Making Data Better – Resolving Inefficiencies in Data Preparation

Making Data Better – Resolving Inefficiencies in Data Preparation

In order to ensure a data analyst in your organization is provided with the right data, it needs to have a credible source, valid and verified, non-problematic metadata, and most importantly consistent data quality of a high level. It becomes hard to trust your data when you constantly come across discrepancies that get in the way of real data science tasks.

Steps to take towards increasing the efficiency of your data

Organize Your Data The best way to make data usable in shorter periods of time with minimal redundancies and discrepancies, is to organize your data. It might sound easy enough, but as we mentioned above, organizing the data you think you need is a trial-and-error process that ultimately results in loss of time and money, therefore building an archive of all the data you could possibly want and have is a mammoth task, especially when you want to also be able to have some command over said data.

Data Profiling and Cataloguing There are plenty of different avenues of action you can take towards organizing and arranging your data, including data profiling and cataloging. Nevertheless, data prepping has been known to take up a crucial 80% of a data scientists time, and the lack of good quality data that can be trusted by every member of your team acts as a hindrance towards enterprise data management, especially when it comes to managing employees and sharing one particular vision for the company.

Appropriated & Best Data Enterprise data management is a well-known term that encompasses the process of ensuring you have the best possible data in order to maximize workflow and streamline communication within your organization, resulting in the improvised ability to make key business decisions, both individually and collaboratively.