Conclusiones del proceso de recapitalización

While businesses across industries recognize the imperative of big data, there are many challenges that face the research and evolution in this field. The most prevalent are skill set shortages, cultural barriers, processes and structures, and technology maturity levels. These issues are discussed in what follows.

1.5.1

Skill Set Shortages

One commonly referred problem with respect to big data is having the right people. Data scientists are often PhD graduates who combine mathematical and program- ming skills, able to interrogate the databases to uncover trends and build predictive models to check different scenarios (Kearney 2014; Davenport and Patil 2012). Unlike large online retailers, such as Amazon, technology companies, such as Google and Financial institutions, most organizations had not invested in such expertise. While Data scientists are a rare commodity and very likely to be highly pricey, assess to it may not be impossible, as outsourcing companies are popping up. For example,Exerfy.com is a Harvard spin off, that specializes in resourcing Data Scientists for organizations or undertake Analytics projects on their behalf (Harvard Innovation Lab 2014). Perhaps, even the pool of expertise is not that small. Big data opens a new employment avenue for mathematicians and data modeling people working forfinancial institutions and perhaps students with strong mathematical skills are likely to be drawn to this new field. Perhaps even open source coders will create ever so user-friendly, predictive analytics freeware that can be interrogated by none experts, in natural language. After all, some freeware tools already exist for those who are statistically inclined.

1.5.2

Cultural Barriers

While market-driven innovations require insight, design-driven innovations call for foresight! Strategic and Technology foresight go hand-in-hand. For company boards who long viewed IT as a support function, the struggle to change their attitude, attract and keep the right talent will be even harder. Spencer Stuart’s 2011 Index shows that the average age of the Standard and Poor’s 500 board members is 62.4 years (Bricker and Eckler2014). C-level management in the US may have a hard time shedding old assumptions and wholeheartedly fostering new ways of conducting businesses.

In addition, in a world where competitive advantage is created and depleted quickly, capability may be a matter of sourcing, investments, and alliances rather internal evolution and development; thus, patterns of capability development may be common in start-ups as well as technology giants (Fine et al.2002).

Organizations that have learned to collaborate, they will now be more able to compete. Innovation 3.0 (Hafkesbrink and Schroll2011), for example, is based on collaborative practices, where it is not companies but communities of interest and communities of practice, with wide participation for suppliers, customers, even competitors who seek a benefit in creating a common purpose. Still many orga- nizations are struggling with internal animosities, silo mentalities and professional territories. Big data has become a new reason tofight about, as marketing compete against IT departments for big data ownership, and of course for the investment budgets that go with it (Gardner2014).

1.5.3

Processes and Structures

For big data driven strategies to succeed they need to be implemented, and they need to be implemented rapidly. This fundamentally means changing processes and possible structures and architecture, with knock on effects on business and technical architecture.

Particular after years of pursuing process standardization to affect operational efficiencies, people and departments are now stuck in their ways. Job descriptions are tight down to particular roles and so are reward systems and remuneration. Departmental and directorial kudos depends on the budget allocate to them. Big data driven innovations demand enterprise-wide collaboration, flexibility and knowledge sharing. Inevitably this will require organizations to take a modular approach to their structure, to enable them to mix and match accordingly. It will also mean that company policies, remuneration and leadership orientation will have to change or perish.

1.5.4

Technology Maturity Levels

To enter afield still in development, particularly one that depends a lot on emerging technologies, one need to plan for the technology obsolescence and technological skills renewal. The big data sphere both in terms of data collection, quality controls and analysis is still in development and inevitably investments in any platform run an inherent risk of becoming outdated or disrupted by new technologies. For example, innovations on database infrastructure, such as orthogonal frequency- division multiplexing (OFDM) (Werbach and Mehta2014), can facilitate real-time analytics, in ways that is not currently possible. Advances in in-memory computing capability, such as Non-volatile memory (NVM) devices (Lankhorst et al.2005), can address energy consumption concerns and analytics speed (Chen et al.2014). Adoption of ubiquitous applications will transform not only what we can do with technology but our attitude towards it, with implications on company, public policy, and the organization of social life (Lesk2013; Adamson et al.2012).

1.5.5

Organizational Advantages and Opportunities

Much like with most disruptive innovations, embarking on big data utilization projects will accrue a number of organizational benefits. Those benefits are discussed below:

a. Improve decision making by lowering the cost of better quality information analysis

b. Improve business performance by disseminating information more effectively across the organization

c. Improve collaboration by developing a common, enterprise-wide business intelligence, integrating views on identified business opportunities

d. Generate and pre-test value propositions utilizing advanced and discovery analytics.

Others focus more on new opportunities that arise from the utilization of big data and big data analytics. For example, Michael and Miller (2013) are arguing for the opportunities that will arise from mining non-text data, such as videos, pictures, and voice, as well as humans—machines and machine-to-machine interactions (LaValle et al.2011). We’ve also assisting at the steadily increasing of the amount of data captured in bidirectional interactions, both people-to-machine and machine- to-machine, by using telematics and telemetry devices in systems of systems. Particularly, interesting is the impact of big data on Health-related industries. Integrating and sharing different forms of biological information, from high-reso- lution imaging such as X-rays, Computed Tomography (CT) scans, and Magnetic Resonance Imaging (MRIs) to health records and lifestyle choices, expert com- munities can get a better understanding of what makes us ill and what keeps us healthy. According to Adrian Usher, Chief Information Security Officer of the of the Skype division at Microsoft, particularly interesting for the sector will be the integration of nanotechnology embedded in people, that will be utilized as a monitoring and diagnostics tool (Shah2013).

While big data visionaries talk about business advantages and opportunities, others warn about its risks, particularly those of infringing on our privacy and abusing our civil liberties, as well as being discriminated against (Lerman2013). Data breaches and security concerns are pertinent both in decentralized, ubiquitous and in centralized cloud conditions; also concerns about privacy and confidentiality are not characteristics about big data, but on social data utilization in general (see digital business identity issues discussed in Morabito (2014)).

The real“elephant in the room”is that big data analysis seeks to make inferences about who we are based on our online behavior as if this is the whole picture. In addition, predictive analytics are modeled based on human theories about cause and effect attributing perhaps the wrong labels to people. For example, Jeffrey Zaslow gives an account of how he‘wrestled’with his Tivo machine algorithms to avoid inaccurate stereotyping based on his TV recordings (Zaslow 2014). While in entertainment this makes a funny account, things could get more serious in healthcare.