Data preparation is a process used to extract actionable information to be shared within an organisation. It ensures the value and quality of the data.

A precise data cleaning and preparation process is essential so that data scientists are making the most of their expertise and time.

Data wrangling is one of the many techniques used in data preparation to preprocess data. It improves complex issues in the analysis process.

Data preparation is essential in data analytics, so data scientists can create reliable service models and algorithms. It’s the basis for successful business decisions.

Data science uses predictive analytics, prescriptive analytics and machine learning to make informed business decisions and projections.

Data discovery helps detect patterns in datasets that can be used as actionable insights and presented as high quality visual data.