The idea of big data has its roots in the 1960s, when computers began to store and process large data sets in data centers. The term really emerged in the early 2000s, though, when statisticians realized the magnitude of information collected online—especially via social media and e-commerce platforms. New software tools such as Hadoop enabled big data analytics on a new level, and also paved the way for distributed data storage.
While human interaction with online platforms painted the early picture of big data (for example, Amazon’s product recommendation algorithm is based on data it collects from individual users), the IoT is increasingly driving its development. IoT sensors and devices generate tremendous amounts of data, and businesses are using analytics to interpret it.
What is Big Data?
Big data is exactly what it sounds like: tremendous data sets that cannot be processed without modern computing technology. Big data is rife with hidden insights—for example, consumer trends that are only detectable if you review millions upon millions of business transactions. Today’s cloud computing processors can do that level of work, making it possible to harness the power of big data to deliver insights for businesses, healthcare organizations, governments, and researchers in many sectors.
To define the term, IBM originated the “Three Vs” of big data. More recently, the list has expanded to five:
Big data typically involves large quantities (terabytes or petabytes) of chaotic, unstructured information. To utilize the data, organizations must find ways to sort and manage it.
Big data isn’t only high in volume, it’s also streaming in at a high velocity (imagine a fire hose). In a digitally connected world, data is generated at staggering speeds and organizations must find ways to keep up—especially if they need to extract real-time insights.
Big data is quite varied in format and can include numerical data, text, video, or audio information. And an organization’s data might come from many different sources social media, customer reviews, enterprise systems, IoT sensors—making it difficult to consolidate.
Big data has intrinsic value, yet that value must be extracted. Discovering the value in an organization’s data set involves multiple factors: powerful analytical tools, intelligent data scientists, and executives who are open to hearing what the data has to say. Predictive analytics is one area that can yield great value, as it helps leaders make predictions based on trends they discern in the data.
It’s true that facts don’t lie—but in its complexity, big data can be deceiving. Data scientists must be careful to cleanse data of errors, duplicate information, and other anomalies that can disrupt their ability to find true insights.
AI and Machine Learning
As the field of big data analytics has unfolded, so too have sophisticated methods for uncovering insights. Today’s analytics tools are often powered by artificial intelligence (AI), a type of computer science that purposes to make machines intelligent. AI can help analysts sort through enormous data sets, seeing patterns and anomalies that humans miss.
Machine learning, a form of AI, applies algorithms that allow computers to learn and change as they perform analytical work. They not only find the patterns in large data sets—but they also interpret them, building a foundation of knowledge that will help to interpret future data more quickly and accurately.
How Does IoT Impact Big Data (and Vice Versa)?
A fast-growing network of connected sensors around the world, the IoT is making a colossal contribution to the body of big data. Because it depends on machines to gather data, rather than humans, the IoT creates huge data sets very quickly. Often, organizations benefit most from data if they can access, analyze, and interpret it in real-time, as it’s flowing into a central platform.
Real-time analytics depends on machine-driven tools like AI, machine learning, and deep learning. The insights they uncover then bounce back to make an impact on IoT devices, users, and organizational decisions.
Here’s an illustration: traffic lights and traffic cameras are connected to a smart city’s IoT network. One camera reveals regular traffic jams at an interchange around rush hour each day, causing traffic to back up onto the interstate. AI tools analyze the data and suggest a solution: increasing the frequency and duration of green lights for traffic exiting the interstate during that period of time each day. Since the traffic lights are also connected to the network, city leaders can easily make that adjustment and cause traffic to flow more smoothly during peak hours.
How Can IoT and Big Data Benefit Industries?
The combination of IoT and big data analytics can empower organizations in many industries to achieve greater efficiencies, anticipate potential problems before they arise, and make smarter decisions. Analytics reveals patterns and trends in data, uncovering insights that enable predictive maintenance for machines and help businesses better understand their customers. Here are a few examples of how the marriage of IoT and big data can impact various industries:
As telehealth and telemedicine continue to expand, connected monitors will become common components of the healthcare system. These devices enable providers to track patients’ readings remotely—from connected blood pressure meters, glucometers, heart monitors, just to name a few. As the connected sensors collect and aggregate data, AI and machine learning tools can identify trends and warning signs, alert patients and providers, and potentially save lives.
IoT devices in the transportation sector include asset trackers, telematics systems, traffic control systems, surveillance, and remote monitoring systems. These devices generate tremendous amounts of disparate data that can be difficult to combine and interpret—but AI and machine learning are helping.
For example, a shipping company might have a thousand trucks traversing the country’s interstates at a given moment. Inside the truck, containers are equipped with asset trackers, while the vehicle itself has an onboard telematics system. Using a centralized cloud platform, the company can view all this information on a single dashboard, along with similar data from the rest of the fleet. AI and machine learning tools can identify trends and notify drivers of potential problems, such as upcoming vehicle maintenance needs or potential traffic backups.
The industrial IoT (IIoT) is a rapidly expanding segment of the wider IoT, and it’s generating massive amounts of data. Putting that data together can be tricky, since the typical factory encompasses information from a conglomeration of legacy machines, cutting edge sensors, and manually entered data. IoT gateways equipped with edge analytics can help to streamline data flowing from different sources—often in different formats—and perform initial analyses and data cleansing before sending it to the cloud. At the enterprise cloud level, the factory data undergo more in-depth analyses. The organization’s data science team, equipped with AI and machine learning tools, can then interpret the data and make recommendations for process adjustments that can increase efficiencies.
On a smart farm, hundreds or thousands of IoT sensors are deployed throughout an agricultural facility—placed far out in a field, inside a remote water tank to monitor usage, or even to track grazing animals. The sensors generate a large body of data on soil conditions, weather patterns, irrigation availability, and other factors. Taken together, the data can provide insights that drive precision agriculture, which takes a focused, individualized approach to cultivation at a particular site. For example, variations between soil conditions in fields on a single farm can be noted and offset with different fertilizing methods.
Retailers are using IoT devices to track assets, supply chain information, customer behavior, and more. For example, a retailer that sources product materials from multiple locations could equip shipping containers with IoT sensors to monitor movement and ensure that the materials remain in a temperature-controlled environment. Viewed as a whole, the data paints a big picture of the supply chain and can be analyzed to reveal insights about how to increase efficiencies or streamline sourcing and assembly.
A Dynamic Duo
While different concepts, IoT and big data analytics are interrelated and dependent upon each other to fulfill their potential. Together, they offer exceptional visionary power. No matter what the application, they can reveal previously unnoticed patterns and present real-time information that helps organizations and individuals make better decisions—or even save lives.