Virtual management systems and methods
A computer-implemented method and system are provided for optimizing resource usage, wherein the resources include employees of an organization. The method includes collecting employee data including structured data and unstructured data through multiple input channels over at least one network and storing the employee data collected over the multiple input channels in at least one computer memory. The method further includes accessing the computer memory using at least one computer processor and executing instructions to perform multiple operations on the stored data. The operations include transforming the unstructured data into structured data and disambiguating the structured data. The operations additionally include applying rule sets to the transformed data and the structured data to derive a digital productivity footprint for each employee and analyzing the derived digital footprints to optimize resource usage.
1. A computer-implemented method for analyzing employee productivity based on a plurality of data sets, the method comprising:
capturing employee data over at least one network, the employee data including structured data, semi-structured data, and unstructured data, from a plurality of data sources including multiple computing applications operating on multiple digital devices and building security systems, the employee data including log-in data, remote access data acquired by monitoring virtual private network usage, building entry and exit data from security badge usage, and application usage data, wherein the captured employee data, including the structured data, the semi-structured data, and the unstructured data, is stored in separate databases in a raw data storage;
utilizing a data gathering engine for collecting the stored employee data from the separate databases in the raw data storage over at least one computer network, applying data cleansing and summarization logic, and parsing the structured data, the semi-structured data, and the unstructured data including at least the building entry and exit data from security badge usage;
aggregating the parsed structured data, semi-structured data, and unstructured data, including the building entry and exit data from security badge usage and the application usage data into a single aggregate data structure including a table having relational fields;
storing the single aggregate data structure including the table having relational fields in a separate aggregate storage database in a computer memory;
accessing the single aggregate data structure including the table having relational fields from the separate aggregate storage database in the computer memory;
disambiguating the aggregated data by comparing the aggregated data from the multiple sources, and processing the aggregated data to eliminate ambiguities across the multiple sources;
generating employee metrics based on the disambiguated data, wherein generating employee metrics includes generating a digital footprint for each employee illustrating disambiguation of employee attendance, an application matrix illustrating frequency of use for each accessed application for each employee, and a skills matrix segmented across multiple parameters to identify skill capabilities of each employee,
wherein generating the skills matrix segmented across multiple parameters comprises:
querying the disambiguated data, and
in response to querying the disambiguated data, identifying, from each employee, a set of qualified employees for each task based on a combination of factors comprising speed, accuracy, skills, location, and availability;
generating a simulated reconfiguration of resources based on the employee metrics; and
providing an interface for visualizing the simulated reconfiguration of resources based on the employee metrics.
2. The method of claim 1 , wherein the structured data comprises:
employee location data;
employee activity data;
employee output data; and
employee cost data.
3. The method of claim 1 , wherein the disambiguating includes comparing the employee data from the multiple data sources.
4. The method of claim 1 , wherein the employee metrics include
employee attendance metrics;
employee activity metrics;
employee workday metrics; and
employee location metrics.
5. The method of claim 1 , further comprising: creating employee benchmarking based on the collected data.
6. The method of claim 1 , further comprising: generating reports based on the employee metrics.
7. The method of claim 1 , further comprising: determining the impact-of-change of employee variables based on the employee metrics.
8. A system for analyzing employee productivity based on a plurality of data sets produced by multiple data sources monitoring employee activities, the system comprising:
a data gathering processor programmed to
receive employee data over at least one network from the plurality of data sources, the data including structured data, semi-structured data, and unstructured data and the data sources including multiple computing applications operating on multiple digital devices and building systems, the employee data including log-in-data, remote access data acquired by monitoring virtual private network usage, building entry and exit data from security badge usage, and application usage data, wherein the captured employee data, including the structured data, the semi-structured data, and the unstructured data, is stored in separate databases in a raw data storage;
collecting the stored employee data from the separate databases in the raw data storage over at least one computer network;
apply data cleansing and summarization logic, and parsing the structured data, the semi-structured data, and the unstructured data including the building entry and exit data from security badge usage;
aggregate the parsed structured data, the semi-structured data, and the unstructured data including the building entry and exit data from security badge usage and the application usage data into a single aggregate data structure including a table having relational fields;
store the single aggregate data structure including the table having relational fields in at least one storage database in a computer memory;
a processing engine including at least one computer processor programmed for
accessing the single aggregate data structure including the table having relational fields from the separate aggregate storage database in the computer memory,
disambiguating the aggregated data by comparing the aggregated data from the multiple sources, and processing the aggregated data to eliminate ambiguities across the multiple sources,
generating employee metrics based on the disambiguated data, including generating a digital footprint for each employee illustrating disambiguation of employee attendance, an application matrix illustrating frequency of use for each accessed application for each employee, and a skills matrix segmented across multiple parameters to identify skill capabilities of each employee,
wherein generating the skills matrix segmented across multiple parameters comprises:
querying the disambiguated data, and
in response to querying the disambiguated data, identifying, from each employee, a set of qualified employees for each task based on a combination of factors comprising speed, accuracy, skills, location, and availability;
a resource management engine generating a simulating reconfiguration of resources based on the employee metrics; and
a visualization engine providing an interface for visualizing the simulated reconfiguration of resources based on the employee metrics.
9. The system of claim 8 , wherein the structured data includes
employee location data;
employee activity data;
employee output data; and
employee cost data.
10. The system of claim 8 , wherein the disambiguating includes comparing the employee data from the multiple data sources.
11. The system of claim 8 , wherein the employee metrics include
employee attendance metrics;
employee activity metrics;
employee workday metrics; and
employee location metrics.
12. The system of claim 8 , wherein the processing engine is further programmed to create employee benchmarking.
13. The system of claim 8 , wherein the processing engine is further programmed to generate reports based on the employee metrics.
14. The system of claim 8 , wherein the processing engine is further programmed to determine the impact-of-change of employee variables based on the employee metrics.