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This chapter provides an overview of the status of recent developments in health information at a national level in selected developed countries, including common strategies, trends, and future directions. It draws primarily on the experience of countries other than the United States. It argues that the evolving and expanding understanding of health, population health, and their determinants has led to increased demands for more and different data and information. In particular, these demands focus on the need for broader data on multiple determinants of health, as well as data capturing a broader concept of health. Different countries have responded to these new demands in different ways, but common themes are emerging in those national responses.

This chapter explores the emergence of passive data capture as a compelling source of Big Data. Various types of passive data capture are discussed, such as those stemming from RFID technology, mobile devices, home monitoring devices, GPS, and data sensors in automobiles and agricultural equipment. The merits of passive data capture and active data capture, which is deliberate and limited, are discussed. Inevitable clashes over the ownership and permissions in using passive data are discussed and privacy issues are addressed. Specific direction is provided on how firms can move to passive data capture processes to increase the scale and velocity of Big Data creation.

The reality that most people in developed countries have a phone or even a smartphone has created a network of sensors and “data reporters” at virtually no cost to firms. Location-based services have emerged, stemming from the network of users connected to mobile apps. This chapter includes a case study of Foursquare through which the merits of location-based services are examined. The opportunities to repurpose data from mobile systems to solve challenges in real estate evaluation, customer behavioral modeling, and even customer communications are explored. Best practices for handling and creating location-based data are presented along with lessons on how to best leverage location-based data for value creation. A great focus is given to the development of location-based services through mobile apps and how such data can change business models.

This chapter demonstrates how the dimensions and features of Big Data allow for a plausible gauge of market, firm, and asset performance and valuation by repurposing data from one business process to solve an economically valuable and challenging problem in another business. An example used illustrates the hypothetical leveraging of mobile phone data to measure physical presence at a store or billboard and correlates it to real estate valuation. Various other examples from the insurance, agricultural, and pharmaceutical industries are provided. The concept of asset surveillance and how Big Data can enable indirect asset surveillance are developed. The challenges and opportunities in creating insightful data on another other firm’s assets are discussed and the implications for ethically and legally challenging industries like health care are examined.

The development of ubiquitous mobile platforms and extensive data capture by mobile apps has brought a new level of, and demand for, precision in measuring customers, transactions, properties, assets, and various other market activities. The exact data captured by mobile phones is examined. The data created by social networks and the ability of firms to measure the previously immeasurable are discussed. The movement to high precision in Big Data is examined in health care as a means of delivering highly personalized medicine. Attention is given to the importance of precision in unraveling complexity in various areas of science and human health and behavioral studies.

The main objective is to help physicians carry out those aspects of clinical trials described in this book. . . .Each individual involved in conducting a trial should be qualified by education, training, and experience to perform his or her respective task(s). . . . . . . —[ICH-GCP 2.8]. . . It is hardly rational to expect people to do things properly unless they are shown how, so that the importance of formal training and education for everyone involved with the pharmaceutical industry should be self-evident. The perpetuation of bad habits by those who are poorly trained and educated, whether in pharmaceutical companies or at investigator sites, is one of the root causes of the inefficiencies in drug development. Yet, while few people (whether or not they actually educate people properly) would dispute the importance of well-trained employees, it is uncommon for most companies to insist that all of those upon whom they depend are properly trained as well. Among those who are rarely trained to meet the required standards of excellence are CRO staff and investigators and their site staff. Negotiations with CROs rarely involve an assessment of how the various tasks are performed and whether or not they are carried out to the standards expected by the sponsor staff. This is not to imply that sponsor employees are better at what they do than CRO staff. What is true, however, is that sponsor employees should know how to do things the way the sponsor wants them done, which in turn should correspond to the sponsor’s notion of excellence. CROs will always profess to have all the processes requested by a sponsor, so that a laissez-faire attitude pervades negotiations. However, CROs quite understandably have their own ways of working, and unless alternatives are specified contractually, what the CRO does is what the sponsor gets. The next logical step after specifying what is wanted is to provide the necessary training for CRO staff. Where education and training are most needed and are carried out most poorly is in the case of clinical trial investigators.

In this digital world in two days we create as much information as we did from the beginning of time until 2003. The volume of data being captured and stored is mind boggling. It seems that there is quantum disruption coming in the decision-making process in the way massive amounts of data and its analysis is being used to make decisions. Data about personal choices are collected at every interaction point, data location flows through the daily use of mobile devices, and these are being used by companies for making business choices. It may seem that those who are in control of data may know more about the person than the person himself; however, if you look at Brexit and the US elections, data analytics pretty much failed to deliver significant insight.