What is Machine Learning?
Machine Learning (ML) is the core subarea of artificial intelligence (AI). It makes computers get into a self-learning mode without explicit programming. When fed new data, these computers learn, grow, change, and develop by themselves.
ML extracts meaningful insights from raw data to solve complex, data-rich business problems. ML algorithms learn from the data. They allow computers to find hidden insights without being programmed to do so. ML is evolving at a rapid rate and gets driven by new computing technologies.
ML is everywhere in the modern world. Self-driving Google cars, online recommendation engines, Amazon offer suggestions, cyber fraud detection… You might have been in touch with these innovations without even knowing it!
The introduction of new technologies requires the development of innovative products. Opportunities for their adoption exist at all organizational levels.
What about ML’s role in business? Its impact is immense! It enhances business scalability and improving business operations across the globe. AI tools and ML algorithms gain tremendous popularity in the business analytics community. Data availability, faster computational processing, and affordable data storage trigger an ML boom.
Machine data as a business-critical asset
Many innovative platforms assist in moving relevant data to operational and business applications. They unlock and optimize machine IoT data in real-time. As data transfers from the edge to the enterprise and cloud, privacy policies get enforced. Making machine data actionable allows your industry to increase operational margins. Minimizing equipment downtime and lowering energy costs are also among the benefits.
Recent studies show that no industry produces more locked data than manufacturing. Advanced analytics offers unique opportunities for improvement. The researchers claim that big data is worth $50 billion in the oil and gas industry. There are tremendous opportunities across other process industries too.
Some manufacturing sectors are still somewhat reluctant to embrace new technology. This creates certain challenges on the way to a data-driven society. For decades, manufacturing businesses have relied on connected machines to streamline operations.
Now, IoT technologies are taking machine data to unprecedented new levels. They are introducing new challenges into data management, analysis and governance.
Unlocking machine data: the dawn of data-driven innovations
IoT data gets locked in its sources. It’s challenging to move the data and control its use. Extracting new data types depends on costly support from the equipment suppliers. That’s why the improvements appear to be reactive and slower, with no real-time insight. Without effective IoT data management, it’s hard to access the right data at the right time. It is even harder to make smarter decisions.
Changes never stop astonishing the world of industry. Businesses can already enjoy real-time visibility and control over their locked machine data. They’ve got access to the systematic approach to collect, process, and analyze IoT data. Now the companies get relevant, actionable insights to achieve meaningful business outcomes.
Data-driven platforms are here to translate data from high-value assets into actionable insights.
The main two ways to access a machine’s data are as follows:
Accessing Real-Time Machine Data: reading the machine’s variables in real time. One can deliver immediate insights into equipment effectiveness and performance issues. Machine data acquisition is passive. The process does not interfere with the critical operations of machines in production.
Accessing Machine Data from External Sensors: attaching the sensors to a machine. There’s no need to change the machine’s historic control programming. One can extract data from these connected machines. The machines are stand-alone, connected via a serial connection, or use proprietary network protocols.
The sensors are often used to capture specific types of data (e.g., temperature, humidity, vibration, pulse inputs, etc.). The Data Control Module aggregates the data from edge devices within the plant. These edge devices include sensor gateways and edge devices.
Advanced analytics and machine learning: “smart” manufacturing
Big data analytics of today addresses a wide variety of issues. It ranges from the high data volumes and challenges to the data insights. It encompasses the cognitive computing technologies, visualization, and calculation of manufacturing data.
Advanced analytics provides more convenient access to data from many data sources. Modern manufacturers get new ways to control all processes throughout their entire operations. ML facilitates the search of correlations/clustering within the tons of process data.
Further research in machine data mining will result in better-organized experiences. The industries will enjoy better data management, storage, and big data analytics capabilities. As a result, businesses will improve their production and business outcomes. The ability to handle locked machine data smartly allows one to overcome obstacles. Accessing the IoT data housed in new and legacy equipment across a plant floor is a vivid example of an intelligent use.
How Is Machine Learning Transforming Data Analysis?
The ML field is changing. Along with evolution comes a rise in demand and importance. Data scientists need ML because of the high-value predictions. They guide better decisions and smart actions in real-time without human intervention.
ML helps to analyze large chunks of data, easing the tasks of data for scientists in an automated process. ML has changed the way data extraction and interpretation work. Now, it involves automatic sets of generic methods that replaced traditional statistical techniques.
As a rule, they study data analysis by the trial and error approach. That is, it’s impossible to use an approach when there are big heterogeneous data sets in question.
More data is directly proportional to the difficulty of introducing new predictive models. Traditional statistical solutions are more focused on static analysis. There’s also a limitation to the analysis of the samples frozen in time. All this results in unreliable and inaccurate conclusions.
ML is offering smart alternatives to analyzing vast volumes of data. It also produces accurate results and analysis. Fast algorithms and data-driven models for data real-time processing are efficient. AI and ML allow businesses to achieve critical goals and get actionable insights. The companies that have already exploited ML are aware of its enormous potential. With a strong focus on data gathering, they recognize the potential value of their data. They use ML to convert a potential value into real, measurable business value.
ML reduces efforts, saves time, and is a cost-effective tool. It can replace many teams working on analyzing and processing of the data. Delivering accurate results it helps to build statistical models based on real-time data.
ML systems optimize the best option for your customers and cut on abundant analytics. ML brings fundamental changes in the way your company collects and processes data. Make sure that your team is well equipped to handle ML systems to get the desired results!