Part II Organization
7.2 Big Data — The Key Innovative Techniques
The big data age has produced innovative principles for business management. In previous eras of technological innovation, business pioneers learnt that the smallest well-organized scale was a crucial factor of competitive success (Cui et al.2014). Similarly, competitive profits are possible to be accumulated by organization’s collecting more and enhanced data but also utilize such data at an efficient scale (Bjelland and Wood2008).
Be it as it may, in what follows we critically reflect on thefive techniques shown in Fig. 7.1 (i.e., integration of data platforms, testing through experimentation, real-time customization, generating data-driven models, and algorithmic and
Integration of Data Platforms Testing through Experimentation Big Data Innovation Algorithmic and Automated- Controlled Analysis Real-time Customization Generating Data-Driven Models
automated-controlled analysis), in order to identify how big data may further assist business organizations in developing innovative strategies, which are adaptable to the rapidly changing models occurring in today’s highly extrinsic technological era.
7.2.1
Integration of Data Platforms
One of the biggest challenges encountered by many business organizations is accrual of datasets lurked departmentally, e.g. sales, marketing, service operations etc. (Dandawate 2013). Unfortunately, this business model impedes the timely utilization of the datasets for complex synthesized analytics (Dean and Ghemawat
2008). In addition, the amassing of information within business units brings a huge challenge for innovation. For example, numerous financial organizations stay limited from the failure to share their huge data between assorted business areas, such as money management, lending and financial markets (Economics 2013). Consequently, business organizations are being prevented from developing a coherent outlook of understanding inter-relationships amongstfinancial markets as well as individual customers (Davenport and Dyché2013).
Due to the growth of technological devices, information is now readily available to almost anyone (Mithas et al.2013; Russom2013; EY2014). This development poses a threat to the innovative potentials of organizations solely depending on exclusiveness of their data as the core resource of their competitive advantage (Davenport and Dyché2013). Considering the real-estate sector, which relies on trades of asymmetric information such as, e.g., access to transaction data and awareness of the‘bid and ask’behavioral pattern of purchasers. Such information entails a momentousfinancial cost as well as energy. Conversely, real-estate online experts in big data and analytics are now bypassing agents; thus creating online forums to allow purchasers and sellers to share their views on the value of properties as well as generating parallel bases for real-estate data (Dandawate2013).
A further put-down at exclusive or proprietary data is the assemblage of freely available satellite imagery that, when processed and analyzed, comprises indica- tions about physical amenities of an organization’s competitors (Stonebraker et al.
2013). These satellite detectives collect insights into business checks as exposed by the capacity of the physical amenities e.g. goods movements etc.
In order to develop innovative products from big data, organizations are now attempting to uncover their departmental reserves of exclusive data: the integration of datasets from multiple systems and the search of external suppliers’ and customers information to create new products is now a crucial determinant of their manufacturing costs. Extra integrated data platforms now permit organizations and their suppliers to pool insightful resources during the design phase (Cui et al.2014).
7.2.2
Testing Through Experimentation
With the advent of big data, there arise a novel potential for a different sort of decision making using controlled experiments. Currently, organizations are testing hypotheses and analyzing results to inform their business operational processes. Testing through experimentation are helping administrators differentiate causality from correlation, therefore decreasing the inconsistency of results and improving product performance (Mithas et al.2013).
For example, many online business organizations are continuous testers; by apportioning a quota of their web page views to undertake experiments that iden- tifies the elements that motivates the active engagement of end-users (Cui et al.
2014; Davenport and Dyché 2013; Parmar et al. 2014). In addition, businesses marketing their products physically also make use of experiments in order to influence their marketing strategies. For example, fast food merchants McDonalds, has equipped some of their outlets with devices that collect data about customer interactions, ordering patterns as well as in-store consumer traffic. The importance of these huge datasets is that experts are able to model the effect of variations on restaurant designs, menus sales and productivity (Kelly et al.2014; Qubole2014).
7.2.3
Real-Time Customization
In this section, we describe how organizations are innovatively using big data by making real-time customization conceivable. Organizations are now able to monitor the behavior of customers from click streams on the Internet, modify their inclina- tions, and most importantly model their possible actions in real time. Furthermore, they are learning to ascertain when customers are close to making a buying decision and then pre-empt the transaction process to afinishing point by bundling desired products combined with a reward offer. Real-time custom analysis can also increase procurements of products by the most valuable customers of a business organization (Drake et al.2014).
As an innovative driver, big data real-time customization is relatively obvious in retail sectors due to the size and value of data available from online procurements, interactions on online social networks (OSNs) and more lately, location-specific smart phone communications. Nevertheless, other sectors can also gain from this novel system of Big-Data application, owing to the growth of sophisticated analytical tools for distributing customers into more insightful micro-segments (De Fortuny et al.2013).
7.2.4
Generating Data-Driven Models
The growth of big data has enhanced the creation of innovative groups of businesses that have accepted data-driven business models. Several of these businesses perform inter-dependent roles in value chains generating valuable consumable data formed
by business transactions (Economics2013; Eisenhardt and Martin 2000; Fan and Bifet2012). For example, a transport business organization recognized that during their daily business operations, enormous volumes of information on product deliveries are being collected. In order to maximize this opportunity, the organiza- tion created a division that trades the data to complement business and economic predictions (Kelly et al.2014).
Another good example of the development of new models is an organization that got so many insights from analyzing data as part of a manufacturing improvement, which prompted a decision to build a business to do similar work for other organizations (Hartmann et al.2014). Currently the business combines supply-chain data for many manufacturing customers and sells software tools to improve their performance. Out- standingly, the newly developed service business now overtakes the income generated by the organizations parent manufacturing business (Provost and Fawcett2013).
7.2.5
Algorithmic and Automated-Controlled Analysis
As also discussed in previous chapters (see, e.g., Chap. 6), big data increase the development and implementation for algorithmic and automated-controlled analysis. In some manufacturing organizations, algorithms are used to analyze sensor data from lines of production, in this manner, the wastage of raw materials are limited, production outcomes are increased and then costly human involvements are avoided (Mithas et al.
2013). This type of data is usually analyzed by groups of computers to enhance production and diminish downtimes through the process of imputing their results to real-time tasks. Products such as photocopying machines used to aircrafts can now create streams of data capable of tracking their usage. In some situations, manufac- turers are able to analyze the inward data and then automatically repair software malfunctions. Besides, computer hardware merchants are collecting and analyzing such data to plan pre-emptive maintenance before customers’ operations are inter- rupted by system failures. As a means of innovation, such automatic-controlled data is also utilized to make necessary changes in product design (Elmqvist and Irani2013). The key point is value-added performance, improved risk assessment, as well as the capability to uncover useful insights that would otherwise remain unknown. Recently, more business organizations are participating in this decision-making industrial revolution due to the massive reduction in price of sensors, communi- cations devices and analytic software (Özcan et al.2014).