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data quality is importantData quality is the foundation for creating value out of data. Companies increasingly trust their data to drive business and take key decisions using analytics and machine learning for example.
Because data often comes from multiple sources or has been historically collected without taking into account the requirements of advanced data analytics, its quality is not always optimal. Yazzoom can help you clean and prepare your data so you can start with a solid base. |
common data quality issues and challenges |
Issues with data range from simple errors, such as duplicate entries or inconsistent format (units, date format, multiple values describing an identical properties...) to missing values that need to be predicted from other values of similar entries and inadequate data (too compressed or sampled too infrequently).
Data sources can also have incompatible formats or not be directly accessible like in the case of data that comes in the form of scanned documents. Different data generators, for example different machines, might also introduce systematic errors through the differences in sensor type, generation and processing systems. Impossible or otherwise anomalous values can also be present. They could come from input error, sensor faults or any other number of cause and can make data anlysis more difficult. |
How we work |
Data preparation and cleaning starts with an assessment of its quality.
We check the data for errors and anomalies to be corrected, establish a strategy for consolidating the different data sources and work together with you to check its adequacy according to future use cases if they are already on the roadmap. Filling in the missing values is also an important task. Missing information can be extrapolated from other entries in the dataset through statistical methods for example. Outliers in the data (anomalous or impossible values) can also be detected by applying advanced anomaly detection algorithms. Data enrichment can also be part of the data preparation. Improving raw data by giving it a context or extracting values from a complex field and categorizing them for example. Data usually continues to be gathered after data preparation and cleaning. Thanks to Yazzoom's advanced knowledge of data science, we are also absle to leverage anomaly detection and predictive modeling to monitor the quality of your future data and check its consistency. |
why work with us |
Working with a team of specialists that are in the field of data cleaning and preparation everyday means:
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