TechTalk
Our knowledge to you.
TechTalk
Our knowledge to you.
Take a minute to consider the astounding amount of data available on the Internet. Every stream, every session, every download, every app—dialed down into data points that can be harnessed to understand behavior, identify patterns and ultimately lead to better customer experiences. With more than 15 billion connected devices in the world, the sheer amount of information available in the form of data is almost hard to fathom. This rise of what we know as big data has opened the door for businesses to begin harnessing this wealth of information by employing more data scientists and leveraging the power of data analytics, AI and machine learning. These closely related technologies provide the framework for processing and understanding big data so that businesses can leverage it in helpful ways that will provide competitive advantages, boost revenues and increase day-to-day efficiency.
We’ve already touched on big data, but to give a more textbook definition, big data refers to the massive amounts of data now available due in part to the ever-increasing number of connected devices we use in our everyday lives. Big data goes beyond that, though. Under the big data umbrella, there are three different types of data: structured data, which includes things like financial records or transaction logs; unstructured data, which encompasses any information that is not easily tracked by a spreadsheet, such as text, documents, pdfs and multimedia files; and finally semi-structured data which includes files with some organizational structure like emails, images, HTML and certain databases. Big data can be classified by what is known as the 5 v’s of big data: volume, value, variety, velocity and veracity.
With the availability of data, businesses are better able than ever before to meet the needs of customers. But how do they harness all of that information?
That’s where artificial intelligence, or AI, and machine learning come into play. Artificial intelligence is the brainpower possessed by machines, akin to human intelligence. AI is meant to replace jobs once only feasible by humans – for example, a chatbot that is able to interact with customers to solve their queries. AI is able to take data, read it and glean insights via natural language processing.
Machine learning is a subset of AI. In machine learning, computers are fed different algorithms in order to make sense of large datasets; therefore, machines become the workhorses of the big data cycle. By leveraging machine learning, businesses are able to rely on machines to aggregate, store, sort and make sense of large data sets and even predict future occurrences. One example of this is leveraging machines in agriculture to predict weather patterns and aid with crop rotation schedules.
Summed up neatly, machine learning is the notion that machines can learn from data, while AI is the concept that those same machines can perform tasks intelligently with the information they know. Therefore, AI uses machine learning as a technique for solving problems.
Altogether, these three closely interwoven concepts of big data, AI and machine learning are capable of producing, processing, analyzing, and using unprecedented amounts of information to enhance business functions and general business interactions.
Employing big data for business is something being closely examined across all industries, and several sectors are already seeing real results from using the power of information, AI and machine learning to improve business interactions. Let’s look at some everyday examples being employed by those in agriculture, retail, automotive, finance, healthcare and customer service.
Farmers’ use of technology has been rapidly evolving, and today data is being leveraged more than ever before thanks to precision agriculture. Precision agriculture uses data to monitor, measure and provide solutions for crop management. AI and machine learning systems are used for this process with the ultimate goal of improving crop quality and being able to make proactive decisions based on data (weather patterns, for example) or to detect issues, such as plant diseases, pests or soil issues.
We’ve all had the experience of clicking on an ad for a sweater and then seeing that sweater pop up, well, seemingly everywhere. Through data processing, those in retail are able to know more about what their customers are looking for so that they can be served more personalized content that makes sense based on their past purchases and interactions. Using customer data to segment users and target them in enhanced ways is a very clever way of pushing these users through the buying cycle. Machine learning capabilities are able to hit undecided customers with not only images of the product they’re eyeing, but also offers, coupons and adjusted pricing to inch them closer to a sale automatically. Impulse buying is now no longer something that only happens in a checkout line, but also when we get served with the exact right deal on our phones at the exact right time.
Self-driving cars, or autonomous vehicles, can’t function without machine learning and AI gathering data about surroundings to aid perception and decision making. This is what makes vehicles able to operate as routinely as if they were being driven by a human. Similar to retail, those in the automotive industry also use data insights to identify which marketing tactics might have led to a sale to improve their buyer’s journey and put money behind tactics most likely to convert leads into sales.
When you’re hit with a message on your phone to verify the latest purchase on your card was made by you, that’s an example of the fraud prevention and detection made possible by machine learning and AI. How’s it accomplished? Large sets are data are analyzed and forecasts can be built upon them to identify patterns and predict future fraudulent actions. Additionally, machine learning can be a huge help in advising on investment opportunities and strategies.
Wearing devices like a Fitbit that can track heart rate, daily steps and sleep patterns, creates a wealth of helpful information that can report on patient health and potentially save lives by identifying triggers for heart attacks or cardiac arrest. Likewise, data gathered from diagnostic tests and regular doctor’s visits aids with personalized treatment plans for better patient care. In general, by analyzing large sets of medical data, healthcare professionals can pinpoint patterns within healthcare data for enhanced treatment.
Similar to the autonomous vehicles mentioned above, something else showing viability to be automated are surgeries. Earlier this year, surgeons in London made headlines for performing a mock surgery on a banana from a continent away: something achieved through top-notch AI. The implications for developing viable autonomous surgery capabilities would mean that everyone in the world could potentially have access to the best medical care possible, which is a life-changing notion.
Have you ever experienced a productive conversation with a chatbot on a website and wonder if there’s truly another person behind the screen talking to you? That’s how good machine learning and AI have gotten when it comes to customer service. Through natural language processing, it’s getting easier for machines to assess, understand and solve customer service issues. Plus, all the data collected from every interaction serves the purpose of making the machines even better able to serve existing customers.
There’s no shortage of ways that businesses are harnessing the knowledge gleaned from big data to know more about customers, automate work processes, predict future events, enhance personalization and streamline systems. In the near future, AI and machine learning will continue to change the ways businesses interact with their customers and teams.
The next time you turn on Netflix and scroll through a personalized list of recommendations or see a promotional email pop up in your inbox from a company website you recently visited, remember that’s the power of big data, AI and machine learning at work.
And what is needed to capture and process all this big data? A fiber network with secure, reliable bandwidth options. To learn more about DQE’s full suite of fiber-based solutions, contact us today.