Laura uses 5 numbers to describe herself: 5, as she is part of a family of five; 16, as she moved from Italy to Germany 16 years ago for her master’s program in Mathematics; 25, as the number of cities she visited for the first-time last year; 442, as her preferred frequency to tune the A string of her violin; 21.157, as the minutes she spent listening to podcasts last year.
Her journey into the world of Big Data was a coincidence. After studying mathematics and abstract subjects, she searched for a job where she could apply her background to reality. She started as a Web Analyst and Online Marketing Manager, and discovered her passion for data, Big Data, data science and machine learning. Over the years, she has taken classes, learnt a lot and defined her focus area.
Currently, as a Principal AI Solution Architect in the Solution Engineering Team at One Data, a software company based in Germany, Laura finds her role to be exciting and stimulating. She works on a wide range of projects, each with its unique set of challenges and requirements. She also enjoys collaborating with different departments such as sales, marketing, product management, and delivery.
She goes on to explain the one of the most beneficial features of One Data’s enabling technology, One Data Cartography, is record linkage combined with data quality. This feature enables holistic and seamless data tracking across system boundaries, based on algorithms and automatic checks for quality anomalies. By freeing data from silos, it enables valuable, linked data products for self-service.
Source: One Data
“Treating data as a product creates value for businesses by shifting the focus from data being a cost center to data being a revenue generator. It encourages the improvement of data quality, which leads to better decision-making and increases data accessibility. This breaks data silos and leads to new insights, giving businesses a competitive advantage.
Data as a Product is a principle of Data Mesh. Data Mesh is a way of organizing and managing data within an organization that focuses on treating data as a product and on sharing it across teams. It creates a decentralized and autonomous system where each team is responsible for their own data and services, but also encourages the sharing and accessing of data across the organization. This allows teams to be more agile, innovative, and efficient with their data. “
Laura recently chose to be a mentor for Women in Big Data because she had and has great female mentors. She wanted to share her experience and empower women in the field of big data. Being a mentor has been a great experience for her, as she has connected with her mentees, learnt a lot from their perspectives and ideas, and developed her own coaching and leadership skills.
Her advice for other women trying to grow their career in big data is to stay authentic and not let self-doubt get in their way. She also suggests finding a good mentor and defining a focus area.
If you want to learn more from Laura, reach out to her on LinkedIn.