Accurate and well-prepared data can make the difference between being a successful business or a company that is quickly forgotten.
Within data science, the goal of preparation is to ensure the accuracy of analyzes and insights. This phase is even more important for MDM (Data Governance) models: the more high-quality data that is collected and used, the greater its accuracy and robustness.
By instilling an efficient and repeatable data preparation process, organizations can enable analysts to speed up this time-consuming, iterative step to free up more time for analysis and the production of constructive insights and models for Power BI, Tableau and custom dashboards. In short, organizations cannot survive without efficient data preparation; if analysts don’t clean and structure them raw the right way, they’re less likely to get meaningful results.
Data collection, combination, structure, and organization are all steps in the process of preparing data for use in business intelligence (BI), analytics, and data visualization applications. Data preprocessing, profiling, cleansing, validation, and transformation are all parts of data preparation. It frequently includes and entails combining data from various internal systems and outside sources.
Ensuring that raw data is correct and consistent before processing and analysis so that the outcomes of BI and analytics applications will be valid is one of the main goals of data preparation. When data is created, they frequently include missing numbers, inaccuracies, or other problems, and when disparate data sets are merged, they frequently have different formats that need to be reconciled. Large portions of data preparation tasks involve correcting data problems, confirming data quality, and consolidating data sets to truly make data driven based decisions.
“Datos Maestros” is spanish for Master Data and thats all we know. With over 40 years of combined experience, accross 40 countries and more than 400 system integrations under our belt, we have seen the good the bad and the ugly. It’s safe to say we know a thing or two about data quality.
Why pay more for less, our unicorn business model allows a one man band and small to medium companies pay on the go, with credits or our pay per match check out options. For the the big dogs we don’t sacrifice quality for quantity and can count on the best bang for your buck with guaranteed lowest price without loosing our Fortune 500 memory.
Getting clean is the first step in data preperation process the next challenge is staying clean. With our business automation integration reap the following benefits:
We integrate prepared data with your preexisting tools and workflow for your team to have easy to access data that is processable and that yields real measurable results.