Abstract: Systems and methods access, from data storage location(s), at least two separate datasets, and perform data analysis on the at least two separate datasets to identify any redundancies. The data analysis includes comparing at least one first column name of first column(s) of first data values of a first dataset with at least one second column name of second column(s) of second data values of a second dataset, the comparing including evaluating similarities of the at least one first column name and the at least one second column name. The data analysis also includes determining whether statistical information of the first data values and the second data values are replicas and deriving semantic logic from the first dataset and the second dataset to interpret importance of retaining both the first dataset and the second dataset.

Abstract: Systems and methods access, from one or more data storage locations, a dataset; perform data analysis on the dataset to detect one or more data quality characteristics each corresponding to at least one data quality dimension including timeliness, uniqueness, accuracy, completeness, validity, or consistency; evaluate the one or more data quality characteristics present in the dataset to identify one or more common patterns; and generate one or more data quality rule recommendations based on the identified one or more common patterns.

Abstract: Systems and methods access, from one or more data storage locations, a dataset; perform data analysis on the dataset to detect one or more data quality characteristics each corresponding to at least one data quality dimension including timeliness, uniqueness, accuracy, completeness, validity, or consistency; evaluate the one or more data quality characteristics present in the dataset to identify one or more common patterns; and generate one or more data quality rule recommendations based on the identified one or more common patterns.

Abstract: Systems and methods access, from one or more data storage locations, a dataset; perform data analysis on the dataset to detect one or more data quality characteristics each corresponding to at least one data quality dimension including timeliness, uniqueness, accuracy, completeness, validity, or consistency; evaluate the one or more data quality characteristics present in the dataset to identify one or more common patterns; and generate one or more data quality rule recommendations based on the identified one or more common patterns.

Abstract: Systems and methods access, from one or more data storage locations, a dataset; perform data analysis on the dataset to detect one or more data quality characteristics each corresponding to at least one data quality dimension including timeliness, uniqueness, accuracy, completeness, validity, or consistency; evaluate the one or more data quality characteristics present in the dataset to identify one or more common patterns; and generate one or more data quality rule recommendations based on the identified one or more common patterns.

Abstract: Systems and methods access, from one or more data storage locations, a dataset; perform data analysis on the dataset to detect one or more data quality characteristics each corresponding to at least one data quality dimension including timeliness, uniqueness, accuracy, completeness, validity, or consistency; evaluate the one or more data quality characteristics present in the dataset to identify one or more common patterns; and generate one or more data quality rule recommendations based on the identified one or more common patterns.

Abstract: Systems and methods perform data processing on dataset(s) and derive, from the dataset(s), data features that would be used in data analysis. The data features are classified as either being categorical or numerical, and a statistical test is applied to the classified data features to determine whether a change between the data features from incoming data is statistically significant compared to historical data features, the statistical test incorporating a population stability index score. Based on the population stability index score surpassing a threshold value, the system indicates that there is a drift in the data features due to the change between the data features from the incoming data being statistically significant compared to the historical data features.

Abstract: Systems and methods monitor, via the database and data structure management processes, data distribution of incoming data to detect recent deviation in parameters influencing statistical properties of the incoming data relative historical parameters of historical data stored to data storage location(s). The monitoring includes extracting parameters associated with users from the incoming data, evaluating the data distribution of the extracted data parameters of the incoming data to determine whether the distribution of the parameters is consistently changing relative the historical parameters, where the parameters include user parameters associated with users, and determining whether changes to the parameters would statistically influence predictive processes implemented by an entity, the predictive processes being reliant upon the parameters.

Abstract: Systems and methods ascertain whether data processing systems maintain reliability by monitoring data drift, the monitoring including extracting data features from incoming input data, evaluating distribution of the extracted data features over time to determine whether the distribution changes over time relative historical data features, determining whether changes to the distribution of input data influence accuracy of predictions made by a machine learning model by comparing the changes to the distribution to a deviation threshold indicative of an acceptable degree of deviation, and identifying a breach of the deviation threshold. Based on the breach having potential to negatively influence the reliability of the data processing systems, a warning signal is triggered to be distributed to user device(s) to facilitate corrective parameter control of machine learning model parameter(s).

Abstract: Systems and methods monitor, via database and data structure management processes, data distribution of the incoming data to detect deviation in resource parameters influencing the statistical properties of the incoming data relative historical resource parameters of historical data stored to one or more data storage locations. The monitoring includes extracting resource parameters associated with user resources from the incoming data, evaluating distribution of the extracted resource parameters to determine whether the distribution of the resource parameters associated with the user resources is consistently changing relative the historical resource parameters, and determining whether changes to the resource parameters would statistically influence predictive processes, reliant upon the resource parameters, implemented by an entity.

Abstract: Systems and methods receive, by a backend system and from a user device, input(s) indicating dataset(s) should be stored to storage location(s) of the backend system, and dynamically compare data of the dataset(s) to stored data of stored dataset(s), the comparing including applying the dataset(s) to a deployed predictive model that is trained to quantify a percentage of similarity between the dataset(s) and the stored dataset(s). Based on the applying, it is dynamically determined that the percentage of similarity of one dataset of the dataset(s) surpasses a predefined threshold percentage, and electronic notification(s) indicating the percentage of similarity between the one dataset and a stored dataset of the dataset(s) is transmitted to the user device based on the percentage of similarity surpassing the predefined threshold percentage.

Abstract: Systems and methods receive input(s) facilitating dataset management, the input(s) initiating a machine learning process configured to detect data redundancies of two or more datasets. Entity data stored to entity data storage location(s) are accessed and processed to conform with formatting requirements for the machine learning process. Validation is performed on the processed entity data, the validation ensuring the processed entity data satisfy the formatting requirements, where the validation produces training data that is inserted into an iterative training and testing loop.

Abstract: Systems and methods facilitate saving data storage through data storage management of storage location(s) by training, via an iterative training and testing loop, a predictive model using training data to detect data redundancies from two or more datasets stored to data storage location(s), the training includes testing the predictive model by predicting a target variable and iteratively adjusting weights and calculations during each subsequent iteration to improve predictability of the target variable, where the predictive model is trained to identify data similarities among the two or more datasets. Based on any error in predicting the target variable being less than a predetermined level, the predictive model is deployed and applied to at least two datasets to quantify a percentage of similarity among the at least two datasets. If it is determined the percentage of similarity surpasses a predefined threshold percentage, then an electronic notification is transmitted.

Abstract: Systems and methods perform data analysis on at least two separate datasets to identify any redundancies. The data analysis includes comparing first data values of a first dataset with second data values of a second dataset, the comparing including evaluating similarities of the first data values and second data values, identifying, from the comparing, that the first data values and the second data values include at least a portion of substantially similar data, and interpreting similarities of the portion of substantially similar data, the interpreting including determining that a dataset of the first dataset and second dataset is a subset of the other dataset. Further, control signal(s) are transmitted to a user device to initiate displaying, via a user interface of the user device, a prompt indicating that the at least one dataset is likely the subset of the other dataset.

Abstract: Systems and methods access, from data storage location(s), at least two separate datasets, and perform data analysis on the at least two separate datasets to identify any redundancies. The data analysis includes comparing at least one first column name of first column(s) of first data values of a first dataset with at least one second column name of second column(s) of second data values of a second dataset, the comparing including evaluating similarities of the at least one first column name and the at least one second column name. The data analysis also includes determining whether statistical information of the first data values and the second data values are replicas and deriving semantic logic from the first dataset and the second dataset to interpret importance of retaining both the first dataset and the second dataset.

Abstract: Systems and methods access, from data storage location(s), at least two separate datasets, and perform data analysis on the at least two separate datasets to identify any redundancies. The data analysis includes comparing at least one first column name of first column(s) of first data values of a first dataset with at least one second column name of second column(s) of second data values of a second dataset, the comparing including evaluating similarities of the at least one first column name and the at least one second column name. The data analysis also includes determining whether statistical information of the first data values and the second data values are replicas and deriving semantic logic from the first dataset and the second dataset to interpret importance of retaining both the first dataset and the second dataset.

Abstract: Systems and methods receive input(s) facilitating dataset management, the input(s) initiating a machine learning process configured to detect data redundancies of two or more datasets. Entity data stored to entity data storage location(s) are accessed and processed to conform with formatting requirements for the machine learning process. Validation is performed on the processed entity data, the validation ensuring the processed entity data satisfy the formatting requirements, where the validation produces training data that is inserted into an iterative training and testing loop.