For years now we have heard the term “big data” — experts have predicted that data will, in essence, take over our world and drive virtually every aspect of our existence, from how we run our businesses to how we conduct our free time.
While we haven’t quite reached that pinnacle just yet, there’s no question that data is becoming more and more of a factor in our day-to-day lives. As technology develops, the amount of applications that rely on big data, data science, and data-driven analytics only increases. The sheer volume of data collected these days is staggering and growing at an almost exponential speed. It is estimated that 2020, 1.7MB of data will be created every second for every person on earth. Because of this, the question for companies in today’s world is not, “should we use data” but “how can we bring data into more and more aspects of our operations?”
In short — how can we compete in today’s data-driven world?
Answering this question is no easy task, but based on current trends and developments it is possible to take at least an educated guess as to what today’s businesses need to think about when it comes to navigating the world of big data today and in the near future.
Artificial Intelligence, otherwise known as AI, is the term used to describe the complex simulation of human intelligence by machines. It’s been the stuff of science-fiction for years (usually, with a terrible outcome for the humans) but in reality, it’s been a slowly developing field for some time now. AI is a wide field that primarily deals with machines learning to apply logical reasoning and self-correction while taking in data and learning about the world around them. As machines learn, the AI they apply allows them to solve problems, learn about their users or change and refine how their applications work. This self-learning can have massive implications for the business world.
Because of its inherent complexity, a lot of what falls under the realm of AI is out of reach for current developers and businesses. However, as AI becomes more and more refined, the applications become obvious and important. Be on the lookout for the term “democratizing AI” to become part of our lexicon in the not too distant future. Whether you’re developing an app for your business or using your computers to help predict future customer interactions, the uses for AI are still being formulated, and experimentation will be key to gaining a competitive advantage.
Machine learning is a specific category of AI. Machine learning deals with how computers are able to learn and adapt to the needs of the world around them without any explicit programming. To accomplish this, programmers/ data scientists/ ML engineers (the job roles continue to grow) make extensive use of algorithms which can learn and adapt based on user data and input. Machine Learning typically falls into one of three categories: supervised, which makes use of labeled data to allow computers to recognize characteristics and use them in the future; unsupervised, which leaves data unlabeled and forces machines to understand and classify various characteristics on their own; and reinforcement learning, which use algorithms to analyze the environment and learn from various causes and effects.
In business, machine learning is a vital part of any model of data analytics today. Machine learning is the driving force behind such various applications as bank fraud detection, email spam filters, and product recommendations. All of these applications can be used to make a customer’s experience and interactions more convenient and pleasant. It’s not too hard to see how each of these skills, and others like them, could either help or truly harm customer relations. As businesses look to become more data-driven, employing Machine Learning will be a vital part of the equation for many years to come.
Deep Learning is just a smaller sub-set of Machine Learning. Deep Learning is designed to more closely resemble the organic neural networks of the human brain. As a result, deep-learning algorithms can tackle much more complex problems that involve unstructured data like images, speech, and text. Typically DL requires significantly more data than ML models, but the benefit is that it has the potential to increase accuracy with less cumbersome fine-tuning or feature selection/engineering.
Deep learning is still a field that is hard to describe or understand — even for those who work within it. The expected high costs and technical expertise required to implement a Deep Learning solution will preclude most organizations from introducing the technology; however, the benefits will certainly be present for an organization that identifies the right use case (i.e., autonomous vehicle, facial recognition).
As companies contemplate ways to stay competitive, some implementation of Artificial Intelligence will be needed – whether building the solution in-house or finding an external partner with a more in-depth knowledge of the technology. However, the organizations that truly begin to embrace AI and forge ahead to develop a comprehensive strategy to experiment and introduce use cases will outpace those laggards that wait.