AI-supported decision support
Becoming a data-driven company is no longer an option, but a necessity.
In today's world, data is collected in all areas. In the process, the amount of data collected is increasing every day. Companies can perform various types of analysis by using this data and make intelligent and insightful business decisions that put them on the path of continuous improvement.
Types of data analysis
The simplest form of analysis is descriptive analytics. Descriptive analytics examines data to find answers to the question "What happened?" or "What is happening?". Descriptive analytics can help manufacturing companies assess productivity levels or track downtime, for example. For example, the answer to the question "How many parts did I produce in the last hour?" can be provided.
The next form of analytics is diagnostic analytics. It helps companies to investigate the reasons or causes for a certain event or problem and provides an answer to the question "Why did something happen? At the same time, solutions can be suggested to remedy the problem. Plant managers can thus determine, for example, whether a machine has failed due to improper operation, overloading of the machine or lack of maintenance.
An illustration of a robot, which stands for the intelligence of humans, hands over its information to its human colleague.© Cognitive vector created by vectorjuice - www.freepik.com
The next higher form of data analytics is predictive analytics. Predictive analytics helps companies answer the question "How can we do it?" by enabling them to take the steps necessary to achieve a specific outcome - and also provide insights into what will happen once the outcome is achieved. For example, next year's sales or demand from a particular customer in a defined period can be forecast from historical data.
The next form is prescriptive analytics, which goes one step further than predictive analytics. It additionally provides recommendations on how to influence a certain trend in a desired direction, prevent a predicted event or react to a future event. For example, it can identify where process improvements will have the greatest impact on the bottom line.
One of the most advanced forms of data analysis to date is cognitive analysis. It finds answers to the question "What don't we know?". It uses multiple analytics techniques to examine large amounts of data with human-like intelligence. Whether it's understanding the background of a customer query or spotting trends in a large data set, cognitive analytics uses artificial intelligence and machine learning algorithms to uncover patterns and correlations that simple analytics cannot. This form of data analysis can identify economic opportunities, minimise risks and make adjustments to changing conditions.
Companies need to be able to respond to changing trends, customer demands and equipment problems with greater flexibility and precision in order to optimise throughput, costs and speed. Using these different types of analysis is a good way to understand what or why something has happened, what is likely to happen in the future and what recommendations for action need to be implemented to achieve the desired outcome. In the long run, all these types of analysis can help optimise businesses.
Prerequisite for use
In order to analyse data, the right data must be available in sufficient quantity and data quality. You can find out how data can be collected in production systems here.