Data is the new language today. Data leads to insights, and insights help organizations to make actionable business decisions. However, sourcing the data and preparing it for the analysis is one of the tedious tasks organizations face these days. Analysts devote a lot of time in searching and gathering the right data. According to a research firm, analysts spend around 60 to 80 percent of their time on data preparation instead of analysis. Consequently, an accurate analysis depends on how well the data has been prepared and managed effectively.
Data preparation is an integral step to generate insights. It is one of the most time-consuming and crucial processes in data mining. In simple words, data preparation is the method of collecting, cleaning, processing and consolidating the data for use in analysis. It enriches the data, transforms it and improves the accuracy of the outcome. Some of the key challenges faced by analysts and data scientists in dealing with data preparation include:
Data preparation is mostly done through analytical or traditional extract, transform, and load (ETL) tools. Both of which have their own advantages and limitations. In order to effectively integrate a variety of data sources, organizations should align the data, transform it and promote the development and adoption of data standards. All these things should effectively manage the volume, variety, veracity and velocity of the data.
Data is everywhere. The ability to integrated it and develop insights faster will drive value across the enterprise. Here are the best practices that will speed up the data preparation and integration process:
: The self-service data preparation tools enable automation and help users handle diverse workloads. It crosses out the manual work of searching, cleansing and transforming the data for analysis. Moreover, the self-service data preparation tools reduce the dependence on IT support and decrease the time to prepare data.
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