape. Although there the innovative techniques of big data are ever evolving, we were able to cover five significant ones, which are paving way for the development of new products and services for many organizations. We gave illustrations on Adobe and HP organizations that have increased the velocity of their decision making processes as well as improving consumer-buying behaviors through the integrated dashboard processing analytics capabilities of big data.
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150
8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157
8.2 Sociometric Solution. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158
8.2.1 Developer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158
8.2.2 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159
8.3 Invenio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160
8.3.1 Developer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160
8.3.2 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161
8.4 Evolv . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161
8.4.1 Developer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162
8.4.2 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163
8.5 Essentia Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163
8.5.1 Developer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164
8.5.2 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164
8.6 Ayasdi Core . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165
8.6.1 Developer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165
8.6.2 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166
8.7 Cogito Dialog . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167
8.7.1 Developer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167
8.7.2 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168
8.8 Tracx . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168
8.8.1 Developer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169
8.8.2 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169
8.9 Kahuna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170
8.9.1 Developer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170
8.9.2 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171
8.10 RetailNext . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172
8.10.1 Developer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172
8.10.2 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173
8.11 Evrythng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173
8.11.1 Developer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173
8.11.2 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174
8.12 Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175
8.12 Summary
This chapter has discussed examples of big data and analytics solutions in practice, providing fact sheets of 10 of the most interesting ones available worldwide in 2014. The evolution trends are going to concern a further focus on convergence of mobile services and social sensing, that is an increased exploitation of advanced analytics for behavioral analysis from intensive data streams as well as from big data.
Businesses and organizations from all sectors began to gain critical insights from the structured data collected through various enterprise systems and analyzed by commercial relational database management systems. However, over the past several years, web intelligence, web analytics, web 2.0, and the ability to mine unstructured user generated contents have ushered in a new data-driven era, leading to unprecedented intelligence on consumer opinion, customer needs, and recognizing new business opportunities. By highlighting several applications such as e-commerce, market intelligence, retail and sentiment analysis and by mapping important initiatives of the current big data and analytics landscape, we hope to contribute to future sources of value.
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175
Abstract
The book has discussed and presented challenges, benefits, and experiences in big data and analytics to a composite audience of practitioners and scholars. In this chapter conclusive remarks are provided as well as key advices for strategic actions as a result of the issues discussed and analyzed in this volume.
In this book we have discussed the key issues and impacts of big data and analytics to a composite audience of practitioners and scholars. In particular, we have focused the attention on their main strategic and organizational challenges and benefits. Thus, we have first framed big data and analytics to question how they can be utilized for achieving competitive advantage (Chap. 1). However, business is only one of the domains impacted by big data and analytics, other areas concern the public sector (Chap. 2) and education (Chap. 3), whose challenges have been consequently investigated. No matter the context and the sector, big data and analytics ask for a new understanding of the potential use of the actual information growth to design appropriate business models (discussed in Chap. 4). Furthermore, we have pointed out what needed at organizational level for improved big data governance (Chap. 5), business oriented evaluation (Chap. 6), and managing change for big data driven innovation (Chap. 7).
The different facets considered can be summarized in the key areas we have identified in 2014 (Morabito 2014a) as representative of a digital business innovative organization (see Fig. 9.1). In the case of big data we consider “business” in a more general sense as “an activity that someone is engaged in” or “work that has to be done or matters that have to be attended to” (Oxford Dictionaries 2014), thus, encompassing both private and public organizations. Consequently, to be innovative by exploiting big data and analytics in their digital business they need to take into account:
• innovation through appropriate business models (Chap. 4),
• collaboration through an effective governance (Chap. 5), and
• control through evaluation frameworks (Chap. 6), and accuracy in managing change (Chap. 7).
As a consequence, IT leaders have to be able to combine an appropriate knowledge of benefits and drawbacks related to big data and analytics utilization, in order to design
effective digital strategies and implement them within they organization.
As for these issues, this book has tried to provide insights as well as inspiring “templates” for putting in practice digital business innovation through big data and analytics. A practice we inaugurated in a former volume (Morabito 2014a), and we actually find useful for managers know-how.
Accordingly, Chap. 8 tells what could be called “10 short stories” about those which have been selected as interesting “global” experiences of the 2014. As for this selection, it is worth mentioning that also in this case as in Morabito (2014a), the choice concerns innovations that are actually applied and “in use”; thus, a pragmatic approach have been adopted, balancing between the so called “Wow” effect (i.e. the perceived novelty and interest in the idea), feasibility, and actual user adoption. Consequently, not only digital innovations potentially inedited if not disruptive, but also “ready-to-use” ones, have been selected and analyzed.
The above arguments and cases lead us to the big data lifecycle management (shown in Fig. 9.2 and discussed in our 2014 contribution on big data, Morabito 2014b) and the main challenges and IT actions identified there for big data for business value:
• Convergence information sources: IT in the organization must enable the construction of a “data asset” from internal and external sources, unique, integrated and of quality.
• Data architecture: IT must support the storage and enable the extraction of valuable information from structured, semi-structured as well as unstructured data (images, recordings, etc.).
• Information infrastructure: IT must define models and adopt techniques for allowing modular and flexible access to information and analysis of data across the enterprise. Furthermore, organizations must commit human resources in recruiting and empowering data scientist skills and capabilities across business lines and management.
• Investments: The IT and the business executives must share decisions on the budget for the management and innovation of information assets.
Taking these issues into account, as also discussed in Morabito (2014b), big data and analytics are key components of the digital asset of today’s organizations (as shown in Fig. 9.3). Indeed, business decisions and actions rely on the digital asset of an organization, although requiring different types of orientation in managing the information systems (IS). As for decisions, integration orientation seems to be required for satisfying the needs for optimization and effective data management of big data. Indeed, the greater the integration of a company’s information system, the faster the overall planning and control cycles (Morabito 2013). Therefore, applying to big data and analytics issues our SIGMA model (discussed in Morabito 2013), we argue that integration orientation constitutes a fundamental lever for facilitating the absorption and transformation of information and knowledge coming from big data and analytics into evidence-driven actions, helping manager’s decision-making and employees perform their work (Morabito 2014b). Furthermore, integration orientation is one of the determinants of organizational absorptive capacity, which, in turn, is theorized to affect business performance (Morabito 2013; Francalanci and Morabito 2008), thus, measuring the ability of an organization to cope with IT complexity or in our case with big data management and use by businesses. As a consequence, moving from decisions to action calls for an organization to improve IS absorptive capacity (Morabito 2013; Francalanci and Morabito 2008) in terms of the set of key orientations considered in the above mentioned SIGMA approach: analytics, information, process, and change orientation.
Considering these issues and what discussed in previous chapters, we point out that the framework in Fig. 9.2 is suitable to provide a systemic and integrated “working” representation of factors and drivers involved in managing digital assets, which aim to exploit the opportunities of big data and analytics for business performance and value. Finally, taking all the above issues into account, we hope the book has provided a toolbox for managerial actions in building what we call a big data intelligence agenda (Morabito 2014b).
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181
