Leyre Murillo-Villar is chief data officer and data control lead at BNP Paribas. She is also a member of the Women in Data 20 in Data and Tech list. She told DataIQ about the need to close the gender gap and how data is at the heart of managing risk.
The problem with stereotypes is that they linger. There isn’t an informed individual on the planet who would argue that STEM industries (science, technology, engineering and maths) ought to be male-dominated, and yet, these sectors remain imbalanced towards male representation. This is partly down to the aforementioned stereotype that these sectors ought to be “man’s work”.
Lyndsay Weir is global data and analytics manager at Nestlé. At the Women in Data conference in November, she was named as one of the new list of female industry role models, the 20 in Data and Tech. She told DataIQ how being a female leader and role model can be done in a quiet way.
For data-driven organisations, some of your most high-potential employees are those with strong data skills and a desire to share them with others. James Eiloart of Tableau explains how to identify and manage these data champions
Bhagya Reddy is principal data engineer at QuantumBlack, a McKinsey company. At the Women in Data conference in November, she was named as one of the new list of female industry role models, the 20 in Data and Tech. She told DataIQ what it means to her and shared her views on the sector.
Dun & Bradstreet, a business information data company that provides business credit reports, has over 330 million business records on its Data Cloud. Andy Crisp tells Toni Sekinah about his observation having completed 20 years in at Dun & Bradstreet and what it means to ‘own’ data at the company.
The Confederation of Laboratories for Artificial Intelligence Research in Europe (CLAIRE), a research network was set up to facilitate collaboration between European labs. Toni Sekinah spoke to Professor Holger Hoos about Europe’s competitive advantage in AI, and how the network can help mitigate the negative effects of AI.
Data scientists are highly sought-after, with demand for specialist data skills increasing over 230%, but they spend too much time on mundane tasks like data preparation. Using automated ETL could free them up for value-adding tasks, as Adverity’s Alexander Igelsböck explains.
Small businesses and start-up can get in on the AI action that may seem to be the domain of the tech giants. This was the view of DataJavelin’s Philip Rooney who made this claim to a room full of entrepreneurs and small business owners at a workshop. Toni Sekinah reports.
Consumers are far more fickle, experimental and granular than ever. To move faster and predict their buying trends more accurately, businesses need to embrace new innovations in data analytics. Andrew Appel of IRI explains why.