For the past couple of decades analytics has made its mark on businesses. During that time, finance professionals learned how to enhance and mature their data and analytics. The progression started with basic gathering of data and became a proactive, automated use of advanced algorithms. Many organizations struggle to achieve predictive analytics because of limited data readiness and skills, it is possible to use a combination of the different technologies simultaneously for different needs and projects. The biggest barriers to widespread use of self-service analytics are typically organizational and cultural barriers, rather than technology or data quality.