Data science – applying science-based methods to data analysis – is becoming increasingly important. However, it often needs to be clarified how this process works, what role it involves and what benefits are derived from using data scientists. In this article, we will attempt to define data science, explain the fundamental processes involved, and illustrate data science’s roles. To move from theory to practice, we briefly illustrate the added value of data science.
What is Data Science?
Data science is an interdisciplinary approach to using data to create added value. It consists of statistical, data and economical methods, which opens up the possibility of developing solutions based on big data.
The term “data science” was introduced to distinguish data science from data processing.
Today, we mostly think about using big data and machine learning to develop problem-oriented solutions when we talk about data science. The process of data science has established itself as a method for finding practical solutions.
The Data Science Process
The application of data science involves understanding the problem and developing a solution based on the data. This solution is based on advanced analytics, such as machine learning. On the other hand, it is essential to develop an iterative and mutual understanding between business and industry experts during the process so that the solution recognizes the customer.
The most crucial step in the data science process is identifying, understanding, and developing the right solution. It adds value to business areas such as sales, marketing and manufacturing.
For this solution, the correct data must be identified, collected and prepared for evaluation, optimally documented in a data catalogue and stored in a data warehouse or data lake for easy access.
In the next step, the data are processed and analyzed using machine learning algorithms to create a schema.
Once the decision has been made to bring the machine learning model into production and deploy it operationally, the next step in the data science process is deployment. Model deployment means the data pipeline or machine learning delivers the model through a dashboard. This means that other systems and channels within the organization can access and process the results.
Roles Involved in Data Science
As mentioned, several times, there are many different roles in data science. All roles are listed here, ordered by the frequency of their presence in the process.
Data Scientists
The central role in data science is that of the data scientists themselves. This role is interpreted in different ways. As a generalist, it usually covers the whole process, but more and more companies that specialize in this role are emerging.
Data Engineers
Without data, there is no analysis. Data scientists are often at the heart of data analysis, and data engineers are the ones who lay the foundations.
Data Architects
You’ll quickly find data architects if you’re looking for an enterprise data infrastructure. Data architects oversee the entire IT infrastructure and are responsible for the security and access control.
Data Analysts
Data analysts typically work only with structured data from the data warehouse and handle ad-hoc analysis from the back office. In contrast, data analysts work with various data visualizations from dashboards.
Data Administrator
The data administrator mediates between data scientists and industry stakeholders. They are responsible for translating the technical results of the data science process into a clear understanding of the business.
Why is Data Science Important?
It has two main aspects that are important for businesses and other organizations. First, data processing is standardized with clearly defined steps. This allows for better, more efficient and transparent use of data. Second, it allows previously unknown patterns to be discovered. This enables initiatives to optimize processes, increase turnover or create innovative business models.
These two aspects, together with the fact that we are creating and storing more and more data, are increasingly at the heart of data science. Like traditional departments such as audit or IT, all companies and organizations will address data science and define it as a business strategy. It will become so deeply embedded in business processes that data science work naturally based on data.
Why Do You Need a Data Science Consultant?
Hiring a data science consultant can be advantageous because professional data science consulting companies like us, are extremely dedicated to your problem. We also provide fast, accurate and well-tested results and have an entire team of experts. Contact us for a free evaluation of your data science project now!
