A constant in the human experience is the ongoing desire to understand the world around us at an ever-deeper level. From the ocean-going explorers of the 15th and 16th centuries to the astronauts of the 20th and 21st centuries, we are on a constant crusade to know more, and to understand life at a deeper level. It was often a matter of surviving and is certainly a key to thriving, but it is also fundamental to our inquisitive nature. Likewise, humans have long desired to know what will happen next, which further enhances our chances to survive and thrive, and to build a world in which our health and happiness are more assured.
Today, this thirst for knowledge and quest for insight are increasingly realized by analyzing the massive amounts of data generated by smartphones, social media, sensors and scanners of all types, the emerging Internet of Things, and increasingly large enterprise IT systems. We’ve always had measurements and data, but we’ve never had data at the scale that we have it today. The rate of data production is growing at a steep, exponential rate, enabled by ever more powerful and plentiful computers and sensors and other digital technology devices.
IDC forecasts that by 2025, the global datasphere will grow to 163 ZB a year, or a trillion gigabytes. That’s 10 times the 16.1 ZB of data generated in 2016. This explosion of data generated by people and things provides opportunities to put all those bits and bytes to work to further our understanding of the people and things we care deeply about.
IDC forecasts that the global datasphere will grow to 163 ZB by 2025.
Data alone, of course, doesn’t do anything for anyone. What we are really after is insight and foresight — a deeper understanding of what is happening now and insights into what is likely to happen in the days, weeks, months, and years ahead — and that takes not just data. It also takes powerful computing, advanced analytics tools and techniques for gaining meaning from massive amounts of unstructured data. And there is good news on this front: We now have an abundance of all of that.
This IT trinity — the combination of big data, powerful computing systems, and sophisticated tools and techniques for data science — paves a path to the widespread use of artificial intelligence (AI), most recently in the form of machine learning and deep learning. For now, let’s cut to the chase and talk about what these data science approaches do for us when we put them into play.
Understanding today and tomorrow
Public and private organizations have been collecting data for as long as they have been around. Data collection is a fundamental aspect of human institutions. Historically, whether that data is in analog or digital form — whether it’s in a hand-written entry on a paper ledger or in binary digits in a relational database management system — it is always a look back in time. The data tells you things about the past.
Let’s consider an example. While it provides useful information, a database report showing quarterly sales totals doesn’t give business leaders all the information they need to make better business decisions for the present and future. It might tell them what they have sold over the past three months, how much they have sold, and whether they have sold more or less of a product compared to earlier quarters. That’s all essential information, but business leaders need more than just a look back in time.
To make more-informed decisions that improve future results, executives need better insight into how the present conditions evolved, and foresight into how future conditions will unfold. They need to understand what’s likely to happen in subsequent quarters, so that decisions can be made that maximize results for those future conditions.
How to get there? For most of history, people used their intuition and insights gained through past experiences to make their best guesses about the future, and to make decisions accordingly. In the era of big data, business leaders have begun to use data analytics to better assess and understand past trends and present conditions, and to give statistical predictions for what seems likely to happen in the future. Data and algorithms can be used to augment human intelligence derived from experience and education.
Artificial intelligence — specifically machine learning and deep learning — changes the ground rules for that decision-making. For example, machine learning and deep learning techniques allow executives to weave in data from many sources, such as social media sites, customer information systems, and e-commerce sites, to make better predictions about what products are likely to sell in the future and who is likely to buy them. They can then tailor their product-development and sales and marketing strategies accordingly.
Let’s not lose sight of the big picture: We are entering an era in which many types of organizations can put AI tools and techniques to work to churn through diverse types of data — including structured, semi-structured, and unstructured data — to gain a better view of the past, the present, and the future.
We’re talking about a huge amount of data that can come into play here. And that’s good, because we need a great deal of data to train the models that yield insights into what is happening today and what is likely to happen in the days to come.
Today we have that data, and we are gathering more of it every day. We have more processing power — power that makes it possible to process data at speeds that were in the past all but unthinkable. And we have the algorithms and other data science tools that allow us to bring this processing power to bear on that data to gain crucial insights. Today we have it all, and that’s going to allow artificial intelligence to take flight in a big way. And while we see AI being used in our daily lives via smartphones, digital assistants, and more as consumers, companies have access to vast data sources and computing power, and have long used data to improve their operations, products, and services. AI may suddenly seem transformative (it is!) and revolutionary (not exactly), but integrating AI into enterprise processes is also the next natural step in how companies innovate, operate, and compete. The time to start is now.
Next up: data analytics, data science, and machine learning
As has been the case for decades, the use of data will continue to evolve. One of the latest and greatest steps in this evolutionary process of data analytics and data science is the rise of machine learning, which brings a paradigm shift to data science. In the past, humans made the rules for data and gave those rules to algorithms. In the era of machine learning, algorithms use data to generate rules.
This fundamental shift opens the door to amazing advances in data science. We are talking about systems that can learn from data, learn from their experiences, and get better at what they do over time. In the next article in this series, we will explain how data, data analytics, data science, and machine learning are related, and then dive deeper into machine learning and deep learning. In subsequent articles, we will give both guidance and examples.
So please follow our progress as we discuss how AI will forever transform business and create new products, services, and jobs.
Jay Boisseau, Ph.D., is an artificial intelligence and high-performance computing technology strategist at Dell EMC.
Lucas Wilson, Ph.D., is an artificial intelligence researcher and lead data scientist in the HPC and AI Innovation Lab at Dell EMC.
About this series
Artificial intelligence has long been shrouded in mystery. It’s often talked about in futuristic terms, although it is in use today and enriching our lives in countless ways. In this series, Jay Boisseau, an AI and HPC strategist for Dell EMC, and Lucas Wilson, an AI researcher for Dell EMC, cut through the talk and explain in simple terms the rapidly evolving technologies and techniques that are coming together to fuel the adoption of AI systems on a broad scale.
 IDC, “Data Age 2025: The Evolution of Data to Life-Critical,” April 2017.