Trayectoria del Grupo

In order to fully explore the existent and prospective capabilities of the big data boom, business organizations need to enforce certain modifications to their in-house operational procedures by considering the three kinds of stakes described in what follows (see also what discussed in Chap.6).

• Data: Business organizations need systems to collect large magnitudes of information in specific industrial formats allowing for stress-free accessibility and analysis. However, due to the enormous proliferation of online social networks and allied technologies, most large organizations now have this—in fact; they normally have more than they are able to utilize (Bughin et al.2010; Groves et al.2013).

• Analytical tools: The second stake required is cutting-edge analytical tools, such as, e.g., NoSQL and Hadoop (see also the Preface and Chap. 6 in this book). NoSQL and Hadoop are proprietary open-source tools and platforms which are universally accessible (Morabito2014); but the biggest challenge is getting the right people proficient enough to make good use of them (Kelly et al. 2014; Olson2010).

• Expertise: This is usually the most challenging category of the stakes: advanced analytics necessitates a workforce with high-tech skills in data science to global privacy laws, as well as a proficient knowledge of the business including relevant sources of value (Lohr2012).

Nevertheless, the mere acquisition of the aforementioned stakes is insufficient in helping organizations because big data is not a standalone additional technology initiative. Frankly, big data is not a technology initiative at all; it is actually a business system that needs technical savvy. Therefore, the sheer addition of capability and expertise alone cannot enable the information technology functions to start engendering database insights. The big leaders of analytics have discovered that to succeed with big data, organizations need to practice the consistent process of deeply embedding big data into their business models (Manyika et al. 2013). This is the most efficient technique to guarantee that derived insights are pooled and shared across all units within the organization. It also guarantees that the entire organization identifies the combined effects and scale benefits that well-conceived analytics proficiency can deliver (Ji et al. 2012). Taking the above issues into account, big data analytics can be relevant to organizations in four areas listed below:

• Refining current products and services

• Refining in-house processes

• Developing innovative product offerings

To get a clearer meaning of the concepts mentioned above, the following sec- tions presents a guideline for business organizations to follow if they are keen on modifying their operational procedures in order to develop innovative products from the insights derived from big data’s analytics.

Firstly, however, let us consider a basic aspect of starting any business organization: goals. This is the best place for organizations to start the big data embedding process; clarifying out their goals. Examples of some useful goal-oriented statements are;“we will integrate cutting-edge analytics and insights as the critical component of all major decisions” or “we will adopt big data systems as a new-fangled way of undertaking our marketing strategies”. A well-defined goal by C-Level executives is a vital prerequisite for the kind of organizational change big data offers. Additionally, C-Level executives also need to answer these sorts of questions e.g.,“How far are we ready to go”?“How will our business performance improve through big data”?“What do we need to focus on”?

7.4.1

An Incentivized Approach

Many organizations are possess opportunities for complex analytics, still, very little of them have the capabilities to further improve their trajectory by accurately defining priorities and selecting the right angle of entry. With the organization’s goals well defined, executives can work on developing a horizontal analytics capability. They learn how to overcome internal struggle and create the skill to utilize big data throughout the organization (Yan2013).

Organizations do not easily change and the value of analytics may not be obvious to everybody. Therefore, a continuous work in helping employees and customers change their daily behaviors as well as continuing in the new innovative path without lapsing is necessary. Business organizations need to clearly define the owners and sponsors for analytics initiatives. By providing adequate inducements for behaviors that are analytics-driven, they will ensure that big data is integrated into processes for making key decisions (Russom2013).

A good example of an incentivized approach for adopting analytics is Nordstrom; Nordstrom, a leader in the retail space when it comes to enhancing the online shopping experience raised the obligation for analytics to an upper managerial level in its organization in order to make analytical tools and insights extensively accessible including integrated analytics-driven goals into its vital tactical initiatives.

7.4.2

Creating a Centralized Organizational‘Home’

Leaders of big data need to create an organizational ‘home’ for their progressive analytics proficiency, which can be supervised by a principal analytics officer. The process of creating an organizational ‘home’ embroils some important design

choices (Demchenko 2013). An organization has to define its approach for the deployment of big data. Furthermore, the collection and ownership of data across corporate functions has to be assigned and a well-structured plan to generate insights must be implemented (Yan2013). The technological infrastructure, privacy policy and access rights must be hosted and maintained by the organization. However these tasks seem quite burdensome to achieve, hence we present four models organizations need to adopt in order to comply with the needed changes for big data optimization.

• Organizational division: If each business organization’s division have their distinct data sets they become capable of making their specific big data decisions with reduced supervision or monitoring. An example of organizations that uses this business model are AT&T and Zynga (Pearson and Wegener2013).

• Organizational division with central support: This is when organizational divisions decide their own choices then co-operate to work on designated initiatives. An example of organizations that uses this business model are Google and Progressive Insurance. Progressive, Insurance depends on it to capture driving behaviors and define customer risk profiles (Bain Insights2013; Pearson and Wegener2013).

• Center of Excellence(CoE): In this model, a self-governing center supervises the organization’s big data. Various organizational divisions follow initiatives guided and coordinated by CoE. Also, Amazon and LinkedIn are examples of organization’s that depend on this model (Demchenko2013).

• Fully centralized: In this model, the corporate center is responsible for priori- tizing business creativities. Netflix is an example of a business organization that adopts this approach (see also Chap.6). The business goals and operating model of an organization greatly affects the type of model they adopt for big data change (EMC2013). An organization with in-depth analytics capabilities as well as a focus on experimentation, testing and innovation, such as, e.g., Google, normally depends on a decentralized method (Couchbase2010). However, CoE is found to be the mostly adopted model due to its enormous advantages and the fewer restrictions. An operational CoE allows cross-business-division access and data sharing capabilities. In addition, CoE assists in providing organizations adequate analytics strategies thereby setting the roadmap for the maintenance of privacy policies (Pearson and Wegener 2013). For example, a foremost European telecommunications organization, is attempting to change its internal procedures by deploying big data to analyze customer data in order deliver improved services and offers as well as utilizing network traffic data to enhance network supervision and investments. These capabilities will all be coordinated by a CoE.

7.4.3

Implementing the Changes—First Steps

As business organizations are already delving into the world of big data, the com- plexities we have discussed necessitate the need for analytics capabilities to be anchored in the organization in order to create substantial insights (Meeker and Hong

2014). Currently, many organizations executives are carting beyond competitors, while others are making intense efforts to keep up with pace. Nevertheless, thefirst step needed is the benchmarking of the organization and determination of the organization’s present situation in big data analytics, compared with that of rivals. This process will further assist the organization in deciding the essential investment needed to advance their relative position. In circumstances were an organization is considerably behind the rivalry, a rapid innovative platform is often compulsory to create and withstand change. This begins by testing hypotheses in order to study where and how cutting-edge analytics is most expected to benefit the organization (Computer Research Association2011).

7.5

Methodologies for Big Data Innovation