I. What does machine learning mean for the Internet of Things?
1. Make the data useful
Today's machine learning algorithms sift through datasets in a way that humans cannot do. The continued growth of the Internet of Things (IoT) is estimated to reach a peak value of $160 million by 2021, meaning that even more algorithms will be needed to keep pace with the corresponding surge in data.
As ABI Research points out, recent advances in machine learning have enabled predictive analytics, meaning businesses that adopt these algorithms can better predict future market trends and more successfully target future customers.
2. Make the Internet of Things more secure
Machine learning is not only used by businesses or innovators, but also for security purposes, with machine learning algorithms already being used to combat cyber threats.
Like data analytics, machine learning algorithms can greatly aid cybersecurity analysis. Whether it's helping to solve labor shortages in industries, attracting top-tier human capital to meet the needs of affluent clients, or finding and closing IoT vulnerabilities, machine learning is a huge boon to the security industry.
The range of operations these algorithms can handle is also noteworthy. Machine learning can be used to more effectively monitor data exchanges, such as Bitcoin mining, and it can also analyze historical data to predict threats and criminal activities even before events occur.
II. Common Mistakes Made by Enterprises in Applying Machine Learning
1: Overly complex machine learning functions
To train for basic unstructured content use cases, organizations can potentially use machine learning tools that require large amounts of data. Validated machine learning tools, which incorporate advanced algorithms, can be trained on small datasets and fully deployed to production within hours, unlike starting a project with hundreds of thousands of documents on a single sample set, which could take weeks or even months.
2: Over-reliance on Robotic Process Automation (RPA)
Robotic Process Automation (RPA) is highly regarded for its ability to improve efficiency by connecting to existing systems and external data sources. It can be deployed quickly, its digital workers are easy to configure, and once in place, they can perform tasks much like humans. The biggest difference between RPA and machine learning techniques is that RPA focuses on repetitive, structured tasks, while machine learning aims to understand both structured and unstructured content. RPA requires machine learning techniques to provide its digital workers with intelligent content, thereby giving them cognitive skills to extract useful information and gain intelligence, learn from various forms of content, grasp the meaning and intent of documents, and increase decision-making capabilities.
3: Assuming they know where to apply machine learning techniques.
When launching automation projects, businesses don't always choose the right processes to start. This is because many companies are fragmented in their knowledge of organizational processes. Furthermore, senior management is not involved in day-to-day workflows, and process documentation is lacking, making it increasingly difficult to truly identify which processes are ready for automation. Integrating process intelligence before a project begins will give businesses a comprehensive understanding of where to apply Robotic Process Automation (RPA) and machine learning solutions, as well as their expected value and savings for the organization—all based on data, not opinions or biases.
4: Missing out on high-value business cases
Typically, businesses rely on routine knowledge and choose tasks that occur frequently because they seem to yield good results. However, this ad-hoc approach to process selection may overlook other business opportunities that offer better ROI. While starting with areas that have minimal disruption to the organization or minimal impact on end-user interactions is perfectly acceptable, it's important to remember how to quickly and easily "land and scale" machine learning across the entire organization.
