The Big Data gold rush transcends business categories. Global spending on Big Data has exploded to reach $125 billion in 2015, making it a major game changer across virtually all industries , likely to grow at double-digit rates in the years to come. Broadly, Big Data are “high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making” .
As this data explosion continues, the amount of stored data is predicted to double every three years until 2020. Furthermore, these data will be generated in new ways, shifting increasingly from those obtained in interactions between humans and computers (e.g., personal computers, smartphones, self-service terminals) to automated machine-to-machine interactions. Smart objects equipped with sensors provide information about their status and their surrounding environments; by 2020, there will be approximately 50 billion of these objects in use.
The development of this so-called Internet of Things likely will lead to value creation for both public and private sectors, through productivity improvements, energy optimization, greater safety, and time savings that may be worth anyway from $3.9 to $11.1 trillion by 2025. Combining all these generated data also will enable data-driven services, providing both firms and their customers with more and better knowledge and enabling them to make better, smarter decisions—or even making the decisions for them.
Big Data enables new services
The potential benefits of delving in Big Data include new insights into unexpected relationships, such as those between a plane passenger’s order of a vegetarian meal and not missing the flight; outcome predictions, such as flu outbreaks anticipated on the basis of Google search terms; or helpful customer recommendations, such as products to stock in the refrigerator for an upcoming party. Business-to-business firms also leverage Big Data opportunities. Rolls-Royce services thousands of airplane engines and propulsion systems, and it already has equipped these products with numerous sensors, to be able to optimize its maintenance scheduling or dispatch engineering teams to make repairs in real time.
In entirely new business models, firms can rely on their superior data collection, aggregation, and analysis capabilities, rather than their physical assets. Waze is a peer-to-peer navigation app that Google recently acquired for $1 billion. Commuting Waze members act as smart sensors, offering real-time updates to other members, as well as aggregated information services to radio stations, advertising firms, and municipalities. The proliferation of mobile devices and connected versions of traditional devices also offer opportunities for new services. In these cases, consumers are buying not the products but rather—as Amazon CEO Jeff Bezos put it in reference to the Kindle device—are paying to access a handheld media store. The continuous flow of data from smart objects thus enables new service offerings, greater safety, and improved asset utilization. For example, by inserting sensors into cement, municipalities can monitor cracks in bridges remotely and communicate real-time traffic information to commuters to optimize traffic flow.
Big Data and their emergent services thus require established and new firms to rethink their strategies, structures, and activities. We believe that service research can be instrumental in driving and supporting this transformation. Accordingly, we describe several illustrative roles for service management in organizations and their activities related to Big Data; we also highlight some options for service researchers that emerge from these new activities.
Service Strategist: Be vigilant and seize the business opportunities of Big Data
A service strategist reviews the impact of new technologies, such as machine-to-machine communication, wearables, virtual reality, or 3D printing, then develops future scenarios for value creation with customers. Emerging data-driven services should stimulate vigilant executives to assess their impact on the firm’s business model and competitive position.
Service researchers are well positioned to identify new logics of interactive value creation, new competitors, and new structures for data-driven business models. Strategic decision making can benefit from in-depth research into complex service ecosystems, including definitions of which roles and activities an organization should adopt. Other research might propose innovative ways to connect service-specific measures (e.g., customer engagement, service operation efficiency) with longitudinal financial data to attain reliable insights into the strategic impact of services.
Service Data Architect: Connect Big Data and validate
The service data architect understands how to store and process Big Data sets, with a customer-centered mindset. Key activities including developing an overview of available data sources (collected continuously, periodically, and ad hoc, both inside and outside the firm); linking them enables holistic, real-time analyses of customer–firm relationships and customer experiences across communication channels.
Service researchers might investigate the interoperability of data and suggest meaningful and rigorous ways to connect them. Studies that identify underlying assumptions and limitations that hinder accurate interpretations, such as correlation as opposed to causation or misleading patterns, represent essential input for service data analyst.
Service Data Analyst: Develop Big Data methods and present results
With its complexity, Big Data demands new analysis methods, such as machine-based learning, text mining, and audio and visual assessments, that are not taught in statistics classes. A service data analyst implements predictive analytics tools to optimize decision making, though the complexity of both the data and the algorithms used to analyze them prevents simple traceability.
Service researchers gradually are shifting their focus to analyses of secondary, behavioral data and new methods. A lack of traceability implies the need for a better understanding of potentially biased interpretations of visual and textual analytics. Service researchers also need to develop new service-specific methods: contextual services feedback using mobile devices that combine sensory with perceptual data (e.g., heat maps of customer traffic plus surveys), behavioral assessment of services encounters (e.g., eye tracking of service encounters), or frequent experiments of customer service provision (e.g., A/B testing with manipulated websites for selected audiences) for example.
Service Development Manager: Rapidly develop Big Data services and involve stakeholders
Middle management’s key responsibility is the continuous development and improvement of data-driven services. Shorter development cycles demand a shift toward agile development processes and new tools for designing smart services. Network-based service systems often involve multiple partners and affect stakeholders beyond the consumers, such as employees, nongovernmental organizations, or society at large.
Service research can support organizations in examining dynamic, nonlinear development processes with rapid service prototyping, as well as in determining success factors for integrating (potentially conflicting) stakeholders in networked service systems. Research opportunities also relate to the types of services enabled by smart objects and customers’ usage, adoption barriers, or willingness to pay for them. The benefits of new services are manifold, ranging from convenience (e.g., self-driving vacuums) to cost savings (e.g., car insurance premiums based on actual driving behavior rather than projections) to improved health (e.g., continuous monitoring of chronic diseases). Research should address mechanisms to balance the inherent need for personalized data to provide these services against consumers’ and policy makers’ increasing concerns about data privacy.
Service Interaction Personnel: Be empowered by Big Data and automate
Integrating customers’ online and offline experiences, by linking various customer information, transforms the role of customer contact personnel. Automated, real-time analytics empower them to be truly customer-centric, even as customers perform more activities themselves, using 3D printers or intelligent autonomous systems such as robots.
Service research might identify real-time information (and suitable presentation formats) derived from Big Data that empower service interaction personnel, then assess the impacts on service performance. For example, a predictive analysis helps personnel anticipate which customers are most likely to defect in the near future. A seamless customer experience demands the orchestration of touch points, using low and high tech channels, with and without the involvement of human service interaction personnel. Research could establish the skills, incentives, mindsets, and profiles that service interaction personnel and customers need for different touch points. Highly automated, neural systems, such as service robots or automated call centers, grant service researchers additional data sources but also demand a deeper understanding of the opportunities and limitations of self-learning service systems.
The proliferation of Big Data thus has already and will continue to transform how organizations create value with their customers. With its interdisciplinary and customer-centric approach, service research is uniquely positioned to take an active role in supporting and advancing this transformation.