Data science has proliferated this past year and gained a recognized function within businesses and other organizations. New applications emerge constantly and the number of startups that are focused on data science and analytics has grown significantly, highlighting a shortage of skills in the workplace and creating a competition on qualified professionals and technologies.
Users’ data is being captured mainly through mobile devices today, whether we like it or not. While collection, analysis, determining relevance, and machine-learning are all combined to bring value. However, spying on users, information security and fraud are the most obvious threats, focus the conversation today around regulatory issues. At some point, we will need a set of principals that everyone can follow and consumers can trust. But would consumers want to stop companies and institutions from leveraging opportunities in data capture? What are the right ways to gather data and what would be the right approaches for its use?
VentureBeat’s Data Science Summit this week presented a series of 'Industry Spotlights' inviting experts to demonstrate a few examples that go beyond the leverage of big data and analytics in the marketing/advertising/customer service domains:
Finance/Banking: Financial institutions have been going through a revolution, moving toward digital banking and mobile banking. Such new financial business models are convenient to customers, enhance productivity, save time and more. At the same time, these mobile banking platforms disrupt traditional banking, leading toward process reengineering and optimization of existing workflow within the industry. Regulation of digital currency is another challenge which has not been addressed by policy. All of these digital financial transactions and mobile commerce proceedings generate data assets, which need to be secure. 'Safe' banking and building/maintaining customer trust are one challenge; another is appropriating value from the data collected, as well as the policy around this domain.
High education: How to collect and analyze complex datasets in order to improve student success and minimize dropping outs. Models take into consideration student behavior, courses lists and academic tracks, financial and personal circumstances, student motivation, and more.
Healthcare: Data science gives the ability to gain visibility to public health aspects that were not apparent before, such as diabetic risks among certain populations or in geographical areas. According to the Mckenzie & Company report (see below under ADDITIONAL INFORMATION), if the healthcare sector in the U.S. were to use big data effectively to drive efficiency and quality, it could create more than $300 billion in value every year. Two-thirds of that would be in the form of reducing US healthcare expenditure by about 8 percent.
Transportation: In automobiles we can collect data on vehicles and the driver's behavior while he or she operate the car. Insights can help make the car more modular, optimize the drivetrain and improve other physical, mechanical and technology systems in the vehicle. People drive cars differently; analyzing the data can enable auto makers to have a better understanding on how to re-design the vehicle operations to improve performance, to incorporate safety features by extracting information on drivers’ habits, the way people accelerate and break and the impact on the vehicle breaks, etc.
Manufacturing: When selling a jet engine, the manufacturer makes revenue not on the sale of the unit itself but on a 30 years maintenance contract to guaranty a top performing and well maintained engine. Therefore, the risk of breakage or engine failure is passed on from the customer to the manufacture. This practice becomes a matter of risk management, driving the manufacturer to maximize their business performance by utilizing data science to best maintain the engines.
Emergency management and services: Data science is used to manage natural disaster events, by using data and analytics in impending and during a heavy storm and helping communities save lives by assessing and providing evacuation lines. Read more about “Utilizing big data modeling and analytics to improve urban resiliency.”
Data science is here to stay and can be used to improve our life, enhance education and create jobs. We are yet to progress with policy and ethics challenges, as well as adjust to various cultures. Time will tell how society and machines can work best together.
1. Mckenzie & Company's “Big data: The next frontier for innovation, competition, and productivity” report studied big data in five domains and found it can generate value in each: healthcare (U.S.), public sector (Europe), retail (U.S.), and manufacturing and personal-location data globally.
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