Big data analytics uses enormous data sets generated by today’s connected devices within manufacturing and service industries to improve business processes. This data contains trends, patterns, and other vital production and consumer data that human analysis may not detect. From this analysis, companies can predict and act on issues in real-time. They can use statistical algorithms, intuitive “what if” scenarios, and predictive models to develop tactical and strategic plans.
How Big Data Analytics Works
All manufacturing equipment produces data. Historically, much of this data was lost or collected sparsely by operators who recorded information in production. This method was error-prone and subject to bias and was easily manipulated. It was not in real-time because the information collected had to be collated, standardized, and analyzed, meaning that the insights were often outdated past the point where a sound decision could be made.
There are several distinct steps in big data analytics, including:
1. Data Collection
Big data analytics uses electronic devices at the point of creation for the data point to capture, record, and forward the information to a cloud-based advanced software platform. These devices take the form of sensors, temperature or pressure gauges, frequency readers, and others. From there, it is put into a data warehouse for access by the analytics engine.
2. Data Processing
Because of the gigantic volume of equipment-generated data, processing is crucial to success. Some platforms batch process data in large blocks before moving it forward, while others stream process data in smaller batches. The difference is the time required between collection and analysis, with stream processing being faster and closer to real-time.
3. Data Cleansing
Raw data is rarely useful as-is. It requires complex formatting and must be checked for missing data points and duplications. Cleaning data also requires the removal of unneeded information so that analysis can be focused on the correct business process for creating actionable insights.
Analysis of big data comes in many forms. Advanced machine learning algorithms and analytics engines using artificial intelligence (AI) are common in big data analytics. Much analysis is devoted to predictive models, where historical data and real-time equipment states are analyzed to determine optimum operating conditions. It can also predict failures, identify quality issues, and pinpoint differences from shift to shift, machine to machine, or operator to operator.
Benefits of Big Data Analytics
Big data analytics is part of the move to “smart” factories, where the equipment throughout the production floor is all connected for full visualization. However, it can be used in service industries, warehousing and logistics, retail, and many other industries.
Key benefits include:
· Reduced Cost
The ability to make accurate decisions in real-time improves efficiency and equipment uptime. And predictive maintenance and quality insights reduce the cost of those issues as well.
· Improved Decision-making
Because big data analytics can identify trends at a deeper level, undetectable patterns emerge. And because data is contextualized and standardized, it can be customized to the user, such as managers, operators, and technicians. This results in faster and better decision-making.
· Value Creation
Because big data analytics captures all operational and manufacturing data, it allows companies a deeper look into consumer needs and preferences. This may include value-added service add-ons for existing products or even new products altogether.
Help for New Businesses
The challenge of adding big data analytics to any company is daunting. And doing so as a new or emerging business is even more so. The Henry Bernick Entrepreneurship Centre (HBEC) at Georgian College offers entrepreneurs assistance in training, mentorship, R&D, and Innovation. Through community partnerships, hands-on student participation, and an experienced staff of experts and educators, HBEC can provide assistance and guidance for new businesses looking to add big data analytics to their company’s strengths at the beginning of their journey to help give them the tools they need to succeed. Contact us today to find out how we can help.