In a world where many businesses handle large amounts of data, getting it sorted out is vital. Data has to be analyzed accurately to get substantial information out of it. This has given rise to data preparation. This is the art of finding, merging, structuring, and sorting out data to make it usable in business intelligence, data visualization, as well as analytics. Data preparation entails several key processes, including preprocessing, profiling, authenticating and transforming. The data is drawn from different internal and external systems.
Why Data Preparation?
One of the main reasons for data preparation is to ensure the raw data being prepared for processing is consistent and accurate. Accurate data yields valid findings once analyzed. However, it is common for data to come with a number of inaccurate or missing sections. In addition, different data come with different data sets and formats, making validation and error removal necessary.
Data preparation is also essential in identifying relevant data to be used in the analysis stage. Having relevant data ensures analytics operations produce meaningful data that sheds light and yields working insights for business decision-making.
Another great purpose of data preparation is curating data sets so business users can perform analysis. Curating data is an excellent way to guide and streamline self-service business intelligence applications.
The Benefits of Data Preparation
Many data scientists always focus on collecting data, cleansing it, and structuring it, forgetting to analyze it adequately. However, effective data preparation enabled end users to focus primarily on data mining and analysis, generating business value. Below are some of the key benefits of data preparation.
Data preparation ensures that the data utilized in analytics applications will yield accurate and reliable results. Therefore, the end goal of a data preparation exercise should be the release of reliable results.
It helps identify and correct data issues that would create inaccuracies. For example, data always come with discrepancies, such as omissions or repetitions. Proper data preparation will point these issues out and provide a solution for fixing them. Data experts sometimes combine iteration with other mathematical and scientific models to fix the data.
Data preparation enables operational workers and business executives to make decisions more informed. Furthermore, once data is analyzed and processed, the end product is accurate information that can be used to tailor solutions toward a given issue. This is, therefore, essential in making decisions at the executive level.
Data preparation also helps reduce the costs associated with data analytics and management. However, when raw data is not prepared accurately and adequately, analytics and management of the same data become a problem. Correcting the issues will require more manpower and expertise, resulting in increased costs.
Conclusion
Adequate and effective data preparation is highly beneficial to businesses. With many businesses relying on huge data to make informed decisions, handling the data plays a vital role in a business’s success. Data preparation weeds out discrepancies and inaccuracies and reduces unnecessary analytics costs. Therefore, investing in proper data preparation is necessary for any business to guarantee success.
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