In the course of digitalization, the Internet of Things, Big Data and Industry 4.0, the amount of available data is growing constantly. But at the same time, it becomes more and more difficult to use these huge amounts without automation. Machine learning can support you here: Producing high-quality data analysis, it can help you make better decisions.
Data is considered the currency of our times. According to a survey by the hard disk manufacturer Seagate and the IT market research institute IDC, 33 zettabytes (the figure 33 with 21 zeros) of data was collected in 2016; by 2025, this number will increase tenfold to 175 zettabytes. In the course of this development, the data sources will change as well: While 70 percent of all data used to be generated by private users in 2015, more than half of all data will be generated by companies in 2025. This means that the future will see even more data, and a shifting of data sources. But what should companies do now to benefit from the “currency of our times”?
Before automated utilization is possible at all, data analysts need to search and process this huge amount of data. As not only the volume has increased, but the data itself has become more complex, 80 percent of analysts' working time today is used for data preparation and 20 percent for predictor modeling. Predictor modeling is the process of finding the optimum ML method for the given problem.
This unfavorable ratio will become even more negative in future, leaving the company little room to make use of the actual value of the data. The solution is machine learning: “ML automates competence”, says Roman Ernst, machine learning expert with solvatio. “Thus, ML is the automation solution for tasks and processes for which automation would not be possible at all without ML, or would cost much more time and money.” But even without process optimization, companies benefit greatly from ML, he adds.
With ML, there is no more need to write programs. Algorithms solve problems independently, identify patters and make predictions based on them. Of course it is important to have a deep understanding of the problem from the start. This is one of the key preconditions for successful implementation of ML-driven use cases. But how does a company implement data analysis with ML so that it generates value?
Machine learning always proceeds in a similar way. Therefore, there exist several templates outlining how the workflow of data analysis with ML could look like. Here you can read about how this works in detail.
The model describes the complex machine learning process in a simplified form.
- Raw data collection: The collection of data is always the first step of machine learning. To this end, companies must first check if and where they are already collecting data, and if so, analyze this data.
- Preprocessing: The quality of the data significantly affects the quality of the results. Therefore, the results must be preprocessed to find faulty data, missing values, and outliers.
- Sampling: Data samples are taken for use in training, validation, and testing.
- Model training and model evaluation: Next, the data is used to create a model by means of an algorithm. This model is then evaluated once more.
- Deployment: The model is deployed for use cases.
Once the company has implemented and run all stages of the model, it will benefit from the process automation options: Data analysts can focus on their actual job and make use of the value of the existing data. On its own, data is not a currency. To generate value from it, a company first must combine the information contained in the data.
In summary:
– Data volumes are constantly increasing, and by 2025 more than half of the global data will be created by companies.
– Machine learning can be used to manage and optimize data, as well as for the automation of tasks and processes.