Pervasive data center architecture systems and methods

1. A system for assessing disparate storage of data among a number of storage devices, the system comprising:

a processor;

a memory;

a data gravity analytics configuration module;

a knowledge database stored in the memory; and

computer code stored in the memory, wherein the computer code, when retrieved from the memory and executed by the processor causes the processor to:

receive information about one or more nodes from a plurality of forensic source submitters, the one or more nodes being associated with:

a network,

mass data storage systems,

data characteristics including at least one of: data mass, data activity, bandwidth between at least two points, or latency;

data storage parameters,

one or more zone indicators, and

one or more Internet Protocol IP addresses,

wherein the submitters are registered contributors in providing aggregated data storage evidence;

identify, using the processor, a selected zone indicator received via a user interface;

select, using the processor, a subset of nodes based on the selected zone indicator, wherein each of the nodes in the subset of nodes is associated with selected zone indicator;

calculate, using the processor, a data gravity index score of the subset of nodes based at least in part on one or more of the selected zone indicator and data characteristics of each of the subset of nodes weighted according to a context;

update the knowledge database with the calculated data gravity index score;

access, using the processor, a historical data set for one or more third party systems associated with the subset of nodes;

calculate, using the processor, a data gravity forecast over a first period of time for the one or more third party systems based on the calculated data gravity index score, the historical data set, and one or more factors; and

automatically generate recommendations for capacity planning for the one or more third party systems based on the data gravity forecast.

2. The System of claim 1 , wherein the one or more factors included one or more of: economy growth rates, third party growth rates, industry growth rates, year-to-year growth index, or compound annual growth rates.

3. The System of claim 1 , wherein the one or more third party systems are associated with a city, a region, a country, or a geographical area.

4. The System of claim 1 , wherein the data gravity forecast is calculated using a machine learning model configured to identify one or more patterns associated with the data characteristics of the subset of nodes and the historical data set.

5. The System of claim 4 , wherein the machine learning model is trained using training data including one or more of: data creation rates, storage capacity, processing capacity, industry growth, cloud usage change, population growth, or annual rate of deployment of enterprise storage.

6. The System of claim 1 , wherein the data gravity analytics configuration module is configured to automatically generate encrypted data packets comprising the recommendations for capacity planning and configured to instruct a network module to send the encrypted data packets to the one or more third party systems.

7. The System of claim 1 , wherein the computer code further causes the processor to:

receive, updated data characteristics associated with the subset of nodes;

calculate, using the processor, an updated data gravity index score associated with the subset of nodes based at least in part on the updated data characteristics; and

calculate, using the processor, an updated data gravity forecast over a second period of time for the one or more third party systems based on the updated data gravity index score, the historical data set, and one or more factors.

8. A computer-implemented method for assessing disparate storage of data among a number of storage devices, the computer-implemented method comprising, as implemented by one or more computing devices configured with specific executable instructions for:

receiving information about one or more nodes from a plurality of forensic source submitters, the one or more nodes being associated with:

a network,

mass data storage systems,

data characteristics including at least one of: data mass, data activity, bandwidth, or latency;

data storage parameters,

one or more zone indicators, and

one or more Internet Protocol IP addresses,

wherein the submitters are registered contributors in providing aggregated data storage evidence;

identifying a selected zone indicator received via a user interface;

selecting a subset of nodes based on the selected zone indicator, wherein each of the nodes in the subset of nodes is associated with selected zone indicator;

calculating a data gravity index score of the subset of nodes based at least in part on one or more of the selected zone indicator and data characteristics of each of the subset of nodes weighted according to a context;

updating a knowledge database with the calculated data gravity index score;

accessing a historical data set for one or more third party systems associated with the subset of nodes;

calculating a data gravity forecast over a first period of time for the one or more third party systems based on the calculated data gravity index score, the historical data, and one or more factors; and

automatically generating recommendations for capacity planning for the one or more third party systems based on the data gravity forecast.

9. The computer-implemented method of claim 8 , wherein the one or more factors included one or more of: economy growth rates, third party growth rates, industry growth rates, year-to-year growth index, or compound annual growth rates.

10. The computer-implemented method of claim 8 , wherein the one or more third party systems are associated with a city, a region, a country, or a geographical area.

11. The computer-implemented method of claim 8 , wherein the data gravity forecast is calculated using a machine learning model configured to identify one or more patterns associated with the data characteristics of the subset of nodes and the historical data set.

12. The computer-implemented method of claim 11 , wherein the machine learning model is trained using training data including one or more of: data creation rates, storage capacity, processing capacity, industry growth, cloud usage change, population growth, or annual rate of deployment of enterprise storage.

13. The computer-implemented method of claim 8 , wherein the specific executable instructions further include:

automatically generating encrypted data packets comprising the recommendations for capacity planning; and

instructing a network module to send the encrypted data packets to the one or more third party systems.

14. The computer-implemented method of claim 8 , wherein the specific executable instructions further include:

receiving updated data characteristics associated with the subset of nodes;

calculating an updated data gravity index score associated with the subset of nodes based at least in part on the updated data characteristics; and

calculating an updated data gravity forecast over a second period of time for the one or more third party systems based on the updated data gravity index score, the historical data set, and one or more factors.

15. A non-transitory computer storage medium storing computer-executable instructions that, when executed by a processor, cause the processor to at least:

receive information about one or more nodes from a plurality of forensic source submitters, the one or more nodes being associated with:

a network,

mass data storage systems,

data characteristics including at least one of: data mass, data activity, bandwidth, or latency;

data storage parameters,

one or more zone indicators, and

one or more Internet Protocol IP addresses,

wherein the submitters are registered contributors in providing aggregated data storage evidence;

identify a selected zone indicator received via a user interface;

select a subset of nodes based on the selected zone indicator, wherein each of the nodes in the subset of nodes is associated with selected zone indicator;

calculate a data gravity index score of the subset of nodes based at least in part on one or more of the selected zone indicator and data characteristics of each of the subset of nodes weighted according to a context;

update a knowledge database with the calculated data gravity index score;

access a historical data set for one or more third party systems associated with the subset of nodes;

calculate a data gravity forecast over a first period of time for the one or more third party systems based on the calculated data gravity index score, the historical data set, and one or more factors; and

automatically generate recommendations for capacity planning for the one or more third party systems based on the data gravity forecast.

16. The non-transitory computer storage medium of claim 15 , wherein the one or more third party systems are associated with a city, a region, a country, or a geographical area.

17. The non-transitory computer storage medium of claim 15 , wherein the data gravity forecast is calculated using a machine learning model configured to identify one or more patterns associated with the data characteristics of the subset of nodes and the historical data set.

18. The non-transitory computer storage medium of claim 17 , wherein the machine learning model is trained using training data including one or more of: data creation rates, storage capacity, processing capacity, industry growth, cloud usage change, population growth, or annual rate of deployment of enterprise storage.

19. The non-transitory computer storage medium of claim 15 , further storing computer-executable instructions that:

automatically generate encrypted data packets comprising the recommendations for capacity planning; and

instruct a network module to send the encrypted data packets to the one or more third party systems.

20. The non-transitory computer storage medium of claim 15 , further storing computer-executable instructions that:

receive updated data characteristics associated with the subset of nodes;

calculate an updated data gravity index score associated with the subset of nodes based at least in part on the updated data characteristics; and

calculate an updated data gravity forecast over a second period of time for the one or more third party systems based on the updated data gravity index score, the historical data set, and one or more factors.