Data is everywhere: 44 zetabytes of data are stored in the digital universe in 2020. That’s a 44 with 27 zeros of bytes. All that data contains a wealth of valuable information. The applications of data are extremely versatile: from describing consumer behavior to predicting and improving performances, processes and developing new products.
Boosting innovation, recognizing, and seizing opportunities, utilizing artificial intelligence… These are all goals that can be achieved with the use of data. An increasing number of organizations are switching to a data-driven work style: analyzing data and using the gained knowledge to improve their business operations.
From using Google Analytics efficiently to gaining insight into online shopping habits. By showing stimulating examples you’ll encourage your employees to start thinking about their role in the company’s transition to data-driven working.
Several companies have already started their digital transition together with RMMBR.
From interpreting models to the deployment of helpful chatbots: data analytics has various implementations. Different organizations use data in different ways. Data analytics is an umbrella term that may seem misleadingly simple at first glance. In practice, every organization has its own view on the deployment and application of data analytics. This makes differentiation in learning solutions essential.
Data analytics is most used for data models. These models are simplified representations of reality. Data models determine how data is linked and how the data is processed and stored within the systems. But how exactly do data models work? What kind of models are there, what can they be used for, and what can be done with the – sometimes abstract – output of these models?
All these questions (and more!) can be answered with the use of creative online training methods. These methods allow participants to navigate through the different layers of data analytics at their own pace.
The data generated by participating in the e-learning module can be used for improvement: What do people find difficult? What do they like? What are the strengths and weaknesses? This way, data analytics become a tool that keeps producing new insights