Who is a data scientist?

A data scientist is an expert who is fluent in the world of big data, able to properly collect and analyze it. His basic duties in the company include:

• preparation of strategy

Together with the company’s management and its employees, he decides what types of data will be most important for analysis to improve business processes.

• searching for data sources

The basic feature of big data is its diversity. They may come from data warehouses and spreadsheets (structured data) or, for example, e-mails, social media or multimedia files (unstructured data). Therefore, selecting data sources is a difficult but crucial task.

•  data visualization and cleansing

The next stage is data visualization so that it is possible to work with IT specialists and data engineers. Its goal is to determine whether the selected sources will provide data that can optimize the company’s decision-making process. The point is to limit their number as much as possible, getting rid of the „noise” of unnecessary information that makes the analysis difficult.

• building and training models

Data scientists often use machine learning and AI to process large amounts of data quickly and accurately. This requires first preparing the model by implementing appropriate algorithms and then training the models on the input data. The most effective models are implemented.

• monitoring

The operation of the entire system is monitored, an expert manages it and measures success based on previously selected criteria. If necessary, the entire process is repeated.

In what areas can data analysis be used

Application areas of data analysis include data extraction, creation of a data platform, processing and visualization of data, connectivity or ensuring privacy and security. The use of this technology allows for:

• new product introduction,
• supply chain planning and management,
• manufacturing engineering,
• production and logistics improvements,
• ensuring the quality of production,
• supporting maintaince,
• better energy and utilities management,
• taking care of health and safety.

The benefits of using data science are also visible at the organizational level. They help in preparing a strategy and roadmap, managing and optimizing processes, as well as optimizing ecosystem partnering. It is also an opportunity to improve the team’s digital skills and capabilities and legal compliance management.

Examples of the benefits of data analysis in the production process

Data science can be successfully used to optimize manufacturing processes. This includes, among others, such aspects like:

• real-time monitoring

Thanks to data analysis, you can identify bottlenecks and make corrections on an ongoing basis, gaining knowledge about machine performance and product quality.

• predictive maintaince

Predictive maintenance is key to preventing equipment failures, reducing downtime and improving safety. Data science can help you analyze machine performance data, identify patterns, and predict failures. It is possible to monitor the status of devices using IoT to gain access to large amounts of data. It also helps in predictive modeling using machine learning to predict equipment life or maintenance schedule.

• quality control: 

Data on defects, customer complaints and returns may be analyzed. By identifying patterns and root causes of quality problems, manufacturers can implement corrective actions and prevent future defects. All this leads to improved product quality

• supply chain optimization

Data science allows you to analyze information about suppliers, inventory levels and demand. Predictive analytics can be performed to forecast demand, adjust inventory levels and optimize supply processes.

• energy consumption management and reduction

Real-time monitoring of machine operation and data analysis allow for optimization of energy consumption. This can lead to significant savings.

Data science for better manufacturing

The benefits of data science in the manufacturing industry are undoubted. To fully use them, you need a professional data analysis team. It should consist of experienced experts skilled in data acquisition and creating algorithms for their analysis.

A suitable solution is to use the services of an IT outsourcing company. You then gain access to high-class specialists with many years of experience. They have already implemented dozens of different types of projects, so they can perfectly adapt to the needs of each company.

If you’re curious about how RITS Professional Services support business partners in their IT projects, be sure to check out our  section: Case studies | RITS Professional Services.