Machine learning and predictive analysis have entered every business. Faster compute power and improved data transfer speed and storage costs has empowered artificial intelligence (AI) to scale up to the highly glorified big data. All these technical terms may seem applicable to business, but enterprises should be wary of their application. Before adopting these technologies, business leaders should be aware of the difference in data sources, types of data sources and different machine-learning models. Understanding these concepts and labels is the key to overcoming the hype behind these terms and getting the real value with true self-service solutions.
The company should learn to ignore observational biases, i.e., looking for things where the search is the easiest. This bias can show up in predictive analytics when algorithms are applied where developers think that they can find an insight or a problem. If a company employs a little feature of machine learning and the rest of the operations are manual, then there’s only marginal impact on functionality and operations for the analysts. Machine learning models predicted what they were trained to anticipate, but the new generation of true machine learning algorithms doesn’t need training for finding insights. These algorithms learn from experience and promise to change the way businesses operate drastically.
Historical Data or Real-Time Data
Historical data provides valuable context which makes predictions based on past experiences. But, IoT technology and connected data sources like sensors show meaningful and insightful data that change over time. The application or real-time data has opened up a new branch in predictive analytics called anomaly or the ability to identify anomalous behavior as they occur. The data-driven insights identify problems that are unknown to the business and are dealt with down the road. This process is critical and beneficial for the business.
Predictive Analytics in Businesses
Shipping giants have started using real-time predictive analytics algorithm. The data collected through this process can optimize the movement of parcels across its delivery network. The data generated helps shipping companies create improved plans to manage the system as a whole. Predictive analytics can help companies forecast demand which in turn allows companies to improve service and be cost-efficient. The next step for shipping companies is to incorporate AI to find best actions in real-time, eliminate human bottlenecks for better decision making.