Katharina Glass
About 3 years ago, my boss decided that it’s time to leverage the superpowers of data. So, I was the first data scientist, a unicorn, amongst 6600 colleges at Aurubis. The primary task was to introduce, to explain, promote and establish data science skillset within the organization. Old industry, like metallurgy and mining, are not the typical examples of successful digital transformation because the related business models are extremely stable, even in the era of hyper-innovation. At least this is what some people believe, and it’s partly true, because for some branches, there is no burning platform for digitization, and hence, the change process is inert. Data science is the fundamental component of digital transformation. Our contribution to the change has a huge impact because we can extract the value from the data and generate the business value, to show people what can be done when the data is there and valid.
I learned that most valuable, essential skills to succeed in our business are not necessarily programming and statistics. We all have training on data science methods at its best. The two must have skills are resilience and communication. Whenever you start something new, you will fail. You must be and stay resilient to rise strongly. Moreover, in the business world is the ability to communicate - tell data-based stories, to visualize and to promote them is crucial. As a data scientist you can only be as good as your communications skills are, since you need to persuade others to make decisions or help to build products based on your analyses. Finally, dare to start simple. When you introduce data science in the industry, you start on the brown field. Simple use cases and projects like metrics, dashboards, reports, historical analysis help you to understand the business model and to assess where is your contribution to success of the company. This is the key to data science success, not only in the multimetal but everywhere else as well.
Commonly known by the term “big data”, Data Science is the study of the generalizable extraction of knowledge from data. It assesses the behaviour of data in a controlled, logic-led, responsive environment for deriving automated solutions and prognostic models for a given situation, problem or business objective. From Tinder to Facebook; LinkedIn to various online giants like Amazon and Google, Data science is playing a pivotal role in making the data scientist the new sought-after job in the market. Using large amounts of data for decision making has become practical now, with industries hiring qualified data scientists to handle a wide range of unprocessed data to come up with modern workable solutions catering to their respective market. Gone are the days when companies used to work on software like Excel only to analyse and store data. Even at that time, only some intelligent ventures worked with SPSS and strata
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