According to Kaggle, an online community for data scientists and machine learners; only 16% of women make up all data scientists. While there are no statistics to confirm, trends from other industries would indicate the percentage for women of color in the data field is even lower. I had the chance to speak to Sadie St. Lawrence, the CEO and founder of Women in Data to discuss women’s role in data and why it’s so crucial to minimize the gender gap in the industry.

In this digital age, marketers have more data to work with than ever before. But actually deriving meaningful insights from that data and converting those insights into action is easier said than done. Often, the problem is that, with so much data at their disposal, marketers are confused about where they should direct their attention.

Can predictive analytics be used to forecast which movie will walk away with the top honor at the Academy Awards? Apparently not. While predictive analytics is useful to forecast future trends, behaviors and patterns- Oscar winners are not one of them. Often times it is used to lower risks, improve software or anticipate customer behavior. Combined with machine learning and algorithms, predictive analytics incorporate past and current data to provide the most logical conclusion.

Is your company a data company? Whether you actually sell software that monitors data or run a brick and mortar store, the answer is yes. Monetizing data a business already collects can significantly increase a company’s revenue.  Gartner defines Data Monetization as “using data for quantifiable economic benefit.”  Whether that entails selling your findings to third party sources or using this intelligence to create a more efficient product is up to the organization itself.