In the final segment of this series about tomorrow’s population, big data, and personalized predictive analytics, I want to get personal. The series has primarily focused on cities because that is where the majority of the world’s population lives. We must remember, however, that the world’s cities are occupied by millions of individuals. Too often we lump all city dwellers together and treat them as a homogenous group (e.g., we say, “He’s a New Yorker”). New York City residents know, however, that the city is a melting pot of cultures and individuals. Understanding these differences is at the very heart of what makes a city smart — and only big data analytics can provide that understanding.

Big data can be empowering and transformative. Individuals, corporations, and governments all over the globe are generating zettabytes of data every year as they connect to networks using their computers and cell phones. People all over the world can search for and purchase consumer products; make dinner reservations at their favorite restaurants; perform research; conduct banking transactions across country boundaries; interact socially with their friends; perform activities associated with their careers; and deepen the interactions of their lives. And, as recent events have demonstrated, newly connected individuals in the developing world can also transform countries and drive social change using mobile telecommunications and social media. As these developing countries grow and millions of more people come “on-line,” the next large marketplace in the global economy is being created for consumer goods and services that cater to the needs and desires of these newly connected consumers (who are estimated to be more than 2 Billion in number). [“Capturing the world’s emerging middle class,” David Court and Laxman Narasimhan, McKinsey Quarterly, July 2010]

However, big data presents challenges for today’s networks and computing systems. Major challenges include: data comes from everywhere, is mostly unstructured, is in many shapes and forms, and is far too large to move. Historically, data has come from traditional structured sources, such as corporate and governmental computer systems. Today it increasingly comes from unstructured data sources such as the Internet, mobile devices, social media interactions, GPS location information, weather models, RFID, transportation and logistics scans that do not reside neatly within the tables and columns of traditional uniform databases and computer systems. What this means is that big data is too “Big” and too “Unstructured” to be currently leveraged by most organizations.

Even if the data could be centralized, today’s computing systems still have difficulty making sense of the data (i.e., understanding and learning) from the interactions between both related and seemingly unrelated data elements. Using current analytic techniques, most decision-making frameworks are challenged to process and understand volumes of data and then instigate actions that foster desired outcomes within timely decision cycles.

That is why it is essential in today’s business and government environments to employ technologies that process and analyze big data in a way much like the human mind senses its environment and processes data. For example, an individual assesses the risks of crossing a street when a car is approaching. The mind processes variables like car speed, distance, obstacles, personal motor skills, and so forth, before making the decision to cross or wait. Like human thought processes, cognitive reasoning ingests and transforms data into prioritized information; creates rich referential connections between data elements; enables understanding and learning; and is then presented as actionable intelligence (within relevant timeframes). This kind of cognitive reasoning can be used by businesses and governments to take on some of today’s most vexing challenges. Many of these challenges are cross-industry and cross-discipline in nature. They require complex simultaneous analysis on many levels that can model real, or open, world considerations. Some of these interdisciplinary challenges that apply to the global Consumer Products/Food and Retail Industries include, amongst many others, how to:

• Feed and provide water to a hungry and thirsty
  planet;
• Efficiently develop and deliver energy to a
  highly consumptive population without increasing the carbon footprint;
• Motivate consumer-centric outcomes with
  differentiated insights to maximize value for the consumer, Consumer Packaged
  Goods companies, and retailers alike;
• Personalize recommendations for global consumers
  about the products they purchase;
• Develop and deliver new drugs to cure disease
  and increase quality of life;
• Manage the global supply chain efficiently and
  with fewer risks; and,
• More accurately predict weather and its impacts on
  the environment.

As noted in previous posts, each of the world’s 50 largest cities is unique. Some have deep historical roots as large cities and others have only recently joined the list as a result of massive building efforts in places like China (see the attached map — based on a map from Free World Maps). As the global population continues to grow, new “greenfield” cities are likely to emerge. Regardless of whether a city labeled a “brownfield” (i.e., older) or “greenfield” (i.e., newer) city, some challenges they face are universal (e.g., infrastructure, sanitation, education, healthcare, food security, and so forth). Meeting those challenges, however, can differ significantly. That is where big data analytics plays its most important role.

Making smart decisions about urban growth and lifestyles is important. I agree with Parag Khanna, Director of the Hybrid Reality Institute and a leading geo-strategist, that cities will play a leading role in world affairs. [“Cities, Not Countries, Will Once Again be Key to World Order,” The National, 26 March 2013] He argues, “Urban corridors are a force multiplier, a source of great strength.” Such corridors can exist within a country as well as between countries. These corridors exist because they link the largest number of people and, therefore, provide the most opportunities (and challenges) associated with life’s endeavors. At the international level, most of the traffic along these corridors involves the flow of information, goods, and services. Ensuring that these flows are optimized is going to require cooperation and technology. Khanna believes there are seven activities that cities must embrace if they are going to provide a good quality of life for their residents. They are:

• First, use technology to empower the population. …
• Second, the use of scenario planning to forecast diverse possibilities and strategies for a turbulent world. …
• Third, complement urban master planning with economic master-planning. This means investing in the vocational training systems that prepare the labour force for rapidly shifting supply chains.
• Fourth, use data and social media as a tool of governance to more efficiently deliver public services and manage traffic.
• Fifth, constantly upgrade infrastructure to meet sustainability standards.
• Sixth, expand the economic footprint through investing in special economic zones in neighbouring countries.
• Seventh, and finally, think of all residents of increasingly multinational/ethnic cities not as citizens versus non-citizens, but as stakeholders.

For the remainder of this post, I want to concentrate on three of those activities: Using technology to empower people and companies; improving “rapidly shifting supply chains”; and upgrading infrastructure.

Using technology to empower people and companies

Since most of the new consumers that form the global middle class are found in cities, companies want to connect with them. Because business, like politics, is local, global companies need to act like local enterprises, regardless of their size, if they want to succeed in this new business landscape. That’s because cities are so diverse. Each neighborhood has its own character, lifestyle, and preferences. From one street to the next, the culture can change dramatically. If companies want to get in front of the money, they need to understand neighborhood differences and tailor their offerings to meet local preferences. Although some of those offerings will be made available in local brick-and-mortar stores, tomorrow’s business landscape is going to be dominated by mobile devices. This makes the digital path-to-purchase a critical element of any company’s business strategy. The most successful companies will find a way to seamlessly weave together multi-channel sales opportunities.

In order to sell a product or service, however, manufacturers need to ensure that consumers know about it. That’s where technology can empower consumers, manufacturers, and retailers. In mega-cities, the ability to target consumers will be the sine qua non of successful business strategies. Even areas that are supposedly “off the grid” have been penetrated by mobile phone technology. The so-called “bottom billion” who live in these areas still require products and services. There are profits to be made selling to the bottom billion, if the products and services can be tailored to their circumstances. Big data analytics can help companies and governments better understand the requirements of people living in these “off the grid” areas so that they are not destined to live forever in poverty and squalor.

Improving “rapidly shifting supply chains”

At Enterra Solutions®, we believe that companies must obtain full visibility into their supply chains — from the issuance of a purchase order (PO), continuing through production milestones, transportation (i.e., ocean shipping, rail/truck), to warehouse delivery, and ultimately shipping to a customer. In order to achieve this, they need to implement what we call Global Network Synchronization. For a company, Global Network Synchronization refers to the ability to understand the complex interactions and dependencies within its supply chain. If a supply chain can be synchronized, it can quickly adjust to disruptive events (e.g., production delays, raw material shortages, weather, port delays, labor disputes, and other events). Global Network Synchronization can also reduce systemic risk by looking for supply chain risk exposures and pre-planning to mitigate those exposures. A company’s global sourcing strategy requires real-time supply chain visibility and understanding the perturbative effects of any delays, so that action can be taken to mitigate any negative consequences. Understanding the perturbative effects of a supply chain disruption is a complex multi-threaded analysis that must take into account the entire range of critical supply chain issues and risks.

Upgrading infrastructure

Cities are not going to get smarter if their infrastructure remains dumb and outdated. The single most important infrastructure a city needs is a good electrical grid. Without a stable and reliable source of electricity, cities can’t attract businesses, create new jobs, or become an “always on” hub of activity. The next most important infrastructure requirement is a good water and sanitation system. Without such a system, the population is likely to remain unhealthy and exposed to disease. With water forecast to be in short supply in the future, having a state-of-the-art water system could mean survival for some cities.

Transportation infrastructure is essential for economic growth. Goods cannot move on dirt roads during severe storms. Container-laden ships cannot offload at ports whose harbors are not deep enough or whose docks can’t handle the containers. Some analysts believe, however, that the greatest infrastructure shortfall in the developing world is found in the so-called “last mile” of distribution. For example, Andrew Youn and Nicholas Fusso write, “Today’s greatest need is not for scientists and engineers to create new tools. The real need is for better distribution of solutions that already work.” [“Distribution, the Key to Unlocking the Development Toolbox,” Next Billion, 25 April 2013] Although developing countries suffer from significant infrastructure shortfalls, even countries like the United States have infrastructure issues. You cannot discuss transportation-related infrastructure issues in isolation. The logistics world has always been intermodal, but the complexity of orchestrating intermodal shipments is increasing. Only technology can deal with this complexity; which leads to the final infrastructure that is becoming essential for economic growth — an information grid.

Analysts are predicting that we will shortly have an Internet of Things that will primarily involve machine-to-machine communication. Smart buildings, smart grids, smart robots, smart cars, and every other “smart” machine will be communicating on the Internet of Things ensuring that systems are functioning efficiently and effectively. Technology has always made progress easier and improved the quality of life for millions of people. I don’t see this trend ending any time soon.