The Role of Big Data in US and Canadian Marketing Strategies

Thompson, an explorer and cartographer who charted most of western and eastern Canada, as well as the northern United States, is regarded as the greatest land geographer of all time. Thompson traveled roughly 90,000 kilometers by foot and canoe and used the information he gathered to produce what he called the "great map"—the most thorough record of the territory of more than 3.9 million km2.1 His chart unleashed North America's commercial potential.

Big data is as significant to Canada in the twenty-first century as Thompson's topographical data was in the nineteenth century

It has the potential to transform Canada's current business and environmental situation. Big data is a word that describes the massive amount of data that today floods the world. In 2013, IBM said that 2.5 exabytes — or 2.5 billion gigabytes — of data were generated everyday. Data is accumulating so swiftly that over 90% of it has been collected in only the last two years (Marr 2015). This data is gathered from a variety of sources, including sensors used to collect shopper information or industrial machinery performance, social media posts, digital images and videos, purchase transactions, and mobile global positioning system signals.

What is crucial is what is done with the data, not how much of it exists. It is the "great map" that data scientists and machine learning specialists can create using the data.

The consulting company Bain & Company highlighted the importance of data by studying over 400 multinational organizations and discovering that those with the most advanced analytics skills outperformed competitors by huge margins. They were:

According to Pearson and Wegener (2013), individuals in the top quartile of their industry are twice as likely to have high financial performance, five times as likely to make faster decisions, three times more likely to execute decisions correctly, and twice as likely to use data frequently when making decisions.

It is evident that the combination of big data and current machine learning will create new economic opportunities while drastically reducing the environmental impacts of Canada's largest sectors through ongoing optimization and the identification and resolution of new problems and challenges. We can do more with less and do it better by utilizing innovative ways to identify previously ignored opportunities. Using the correct data, algorithms can identify chances for improvement that humans do not see.

Google, Facebook, and Amazon dominate the consumer big data industry, demonstrating that data-driven innovations may affect every part of our lives

Big data and machine learning have resulted in considerable productivity gains and novel approaches to a variety of consumer applications. So far, these strategies have not been widely used in primary industry, owing mostly to a lack of high quality data. It is the one area of big data where Canadians do not have a disadvantage due to their smaller population.

For the past century, Canada has led the globe in the core industries of mining, energy, forestry, and agriculture. For the most part, the emphasis has been on digging, cutting, and planting, followed by sales after primary processing.

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Google, Facebook, and Amazon dominate the consumer big data industry, and they have proved how data-driven innovations can affect every part of our lives. (Photo by Shutterstock.com)

Not just big data, but a huge opportunity.

Applying big data to our key sectors results in lower costs and environmental impacts: fewer waste, pollution, and land disturbance, while also producing significant new-economy jobs that will define Canada's performance over the next 100 years.

Although Canadian institutions create a disproportionate number of the world's big data experts, Canada ranks low in terms of big data possibilities. Because of the country's small population, there is limited customer potential. Many of the best and brightest big data experts migrate to the United States since there are no possibilities in Canada.

When it comes to core industry opportunities, Canada's population is not a disadvantage. Canada is more reliant on and has more opportunities in the primary industry than the other Group of Seven countries. Primary industries make significant contributions to Canadian employment, capital investment, exports, and GDP. The scale and modernity of Canadian industry provide a competitive advantage. We also have world-class subject matter expertise, which is critical for identifying opportunities when working with machine learning approaches based on big data.

Ironically, Canada lacks the necessary type and quantity of data to capitalize on these new big data prospects. The opportunity—the "big idea"—is to enable revolutionary change by gathering and categorizing the necessary data for the rapid application of machine learning and artificial intelligence (AI) to our most important main industries.

What is crucial is what is done with the data, not how much of it exists

This should begin with a pilot program to build the infrastructure and populate what will eventually grow into a major open-source data library for primary industries. Once we learn by doing, we can quickly advance, allowing Canada to fill the rest of the library and become a global leader in the developing domain of primary industry big data.

The reasons for the lack of data may be traced back to the 1960s, when main industries around the world began employing computers to collect data, measure, and regulate processes. The sensors2 they utilized were linked to computers via SCADA (Supervisory Control and Data Acquisition). The SCADA protocol, which is now widely used, allows communication between a computer and a distant sensor or control device. Simple instances include requesting a temperature or pressure reading or remotely operating a valve. The amount, type, and contextualization of data that are today commonplace in big data were unknown when SCADA was first created. Although much progress has been achieved since the 1960s, SCADA and SCADA-like systems are just not appropriate for this huge data job, for a variety of reasons:

SCADA uses a serial connection between a computer and a sensor to record readings and progress to the next sensor. However, the sensors lack intelligence and cannot encrypt or compress data. Additionally, communication methods are outdated and costly.

Using SCADA systems for big data applications is analogous to developing a self-driving automobile using data from a backup beeper.